Feeds:
Posts
Comments

Archive for the ‘Technology Advance Assessment of’ Category

Compilation of References in Leaders in Pharmaceutical Intelligence about proteomics, metabolomics, signaling pathways, and cell regulation

Compilation of References in Leaders in Pharmaceutical Intelligence about
proteomics, metabolomics, signaling pathways, and cell regulation

Curator: Larry H. Bernstein, MD, FCAP

 

Proteomics

  1. The Human Proteome Map Completed
    Reporter and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/28/the-human-proteome-map-completed/
  1. Proteomics – The Pathway to Understanding and Decision-making in Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/06/24/proteomics-the-pathway-to-understanding-and-decision-making-in-medicine/
  1. Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/10/22/advances-in-separations-technology-for-the-omics-and-clarification-of-therapeutic-targets/
  1. Expanding the Genetic Alphabet and Linking the Genome to the Metabolome
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/
  1. Synthesizing Synthetic Biology: PLOS Collections
    Reporter: Aviva Lev-Ari
    http://pharmaceuticalintelligence.com/2012/08/17/synthesizing-synthetic-biology-plos-collections/

 

Metabolomics

  1. Extracellular evaluation of intracellular flux in yeast cells
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/25/extracellular-evaluation-of-intracellular-flux-in-yeast-cells/ 
  2. Metabolomic analysis of two leukemia cell lines. I.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/ 
  3. Metabolomic analysis of two leukemia cell lines. II.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/ 
  4. Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics
    Reviewer and Curator, Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/ 
  5. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    http://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/

 

Metabolic Pathways

  1. Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief
    Reviewer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/21/pentose-shunt-electron-transfer-galactose-more-lipids-in-brief/
  2. Mitochondria: More than just the “powerhouse of the cell”
    Reviewer and Curator: Ritu Saxena
    http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/
  3. Mitochondrial fission and fusion: potential therapeutic targets?
    Reviewer and Curator: Ritu saxena
    http://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/ 
  4. Mitochondrial mutation analysis might be “1-step” away
    Reviewer and Curator: Ritu Saxena
    http://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/
  5. Selected References to Signaling and Metabolic Pathways in PharmaceuticalIntelligence.com
    Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/
  6. Metabolic drivers in aggressive brain tumors
    Prabodh Kandal, PhD
    http://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/ 
  7. Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes
    Author and Curator: Aviva Lev-Ari, PhD, RD
    http://pharmaceuticalintelligence.com/2012/10/22/metabolite-identification-combining-genetic-and-metabolic-information-genetic-association-links-unknown-metabolites-to-functionally-related-genes/
  8. Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation
    Author and curator:Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/09/26/mitochondria-origin-from-oxygen-free-environment-role-in-aerobic-glycolysis-metabolic-adaptation/
  9. Therapeutic Targets for Diabetes and Related Metabolic Disorders
    Reporter, Aviva Lev-Ari, PhD, RD
    http://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
  10. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    http://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/
  11. The multi-step transfer of phosphate bond and hydrogen exchange energy
    Curator:Larry H. Bernstein, MD, FCAP,
    http://pharmaceuticalintelligence.com/2014/08/19/the-multi-step-transfer-of-phosphate-bond-and-hydrogen-exchange-energy/
  12. Studies of Respiration Lead to Acetyl CoA
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/
  13. Lipid Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/15/lipid-metabolism/
  14. Carbohydrate Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/
  15. Prologue to Cancer – e-book Volume One – Where are we in this journey?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/04/13/prologue-to-cancer-ebook-4-where-are-we-in-this-journey/
  16. Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/04/04/introduction-the-evolution-of-cancer-therapy-and-cancer-research-how-we-got-here/
  17. Inhibition of the Cardiomyocyte-Specific Kinase TNNI3K
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/11/01/inhibition-of-the-cardiomyocyte-specific-kinase-tnni3k/
  18. The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/05/15/the-binding-of-oligonucleotides-in-dna-and-3-d-lattice-structures/
  19. Mitochondrial Metabolism and Cardiac Function
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
  20. How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia
    Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/
  21. AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
    Author and Curator: SJ. Williams
    http://pharmaceuticalintelligence.com/2013/03/12/ampk-is-a-negative-regulator-of-the-warburg-effect-and-suppresses-tumor-growth-in-vivo/
  22. A Second Look at the Transthyretin Nutrition Inflammatory Conundrum
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
  23. Overview of Posttranslational Modification (PTM)
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/
  24. Malnutrition in India, high newborn death rate and stunting of children age under five years
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/15/malnutrition-in-india-high-newborn-death-rate-and-stunting-of-children-age-under-five-years/
  25. Update on mitochondrial function, respiration, and associated disorders
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/08/update-on-mitochondrial-function-respiration-and-associated-disorders/
  26. Omega-3 fatty acids, depleting the source, and protein insufficiency in renal disease
    Larry H. Bernstein, MD, FCAP, Curator
    http://pharmaceuticalintelligence.com/2014/07/06/omega-3-fatty-acids-depleting-the-source-and-protein-insufficiency-in-renal-disease/ 
  27. Late Onset of Alzheimer’s Disease and One-carbon Metabolism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    http://pharmaceuticalintelligence.com/2013/05/06/alzheimers-disease-and-one-carbon-metabolism/
  28. Problems of vegetarianism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    http://pharmaceuticalintelligence.com/2013/04/22/problems-of-vegetarianism/

 

Signaling Pathways

  1. Introduction to e-Series A: Cardiovascular Diseases, Volume Four Part 2: Regenerative Medicine
    Larry H. Bernstein, MD, FCAP, writer, and Aviva Lev- Ari, PhD, RN  http://pharmaceuticalintelligence.com/2014/04/27/larryhbernintroduction_to_cardiovascular_diseases-translational_medicine-part_2/
  2. Epilogue: Envisioning New Insights in Cancer Translational Biology
    Series C: e-Books on Cancer & Oncology
    Author & Curator: Larry H. Bernstein, MD, FCAP, Series C Content Consultant
    http://pharmaceuticalintelligence.com/2014/03/29/epilogue-envisioning-new-insights/
  3. Ca2+-Stimulated Exocytosis:  The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter  Writer and Curator: Larry H Bernstein, MD, FCAP and Curator and Content Editor: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/12/23/calmodulin-and-protein-kinase-c-drive-the-ca2-regulation-of-hormone-and-neurotransmitter-release-that-triggers-ca2-stimulated-exocy
  4. Cardiac Contractility & Myocardial Performance: Therapeutic Implications of Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
    Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC
    Author and Curator: Larry H Bernstein, MD, FCAP and Article Curator: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/
  5. Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
    Author and Curator: Larry H Bernstein, MD, FCAP Author: Stephen Williams, PhD, and Curator: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/
  6. Identification of Biomarkers that are Related to the Actin Cytoskeleton
    Larry H Bernstein, MD, FCAP, Author and Curator
    http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/
  7. Advanced Topics in Sepsis and the Cardiovascular System at its End Stage
    Author and Curator: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-Sepsis-and-the-Cardiovascular-System-at-its-End-Stage/
  8. The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology
    Demet Sag, PhD, Author and Curator
    http://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  9. IDO for Commitment of a Life Time: The Origins and Mechanisms of IDO, indolamine 2, 3-dioxygenase
    Demet Sag, PhD, Author and Curator
    http://pharmaceuticalintelligence.com/2013/08/04/ido-for-commitment-of-a-life-time-the-origins-and-mechanisms-of-ido-indolamine-2-3-dioxygenase/
  10. Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad
    Author and Curator: Demet Sag, PhD, CRA, GCP
    http://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
  11. Signaling Pathway that Makes Young Neurons Connect was discovered @ Scripps Research Institute
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/06/26/signaling-pathway-that-makes-young-neurons-connect-was-discovered-scripps-research-institute/
  12. Naked Mole Rats Cancer-Free
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
  13. Amyloidosis with Cardiomyopathy
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/03/31/amyloidosis-with-cardiomyopathy/
  14. Liver endoplasmic reticulum stress and hepatosteatosis
    Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2013/03/10/liver-endoplasmic-reticulum-stress-and-hepatosteatosis/
  15. The Molecular Biology of Renal Disorders: Nitric Oxide – Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
  16. Nitric Oxide Function in Coagulation – Part II
    Curator and Author: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-function-in-coagulation/
  17. Nitric Oxide, Platelets, Endothelium and Hemostasis
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/08/nitric-oxide-platelets-endothelium-and-hemostasis/
  18. Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/09/14/interaction-of-nitric-oxide-and-prostacyclin-in-vascular-endothelium/
  19. Nitric Oxide and Immune Responses: Part 1
    Curator and Author:  Aviral Vatsa PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/18/nitric-oxide-and-immune-responses-part-1/
  20. Nitric Oxide and Immune Responses: Part 2
    Curator and Author:  Aviral Vatsa PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  21. Nitric Oxide and iNOS have Key Roles in Kidney Diseases – Part II
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-and-inos-have-key-roles-in-kidney-diseases/
  22. New Insights on Nitric Oxide donors – Part IV
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/
  23. Crucial role of Nitric Oxide in Cancer
    Curator and Author: Ritu Saxena, Ph.D.
    http://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
  24. Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-a-concomitant-influence-on-mitochondrial-function/
  25. Nitric Oxide and Immune Responses: Part 2
    Author and Curator: Aviral Vatsa, PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  26. Mitochondrial Damage and Repair under Oxidative Stress
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/
  27. Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/
  28. Targeting Mitochondrial-bound Hexokinase for Cancer Therapy
    Curator and Author: Ziv Raviv, PhD, RN 04/06/2013
    http://pharmaceuticalintelligence.com/2013/04/06/targeting-mitochondrial-bound-hexokinase-for-cancer-therapy/
  29. Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/
  30. Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/
  31. Biochemistry of the Coagulation Cascade and Platelet Aggregation – Part I
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/biochemistry-of-the-coagulation-cascade-and-platelet-aggregation/

 

Genomics, Transcriptomics, and Epigenetics

  1. What is the meaning of so many RNAs?
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/06/what-is-the-meaning-of-so-many-rnas/
  2. RNA and the transcription the genetic code
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    http://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  3. A Primer on DNA and DNA Replication
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/29/a_primer_on_dna_and_dna_replication/
  4. Pathology Emergence in the 21st Century
    Author and Curator: Larry Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  5. RNA and the transcription the genetic code
    Writer and Curator, Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  6. Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease: Views by Larry H Bernstein, MD, FCAP
    Author: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/16/commentary-on-biomarkers-for-genetics-and-genomics-of-cardiovascular-disease-views-by-larry-h-bernstein-md-fcap/
  7. Observations on Finding the Genetic Links in Common Disease: Whole Genomic Sequencing Studies
    Author an Curator: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/05/18/observations-on-finding-the-genetic-links/
  8. Silencing Cancers with Synthetic siRNAs
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2013/12/09/silencing-cancers-with-synthetic-sirnas/
  9. Cardiometabolic Syndrome and the Genetics of Hypertension: The Neuroendocrine Transcriptome Control Points
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/12/12/cardiometabolic-syndrome-and-the-genetics-of-hypertension-the-neuroendocrine-transcriptome-control-points/
  10. Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2013/12/08/developments-in-the-genomics-and-proteomics-of-type-2-diabetes-mellitus-and-treatment-targets/
  11. CT Angiography & TrueVision™ Metabolomics (Genomic Phenotyping) for new Therapeutic Targets to Atherosclerosis
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/11/15/ct-angiography-truevision-metabolomics-genomic-phenotyping-for-new-therapeutic-targets-to-atherosclerosis/
  12. CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
    Genomics Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/30/cracking-the-code-of-human-life-the-birth-of-bioinformatics-computational-genomics/
  13. Big Data in Genomic Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/
  14.  From Genomics of Microorganisms to Translational Medicine
    Author and Curator: Demet Sag, PhD
    http://pharmaceuticalintelligence.com/2014/03/20/without-the-past-no-future-but-learn-and-move-genomics-of-microorganisms-to-translational-medicine/
  15.  Summary of Genomics and Medicine: Role in Cardiovascular Diseases
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/01/06/summary-of-genomics-and-medicine-role-in-cardiovascular-diseases/

Read Full Post »

Extracellular evaluation of intracellular flux in yeast cells

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence

This is the fourth article in a series on metabolomics, which is a major development in -omics, integrating transcriptomics, proteomics,  genomics, metabolic pathways analysis, metabolic and genomic regulatory control using computational mapping.  In the previous two part presentation, flux analysis was not a topic for evaluation, but here it is the major focus.  It is a study of yeast cells, and bears some relationship to the comparison of glycemia, oxidative phosphorylation, TCA cycle, and ETC in leukemia cell lines.  In the previous study – system flux was beyond the scope of analysis, and explicitly stated.  The inferences made in comparing the two lymphocytic leukemia cells was of intracellular metabolism from extracellular measurements.  The study of yeast cells is aimed at looking at cellular effluxes, which is also an important method for studying pharmacological effects and drug resistance.

Metabolomic series

1.  Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

http://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/

2.  Metabolomic analysis of two leukemia cell lines. I

http://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/

3.  Metabolomic analysis of two leukemia cell lines. II.

 http://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/

4.  Extracellular evaluation of intracellular flux in yeast cells

Q1. What is efflux?

Q2. What measurements were excluded from the previous study that would not allow inference about fluxes?

Q3. Would this study bear any relationship to the Pasteur effect?

Q4 What is a genome scale network reconstruction?

Q5 What type of information is required for a network prediction model?

Q6. Is there a difference between the metabolites profiles for yeast grown under aerobic and anaerobuc conditions – under the constrainsts?

Q7.  If there is a difference in the S metabolism, would there be an effect on ATP production?

 

 

Connecting extracellular metabolomic measurements to intracellular flux
states in yeast

Monica L Mo1Bernhard Ø Palsson1 and Markus J Herrgård12*

Author Affiliations

1 Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA

2 Current address: Synthetic Genomics, Inc, 11149 N Torrey Pines Rd, La Jolla, CA 92037, USA

For all author emails, please log on.

BMC Systems Biology 2009, 3:37  doi:10.1186/1752-0509-3-37

 

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1752-0509/3/37

 

Received: 15 December 2008
Accepted: 25 March 2009
Published: 25 March 2009

© 2009 Mo et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Metabolomics has emerged as a powerful tool in the

  • quantitative identification of physiological and disease-induced biological states.

Extracellular metabolome or metabolic profiling data, in particular,

  • can provide an insightful view of intracellular physiological states in a noninvasive manner.

Results

We used an updated genome-scale

  • metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate
  1. how changes in the extracellular metabolome can be used
  2. to study systemic changes in intracellular metabolic states.

The iMM904 metabolic network was reconstructed based on

  • an existing genome-scale network, iND750,
  • and includes 904 genes and 1,412 reactions.

The network model was first validated by

  • comparing 2,888 in silico single-gene deletion strain growth phenotype predictions
  • to published experimental data.

Extracellular metabolome data measured

  • of ammonium assimilation pathways 
  • in response to environmental and genetic perturbations

was then integrated with the iMM904 network

  • in the form of relative overflow secretion constraints and
  • a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints.

Predicted intracellular flux changes were

  • consistent with published measurements
  • on intracellular metabolite levels and fluxes.

Patterns of predicted intracellular flux changes

  • could also be used to correctly identify the regions of
  • the metabolic network that were perturbed.

Conclusion

Our results indicate that

  • integrating quantitative extracellular metabolomic profiles
  • in a constraint-based framework
  • enables inferring changes in intracellular metabolic flux states.

Similar methods could potentially be applied

  • towards analyzing biofluid metabolome variations
  • related to human physiological and disease states.

Background

“Omics” technologies are rapidly generating high amounts of data

  • at varying levels of biological detail.

In addition, there is a rapidly growing literature and

  • accompanying databases that compile this information.

This has provided the basis for the assembly of

  • genome-scale metabolic networks for various microbial and eukaryotic organisms [111].

These network reconstructions serve

  • as manually curated knowledge bases of
  • biological information as well as
  • mathematical representations of biochemical components and
  • interactions specific to each organism.

genome-scale network reconstruction is

  • structured collection of genes, proteins, biochemical reactions, and metabolites
  • determined to exist and operate within a particular organism.

This network can be converted into a predictive model

  • that enables in silico simulations of allowable network states based on
  • governing physico-chemical and genetic constraints [12,13].

A wide range of constraint-based methods have been developed and applied

  • to analyze network metabolic capabilities under
  • different environmental and genetic conditions [13].

These methods have been extensively used to

  • study genome-scale metabolic networks and have successfully predicted, for example,
  1. optimal metabolic states,
  2. gene deletion lethality, and
  3. adaptive evolutionary endpoints [1416].

Most of these applications utilize

  • optimization-based methods such as flux balance analysis (FBA)
  • to explore the metabolic flux space.

However, the behavior of genome-scale metabolic networks can also be studied

  • using unbiased approaches such as
  • uniform random sampling of steady-state flux distributions [17].

Instead of identifying a single optimal flux distribution based on

  • a given optimization criterion (e.g. biomass production),

these methods allow statistical analysis of

  • a large range of possible alternative flux solutions determined by
  • constraints imposed on the network.

Sampling methods have been previously used to study

  1. global organization of E. coli metabolism [18] as well as
  2. to identify candidate disease states in the cardiomyocyte mitochondria [19].

Network reconstructions provide a structured framework

  • to systematically integrate and analyze disparate datasets
  • including transcriptomic, proteomic, metabolomic, and fluxomic data.

Metabolomic data is one of the more relevant data types for this type of analysis as

  1. network reconstructions define the biochemical links between metabolites, and
  2. recent advancements in analytical technologies have allowed increasingly comprehensive
  • intracellular and extracellular metabolite level measurements [20,21].

The metabolome is

  1. the set of metabolites present under a given physiological condition
  2. at a particular time and is the culminating phenotype resulting from
  • various “upstream” control mechanisms of metabolic processes.

Of particular interest to this present study are

  • the quantitative profiles of metabolites that are secreted into the extracellular environment
  • by cells under different conditions.

Recent advances in profiling the extracellular metabolome (EM) have allowed

  • obtaining insightful biological information on cellular metabolism
  • without disrupting the cell itself.

This information can be obtained through various

  • analytical detection,
  • identification, and
  • quantization techniques

for a variety of systems ranging from

  • unicellular model organisms to human biofluids [2023].

Metabolite secretion by a cell reflects its internal metabolic state, and

  • its composition varies in response to
  • genetic or experimental perturbations
  • due to changes in intracellular pathway activities
  • involved in the production and utilization of extracellular metabolites [21].

Variations in metabolic fluxes can be reflected in EM changes which can

  • provide insight into the intracellular pathway activities related to metabolite secretion.

The extracellular metabolomic approach has already shown promise

  • in a variety of applications, including
  1. capturing detailed metabolite biomarker variations related to disease and
  2. drug-induced states and
  3. characterizing gene functions in yeast [2427].

However, interpreting changes in the extracellular metabolome can be challenging

  • due to the indirect relationship between the proximal cause of the change
    (e.g. a mutation)
  • and metabolite secretion.

Since metabolic networks describe

  • mechanistic,
  • biochemical links between metabolites,

integrating such data can allow a systematic approach

  • to identifying altered pathways linked to
  • quantitative changes in secretion profiles.

Measured secretion rates of major byproduct metabolites

  • can be applied as additional exchange flux constraints
  • that define observed metabolic behavior.

For example, a recent study integrating small-scale EM data

  • with a genome-scale yeast model
  • correctly predicted oxygen consumption and ethanol production capacities
  • in mutant strains with respiratory deficiencies [28].

The respiratory deficient mutant study

  • used high accuracy measurements for a small number of
  • major byproduct secretion rates
  • together with an optimization-based method well suited for such data.

Here, we expand the application range of the model-based method used in [28]

  • to extracellular metabolome profiles,
  • which represent a temporal snapshot of the relative abundance
  • for a larger number of secreted metabolites.

Our approach is complementary to

  • statistical (i.e. “top-down”) approaches to metabolome analysis [29]
  • and can potentially be used in applications such as biofluid-based diagnostics or
  • large-scale characterization of mutants strains using metabolite profiles.

This study implements a constraint-based sampling approach on

  • an updated genome-scale network of yeast metabolism
  • to systematically determine how EM level variations

are linked to global changes in intracellular metabolic flux states.

By using a sampling-based network approach and statistical methods (Figure 1),

  • EM changes were linked to systemic intracellular flux perturbations
    in an unbiased manner
  • without relying on defining single optimal flux distributions
  • used in the previously mentioned study [28].

The inferred perturbations in intracellular reaction fluxes were further analyzed

  • using reporter metabolite and subsystem (i.e., metabolic pathway) approaches [30]
  • in order to identify dominant metabolic features that are collectively perturbed (Figure 2).

The sampling-based approach also has the additional benefit of

  • being less sensitive to inaccuracies in metabolite secretion profiles than
  • optimization-based methods and can effectively be used – in biofluid metabolome analysis.
integration of exometabolomic (EM) data

integration of exometabolomic (EM) data

Figure 1. Schematic illustrating the integration of exometabolomic (EM) data with the constraint-based framework.

(A) Cells are subjected to genetic and/or environmental perturbations to secrete metabolite patterns unique to that condition.
(B) EM is detected, identified, and quantified.
(C) EM data is integrated as required secretion flux constraints to define allowable solution space.
(D) Random sampling of solution space yields the range of feasible flux distributions for intracellular reactions.
(E) Sampled fluxes were compared to sampled fluxes of another condition to determine

  • which metabolic regions were altered between the two conditions (see Figure 2).

(F) Significantly altered metabolic regions were identified.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-1.jpg

 

sampling and scoring analysis to determine intracellular flux changes

sampling and scoring analysis to determine intracellular flux changes

Figure 2. Schematic of sampling and scoring analysis to determine intracellular flux changes.

(A) Reaction fluxes are sampled for two conditions.
(B & C) Sample of flux differences is calculated by selecting random flux values from each condition

  • to obtain a distribution of flux differences for each reaction.

(D) Standardized reaction Z-scores are determined, which represent

  • how far the sampled flux differences deviates from a zero flux change.

Reaction scores can be used in

  1. visualizing perturbation subnetworks and
  2. analyzing reporter metabolites and subsystems.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-2.jpg

This study was divided into two parts and describes:

(i) the reconstruction and validation of an expanded S. cerevisiae metabolic network, iMM904; and
(ii) the systematic inference of intracellular metabolic states from

  • two yeast EM data sets using a constraint-based sampling approach.

The first EM data set compares wild type yeast to the gdh1/GDH2 (glutamate dehydrogenase) strain [31],

  • which indicated good agreement between predicted metabolic changes
  • of intracellular metabolite levels and fluxes [31,32].

The second EM data set focused on secreted amino acid measurements

  • from a separate study of yeast cultured in different
    ammonium and potassium concentrations [33].

We analyzed the EM data to gain further insight into

  • perturbed ammonium assimilation processes as well as
  1. metabolic states relating potassium limitation and
  2. ammonium excess conditions to one another.

The model-based analysis of both

  • separately published extracellular metabolome datasets
  • suggests a relationship between
  1. glutamate,
  2. threonine and
  3. folate metabolism,
  • which are collectively perturbed when
    ammonium assimilation processes are broadly disrupted
  1. either by environmental (excess ammonia) or
  2. genetic (gene deletion/overexpression) perturbations.

The methods herein present an approach to

  • interpreting extracellular metabolome data and
  • associating these measured secreted metabolite variations
  • to changes in intracellular metabolic network states.

Additional file 1. iMM904 network content.

The data provided represent the content description of the iMM904 metabolic network and
detailed information on the expanded content.

Format: XLS Size: 2.7MB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 2. iMM904 model files.

The data provided are the model text files of the iMM904 metabolic network
that is compatible with the available COBRA Toolbox [13]. The model structure
can be loaded into Matlab using the ‘SimPhenyPlus’ format with GPR and compound information.

Format: ZIP Size: 163KB Download file

Conversion of the network to a predictive model

The network reconstruction was converted to a constraint-based model using established procedures [13].

Network reactions and metabolites were assembled into a stoichiometric matrix 

  • containing the stoichiometric coefficients of the reactions in the network.

The steady-state solution space containing possible flux distributions

  • is determined by calculating the null space of S= 0,

where is the reaction flux vector.

Minimal media conditions were set through constraints on exchange fluxes

  • corresponding to the experimental measured substrate uptake rates.

All the model-based calculations were done using the Matlab COBRA Toolbox [13]

  • utilizing the glpk or Tomlab/CPLEX (Tomopt, Inc.) optimization solvers.

Chemostat growth simulations

The iMM904 model was initially validated by

  1. simulating wild type yeast growth in aerobic and anaerobic
    carbon-limited chemostat conditions
  2. and comparing the simulation results to published experimental data

on substrate uptake and byproduct secretion in these conditions [34].

The study was performed following the approach taken to validate the iFF708 model in a previous study [35].

The predicted glucose uptake rates were determined

  1. by setting the in silico growth rate to the measured dilution rate,
    – equivalent under continuous culture growth,
  2. and minimizing the glucose uptake rate.

The accuracy of in silico predictions of

  • substrate uptake and byproduct secretion by the iMM904 model
  • was similar to the accuracy obtained using the iFF708 model
  • and results are shown in Figure S1 [see Additional file 3].

Additional file 3. Supplemental figures. 

The file provides the supplemental figures and descriptions of S1, S2, S3, and S4.

Format: PDF Size: 513KB Download file

This file can be viewed with: Adobe Acrobat Reader

Genome-scale gene deletion phenotype predictions

The iMM904 network was further validated by

  • performing genome-scale gene lethality computations
  • following established procedures to determine growth phenotypes
  1. under minimal medium conditions and
  2. compared to published data.

A modified version of the biomass function used in previous iND750 studies

  1. was set as the objective to be maximized and
  2. gene deletions were simulated by

setting the flux through the corresponding reaction(s) to zero.

The biomass function was based on the experimentally measured

  1. composition of major cellular constituents
  2. during exponential growth of yeast cells and
  3. was reformulated to include trace amounts of
  4. additional cofactors and metabolites
  5. with the assumed fractional contribution of 10-.

These additional biomass compounds were included

according to the biomass formulation used in the iLL672 study

  • to improve lethality predictions through
  • the inclusion of additional essential biomass components [3].

The model was constrained by limiting

  1. the carbon source uptake to 10 mmol/h/gDW
  2. and oxygen uptake to 2 mmol/h/gDW.

Ammonia, phosphate, and sulfate were assumed to be non-limiting.

The experimental phenotyping data was obtained

  • using strains that were auxotrophic for
  1. methionine,
  2. leucine,
  3. histidine, and
  4. uracil.

These auxotrophies were simulated

  1. by deleting the appropriate genes from the model and
  2. supplementing the in silico strain with the appropriate supplements
  3. at non-limiting, but low levels.

Furthermore, trace amounts of essential nutrients that are present

  • in the experimental minimal media formulation
  1. 4-aminobenzoate,
  2. biotin,
  3. inositol,
  4. nicotinate,
  5. panthothenate,
  6. thiamin)
  • were supplied in the simulations [3].

Three distinct methods to simulate the outcome of gene deletions were utilized:

  1. Flux-balance analysis (FBA) [36-38],
  2. Minimization of Metabolic Adjustment (MoMA) [39], and
  3. a linear version of MoMA (linearMoMA).

In the linearMoMA method, minimization of the quadratic objective function
of the original MoMA algorithm

  • was replaced by minimization of the corresponding 1-norm objective function
    (i.e. sum of the absolute values of the differences of wild type FBA solution
    and the knockout strain flux solution).

The computed results were then compared to growth phenotype data
(viable/lethal) from a previously published experimental gene deletion study [3].

The comparison between experimental and in silico deletion phenotypes involved

  • choosing a threshold for the predicted relative growth rate of
  • a deletion strain that is considered to be viable.

We used standard ROC curve analysis

  • to assess the accuracy of different prediction methods and models
  • across the full range of the viability threshold parameter,
    results shown in Figure S2 [see Additional file 3].

The ROC curve plots the true viable rate against the false viable rate

  • allowing comparison of different models and methods
  • without requiring arbitrarily choosing this parameter a priori [40].

The optimal prediction performance corresponds to

  • the point closest to the top left corner of the ROC plot
    (i.e. 100% true viable rate, 0% false viable rate).

Table 1

Table 1 Comparison of iMM904 and iLL672 gene deletion predictions and experimental data under minimal media conditions
Media Model Method True viable False viable False lethal True lethal True viable % False viable % MCC
Glucose iMM904 full FBA 647 10 32 33 95.29 23.26 0.6
iMM904 full linMOMA 644 10 35 33 94.85 23.26 0.58
iMM904 full MOMA 644 10 35 33 94.85 23.26 0.58
iMM904 red FBA 440 9 28 33 94.02 21.43 0.61
iMM904 red linMOMA 437 9 31 33 93.38 21.43 0.6
iMM904 red MOMA 437 9 31 33 93.38 21.43 0.6
iLL672 full MOMA 433 9 35 33 92.52 21.43 0.57
Galactose iMM904 full FBA 595 32 36 59 94.29 35.16 0.58
iMM904 full linMOMA 595 32 36 59 94.29 35.16 0.58
iMM904 full MOMA 595 32 36 59 94.29 35.16 0.58
iMM904 red FBA 409 12 33 56 92.53 17.65 0.67
iMM904 red linMOMA 409 12 33 56 92.53 17.65 0.67
iMM904 red MOMA 409 12 33 56 92.53 17.65 0.67
iLL672 full MOMA 411 19 31 49 92.99 27.94 0.61
Glycerol iMM904 full FBA 596 43 36 47 94.3 47.78 0.48
iMM904 full linMOMA 595 44 37 46 94.15 48.89 0.47
iMM904 full MOMA 598 44 34 46 94.62 48.89 0.48
iMM904 red FBA 410 20 34 46 92.34 30.3 0.57
iMM904 red linMOMA 409 21 35 45 92.12 31.82 0.56
iMM904 red MOMA 412 21 32 45 92.79 31.82 0.57
iLL672 full MOMA 406 20 38 46 91.44 30.3 0.55
Ethanol iMM904 full FBA 593 45 29 55 95.34 45 0.54
iMM904 full linMOMA 592 45 30 55 95.18 45 0.54
iMM904 full MOMA 592 44 30 56 95.18 44 0.55
iMM904 red FBA 408 21 27 54 93.79 28 0.64
iMM904 red linMOMA 407 21 28 54 93.56 28 0.63
iMM904 red MOMA 407 20 28 55 93.56 26.67 0.64
iLL672 full MOMA 401 13 34 62 92.18 17.33 0.68
MCC, Matthews correlation coefficient (see Methods). Note that the iLL672 predictions were obtained directly from [3] and thus the viability threshold was not optimized using the maximum MCC approach.
Mo et al. BMC Systems Biology 2009 3:37  http://dx.doi.org:/10.1186/1752-0509-3-37

 

The values reported in Table 1 correspond to selecting

  • the optimal viability threshold based on this criterion.

We summarized the overall prediction accuracy of a model and method

  • using the Matthews Correlation Coefficient (MCC) [40].

The MCC ranges from -1 (all predictions incorrect) to +1 (all predictions correct) and

  • is suitable for summarizing overall prediction performance

in our case where there are substantially more viable than lethal gene deletions.

ROC plots were produced in Matlab (Mathworks, Inc.).

 

Table 1. Comparison of iMM904 and iLL672

  • gene deletion predictions and
  • experimental data

Inferring perturbed metabolic regions based on EM profiles

The method implemented in this study is shown schematically in Figures 1 and 2

Constraining the iMM904 network 

Relative levels of quantitative EM data were incorporated into the constraint-based framework

  • as overflow secretion exchange fluxes to simulate the required low-level production of
  • experimentally observed excreted metabolites.

The primary objective of this study is to associate

  • relative metabolite levels that are generally measured for metabonomic or biofluid analyses
  • to the quantitative ranges of intracellular reaction fluxes required to produce them.

However, without detailed kinetic information or dynamic metabolite measurements available,

  • we approximated EM datasets of relative quantitative metabolite levels
  • to be proportional to the rate in which they are secreted and detected
  • (at a steady state) – into the extracellular media.

This approach is analogous to approximating uptake rates based

  • on metabolite concentrations from a previous study performing sampling analysis
  • on a cardiomyocyte mitochondrial network
  • to identify differential flux distribution ranges

for various environmental (i.e. substrate uptake) conditions [19].

The raw data was normalized by the raw maximum value of the dataset
(thus the maximum secretion flux was 1 mmol/hr/gDW) with

  • an assumed error of 10%
  • to set the lower and upper bounds and thus
  • inherently accounting for sampling calculation sensitivity.

The gdh1/GDH2 strains were flask cultured under minimal glucose media conditions; thus,

  • glucose and oxygen uptake rates were set at 15 and 2 mmol/hr/gDW, respectively,
  • for the gdh1/GDH2 strain study.

In the anaerobic case the oxygen uptake rate was set to zero, and

  • sterols and fatty acids were provided as in silico supplements as described in [35].

For the potassium limitation/ammonium toxicity study

  • the growth rate was set at 0.17 1/h, and
  • the glucose uptake rate was minimized
  • to mimic experimental chemostat cultivation conditions.

These input constraints were constant for each perturbation and comparative wild-type condition

  • such that the calculated solution spaces between the conditions
  • differed based only on variations in the output secretion constraints.

FBA optimization of EM-constrained networks

A modified FBA method with minimization of the 1-norm objective function

  • between two optimal flux distributions was used
  • to determine optimal intracellular fluxes
  • based on the EM-constrained metabolic models.

This method determines two optimal flux distributions simultaneously

  • for two differently constrained models (e.g. wild type vs. mutant) –
  • these flux distributions maximize biomass production in each case and
  • the 1-norm distance between the distributions is as small as possible
  • given the two sets of constraints.

This approach avoids problems with

  • alternative optimal solutions when comparing two FBA-computed flux distributions
  • by assuming minimal rerouting of flux distibution between a perturbed network and its reference network.

Reaction flux changes from the FBA optimization results were determined

  • by computing the relative percentage fold change for each reaction
  • between the mutant and wild-type flux distributions.

Random sampling of the steady-state solution space

We utilized artificial centering hit-and-run (ACHR) Monte Carlo sampling [19,41]

  • to uniformly sample the metabolic flux solution space
  • defined by the constraints described above.

Reactions, and their participating metabolites, found to participate in intracellular loops [42]

  • were discarded from further analysis as these reactions can have arbitrary flux values.

The following sections describe the approaches used for the analysis of the different datasets.

Sampling approach used in the gdh1/GDH2 study

Due to the overall shape of the metabolic flux solution space,

  • most of the sampled flux distributions resided close to the minimally allowed growth rate
    (i.e. biomass production) and
  • corresponded to various futile cycles that utilized substrates but
  • did not produce significant biomass.

In order to study more physiologically relevant portions of the flux space

  • we restricted the sampling to the part of the solution space
  • where the growth rate was at least 50% of the maximum growth rate
  • for the condition as determined by FBA.

This assumes that cellular growth remains an important overall objective by the yeast cells

  • even in batch cultivation conditions, but
  • that the intracellular flux distributions
  • may not correspond to maximum biomass production [43].

To test the sensitivity of the results to the minimum growth rate threshold,

  • separate Monte Carlo samples were created for each minimum threshold
  • ranging from 50% to 100% at 5% increments.

We also tested the sensitivity of the results

  • to the relative magnitude of the extracellular metabolite secretion rates
  • by performing the sampling at three different relative levels

(0 corresponding to no extracellular metabolite secretion, maximum rate of 0.5 mmol/hr/gDW,
and maximum rate of 1.0 mmol/hr/gDW).

For each minimum growth rate threshold and extracellular metabolite secretion rate,

  • the ACHR sampler was run for 5 million steps and
  • a flux distribution was stored every 5000 steps.

The sensitivity analysis results are presented in Figures S3 and S4 [see Additional File 3], and

  • the results indicate that the reaction Z-scores (see below) are not significantly affected by
  1. either the portion of the solution space sampled or
  2. the exact scaling of secretion rates.

The final overall sample used was created by combining the samples for all minimum growth rate thresholds

  • for the highest extracellular metabolite secretion rate (maximum 1 mmol/hr/gDW).

This approach allowed biasing the sampling towards

  • physiologically relevant parts of the solution space
  • without imposing the requirement of strictly maximizing a predetermined objective function.

The samples obtained with no EM data were used as control samples

  • to filter reporter metabolites/subsystems whose scores were significantly high
  • due to only random differences between sampling runs.

Sampling approach used in the potassium limitation/ammonium toxicity study

Since the experimental data used in this study was generated in chemostat conditions, and

  • previous studies have indicated that chemostat flux patterns predicted by FBA are
  • close to the experimentally measured ones [43],
  • we assumed that sampling of the optimal solution space was appropriate for this study.

In order to sample a physiologically reasonable range of flux distributions,

  • samples for four different oxygen uptake rates
    (1, 2, 3, and 4 mmol/hr/gDW with 5 million steps each)
  • were combined in the final analysis.

Standardized scoring of flux differences between perturbation and control conditions

Z-score based approach was implemented to quantify differences in flux samples between two conditions (Figure 2).
First, two flux vectors were chosen randomly,

  • one from each of the two samples to be compared and
  • the difference between the flux vectors was computed.

This approach was repeated to create a sample of 10,000 (n) flux difference vectors

  • for each pair of conditions considered (e.g. mutant or perturbed environment vs. wild type).

Based on this flux difference sample, the sample mean (μdiff,i) and standard deviation (σdiff,i)

  • between the two conditions was calculated for each reaction i. The reaction Z-score was calculated as:

 

reaction Z-score

reaction Z-score

which describes the sampled mean difference deviation

  • from a population mean change of zero (i.e. no flux difference
    between perturbation and wild type).

Note that this approach allows accounting for uncertainty in the

  • flux distributions inferred based on the extracellular metabolite secretion constraints.

This is in contrast to approaches such as FBA or MoMA that would predict

  • a single flux distribution for each condition and thus potentially
  • overestimate differences between conditions.

The reaction Z-scores can then be further used in analysis

  • to identify significantly perturbed regions of the metabolic network
  • based on reporter metabolite [44] or subsystem [30] Z-scores.

These reporter regions indicate, or “report”, dominant perturbation features

  • at the metabolite and pathway levels for a particular condition.

The reporter metabolite Z-score for any metabolite can be derived from the reaction Z-scores

  • of the reactions consuming or producing j (set of reactions denoted as Rj) as:

 

reporter z-score for any metabolite j

reporter z-score for any metabolite j

where Nis the number of reactions in Rand mmet,is calculated as

 

distributional correction for m_met,j SQRT

distributional correction for m_met,j SQRT

To account and correct for background distribution, the metabolite Z-score was normalized

  • by computing μmet,Nj and σmet,,Nj corresponding to the mean mmet and
  • its standard deviation for 1,000 randomly generated reaction sets of size Nj.

Z-scores for subsystems were calculated similarly by considering the set of reactions R

  • that belongs to each subsystem k.

Hence, positive metabolite and subsystem scores indicate a significantly perturbed metabolic region

  • relative to other regions, whereas
  • a negative score indicate regions that are not perturbed
  • more significantly than what is expected by random chance.

Perturbation subnetworks of reactions and connecting metabolites were visualized using Cytoscape [45].

Results and discussion

  1. Reconstruction and validation of iMM904 network iMM904 network content 

A previously reconstructed S. cerevisiae network, iND750,

  • was used as the basis for the construction of the expanded iMM904 network.
  • Prior to its presentation here, the
    iMM904 network content was the basis for a consensus jamboree network that was recently published
  • but has not yet been adapted for FBA calculations [46].

The majority of iND750 content was carried over and

  • further expanded on to construct iMM904, which accounts for
  1. 904 genes,
  2. 1,228 individual metabolites, and
  3. 1,412 reactions of which
  •                       395 are transport reactions.

Both the number of gene-associated reactions and the number of metabolites

  • increased in iMM904 compared with the iND750 network.

Additional genes and reactions included in the network primarily expanded the

  • lipid,
  • transport, and
  • carbohydrate subsystems.

The lipid subsystem includes

  • new genes and
  • reactions involving the degradation of sphingolipids and glycerolipids.

Sterol metabolism was also expanded to include

  • the formation and degradation of steryl esters, the
  •                      storage form of sterols.

The majority of the new transport reactions were added

  • to connect network gaps between intracellular compartments
  • to enable the completion of known physiological functions.

We also added a number of new secretion pathways

  • based on experimentally observed secreted metabolites [31].

A number of gene-protein-reaction (GPR) relationships were modified

  • to include additional gene products that are required to catalyze a reaction.

For example, the protein compounds

  • thioredoxin and
  • ferricytochrome C

were explicitly represented as compounds in iND750 reactions, but

  • the genes encoding these proteins were not associated with their corresponding GPRs.

Other examples include glycogenin and NADPH cytochrome p450 reductases (CPRs),

  1. which are required in the assembly of glycogen and
  2. to sustain catalytic activity in cytochromes p450, respectively.

These additional proteins were included in iMM904 as

  • part of protein complexes to provide a more complete
  • representation of the genes and
  • their corresponding products necessary for a catalytic activity to occur.

Major modifications to existing reactions were in cofactor biosynthesis, namely in

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways.

Reactions from previous S. cerevisiae networks associated with

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways

were essentially inferred from known reaction mechanisms based on

  • reactions in previous network reconstructions of E. coli [2,47].

These pathways were manually reviewed

  • based on current literature and subsequently replaced by
  • reactions and metabolites specific to yeast.

Additional changes in other subsystems were also made, such as

  1. changes to the compartmental location of a gene and
  2. its corresponding reaction(s),
  3. changes in reaction reversibility and cofactor specificity, and
  4. the elucidation of particular transport mechanisms.

A comprehensive listing of iMM904 network contents as well as

  • a detailed list of changes between iND750 and iMM904 is included
    [see Additional file 1].

Predicting deletion growth phenotypes

The updated genome-scale iMM904 metabolic network was validated

  • by comparing in silico single-gene deletion predictions to
  • in vivo results from a previous study used
  • to analyze another S. cerevisiae metabolic model, iLL672 [3].

This network was constructed based on the iFF708 network [22],

  • which was also the starting point for
  • reconstructing the iND750 network [2].

The experimental data used to validate the iLL672 model consisted of

3,360 single-gene knockout strain phenotypes evaluated

  • under minimal media growth conditions with
  1. glucose,
  2. galactose,
  3. glycerol, and
  4. ethanol

as sole carbon sources. Growth phenotypes for the iMM904 network were predictedusing

  1. FBA [3234],
  2. MoMA [35], and
  3. linear MoMA methods

as described in Methods and subsequently compared to the experimental data (Table 1).

Each deleted gene growth prediction comparison was classified as

  1. true lethal,
  2. true viable,
  3. false lethal, or
  4. false viable.

The growth rate threshold for considering a prediction viable was chosen

  • for each condition and method separately
  • to optimize the tradeoff between true viable and false viable predictions
    (maximum Matthews correlation coefficient, see Methods).

Since iMM904 has 212 more genes than iLL672 with experimental data, we also present results

  • for the subset of iMM904 predictions with genes included in iLL672 (reduced iMM904 set).

When the same gene sets are compared, iMM904 improves gene lethality predictions under

  • glucose,
  • galactose, and
  • glycerol conditions

over iLL672 somewhat, but is less accurate

  • at predicting growth phenotypes under the ethanol condition.

It should be noted that the iLL672 predictions were obtained directly from [3]

  • thus the growth rate threshold was not optimized similarly to iMM904 predictions.

Overall, when viability cutoff is chosen

  • as indicated above for each method separately,
  • the three prediction methods perform similarly
  1. FBA,
  2. MOMA, and
  3. linear MOMA) .

While the full gene complement in iMM904 greatly increased

  • the number of true viable predictions,
  • the full model also made significantly more false viable predictions
  • compared with reduced iMM904 and iLL672 predictions.

However, it is important to note that 143 reactions involved in dead-end biosynthetic pathways were actually

  • removed from iFF708 to build the iLL672 reconstruction [3].

These dead-ends are considered “knowledge gaps” in pathways

  • that have not been fully characterized and, as a result,
  • lead to false viable predictions when determining gene essentiality
  • if the pathway is in fact required for growth under a certain condition [2,26].

As more of these pathways are elucidated and

  • included in the model to
  • fill in existing network gaps,
  • we can expect false viable prediction rates to consequently decrease.

Thus, while a larger network has a temporarily reduced capacity to accurately predict gene deletion phenotypes,

  • it captures a more complete picture of currently known metabolic functions and
  • provides a framework for network expansion as new pathways are elucidated [48].

 

Inferring intracellular perturbation states from metabolic profiles – Aerobic and anaerobic gdh1/GDH2 mutant behavior

The gdh1/GDH2 mutant strain was previously developed [49,50]

  • to lower NADPH consumption in ammonia assimilation, which would
  • favor the NADPH-dependent fermentation of xylose.

In this strain, the NADPH-dependent glutamate dehydrogenase, Gdh1, was

  • deleted and the NADH-dependent form of the enzyme, Gdh2,
  •                     was overexpressed.

The net effect is to allow efficient assimilation of ammonia

  • into glutamate using NADH instead of NADPH as a cofactor.

While growth characteristics remained unaffected,

  • relative quantities of secreted metabolites differed between the wild-type and mutant strain
  • under aerobic and anaerobic conditions.

We analyzed EM data for the gdh1/GDH2 and wild-type strains reported

  • in [31] under aerobic and anaerobic conditions separately using
  • both FBA optimization and
  • sampling-based approaches as described in Methods.

43 measured extracellular and intracellular metabolites from the original dataset [31],

  • primarily of central carbon and amino acid metabolism,
  • were explicitly represented in the iMM904 network [see Additional file 4].

Extracellular metabolite levels were used

  • to formulate secretion constraints and
  • differential intracellular metabolites were used
  • to compare and validate the intracellular flux predictions.

Perturbed reactions from the FBA results were

  • determined by calculating relative flux changes, and
  • reaction Z-scores were calculated from the sampling analysis
  • to quantify flux changes between the mutant and wild-type strains,
  • with Z reaction > 1.96 corresponding to a two-tailed p-value < 0.05 and
  • considered to be significantly perturbed [see Additional file 4].

Additional file 4. Gdh mutant aerobic and anaerobic analysis results. 

The data provided are the full results for the exometabolomic analysis of aerobic and anerobic gdh1/GDH2 mutant.

Format: XLS Size: 669KB Download file

This file can be viewed with: Microsoft Excel Viewer

To validate the predicted results, reaction flux changes from both FBA and sampling methods were compared to differential intracellular metabolite level data measured from the same study. Intracellular metabolites involved in highly perturbed reactions (i.e. reactants and products) predicted from FBA and sampling analyses were identified and
compared to metabolites that were experimentally identified as significantly changed (< 0.05) between mutant and wild-type. Statistical measures of recall, accuracy, and
precision were calculated and represent the predictive sensitivity, exactness, and reproducibility respectively. From the sampling analysis, a considerably larger number of
significantly perturbed reactions are predicted in the anaerobic case (505 reactions, or 70.7% of active reactions) than in aerobic (394 reactions, or 49.8% of active reactions). The top percentile of FBA flux changes equivalent to the percentage of significantly perturbed sampling reactions were compared to the intracellular data. Results from both analyses are summarized in Table 2. Sampling predictions were considerably higher in recall than FBA predictions for both conditions, with respective ranges of 0.83–1
compared to 0.48–0.96. Accuracy was also higher in sampling predictions; however, precision was slightly better in the FBA predictions as expected due to the smaller
number of predicted changes. Overall, the sampling predictions of perturbed intracellular metabolites are strongly consistent with the experimental data and significantly
outperforms that of FBA optimization predictions in accurately predicting differential metabolites involved in perturbed intracellular fluxes.

Table 2. Statistical comparison of the differential intracellular metabolite data set (< 0.05) with metabolites involved in perturbed reactions predicted by FBA optimization and sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.

 

Table 2 Statistical comparison of the differential intracellular metabolite data set (p < 0.05)
with metabolites involved in perturbed reactions predicted by FBA optimization and
sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.
                           Aerobic                         Anaerobic                             Overall
FBA Sampling FBA Sampling FBA
Recall 0.48 0.83 0.96 1 0.71 0.91
Accuracy 0.55 0.62 0.64 0.64 0.6 0.63
Precision 0.78 0.69 0.64 0.63 0.68 0.66
Overall statistics indicate combined results of both conditions.
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37


Figure 3.
 Perturbation reaction subnetwork of gdh1/GDH2 mutant under aerobic conditions.

The network illustrates a simplified subset of highly perturbedPerturbation subnetworks can be drawn to visualize predicted significantly perturbed intracellular reactions and illustrate their connection to the observed secreted metabolites in the aerobic and anaerobic gdh1/GDH2 mutants.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Figure 3 shows an example of a simplified aerobic perturbation subnetwork consisting primarily of proximal pathways connected directly to a subset of major secreted
metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate.

Figure 4 displays anaerobic reactions with Z-scores of similar magnitude to the perturbed reactions in Figure 3. The same subset of metabolites is also present in the
larger anaerobic perturbation network and indicates that the NADPH/NADH balance perturbation induced by the gdh1/GDH2 manipulation has widespread effects
beyond just altering glutamate metabolism anaerobically.

Interestingly, it is clear that the majority of the secreted metabolite pathways involve connected perturbed reactions that broadly converge on glutamate.

Note that Figures 3 and 4 only show the subnetworks that consisted of two or more connected reactions  for a number of secreted metabolites no contiguous perturbed pathway could be identified by the sampling approach. This indicates that the secreted metabolite pattern alone is not sufficient to determine which specific
production and secretion pathways are used by the cell for these metabolites.

Reactions connected to aerobically-secreted metabolites predicted from the sampling analysis of the gdh1/GDH2 mutant strain.
The major secreted metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate

were also detected in the anaerobic condition. Metabolite abbreviations are found in Additional file 1.

Figure 4.

Perturbation reaction subnetwork of gdh1/GDH2 mutant under anaerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

Subnetwork illustrates the highly perturbed anaerobic reactions of similar Z-reaction magnitude to the reactions in Figure 3.

A significantly larger number of reactions indicates mutant metabolic effects are more widespread in the anaerobic environment.
The network shows that perturbed pathways converge on glutamate, the main site in which the gdh1/GDH2 modification was introduced, which
suggests that the direct genetic perturbation effects are amplified under this environment. Metabolite abbreviations are found in Additional file 1.

To further highlight metabolic regions that have been systemically affected by the gdh1/GDH2 modification, reporter metabolite and subsystem methods [30] were used to
summarize reaction scores around specific metabolites and in specific metabolic subsystems. The top ten significant scores for metabolites/subsystems associated with more
than three reactions are summarized in Tables 3 (aerobic) and 4 (anaerobic), with Z > 1.64 corresponding to < 0.05 for a one-tailed distribution. Full data for all reactions,
reporter metabolites, and reporter subsystems is included [see Additional file 4].

Table 3. List of the top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in aerobic conditions.

Table 3
List of the top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in aerobic conditions.
Reporter metabolite Z-score No of reactions*
L-proline [c] 2.71 4
Carbon dioxide [m] 2.51 15
Proton [m] 2.19 51
Glyceraldehyde 3-phosphate [c] 1.93 7
Ubiquinone-6 [m] 1.82 5
Ubiquinol-6 [m] 1.82 5
Ribulose-5-phosphate [c] 1.8 4
Uracil [c] 1.74 4
L-homoserine [c] 1.72 4
Alpha-ketoglutarate [m] 1.71 8
Reporter subsystem Z-score No of reactions
Citric Acid Cycle 4.58 7
Pentose Phosphate Pathway 3.29 12
Glycine and Serine Metabolism 2.69 17
Alanine and Aspartate Metabolism 2.65 6
Oxidative Phosphorylation 1.79 8
Thiamine Metabolism 1.54 8
Arginine and Proline Metabolism 1.44 20
Other Amino Acid Metabolism 1.28 5
Glycolysis/Gluconeogenesis 0.58 14
Anaplerotic reactions 0.19 9
*Number of reactions categorized in a subsystem or found to be neighboring each metabolite
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37

Table 4. List of top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in anaerobic conditions.

 

Table 4
List of top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in anaerobic conditions.
Reporter metabolite Z-score No of reactions
Glutamate [c] 4.52 35
Aspartate [c] 3.21 11
Alpha-ketoglutarate [c] 2.66 17
Glycine [c] 2.65 7
Pyruvate [m] 2.56 7
Ribulose-5-phosphate [c] 2.43 4
Threonine [c] 2.28 6
10-formyltetrahydrofolate [c] 2.27 5
Fumarate [c] 2.27 5
L-proline [c] 2.04 4
Reporter subsystem Z-score No of reactions
Valine, Leucine, and Isoleucine Metabolism 3.97 15
Tyrosine, Tryptophan, and Phenylalanine Metabolism 3.39 23
Pentose Phosphate Pathway 3.29 11
Purine and Pyrimidine Biosynthesis 3.08 40
Arginine and Proline Metabolism 2.96 19
Threonine and Lysine Metabolism 2.74 14
NAD Biosynthesis 2.66 7
Alanine and Aspartate Metabolism 2.65 6
Histidine Metabolism 2.24 10
Cysteine Metabolism 1.85 10
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37
Open Data

Perturbations under aerobic conditions largely consisted of pathways involved in mediating the NADH and NADPH balance. Among the highest scoring aerobic subsystems
are TCA cycle and pentose phosphate pathway – key pathways directly involved in the generation of NADH and NADPH. Reporter metabolites involved in these
subsystems –

  • glyceraldehyde-3-phosphate,
  • ribulose-5-phosphate, and
  • alpha-ketoglutarate – were also identified.

These results are consistent with flux and enzyme activity measurements

  • of the gdh1/GDH2 strain under aerobic conditions [32],
  1. which reported significant reduction in the pentose phosphate pathway flux
  2. with concomitant changes in other central metabolic pathways.

Levels of several TCA cycle intermediates (e.g. fumarate, succinate, malate) were also elevated

  • in the gdh1/GDH2 mutant according to the differential intracellular metabolite data.

Altered energy metabolism, as indicated by

  • reporter metabolites (i.e. ubiquinone- , ubiquinol, mitochondrial proton)
  • and subsystem (oxidative phosphorylation),

is certainly feasible as NADH is a primary reducing agent for ATP production.

Pentose phosphate pathway and NAD biosynthesis also appears

  • among the most perturbed anaerobic subsystems, further suggesting
  • perturbed cofactor balance as a common, dominant effect under both conditions.

Glutamate dehydrogenase is a critical enzyme of amino acid biosynthesis as it acts as

  • the entry point for ammonium assimilation via glutamate.

Consequently, metabolic subsystems involved in amino acid biosynthesis were broadly perturbed

  • as a result of the gdh1/GDH2 modification in both aerobic and anaerobic conditions.

For example, the proline biosynthesis pathway that uses glutamate as a precursor

  • was significantly perturbed in both conditions,
  • with significantly changed intracellular and extracellular levels.

There were differences, however, in that more amino acid related subsystems were

  • significantly affected in the anaerobic case (Table 4),
  • further highlighting that altered ammonium assimilation in the mutant
  • has a more widespread effect under anaerobic conditions.

This effect is especially pronounced for

  • threonine and nucleotide metabolism,
  • which were predicted to be significantly perturbed only in anaerobic conditions.

Intracellular threonine levels were amongst the most significantly reduced

  • relative to other differential intracellular metabolites in the anaerobically grown gdh1/GDH2 strain
    (see [31] and Additional file 4), and
  • the relationship between threonine and nucleotide biosynthesis is further supported

by threonine’s recently discovered role as a key precursor in yeast nucleotide biosynthesis [51].

Other key anaerobic reporter metabolites are

  • glycine and 10-formyltetrahydrofolate,
  • both of which are involved in the cytosolic folate cycle (one-carbon metabolism).

Folate is intimately linked to biosynthetic pathways of

  • glycine (with threonine as its precursor) and purines
  • by mediating one-carbon reaction transfers necessary in their metabolism and
  • is a key cofactor in cellular growth [52].

Thus, the anaerobic perturbations identified in the analysis emphasize the close relationship

  • between threonine, folate, and nucleotide metabolic pathways as well as
  • their potential connection to perturbed ammonium assimilation processes.

Interestingly, this association has been previously demonstrated at the transcriptional level

  • as yeast ammonium assimilation (via glutamine synthesis) was found to be
  • co-regulated with genes involved in glycine, folate, and purine synthesis [53].

In summary, the overall differences in predicted gdh1/GDH2 mutant behavior

  • under aerobic and anaerobic conditions show that changes in flux states
  • directly related to modified ammonium assimilation pathway
  1. are amplified anaerobically whereas the
  2. indirect effects through NADH/NADPH balance are more significant aerobically.

Perturbed metabolic regions under aerobic conditions were predominantly

  • in central metabolic pathways involved in responding to the changed NADH/NADPH demand
  • and did not necessarily emphasize that glutamate dehydrogenase was the site of the genetic modification.

The majority of affected anaerobic pathways were involved directly

  • in modified ammonium assimilation as evidenced by

1) significantly perturbed amino acid subsystems,

2) a broad perturbation subnetwork converging on glutamate (Figure 4), and

3) glutamate as the most significant reporter metabolite (Table 4).

Potassium-limited and excess ammonium environments

A recent study reported that potassium limitation resulted in significant

  • growth retardation effect in yeast due to excess ammonium uptake
  • when ammonium was provided as the sole nitrogen source [33].

The proposed mechanism for this effect was that ammonium

  • could to be freely transported through potassium channels
  • when potassium concentrations were low in the media environment, thereby
  • resulting in excess ammonium uptake [33].

As a result, yeast incurred a significant metabolic cost

  • in assimilating ammonia to glutamate and
  • secreting significant amounts of glutamate and other amino acids
  • in potassium-limited conditions as a means to detoxify the excess ammonium.

A similar effect was observed when yeast was grown

  • with no potassium limitation,
  • but with excess ammonia in the environment.

While the observed effect of both environments (low potassium or excess ammonia) was similar,

  • quantitatively unique amino acid secretion profiles suggested that
  • internal metabolic states in these conditions are potentially different.

In order to elucidate the differences in internal metabolic states, we utilized

  • the iMM904 model and the EM profile analysis method to analyze amino acid secretion profiles
  • for a range of low potassium and high ammonia conditions reported in [33].

As before, we utilized amino acid secretion patterns as constraints to the iMM904 model,

  1. sampled the allowable solution space,
  2. computed reaction Z-scores for changes from a reference condition (normal potassium and ammonia), and
  3. finally summarized the resulting changes using reporter metabolites.

Figure 5 shows a clustering of the most significant reporter metabolites (Z ≥ 1.96 in any of the four conditions studied)

  • obtained from this analysis across the four conditions studied.

Interestingly, the potassium-limited environment perturbed only a subset of

  • the significant reporter metabolites identified in the high ammonia environments.

Both low potassium environments shared a consistent pattern of

  • highly perturbed amino acids and related precursor biosynthesis metabolites
    (e.g. pyruvate, PRPP, alpha-ketoglutarate)
  • with high ammonium environments.

The amino acid perturbation pattern (indicated by red labels in Figure 5) was present in

  • the ammonium-toxic environments, although the pattern was
  • slightly weaker for the lower ammonium concentration.

Nevertheless, the results clearly indicate that a similar

  • ammonium detoxifying mechanism that primarily perturbs pathways
  • directly related to amino acid metabolism
  • exists under both types of media conditions.

Figure 5.

Clustergram of top reporter metabolites - y in ammonium-toxic and potassium-limited conditions

Clustergram of top reporter metabolites – y in ammonium-toxic and potassium-limited conditions

Clustergram of top reporter metabolites (i.e. in yellow) in ammonium-toxic and potassium-limited conditions.

Amino acid perturbation patterns (shown in red) were shown to be consistently scored across conditions, indicating that potassium-limited environments K1 (lowest
concentration) and K2 (low concentration) elicited a similar ammonium detoxification response as ammonium-toxic environments N1 (high concentration) and N2
(highest concentration). Metabolites associated with folate metabolism (highlighted in green) are also highly perturbed in ammonium-toxic conditions. Metabolite
abbreviations are found in Additional file 1.

In addition to perturbed amino acids, a secondary effect notably appears at high ammonia levels in which metabolic regions related to folate metabolism are significantly affected. As highlighted in green in Figure 3, we predicted significantly perturbed key metabolites involved in the cytosolic folate cycle. These include tetrahydrofolate derivatives and other metabolites connected to the folate pathway, namely glycine and the methionine-derived methylation cofactors S-adenosylmethionine and S-adenosyl-homocysteine. Additionally, threonine was identified to be a key perturbed metabolite in excess ammonium conditions. These results further illustrate the close
connection between threonine biosynthesis, folate metabolism involving glycine derived from its threonine precursor, and nucleotide biosynthesis [51] that was discussed in
conjunction with the gdh1/GDH2 strain data. Taken together with the anaerobic gdh1/GDH2 data, the results consistently suggest highly perturbed threonine and folate
metabolism when amino acid-related pathways are broadly affected.

In both ammonium-toxic and potassium-limited environments, impaired cellular growth was observed, which can be attributed to high energetic costs of increased
ammonium assimilation to synthesize and excrete amino acids. However, under high ammonium environments, reporter metabolites related to threonine and folate
metabolism indicated that their perturbation, and thus purine supply, may be an additional factor in decreasing cellular viability as there is a direct relationship between
intracellular folate levels and growth rate [54]. Based on these results, we concluded that while potassiumlimited growth in yeast indeed shares physiological features with
growth in ammonium excess, its effects are not as detrimental as actual ammonium excess. The effects on proximal amino acid metabolic pathways are similar in both
environments as indicated by the secretion of the majority of amino acids. However, when our method was applied to analyze the physiological basis behind differences in
secretion profiles between low potassium and high ammonium conditions, ammonium excess was predicted to likely disrupt physiological ammonium assimilation processes,
which in turn potentially impacts folate metabolism and associated cellular growth.

Conclusion

The method presented in this study presents an approach to connecting intracellular flux states to metabolites that are excreted under various physiological conditions. We
showed that well-curated genome-scale metabolic networks can be used to integrate and analyze quantitative EM data by systematically identifying altered intracellular
pathways related to measured changes in the extracellular metabolome. We were able to identify statistically significant metabolic regions that were altered as a result of
genetic (gdh1/GD2 mutant) and environmental (excess ammonium and limited potassium) perturbations, and the predicted intracellular metabolic changes were consistent
with previously published experimental data including measurements of intracellular metabolite levels and metabolic fluxes. Our reanalysis of previously published EM data
on ammonium assimilation-related genetic and environmental perturbations also resulted in testable hypotheses about the role of threonine and folate pathways in mediating
broad responses to changes in ammonium utilization. These studies also demonstrated that the samplingbased method can be readily applied when only partial secreted
metabolite profiles (e.g. only amino acids) are available.

With the emergence of metabolite biofluid biomarkers as a diagnostic tool in human disease [55,56] and the availability of genome-scale human metabolic networks [1],
extensions of the present method would allow identifying potential pathway changes linked to these biomarkers. Employing such a method for studying yeast metabolism was possible as the metabolomic data was measured under controllable environmental conditions where the inputs and outputs of the system were defined. Measured metabolite biomarkers in a clinical setting, however, is far from a controlled environment with significant variations in genetic, nutritional, and environmental factors between different
patients. While there are certainly limitations for clinical applications, the method introduced here is a progressive step towards applying genome-scale metabolic networks
towards analyzing biofluid metabolome data as it 1) avoids the need to only study optimal metabolic states based on a predetermined objective function, 2) allows dealing with noisy experimental data through the sampling approach, and 3) enables analysis even with limited identification of metabolites in the data. The ability to establish potential
connections between extracellular markers and intracellular pathways would be valuable in delineating the genetic and environmental factors associated with a particular
disease.

Authors’ contributions

Conceived and designed the experiments: MLM MJH BOP. Performed experiments: MLM MJH. Analyzed the data: MLM MJH. Wrote the paper: MLM MJH BOP. All authors have read and approved the final manuscript.

Acknowledgements

We thank Jens Nielsen for providing the raw metabolome data for the mutant strain, and Jan Schellenberger and Ines Thiele for valuable discussions. This work was supported by NIH grant R01 GM071808. BOP serves on the scientific advisory board of Genomatica Inc.

 

References

  1. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BO: Global reconstruction of the human metabolic network based on genomic and bibliomic data. 

Proc Natl Acad Sci USA 2007, 104(6):1777-1782. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Duarte NC, Herrgard MJ, Palsson B: Reconstruction and Validation of Saccharomyces cerevisiae iND750, a Fully Compartmentalized Genome-Scale Metabolic Model. 

Genome Res 2004, 14(7):1298-1309. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Kuepfer L, Sauer U, Blank LM: Metabolic functions of duplicate genes in Saccharomyces cerevisiae. 

Genome Res 2005, 15(10):1421-1430. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Nookaew I, Jewett MC, Meechai A, Thammarongtham C, Laoteng K, Cheevadhanarak S, Nielsen J, Bhumiratana S: The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. 

BMC Syst Biol 2008, 2:71. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text

  1. Edwards JS, Palsson BO: Systems properties of the Haemophilus influenzae Rd metabolic genotype. 

J biol chem 1999, 274(25):17410-17416. PubMed Abstract | Publisher Full Text

  1. Edwards JS, Palsson BO: The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. 

Proc Natl Acad Sci USA 2000, 97(10):5528-5533. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Thiele I, Vo TD, Price ND, Palsson B: An Expanded Metabolic Reconstruction of Helicobacter pylori (IT341 GSM/GPR): An in silico genome-scale characterization of single and double deletion mutants. 

J Bacteriol 2005, 187(16):5818-5830. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Vo TD, Greenberg HJ, Palsson BO: Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. 

J Biol Chem 2004, 279(38):39532-39540. PubMed Abstract | Publisher Full Text

  1. Reed JL, Vo TD, Schilling CH, Palsson BO: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). 

Genome Biology 2003, 4(9):R54.51-R54.12. BioMed Central Full Text

  1. Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, V H, Palsson BO: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1261 ORFs and thermodynamic information. 

Molecular Systems Biology 2007, 3:121. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Suthers PF, Dasika MS, Kumar VS, Denisov G, Glass JI, Maranas CD: A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189. 

PLos Comp Biol 2009, 5(2):e1000285. Publisher Full Text

  1. Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. 

Nat Rev Microbiol 2004, 2(11):886-897. PubMed Abstract | Publisher Full Text

  1. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ: Quantitative Prediction of Cellular Metabolism with Constraint-based Models: The COBRA Toolbox. 

Nature protocols 2007, 2(3):727-738. PubMed Abstract | Publisher Full Text

  1. Reed JL, Palsson BO: Genome-Scale In Silico Models of E. coli Have Multiple Equivalent Phenotypic States: Assessment of Correlated Reaction Subsets That Comprise Network States. 

Genome Res 2004, 14(9):1797-1805. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Fong SS, Palsson BO: Metabolic gene deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. 

Nature Genetics 2004, 36(10):1056-1058. PubMed Abstract | Publisher Full Text

  1. Ibarra RU, Edwards JS, Palsson BO: Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. 

Nature 2002, 420(6912):186-189. PubMed Abstract | Publisher Full Text

  1. Schellenberger J, Palsson BØ: Use of randomized sampling for analysis of metabolic networks. 

J Biol Chem 2009, 284(9):5457-5461. PubMed Abstract | Publisher Full Text

  1. Almaas E, Kovács B, Vicsek T, Oltvai ZN, Barabási AL: Global organization of metabolic fluxes in the bacterium Escherichia coli. 

Nature 2004, 427(6977):839-843. PubMed Abstract | Publisher Full Text

  1. Thiele I, Price ND, Vo TD, Palsson BO: Candidate metabolic network states in human mitochondria: Impact of diabetes, ischemia, and diet. 

J Biol Chem 2005, 280(12):11683-11695. PubMed Abstract | Publisher Full Text

  1. Kell DB: Metabolomics and systems biology: making sense of the soup. 

Curr Opin Microbiol 2004, 7(3):296-307. PubMed Abstract | Publisher Full Text

  1. Kell DB, Brown M, Davey HM, Dunn WB, Spasic I, Oliver SG: Metabolic footprinting and systems biology: the medium is the message. 

Nat Rev Microbiol 2005, 3(7):557-565. PubMed Abstract | Publisher Full Text

  1. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB: Metabolomics by numbers: acquiring and understanding global metabolite data. 

Trends Biotechnol 2004, 22(5):245-252. PubMed Abstract | Publisher Full Text

  1. Lenz EM, Bright J, Wilson ID, Morgan SR, Nash AF: A 1H NMR-based metabonomic study of urine and plasma samples obtained from healthy human subjects. 

J Pharm Biomed Anal 2003, 33(5):1103-1115. PubMed Abstract | Publisher Full Text

  1. Allen J, Davey HM, Broadhurst D, Heald JK, Rowland JJ, Oliver SG, Kell DB: High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. 

Nat Biotech 2003, 21(6):692-696. Publisher Full Text

  1. Nicholson JK, Connelly J, Lindon JC, Holmes E: Metabonomics: a platform for studying drug toxicity and gene function. 

Nat Rev Drug Discov 2002, 1(2):153-161. PubMed Abstract | Publisher Full Text

  1. Mortishire-Smith RJ, Skiles GL, Lawrence JW, Spence S, Nicholls AW, Johnson BA, Nicholson JK: Use of metabonomics to identify impaired fatty acid metabolism as the mechanism of a drug-induced toxicity. 

Chem Res Toxicol 2004, 17(2):165-173. PubMed Abstract | Publisher Full Text

  1. Sabatine MS, Liu E, Morrow DA, Heller E, McCarroll R, Wiegand R, Berriz GF, Roth FP, Gerszten RE: Metabolomic identification of novel biomarkers of myocardial ischemia. 

Circulation 2005, 112(25):3868-3875. PubMed Abstract | Publisher Full Text

  1. Cakir T, Efe C, Dikicioglu D, Hortaçsu AKB, Oliver SG: Flux balance analysis of a genome-scale yeast model constrained by exometabolomic data allows metabolic system identification of genetically different strains. 

Biotechnol Prog 2007, 23(2):320-326. PubMed Abstract | Publisher Full Text

  1. Bang JW, Crockford DJ, Holmes E, Pazos F, Sternberg MJ, Muggleton SH, Nicholson JK: Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods. 

J Proteome Res 2008, 7(2):497-503. PubMed Abstract | Publisher Full Text

  1. Oliveira AP, Patil KR, Nielsen J: Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks. 

BMC Syst Biol 2008, 2:17. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text

  1. Villas-Boas SG, Moxley JF, Akesson M, Stephanopoulos G, Nielsen J: High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. 

Biochem J 2005, 388(Pt 2):669-677. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Moreira dos Santos M, Thygesen G, Kötter P, Olsson L, Nielsen J: Aerobic physiology of redox-engineered Saccharomyces cerevisiae strains modified in the ammonium assimilation for increased NADPH availability. 

FEMS Yeast Res 2003, 4(1):59-68. PubMed Abstract | Publisher Full Text

  1. Hess DC, Lu W, Rabinowitz JD, Botstein D: Ammonium toxicity and potassium limitation in yeast. 

PLoS Biol 2006, 4(11):e351. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Nissen TL, Schulze U, Nielsen J, Villadsen J: Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae. 

Microbiology 1997, 143(Pt 1):203-218. PubMed Abstract | Publisher Full Text

  1. Famili I, Forster J, Nielsen J, Palsson BO: Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. 

Proc Natl Acad Sci USA 2003, 100(23):13134-13139. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Bonarius HPJ, Schmid G, Tramper J: Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. 

Trends in Biotechnology 1997, 15(8):308-314. Publisher Full Text

  1. Edwards JS, Palsson BO: Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. 

BMC Bioinformatics 2000, 1:1. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text

  1. Varma A, Palsson BO: Metabolic Flux Balancing: Basic concepts, Scientific and Practical Use. 

Nat Biotechnol 1994, 12:994-998. Publisher Full Text

  1. Segre D, Vitkup D, Church GM: Analysis of optimality in natural and perturbed metabolic networks. 

Proc Natl Acad Sci USA 2002, 99(23):15112-15117. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. 

Bioinformatics 2000, 16(5):412-424. PubMed Abstract | Publisher Full Text

  1. Price ND, Schellenberger J, Palsson BO: Uniform Sampling of Steady State Flux Spaces: Means to Design Experiments and to Interpret Enzymopathies. 

Biophysical Journal 2004, 87(4):2172-2186. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Price ND, Thiele I, Palsson BO: Candidate states of Helicobacter pylori’s genome-scale metabolic network upon application of “loop law” thermodynamic constraints. 

Biophysical J 2006, 90(11):3919-3928. Publisher Full Text

  1. Schuetz R, Kuepfer L, Sauer U: Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. 

Mol Syst Biol 2007, 3:119. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Patil KR, Nielsen J: Uncovering transcriptional regulation of metabolism by using metabolic network topology. 

Proc Natl Acad Sci USA 2005, 102(8):2685-2689. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. 

Genome Res 2003, 13(11):2498-2504. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Herrgard MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Bluthgen N, Borger S, Costenoble R, Heinemann M, et al.: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. 

Nat Biotech 2008, 26:1155-1160. Publisher Full Text

  1. Forster J, Famili I, Fu PC, Palsson BO, Nielsen J: Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network. 

Genome Research 2003, 13(2):244-253. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, Bui OT, Knight EM, Fong SS, Palsson BO: Systems Approach to Genome Annotation: Prediction and Validation of Metabolic Functions. 

Proc Natl Acad Sci USA 2006, 103(46):17480-17484. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Nissen TL, Kielland-Brandt MC, Nielsen J, Villadsen J: Optimization of ethanol production in Saccharomyces cerevisiae by metabolic engineering of the ammonium assimilation. 

Metab Eng 2000, 2(1):69-77. PubMed Abstract | Publisher Full Text

  1. Roca C, Nielsen J, Olsson L: Metabolic engineering of ammonium assimilation in xylose-fermenting Saccharomyces cerevisiae improves ethanol production. 

Appl Environ Microbiol 2003, 69(8):4732-4736. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Hartman JL IV: Buffering of deoxyribonucleotide pool homeostasis by threonine metabolism. 

Proc Natl Acad Sci USA 2007, 104(28):11700-11705. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Gelling CL, Piper MD, Hong SP, Kornfeld GD, Dawes IW: Identification of a novel one-carbon metabolism regulon in Saccharomyces cerevisiae. 

J Biol Chem 2004, 279(8):7072-7081. PubMed Abstract | Publisher Full Text

  1. Denis V, Daignan-Fornier B: Synthesis of glutamine, glycine and 10-formyl tetrahydrofolate is coregulated with purine biosynthesis in Saccharomyces cerevisiae. 

Mol Gen Genet 1998, 259(3):246-255. PubMed Abstract | Publisher Full Text

  1. Hjortmo S, Patring J, Andlid T: Growth rate and medium composition strongly affect folate content in Saccharomyces cerevisiae. 

Int J Food Microbiol 2008, 123(1–2):93-100. PubMed Abstract | Publisher Full Text

  1. Kussmann MRF, Affolter M: OMICS-driven biomarker discovery in nutrition and health. 

J Biotechnol 2006, 124(4):758-787. PubMed Abstract | Publisher Full Text

  1. Serkova NJ, Niemann CU: Pattern recognition and biomarker validation using quantitative 1H-NMR-based metabolomics. 

Expert Rev Mol Diagn 2006, 6(5):717-731. PubMed Abstract | Publisher Full Text

 

Read Full Post »

Metabolomic analysis of two leukemia cell lines. II.

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence

 

In Part I of metabolomics of two leukemia cell lines, we have established a major premise for the study, an insight into the use of an experimental model, and some insight into questions raised.

I here return to examine these before pursuing more detail in the study.

Q1. What strong metabolic pathways come into focus in this study?

Answer – The aerobic and anaerobic glycolytic pathways, with a difference measured in the extent of participation of mitochondrial oxidative phosphorylation.

Q2. Would we expect to also gain insight into the effect, on balance, played by a suppressed ubiquitin pathway?

Answer – lets look into this in Part II.

Q3. Would the synthesis of phospholipid and the maintenance of membrane structures requires availability of NADPH, which would be a reversal of the TCA cycle at the cost of delta G in catabolic energy, be consistent with increased dependence of anaerobic glycolysis  with unchecked replication?

Answer: Part II might show this, as the direction and the difference between the cell lines is consistent with a Warburg (Pasteur) effect.

Recall the observation that the model is based on experimental results from  lymphocytic leukemia cell lines in cell culture.  The internal metabolic state is inferred from measurement of external metabolites.

The classification of the lymphocytic leukemias in humans is based on T-cell and B-cell lineages, but actually uses cell differentiation (CD) markers on the cytoskeleton for recognition.  It is only a conjecture that if the cells line were highly anaplastic, they might not be sustainable in cell culture in perpetuity.
The analogue of these cells to what I would expect to see in humans is the SLL having the characteristic marking: CD5, see http://www.pathologyoutlines.com/topic/lymphomaSLL.html

Micro description
=======================================================

● Effacement of nodal architecture by pale staining pseudofollicles or proliferation centers with ill-defined borders, containing small round mature lymphocytes, prolymphocytes (larger than small lymphocytes, abundant basophilic cytoplasm, prominent nucleoli), paraimmunoblasts (larger cells with distinct nucleoli) and many smudge cells
● Pseudofollicular centers are highlighted by decreasing light through the condenser at low power; cells have pale cytoplasm but resemble soccer balls or smudge cells on peripheral smear (cytoplasm is bubbly in mantle cell lymphoma); may have plasmacytoid features
● May have marginal zone, perifollicular or interfollicular patterns, but these cases also have proliferation centers (Mod Pathol 2000;13:1161)
● Interfollicular pattern: large, reactive germinal centers; resembles follicular lymphoma but germinal centers are bcl2 negative and tumor cells resemble SLL by morphology and immunostains
(Am J Clin Path 2000;114:41)
● Paraimmunoblastic variant: diffuse proliferation of paraimmunoblasts (normally just in pseudoproliferation centers); rare, <30 reported cases; usually multiple lymphadenopathies and rapid disease progression; case report in 69 year old man (Hum Pathol 2002;33:1145); consider as mantile cell lymphoma if t(11;14)(q13;q32) is present; may also represent CD5+ diffuse large B cell lymphoma
Bone marrow: small focal aggregates of variable size with irregular, poorly circumscribed outlines; lymphocytes are well differentiated, small, round with minimal atypia; may have foci of transformation; rarely has granulomas (J Clin Pathol 2005;58:815)
● Marrow infiltrative patterns are also described as diffuse (unmutated IgH genes, ZAP-70+, more aggressive), nodular (associated with IgH hypermutation, ZAP-70 negative) or mixed (variable mutation of IgH, variable ZAP-70, Hum Pathol 2006;37:1153)

 

Positive stains
=======================================================

● CD5, CD19, CD20 (dim), CD23, surface Ig light chain, surface IgM (dim)
● Also CD43, CD79a, CD79b (dim in 20%, Arch Pathol Lab Med 2003;127:561), bcl2
● Variable CD11c, FMC7 (42%)
Negative stains
=======================================================

● CD10, cyclin D1
Molecular
=======================================================

● Trisomy 12 (30%, associated with atypical CLL and CD79b), deletion 13q14 (25-50%),
deletion of 11q23 (worse prognosis, 10-20%)

 

Results

We set up a pipeline that could be used to

  • infer intracellular metabolic states from semi-quantitative data
  • regarding metabolites exchanged between cells and their environment.

Our pipeline combined the following four steps:

  1. data acquisition,
  2. data analysis,
  3. metabolic modeling and
  4.  experimental validation of
  • the model predictions (Fig. 1A).

We demonstrated the pipeline and the predictive potential

  • to predict metabolic alternations in diseases such as cancer
  • based on two lymphoblastic leukemia cell lines.

The resulting Molt-4 and CCRF-CEM condition-specific cell line models were able

  • to explain metabolite uptake and secretion
  •  by predicting the distinct utilization of central metabolic pathways by the two cell lines.

Whereas the CCRF-CEM model

  • resembled more a glycolytic, commonly referred to as ‘Warburg’ phenotype,
  • our predictions suggested  a more respiratory phenotype for the Molt-4  model.

We found these predictions to be in agreement with measured gene expression differences

  • at key regulatory steps in the central metabolic pathways, and
  • they were also consistent with  data regarding the energy and redox states of the cells.

After a brief discussion of the data generation and analysis steps, the results

  • derived from model generation and analysis will be described in detail.

 

2.1 Pipeline for generation of condition-specific metabolic cell line models

2.1.1 Generation of experimental data

We monitored the growth and viability of lymphoblastic leukemia cell lines in
serum- free medium (File S2, Fig. S1). Multiple omics  data sets  were derived  from these cells.

Extracellular metabolomics (exo-metabolomic) data,

  • comprising measurements of the metabolites in the spent medium of the cell cultures
    (Paglia et al. 2012a),
  • were collected along with transcriptomic data, and
  • these data sets were used to construct the models.

 

2.1.4 Condition-specific models for CCRF-CEM and Molt-4 cells

To determine whether we had obtained two distinct models,

  • we evaluated the reactions, metabolites, and genes of the two models.

Both the Molt-4 and CCRF-CEM models contained approximately

  • half of the reactions and metabolites present in the global model (Fig. 1C).

They were very similar to each other in terms of their

  • reactions,
  • metabolites, and
  • genes (File S1, Table S5A–C).

The Molt– 4 model contained

  • seven reactions that were not present in the CCRF-CEM model
    (Co-A biosynthesis pathway and exchange reactions).

In contrast, the CCRF-CEM  contained

31 unique reactions

  • arginine and proline metabolism,
  • vitamin B6  metabolism,
  • fatty acid activation,
  • transport, and exchange reaction.
  • There  were 2 and 15 unique metabolites in the Molt-4 and CCRF-CEM models,  respectively
    (File S1, Table S5B).
    Approximately three quarters of the global  model  genesremained in the condition-specific cell line models  (Fig. 1C).

The Molt-4 model contained

  • 15 unique genes, and

the CCRF-CEM model had

  • 4 unique genes (File S1, Table S5C).

Both models lacked NADH dehydrogenase
(complex I of the electron transport chain—ETC),

  •  determined by  the  absence of expression of a mandatory subunit
    (NDUFB3, Entrez gene ID 4709).

The ETC was fueled by FADH2 originating from

  1. succinate dehydrogenase and
  2. from fatty acid oxidation, which
  • through flavoprotein electron transfer
  • could contribute to the same ubiquinone pool as
  • complex I and complex II (succinate dehydrogenase).

Despite their different in vitro growth rates
(which differed by 11 %, see File S2, Fig. S1) and

  • differences in exo-metabolomic data (Fig. 1B) and
  • transcriptomic data,
  • the internal networks were largely conserved
  • in the two condition-specific cell line models.

 

2.1.5 Condition-specific cell line models predict distinct metabolic strategies

Despite the overall similarity of the metabolic models,

  • differences in their cellular uptake and secretion patterns suggested
  • distinct metabolic states in the two cell lines
    (Fig. 1B and see “Materials and methods” section for more detail).

To interrogate the metabolic differences, we sampled the solution space

  • of each model  using an Artificial Centering Hit-and-Run (ACHR) sampler (Thiele et al. 2005).

For this  analysis, additional constraints were applied, emphasizing

  • the  quantitative differences in commonly uptaken and secreted metabolites.

The  maximum possible uptake and maximum possible secretion flux rates were

  • reduced according to the measured relative differences between the cell lines
    (Fig. 1D, see “Materials and methods” section).

We plotted the number of sample points containing a particular flux rate for each reaction. The resulting

  • binned histograms can be understood as representing the probability that
  • a particular reaction can have a certain flux value.

A comparison of the sample points obtained for the Molt-4 and CCRF-CEM models revealed

  • a  considerable shift in the distributions, suggesting
  • a higher utilization of  glycolysis by the CCRF-CEM model (File S2, Fig. S2).

This result  was further  supported by differences

  • in medians calculated from sampling points (File S1,  Table S6).

The shift persisted throughout all reactions of the pathway and

  • was  induced by the higher glucose uptake (35 %) from
  • the extracellular medium in CCRF-CEM cells.

The sampling median for glucose uptake was 34 % higher

  • in the  CCRF-CEM model than in Molt-4 model (File S2, Fig. S2).

The usage of the  TCA cycle was also distinct in the two condition-specific cell-line models (Fig. 2).

  • the models used succinate dehydrogenase differently (Figs. 23).

The Molt-4 model utilized an associated reaction to generate FADH2, whereas

  • in  the CCRF-CEM model, the histogram was shifted in the opposite direction,
  • toward  the generation of succinate.

Additionally, there was a higher efflux of  citrate toward

  • amino acid and lipid metabolism in the CCRF-CEM model (Fig. 2).

There was higher flux through anaplerotic and cataplerotic reactions

  • in the CCRF-CEM model than in the Molt-4 model (Fig. 2);
  • these reactions include the efflux  of citrate through

 

  1. ATP-citrate lyase,
  2. uptake of glutamine,
  3. generation of  glutamate from glutamine,
  4. transamination of pyruvate and
  5.  glutamate to alanine  and to 2-oxoglutarate,
  6. secretion of nitrogen, and
  7. secretion of alanine.

The Molt-4 model showed higher utilization of oxidative phosphorylation (Fig. 3),

  • supported by elevated median flux through ATP synthase (36 %) and other  enzymes,
  • which contributed to higher oxidative metabolism.

The sampling  analysis therefore revealed different usage of

  • central metabolic pathways by the condition-specific models.

 

Fig. 2

Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM model (red) and the Molt-4 model (blue).
The table provides the median values of the sampling results. Negative values in histograms and Table

  • describe reversible  reactions with flux in the reverse direction.

There are multiple reversible  reactions for the transformation of

  1. isocitrate and α-ketoglutarate,
  2. malate and  fumarate, and
  3. succinyl-CoA and succinate.

These reactions are  unbounded,  and therefore histograms are not shown.
The details of participating cofactors  have been removed.

Atp ATP, cit citrate, adp ADP, pi phosphate, oaa oxaloacetate, accoa acetyl-CoAcoa coenzyme-A,
icit isocitrate, αkg α-ketoglutarate, succcoa succinyl-CoAsucc succinate, fumfumarate, mal malate,
oxa oxaloacetate,  pyr pyruvate, lac lactate, ala alanine, gln glutamine, ETC electron transport  chain.

 

Electronic supplementary material The online version of this article
http://dx.doi.org:/10.1007/s11306-014-0721-3 
contains supplementary material,  which  is available to authorized users.

  1.  K. Aurich _ G. Paglia _ O ´ . Rolfsson _ S. Hrafnsdo´ ttir _
  2. Magnu´sdo´ ttir _ B. Ø. Palsson _ R. M. T. Fleming _ I. Thiele. Center for Systems Biology,
    University of Iceland, Reykjavik, Iceland
  3.  K. Aurich _ R. M. T. Fleming _ I. Thiele (&). Luxembourg Centre for Systems Biomedicine,
    University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
    e-mail: ines.thiele@uni.lu
  4. M. Stefaniak. School of Health Science, Faculty of Food Science and Nutrition,
    University of Iceland, Reykjavik, Iceland
  5. Ø. Palsson. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

http://link.springer.com/static-content/images/404/art%253A10.1007%252
Fs11306-014-0721-3/MediaObjects/11306_2014_721_Fig3_HTML.gif

 

Fig. 3

Fatty acid oxidation and ETC _Fig3

Fatty acid oxidation and ETC _Fig3

 

Sampling reveals different utilization of oxidative phosphorylation by the

  • generated models.

Different distributions are observed for the CCRF-CEM model (red) and the Molt-4 model (blue).

  • Molt-4 has higher  median  flux through ETC reactions II–IV.

The table provides the median values  of the sampling results. Negative values in the histograms and in the table describe

  • reversible reactions with flux in the reverse direction.

Both models lack Complex I of the ETC because of constraints

  • arising from the mapping of transcriptomic data.

Electron transfer flavoprotein and

  • electron transfer flavoprotein–ubiquinone oxidoreductase
  •  both also carry higher flux in the Molt-4 model

 

2.1.6 Experimental validation of energy and redox status of CCRF-CEM and Molt-4 cells

Cancer cells have to balance their needs

  •  for energy and biosynthetic precursors, and they have
  • to maintain redox homeostasis to proliferate (Cairns et al. 2011).

We conducted enzymatic assays of cell lysates to measure levels and/or ratios of

  • ATP,
  • NADPH + NADP,
  • NADH + NAD, and
  • glutathione.

These measurements were used to provide support for

  • the in silico predicted metabolic differences (Fig. 4).

Additionally, an Oxygen Radical Absorbance Capacity (ORAC) assay was used

  • to evaluate the cellular antioxidant status (Fig. 4B).

Total concentrations of NADH + NAD, GSH + GSSG, NADPH + NADP and ATP, were higher in Molt-4 cells  (Fig. 4A).

The higher ATP concentration in Molt-4 cells could either result from

  • high production rates, or intracellular  accumulation connected to high or
  • low reactions fluxes (Fig. 4A).

Our simplified view that oxidative Molt-4 produces less ATP and was contradicted by

  • the higher ATP concentrations measured (Fig. 4L).

Yet we want to emphasize that concentrations

  • cannot be compared to flux values,
  • since we are modeling at steady-state.

NADH/NAD+ ratios for both cell lines were shifted toward NADH (Fig. 4D, E), but

  • the shift toward NADH was more pronounced in CCRF-CEM (Fig. 4E),
  • which matched  our expectation based on the higher utilization of
  • glycolysis and 2-oxoglutarate  dehydrogenase in the CCRF-CEM model (Fig. 4L).

 

Fig. 4 (not shown)

A–K  Experimentally determined ATP, NADH + NAD, NADPH + NADP, and GSH + GSSG concentrations, and ROS detoxification in the CCRF-CEM and Molt-4 cells.

L Expectations for cellular energy and redox states. Expectations are based on predicted metabolic differences of the Molt-4 and CCRF-CEM models

2.1.7 Comparison of network utilization and alteration in gene expression

With the assumption that

  • differential expression of particular genes would cause reaction flux changes,

we determined how the differences in gene expression (between CCRF-CEM and Molt-4)

  • compared to the flux differences observed in the  models.

Specifically, we checked whether the reactions associated with genes upregulated
(significantly more expressed in CCRF-CEM cells compared to Molt-4  cells)

  • were indeed more utilized by the CCRF-CEM model,

and we  checked  whether downregulated genes

  • were associated with reactions more utilized by the Molt-4 model.

The set of downregulated genes was associated with 15 reactions, and

  • the set of 49 upregulated genes was associated with 113 reactions in the models.

Reactions were defined as differently utilized

  • if the difference in flux exceeded 10 % (considering only non-loop reactions).

Of the reactions associated with upregulated genes,

  • 72.57 % were more utilized by the CCRF-CEM model, and
  • 2.65 % were more utilized by the Molt-4 model (File S1, Table S7).

In contrast, all 15 reactions associated with the 12 downregulated genes

  • were more utilized in the CCRF-CEM model (File S1, Table S8).

After this initial analysis, we approached the question from a different angle, asking

  • whether the majority of the reactions associated with each individual gene
  • upregulated in CCRF-CEM were more utilized by the CCRF-CEM model.
  •  this was the case for 77.55 % of the upregulated genes.

The majority of reactions associated with two (16.67 %) downregulated genes

  • were more utilized by the Molt-4 model.

Taken together, our comparisons of the

  • direction of gene expression with the fluxes of the two cancer cell-line models
  • confirmed that reactions associated with upregulated genes in the CCRF-CEM
    cells were generally more utilized by the CCRF-CEM model.

2.1.8 Accumulation of DEGs and AS genes at key metabolic steps

After we confirmed that most reactions associated with upregulated genes

  • were more utilized by the CCRF-CEM model,

we checked the locations of DEGs within the network. In this analysis, we paid special attention to

  • the central metabolic pathways that we had found
  • to be distinctively utilized by the two models.

Several DEGs and AS events were associated with

  • glycolysis,
  • the ETC,
  • pyruvate metabolism, and
  • the PPP (Table 1).

 

Table 1

DEGs and AS events of central metabolic and cancer-related pathways

Full lists of DEGs and AS are provided in the supplementary material.

Upregulated significantly more expressed in CCRF-CEM compared to Molt-4 cells

PPP pentose phosphate pathway, OxPhos oxidative phosphorylation, Glycolysis/gluconglycolysis/gluconeogenesis, Pyruvate met. pyruvate metabolism

Moreover, in glycolysis, the DEGs and/or AS genes

  • were associated with all three rate-limiting steps, i.e., the steps mediated by
  1. hexokinase,
  2. pyruvate kinase, and
  3. phosphofructokinase.

Of these key enzymes,

  • hexokinase 1 (Entrez Gene ID: 3098) was alternatively spliced,
  • pyruvate kinase (PKM, Entrez gene ID: 5315) was significantly more
    expressed in the CCRF-CEM cells (Table 1),

in agreement with the higher in silico predicted flux.

However, in contrast to the observed

  • higher utilization of glycolysis in the CCRF-CEM model,
  • the gene associated with the rate-limiting glycolysis step, phosphofructokinase (Entrez Gene ID: 5213),
  • was significantly upregulated in Molt-4 cells relative to CCRF-CEM cells.

This higher expression was detected for only a single isozyme, however. Two of
the three genes associated with phosphofructokinase were also subject to
alternative splicing (Table 1). In addition to the key enzymes, fructose
bisphosphate aldolase (Entrez Gene ID: 230) was also significantly

  • upregulated in Molt-4 cells relative to CCRF-CEM cells,
  • in contrast to the predicted higher utilization of glycolysis in the CCRF-CEM model.

Additionally, glucose-6P-dehydrogenase (G6PD), which catalyzes

  • the first reaction and committed step of the PPP,
  • was an AS gene (Table 1).

A second AS gene associated with

  •  the PPP reaction of the deoxyribokinase
  • was RBKS (Entrez Gene ID: 64080).

This gene is also associated with ribokinase, but ribokinase was removed

  • because of the lack of ribose uptake or secretion.

Single AS genes were associated with different complexes of the ETC (Table 1).

Literature query revealed that at least 13 genes associated with alternative

  • splicing events were mentioned previously in connection with both alternative
    splicing and cancer (File S1, Table S14), and
  • 37 genes were associated with cancer, e.g., upregulated, downregulated at the
    level of mRNA or protein, or otherwise
  • connected to cancer metabolism and signaling.

One general observation was that there was a surprising

  • accumulation of metabolite transporters among the AS.

Overall, the high incidence of

  • differential gene expression events at metabolic control points
  • increases the plausibility of the in silico predictions.

 

2.1.9 Single gene deletion

Analyses of essential genes in metabolic models have been used

  • to predict candidate drug targets for cancer cells (Folger et al. 2011).

Here, we conducted an in silico gene deletion study for all model genes to identify

  • a unique set of knock-out (KO) genes
  • for each condition-specific cell line model.

The analysis yielded 63 shared lethal KO genes and

  • distinct sets of KO genes for the CCRF-CEM model (11 genes) and the Molt-4 model (3 genes).

For three of the unique CCRF-CEM KO genes,

  • the genes were only present in the CCRF-CEM model (File S1, Table S9).

 

The essential genes for both models were then

  • related to the cell-line-specific differences in metabolite uptake and secretion (Fig. 1B).

The CCRF-CEM model

  1. needed to generate putrescine from ornithine
    (ORNDC, Entrez Gene ID: 4953)
  2. to subsequently produce 5-methylthioadenosine for secretion (Fig. 1B).
  3. S-adenosylmethioninamine produced by adenosylmethionine decarboxylase
    (arginine and proline metabolism, associated with Entrez Gene ID: 262)
  • is a substrate required for generation of 5-methylthioadenosine.

Another example of a KO gene connected to an enforced exchange reaction was

  • glutamic-oxaloacetic transaminase 1 (GOT1, Entrez Gene ID: 2805).

Without GOT1, the CCRF-CEM model was forced to secrete

  • 4-hydroxyphenylpyruvate (Fig. 1B),
  • the second product of tyrosine transaminase,
  • which is produced only by that enzyme.

 

One KO gene in the Molt-4 model (Entrez Gene ID: 26227) was associated with

  • phosphoglycerate dehydrogenase (PGDH),
  • which catalyzes the conversion of 3-phospho-d-glycerate to 3-phosphohydroxypyruvate
  • while generating NADH from NAD+.

This KO gene is particularly interesting, given

  • the involvement of this reaction in a novel pathway for ATP generation in rapidly proliferating cells
    (Locasale et al. 2011; Vander Heiden 2011; Vazquez et al. 2011).

Reactions associated with unique KO genes were in many cases utilized more by the model, in which

  • the gene KO was lethal,
  • underlining the potential importance of these reactions for the models.

Thus, single gene deletion provided unique sets of lethal genes that could be

  • specifically targeted to kill these cells.

 

3 Discussion

In the current study, we explored the possibility of

  • semi-quantitatively integrating metabolomic data with
  • the human genome-scale reconstruction to facilitate analysis.

By constructing condition-specific cell line models

  • to provide a structured framework,
  • we derived insights that could not have been obtained from data analysis alone.

We derived condition-specific cell line models

  • for CCRF-CEM and
  • Molt-4 cells

that were able to explain the observed exo-metabolomic differences (Fig. 1B).

Despite the overall similarities between the models, the analysis revealed

  • distinct usage of central metabolic pathways (Figs. 234),
  • which we validated based on experimental data and
  • differential gene expression.

The additional data sufficiently supported

  • metabolic differences in the cell lines,
  • providing confidence in the generated models and the model-based predictions.

We used the validated models

  • to predict unique sets of lethal genes
  • to identify weak links in each model.

These weak links may represent potential drug targets.

Integrating omics data with the human genome-scale reconstruction

  • provides a structured framework (i.e., pathways)
  • that is based on careful consideration of the available biochemical literature
    (Thiele and Palsson2010).

This network context can simplify omics data analysis, and

  • it allows even non-biochemical experts
  • to gain fast and comprehensive insights
  • into the metabolic aspects of omics data sets.

Compared to transcriptomic data,

  • methods for the integration and analysis of metabolomic data
  • in the context of metabolic models are less well established,

although it is an active field of research (Li et al. 2013; Paglia et al. 2012b).
In contrast to other studies, our approach emphasizes

  • the representation of experimental conditions rather than
  • the reconstruction of a generic, cell-line-specific network,
  • which would require the combination of data sets from
  • many experimental conditions and extensive manual curation.

Rather, our way of model construction allowed us to efficiently

  • assess the metabolic characteristics of cells.

Despite the fact, that only a limited number of exchanged metabolites can be

  • measured by available metabolomics platforms and
  • at reasonable time-scale,

and that pathways of measured metabolites might still be unknown to date
(File S1, Tables S2–S3), our methods have the potential

  • to reveal metabolic characteristics of cells
  • which could be useful for biomedicine and personalized health.

The reasons why some cancers respond to certain treatments and not others
remain unclear, and choosing a treatment for a specific patient is often difficult
(Vander Heiden 2011). One potential application of our approach could be the
characterization of cancer phenotypes to explore how cancer cells or other cell
types

  • with particular metabolic characteristics respond to drugs.

The generation of our condition-specific cell line models involved

  • only limited manual curation,
  • making this approach a fast way to place metabolomic data
  • into a network context.

Model building mainly involves

  • the rigid reduction of metabolite exchanges
  • to match the observed metabolite exchange pattern
  • with as few additional metabolite exchanges as possible.

It should be noted that this reduction determines,

  • which pathways can be utilized by the model.

Our approach mostly conserved the internal network redundancy. However, a

  • more significant reduction may be achieved using different data.

Generally, a trade-off exists between the reduction of the internal network and

  • the increasing number of network gaps that need to be curated
  • by using additional omics data, such as transcriptomics and proteomics.

One way to prevent the emergence of network gaps would be

  • to use mapping algorithms that conserve network functionality,
    such as GIMME (Becker and Palsson 2008).

However, several additional methods exist for the integration of
transcriptomic data (Blazier and Papin 2012), and

  • which model-building method is best depends on the available data.

Interestingly, the lack of a significant contribution of our

  • gene expression data to the reduction of network size
  • suggests that the use of transcriptomic data is not necessary
  • to identify distinct metabolic strategies;
  • rather, the integration of exo-metabolomic data alone
    may provide sufficient insight.

However, sampling of the cell line models constrained

  • according to the exo-metabolomic profiles only, or
  • increasing the cutoff for the generation of absent and present calls (p < 0.01),
  • did not yield the same insights as presented herein (File S1, Table S18).

Only recently Gene Inactivation Moderated by Metabolism, Metabolomics and
Expression (GIM(3)E) became available, which

  • enforces minimum turnover of detected metabolites
  • based on intracellular metabolomics data as well as
  • gene expression microarray data (Schmidt et al. 2013).

In contrast to this approach, we emphasized our analysis on the

  • relative differences in the exo-metabolomic data of two cell lines.

GIM(3)E constitutes another integration method when the analysis should be

  • emphasized on intracellular metabolomics data (Schmidt et al. 2013).

The metabolic differences predicted by the models are generally plausible.
Cancers are known to be heterogeneous (Cairns et al. 2011), and

  • the contribution of oxidative phosphorylation to cellular ATP production
    may vary (Zu and Guppy 2004).

Moreover, leukemia cell lines have been shown

  • to depend on glucose, glutamine, and fatty acids to varying extents
  • to support proliferation.

Such dependence may cause the cells to adapt their metabolism

  • to the environmental conditions (Suganuma et al. 2010).

In addition to identifying supporting data in the literature, we performed

  • several analyses to validate the models and model predictions.

Our expectations regarding the levels and ratios of metabolites

  • relevant to energy and redox state were largely met (Fig. 4L).

The more pronounced shift of the NADH/NAD+ ratio

  • toward NADH in the CCRF-CEM cells
  • was in agreement with the predicted Warburg phenotype (Fig. 4),
  • and the higher lactate secretion in the CCRF-CEM cells (File S2, Fig. S2)
  • implies an increase in NADH relative to NAD+
    (Chiarugi et al. 2012; Nikiforov et al. 2011), again
  • matching the known Warburg phenotype.

ROS production is enhanced in certain types of cancer (Droge 2002; Ha et al. 2000), and

  • the generation of ROS is thought to contribute to
  1. mutagenesis,
  2. tumor promotion, and
  3. tumor progression (Dreher and Junod1996; Ha et al. 2000).

However, decreased mitochondrial glucose oxidation and

  • a transition to aerobic glycolysis
  • protect cells against ROS damage during biosynthesis and cell division
    (Brand and Hermfisse1997).

The higher ROS detoxification capability in Molt-4 cells, in combination with

  • higher spermidine dismutase utilization by the Molt-4 model (Fig. 4),
  • provided a consistent picture of the predicted respiratory phenotype (Fig. 4L).

Control of NADPH maintains the redox potential through GSH and

  • protects against oxidative stress, yet
  • changes in the NADPH ratio in response to oxidative damage
  • are not well understood (Ogasawara et al.2009).

Under stress conditions, as assumed for Molt-4 cells,

  • the NADPH/NADP+ ratio is expected to decrease because of
  • the continuous reduction of GSSG (Fig. 4L), and
  • this was confirmed in the Molt-4 cells (Fig. 4).

The higher amounts of GSH found in Molt-4 cells in vitro may demonstrate

  • an additional need for ROS scavengers because of
  • a greater reliance on oxidative metabolism.

Cancer is related to metabolic reprogramming, which results from

  • alterations of gene expression and
  • the expression of specific isoforms or
  • splice forms to support proliferation
    (Cortes-Cros et al. 2013; Marin-Hernandez et al. 2009).

The gene expression differences detected between the two cell lines in this study
supported the existence of

  • metabolic differences in these cell lines, particularly because
  • key steps of the metabolic pathways central to cancer metabolism
  • seemed to be differentially regulated (Table 1).

The detailed analysis of the respective

  • differences on the pathway fluxes exceeds the scope of this study, which was to
  • demonstrate the potential of the integration of exo-metabolomic data into the network context.

We found discrepancies between differential gene regulation and

  • the flux differences between the two models as well as
  • the utilization AS gene-associated reaction.

This is not surprising, since analysis of the detailed system is required

  • to make any further assumptions on the impact that
  • the differential regulation or splicing might have on the reaction flux,
  • given that for many of the concerned enzymes isozymes exist, or
  • only one of multiple subunits of a protein complex was concerned.

Additionally, reaction fluxes are regulated by numerous post-translational factors, e.g.,

  • protein modification,
  • inhibition through proteins or metabolites,
  • alter reaction fluxes (Lenzen 2014),

which are out of the scope of constraint-based steady-state modeling.

Rather, the results of the presented  approach

  • demonstrate how the models can be used to generate
  • informed hypothesis that can guide experimental work.

The combination of our tailored metabolic models and

  • differential gene expression analysis seems well-suited
  • to determine the potential drivers
  • involved in metabolic differences between cells.

Such information could be valuable for drug discovery, especially when more

  • peripheral metabolic pathways are considered.

Statistical comparisons of gene expression data with sampling-derived flux data

  • could be useful in future studies (Mardinoglu et al. 2013).

A single-gene-deletion analysis revealed that PGDH was

  • a lethal KO gene for the Molt-4 model only.

Differences in PGDH protein levels

  • correspond to the amount of glycolytic carbon
  • diverted into glycine biosynthesis.

Rapidly proliferating cells may use an

  • alternative glycolytic pathway for ATP generation,
  • which may provide an advantage in the case of
  • extensive oxidative phosphorylation and proliferation
    (Locasale et al.2011; Vander Heiden 2011; Vazquez et al. 2011).

For breast cancer cell lines, variable dependency on

  • the expression of PGDH has already been demonstrated
    (Locasale et al. 2011).

This example of a unique KO gene demonstrates how

  • in silico gene deletion in metabolomics-driven models
  • can identify the metabolic pathways used by cancer cells.

This approach can provide valuable information for drug discovery.

In conclusion, our contextualization method produced

  • metabolic models that agreed in many ways with the validation data sets.

The analyses described in this study have great potential to reveal

  • the mechanisms of metabolic reprogramming,
  • not only in cancer cells but also in other cells affected by diseases, and
  • for drug discovery in general.

 

4.3 Analysis of the extracellular metabolome

Mass spectrometry analysis of the exo-metabolome was performed by
Metabolon®, Inc. (Durham, NC, USA) using a standardized analytical platform.
In total, 75 extracellular metabolites were detected in the initial data set for at
least 1 of the 2 cell lines (Paglia et al. 2012a). Of these metabolites, 15 were not
part of our global model and were discarded. Apart from being absent in our
global model, an independent search in HMDB (Wishart et al. 2013) revealed no
pathway information was available for most of these metabolites (File S1, Tables S2–S3).
It should be noted that metabolites e.g.,

  • N-acetylisoleucine,
  • N-acetyl-methionine or pseudouridine,

constitute protein and RNA degradation products, which were out of the scope
of the metabolic network.

Thiamin (Vitamin B1) was part of the minimal medium of essential compounds
supplied to both models.Riboflavin (Vitamin B2) and Trehalose were excluded
since these compounds cannot be produced by human cells. Erythrose and
fructose were also excluded. In contrast 46 metabolites that were part of the
global model. The data set included two different time points, which allowed us
to treat the increase/decrease of a metabolite signal between time points as

  • evidence for uptake or secretion when the change was greater than 5 %
    from what was observed in the control (File S1, Tables S2–S3).

We found 12 metabolites that were taken up by both cell lines and
10 metabolites that were commonly secreted by both cell lines over
the course of the experiment.

Molt-4 cells took up three metabolites not taken up by CCRF-CEM cells, and
secreted one metabolite not secreted by CCRF-CEM cells. Two of the three
uniquely uptaken metabolites were essential amino acids:

  1. valine and
  2. methionine.

It is unlikely that these metabolites were not taken up by the CCRF-CEM cells,
and the CCRF-CEM model was allowed to take up this metabolite. Therefore,
no quantitative constraints were applied for the sampling analysis either.
CCRF-CEM cells had

  • four unique uptaken
  • and seven unique secreted metabolites
    (exchange not detected in Molt-4 cells).

 

4.4 Network refinement based on exo-metabolic data

Despite its comprehensiveness, the human metabolic reconstruction is

  • not complete with respect to extracellular metabolite transporters
    (Sahoo et al. 2014; Thiele et al. 2013).

Accordingly, we identified metabolite transport systems

  • from the literature for metabolites that were already part of the global model,
  • but whose extracellular transport was not yet accounted for.

Diffusion reactions were included whenever a respective transporter could not be identified.

In total, 34 reactions [11 exchange reactions, 16 transport reactions and 7 demand reactions
(File S1, Table S11)] were added to Recon 2 (Thiele et al. 2013), and 2 additional reactions
were added to the global model (File S1, Table S10).

4.5 Expression profiling

Molt-4 and CCRF-CEM cells were grown in advanced RPMI 1640 and 2 mM
GlutaMax, and the cells were resuspended in medium containing DMSO
(0.67 %) at a concentration of 5 × 105 cells/mL. The cell suspension (2 mL)
was seeded in 12-well plates in triplicate. After 48 h of growth, the cells
were collected by centrifugation at 201×g for 5 min. Cell pellets were snap-frozen
in liquid N2 and kept frozen until RNA extraction and analysis by Aros
(Aarhus, Denmark).

4.6 Analysis of transcriptomic data

We used the Affymetrix GeneChip Human Exon 1.0 ST Array to measure whole
genome exon expression. We generated detection above background (DABG) calls
using ROOT (version 22) and the XPS package for R (version 11.1), with Robust
Multi-array Analysis summarization. Calls for data mapping were assigned based
on p < 0.05 as the cutoff probability to distinguish presence versus absence for
the 1,278 model genes (File S1, Table S12).

Differential gene expression and alternative splicing analyses were performed by
using AltAnalyse software (v2.02beta) with default options on the raw data files
(CEL files). The Homo sapiens Ensemble 65 database was used, probe set filtering
was kept as DABG p < 0.05, and non-log expression < 70 was used for
constitutive probe sets to determine gene expression levels. For the comparison,
CCRF-CEM was the experimental group and Molt-4 was the baseline group. The
set of DEGs between cell lines was identified based on a p < 0.05 FDR cutoff
(File S1, Table S13A–B). Alternative splicing analysis was performed on core probe sets
with a minimum alternative exon score of 2 and a maximum absolute gene
expression change of 3 because alternative splicing is a less critical factor among
highly DEGs (File S1, Table S14). Gene expression data, complete lists of DABG p-values,
DEGs and alternative splicing events have been deposited in the Gene
Expression Omnibus
 (GEO) database (Accession number: GSE53123).

 

4.7 Deriving cell-type-specific subnetworks

Transcriptomic data were mapped to the model in a manual fashion (COBRA
function: deleteModelGenes). Specifically, reactions dependent on gene products
that were called as “absent” were constrained to zero, such that fluxes through
these reactions were disabled. Submodels were extracted based on the set of
reactions carrying flux (network pruning) by running fastFVA
(Gudmundsson and Thiele 2010) after mapping the metabolomic and
transcriptomic data using the COBRA toolbox (Schellenberger et al. 2011).

 

…..

 

Electronic supplementary material

Below is the link to the electronic supplementary material.

File S1. Supplementary material 1 (XLSX 915 kb)

File S2. Supplementary material 2 (DOCX 448 kb)

References

Antonucci, R., Pilloni, M. D., Atzori, L., & Fanos, V. (2012). Pharmaceutical research and metabolomics in the newborn. Journal of Maternal-Fetal and Neonatal Medicine, 25, 22–26.PubMedCrossRef

Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., et al. (2011). NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Research, 39, D1005–D1010.PubMedCentralPubMedCrossRef

Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., et al. (2011). The quantitative proteome of a human cell line.Molecular Systems Biology, 7, 549.PubMedCentralPubMedCrossRef

Becker, S. A., & Palsson, B. O. (2008). Context-specific metabolic networks are consistent with experiments. PLoS Computational Biology, 4, e1000082.PubMedCentralPubMedCrossRef

Blazier, A. S., & Papin, J. A. (2012). Integration of expression data in genome-scale metabolic network reconstructions. Frontiers in Physiology, 3, 299.PubMedCentralPubMedCrossRef

Bordbar, A., Lewis, N. E., Schellenberger, J., Palsson, B. O., & Jamshidi, N. (2010). Insight into human alveolar macrophage and M. tuberculosisinteractions via metabolic reconstructions. Molecular Systems Biology, 6, 422.PubMedCentralPubMedCrossRef

Bordbar, A., & Palsson, B. O. (2012). Using the reconstructed genome-scale human metabolic network to study physiology and pathology. Journal of Internal Medicine, 271, 131–141.PubMedCentralPubMedCrossRef

Brand, K. A., & Hermfisse, U. (1997). Aerobic glycolysis by proliferating cells: a protective strategy against reactive oxygen species. FASEB Journal, 11, 388–395.PubMed

Cairns, R. A., Harris, I. S., & Mak, T. W. (2011). Regulation of cancer cell metabolism. Nature Reviews Cancer, 11, 85–95.PubMedCrossRef

Chance, B., Sies, H., & Boveris, A. (1979). Hydroperoxide metabolism in mammalian organs. Physiological Reviews, 59, 527–605.PubMed

Chapman, E. H., Kurec, A. S., & Davey, F. R. (1981). Cell volumes of normal and malignant mononuclear cells. Journal of Clinical Pathology, 34, 1083–1090.PubMedCentralPubMedCrossRef

Chiarugi, A., Dolle, C., Felici, R., & Ziegler, M. (2012). The NAD metabolome—a key determinant of cancer cell biology. Nature Reviews Cancer, 12, 741–752.PubMedCrossRef

Cortes-Cros, M., Hemmerlin, C., Ferretti, S., Zhang, J., Gounarides, J. S., Yin, H., et al. (2013). M2 isoform of pyruvate kinase is dispensable for tumor maintenance and growth. Proceedings of the National Academy of Sciences of the United States of America, 110, 489–494.PubMedCentralPubMedCrossRef

Dreher, D., & Junod, A. F. (1996). Role of oxygen free radicals in cancer development. European Journal of Cancer, 32a, 30–38.PubMedCrossRef

Droge, W. (2002). Free radicals in the physiological control of cell function. Physiological Reviews, 82, 47–95.PubMed

Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., et al. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences of the United States of America, 104, 1777–1782.PubMedCentralPubMedCrossRef

Durot, M., Bourguignon, P. Y., & Schachter, V. (2009). Genome-scale models of bacterial metabolism: Reconstruction and applications. FEMS Microbiology Reviews, 33, 164–190.PubMedCentralPubMedCrossRef

Fleming, R. M., Thiele, I., & Nasheuer, H. P. (2009). Quantitative assignment of reaction directionality in constraint-based models of metabolism: Application to Escherichia coliBiophysical Chemistry, 145, 47–56.PubMedCentralPubMedCrossRef

Folger, O., Jerby, L., Frezza, C., Gottlieb, E., Ruppin, E., & Shlomi, T. (2011). Predicting selective drug targets in cancer through metabolic networks. Molecular Systems Biology, 7, 501.PubMedCentralPubMedCrossRef

Frezza, C., Zheng, L., Folger, O., Rajagopalan, K. N., MacKenzie, E. D., Jerby, L., et al. (2011). Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature, 477, 225–228.PubMedCrossRef

Ganske, F., & Dell, E. J. (2006). ORAC assay on the FLUOstar OPTIMA to determine antioxidant capacity. BMG LABTECH.

Gudmundsson, S., & Thiele, I. (2010). Computationally efficient flux variability analysis. BMC Bioinformatics, 11, 489.PubMedCentralPubMedCrossRef

Ha, H. C., Thiagalingam, A., Nelkin, B. D., & Casero, R. A, Jr. (2000). Reactive oxygen species are critical for the growth and differentiation of medullary thyroid carcinoma cells. Clinical Cancer Research, 6, 3783–3787.PubMed

Hyduke, D. R., Lewis, N. E., & Palsson, B. O. (2013). Analysis of omics data with genome-scale models of metabolism. Molecular BioSystems, 9, 167–174.PubMedCentralPubMedCrossRef

Jerby, L., & Ruppin, E. (2012). Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clinical Cancer Research,18, 5572–5584.PubMedCrossRef

Jerby, L., Shlomi, T., & Ruppin, E. (2010). Computational reconstruction of tissue-specific metabolic models: Application to human liver metabolism.Molecular Systems Biology, 6, 401.PubMedCentralPubMedCrossRef

Jerby, L., Wolf, L., Denkert, C., Stein, G. Y., Hilvo, M., Oresic, M., et al. (2012). Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Research, 72, 5712–5720.PubMedCrossRef

Lenzen, S. (2014). A fresh view of glycolysis and glucokinase regulation: History and current status. Journal of Biological Chemistry, 289, 12189–12194.PubMedCrossRef

Lewis, N. E., Nagarajan, H., & Palsson, B. O. (2012). Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology, 10, 291–305.PubMedCentralPubMed

Lewis, N. E., Schramm, G., Bordbar, A., Schellenberger, J., Andersen, M. P., Cheng, J. K., et al. (2010). Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nature Biotechnology, 28, 1279–1285.PubMedCentralPubMedCrossRef

Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., et al. (2013). Predicting network activity from high throughput metabolomics. PLoS Computational Biology, 9, e1003123.PubMedCentralPubMedCrossRef

Locasale, J. W., Grassian, A. R., Melman, T., Lyssiotis, C. A., Mattaini, K. R., Bass, A. J., et al. (2011). Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nature Genetics, 43, 869–874.PubMedCentralPubMedCrossRef

Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., et al. (2013). Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Molecular Systems Biology, 9, 649.PubMedCentralPubMedCrossRef

Marin-Hernandez, A., Gallardo-Perez, J. C., Ralph, S. J., Rodriguez-Enriquez, S., & Moreno-Sanchez, R. (2009). HIF-1alpha modulates energy metabolism in cancer cells by inducing over-expression of specific glycolytic isoforms. Mini Reviews in Medicinal Chemistry, 9, 1084–1101.PubMedCrossRef

Mir, M., Wang, Z., Shen, Z., Bednarz, M., Bashir, R., Golding, I., et al. (2011). Optical measurement of cycle-dependent cell growth. Proceedings of the National Academy of Sciences of the United States of America, 108, 13124–13129.PubMedCentralPubMedCrossRef

Mo, M. L., Palsson, B. O., & Herrgard, M. J. (2009). Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Systems Biology, 3, 37.PubMedCentralPubMedCrossRef

Nikiforov, A., Dolle, C., Niere, M., & Ziegler, M. (2011). Pathways and subcellular compartmentation of NAD biosynthesis in human cells: From entry of extracellular precursors to mitochondrial NAD generation. The Journal of biological chemistry, 286, 21767–21778.PubMedCentralPubMedCrossRef

Ogasawara, Y., Funakoshi, M., & Ishii, K. (2009). Determination of reduced nicotinamide adenine dinucleotide phosphate concentration using high-performance liquid chromatography with fluorescence detection: Ratio of the reduced form as a biomarker of oxidative stress. Biological & Pharmaceutical Bulletin, 32, 1819–1823.CrossRef

Paglia, G., Hrafnsdottir, S., Magnusdottir, M., Fleming, R. M., Thorlacius, S., Palsson, B. O., et al. (2012a). Monitoring metabolites consumption and secretion in cultured cells using ultra-performance liquid chromatography quadrupole-time of flight mass spectrometry (UPLC-Q-ToF-MS).Analytical and Bioanalytical Chemistry, 402, 1183–1198.PubMedCrossRef

Paglia, G., Palsson, B. O., & Sigurjonsson, O. E. (2012b). Systems biology of stored blood cells: Can it help to extend the expiration date? Journal of Proteomics, 76, 163–167.PubMedCrossRef

Price, N. D., Schellenberger, J., & Palsson, B. O. (2004). Uniform sampling of steady-state flux spaces: Means to design experiments and to interpret enzymopathies. Biophysical Journal, 87, 2172–2186.PubMedCentralPubMedCrossRef

Reed, J. L., Famili, I., Thiele, I., & Palsson, B. O. (2006). Towards multidimensional genome annotation. Nature Reviews Genetics, 7, 130–141.PubMedCrossRef

Sahoo, S., Aurich, M. K., Jonsson, J. J., & Thiele, I. (2014). Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease. Frontiers in Physiology, 5, 91.PubMedCentralPubMedCrossRef

Sahoo, S., & Thiele, I. (2013). Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. Human Molecular Genetics, 22, 2705–2722.PubMedCentralPubMedCrossRef

Schellenberger, J., & Palsson, B. O. (2009). Use of randomized sampling for analysis of metabolic networks. The Journal of biological chemistry,284, 5457–5461.PubMedCrossRef

Schellenberger, J., Que, R., Fleming, R. M., Thiele, I., Orth, J. D., Feist, A. M., et al. (2011). Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox v2.0. Nature Protocols, 6, 1290–1307.PubMedCentralPubMedCrossRef

Schmidt, B. J., Ebrahim, A., Metz, T. O., Adkins, J. N., Palsson, B. O., & Hyduke, D. R. (2013). GIM3E: Condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics (Oxford, England), 29, 2900–2908.CrossRef

Suganuma, K., Miwa, H., Imai, N., Shikami, M., Gotou, M., Goto, M., et al. (2010). Energy metabolism of leukemia cells: Glycolysis versus oxidative phosphorylation. Leukemia & Lymphoma, 51, 2112–2119.CrossRef

Thiele, I., & Palsson, B. O. (2010). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols, 5, 93–121.PubMedCentralPubMedCrossRef

Thiele, I., Price, N. D., Vo, T. D., & Palsson, B. O. (2005). Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. The Journal of biological chemistry, 280, 11683–11695.PubMedCrossRef

Thiele, I., Swainston, N., Fleming, R. M., Hoppe, A., Sahoo, S., Aurich, M. K., et al. (2013). A community-driven global reconstruction of human metabolism. Nature Biotechnology, 31, 419–425.PubMedCrossRef

Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., et al. (2010). Towards a knowledge-based human protein Atlas.Nature Biotechnology, 28, 1248–1250.PubMedCrossRef

Vander Heiden, M. G. (2011). Targeting cancer metabolism: A therapeutic window opens. Nature Reviews Drug Discovery, 10, 671–684.PubMedCrossRef

Vazquez, A., Markert, E. K., & Oltvai, Z. N. (2011). Serine biosynthesis with one carbon catabolism and the glycine cleavage system represents a novel pathway for ATP generation. PLoS ONE, 6, e25881.PubMedCentralPubMedCrossRef

Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41, D801–D807.PubMedCentralPubMedCrossRef

Zu, X. L., & Guppy, M. (2004). Cancer metabolism: Facts, fantasy, and fiction. Biochemical and Biophysical Research Communications, 313, 459–465.PubMedCrossRef

 

Read Full Post »

Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

Reviewer and Curator: Larry H. Bernstein, MD, FCAP 

 

The human genome is estimated to encode over 30,000 genes, and to be responsible for generating more than 100,000 functionally distinct proteins. Understanding the interrelationships among

  1. genes,
  2. gene products, and
  3. dietary habits

is fundamental to identifying those who will benefit most from or be placed at risk by intervention strategies.

Unraveling the multitude of

  • nutrigenomic,
  • proteomic, and
  • metabolomic patterns

that arise from the ingestion of foods or their

  • bioactive food components

will not be simple but is likely to provide insights into a tailored approach to diet and health. The use of new and innovative technologies, such as

  • microarrays,
  • RNA interference, and
  • nanotechnologies,

will provide needed insights into molecular targets for specific bioactive food components and

  • how they harmonize to influence individual phenotypes(1).

Nutrigenetics asks the question how individual genetic disposition, manifesting as

  • single nucleotide polymorphisms,
  • copy-number polymorphisms and
  • epigenetic phenomena,

affects susceptibility to diet.

Nutrigenomics addresses the inverse relationship, that is how diet influences

  • gene transcription,
  • protein expression and
  • metabolism.

A major methodological challenge and first pre-requisite of nutrigenomics is integrating

  • genomics (gene analysis),
  • transcriptomics (gene expression analysis),
  • proteomics (protein expression analysis) and
  • metabonomics (metabolite profiling)

to define a “healthy” phenotype. The long-term deliverable of nutrigenomics is personalised nutrition (2).

Science is beginning to understand how genetic variation and epigenetic events

  • alter requirements for, and responses to, nutrients (nutrigenomics).

At the same time, methods for profiling almost all of the products of metabolism in a single sample of blood or urine are being developed (metabolomics). Relations between

  • diet and nutrigenomic and metabolomic profiles and
  • between those profiles and health

have become important components of research that could change clinical practice in nutrition.

Most nutrition studies assume that all persons have average dietary requirements, and the studies often

  • do not plan for a large subset of subjects who differ in requirements for a nutrient.

Large variances in responses that occur when such a population exists

  • can result in statistical analyses that argue for a null effect.

If nutrition studies could better identify responders and differentiate them from nonresponders on the basis of nutrigenomic or metabolomic profiles,

  • the sensitivity to detect differences between groups could be greatly increased, and
  • the resulting dietary recommendations could be appropriately targeted (3).

In recent years, nutrition research has moved from classical epidemiology and physiology to molecular biology and genetics. Following this trend,

  • Nutrigenomics has emerged as a novel and multidisciplinary research field in nutritional science that
  • aims to elucidate how diet can influence human health.

It is already well known that bioactive food compounds can interact with genes affecting

  • transcription factors,
  • protein expression and
  • metabolite production.

The study of these complex interactions requires the development of

  • advanced analytical approaches combined with bioinformatics.

Thus, to carry out these studies

  • Transcriptomics,
  • Proteomics and
  • Metabolomics

approaches are employed together with an adequate integration of the information that they provide(4).

Metabonomics is a diagnostic tool for metabolic classification of individuals with the asset of quantitative, non-invasive analysis of easily accessible human body fluids such as urine, blood and saliva. This feature also applies to some extent to Proteomics, with the constraint that

  • the latter discipline is more complex in terms of composition and dynamic range of the sample.

Apart from addressing the most complex “Ome”, Proteomics represents

  • the only platform that delivers not only markers for disposition and efficacy
  • but also targets of intervention.

Application of integrated Omic technologies will drive the understanding of

  • interrelated pathways in healthy and pathological conditions and
  • will help to define molecular ‘switchboards’,
  • necessary to develop disease related biomarkers.

This will contribute to the development of new preventive and therapeutic strategies for both pharmacological and nutritional interventions (5).

Human health is affected by many factors. Diet and inherited genes play an important role. Food constituents,

  • including secondary metabolites of fruits and vegetables, may
  • interact directly with DNA via methylation and changes in expression profiles (mRNA, proteins)
  • which results in metabolite content changes.

Many studies have shown that

  • food constituents may affect human health and
  • the exact knowledge of genotypes and food constituent interactions with
  • both genes and proteins may delay or prevent the onset of diseases.

Many high throughput methods have been employed to get some insight into the whole process and several examples of successful research, namely in the field of genomics and transcriptomics, exist. Studies on epigenetics and RNome significance have been launched. Proteomics and metabolomics need to encompass large numbers of experiments and linked data. Due to the nature of the proteins, as well as due to the properties of various metabolites, experimental approaches require the use of

  • comprehensive high throughput methods and a sufficiency of analysed tissue or body fluids (6).

New experimental tools that investigate gene function at the subcellular, cellular, organ, organismal, and ecosystem level need to be developed. New bioinformatics tools to analyze and extract meaning

  • from increasingly systems-based datasets will need to be developed.

These will require, in part, creation of entirely new tools. An important and revolutionary aspect of “The 2010 Project”  is that it implicitly endorses

  • the allocation of resources to attempts to assign function to genes that have no known function.

This represents a significant departure from the common practice of defining and justifying a scientific goal based on the biological phenomena. The rationale for endorsing this radical change is that

  • for the first time it is feasible to envision a whole-systems approach to gene and protein function.

This whole-systems approach promises to be orders of magnitude more efficient than the conventional approach (7).

The Institute of Medicine recently convened a workshop to review the state of the various domains of nutritional genomics research and policy and to provide guidance for further development and translation of this knowledge into nutrition practice and policy (8). Nutritional genomics holds the promise to revolutionize both clinical and public health nutrition practice and facilitate the establishment of

(a) genome-informed nutrient and food-based dietary guidelines for disease prevention and healthful aging,

(b) individualized medical nutrition therapy for disease management, and

(c) better targeted public health nutrition interventions (including micronutrient fortification and supplementation) that

  • maximize benefit and minimize adverse outcomes within genetically diverse human populations.

As the field of nutritional genomics matures, which will include filling fundamental gaps in

  • knowledge of nutrient-genome interactions in health and disease and
  • demonstrating the potential benefits of customizing nutrition prescriptions based on genetics,
  • registered dietitians will be faced with the opportunity of making genetically driven dietary recommendations aimed at improving human health.

The new era of nutrition research translates empirical knowledge to evidence-based molecular science (9). Modern nutrition research focuses on

  • promoting health,
  • preventing or delaying the onset of disease,
  • optimizing performance, and
  • assessing risk.

Personalized nutrition is a conceptual analogue to personalized medicine and means adapting food to individual needs. Nutrigenomics and nutrigenetics

  • build the science foundation for understanding human variability in
  • preferences, requirements, and responses to diet and
  • may become the future tools for consumer assessment

motivated by personalized nutritional counseling for health maintenance and disease prevention.

The primary aim of ―omic‖ technologies is

  • the non-targeted identification of all gene products (transcripts, proteins, and metabolites) present in a specific biological sample.

By their nature, these technologies reveal unexpected properties of biological systems.

A second and more challenging aspect of ―omic‖ technologies is

  • the refined analysis of quantitative dynamics in biological systems (10).

For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with

  • high sample numbers in reliable measurement times with respect to
  • both technical accuracy and the identification and quantitation of small-molecular-weight metabolites.

This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology.

In modern nutrition research, mass spectrometry has developed into a tool

  • to assess health, sensory as well as quality and safety aspects of food.

In this review, we focus on health-related benefits of food components and, accordingly,

  • on biomarkers of exposure (bioavailability) and bioefficacy.

Current nutrition research focuses on unraveling the link between

  • dietary patterns,
  • individual foods or
  • food constituents and

the physiological effects at cellular, tissue and whole body level

  • after acute and chronic uptake.

The bioavailability of bioactive food constituents as well as dose-effect correlations are key information to understand

  • the impact of food on defined health outcomes.

Both strongly depend on appropriate analytical tools

  • to identify and quantify minute amounts of individual compounds in highly complex matrices–food or biological fluids–and
  • to monitor molecular changes in the body in a highly specific and sensitive manner.

Based on these requirements,

  • mass spectrometry has become the analytical method of choice
  • with broad applications throughout all areas of nutrition research (11).

Recent advances in high data-density analytical techniques offer unrivaled promise for improved medical diagnostics in the coming decade. Genomics, proteomics and metabonomics (as well as a whole slew of less well known ―omics‖ technologies) provide a detailed descriptor of each individual. Relating the large quantity of data on many different individuals to their current (and possibly even future) phenotype is a task not well suited to classical multivariate statistics. The datasets generated by ―omics‖ techniques very often violate the requirements for multiple regression. However, another statistical approach exists, which is already well established in areas such as medicinal chemistry and process control, but which is new to medical diagnostics, that can overcome these problems. This approach, called megavariate analysis (MVA),

  • has the potential to revolutionise medical diagnostics in a broad range of diseases.

It opens up the possibility of expert systems that can diagnose the presence of many different diseases simultaneously, and

  • even make exacting predictions about the future diseases an individual is likely to suffer from (12).

Cardiovascular diseases

Cardiovascular diseases are the leading cause of morbidity and mortality in Western countries. Although coronary thrombosis is the final event in acute coronary syndromes,

  • there is increasing evidence that inflammation also plays a role in development of atherosclerosis and its clinical manifestations, such as
  • myocardial infarction, stroke, and peripheral vascular disease.

The beneficial cardiovascular health effects of

  • diets rich in fruits and vegetables are in part mediated by their flavanol content.

This concept is supported by findings from small-scale intervention studies with surrogate endpoints including

  1. endothelium-dependent vasodilation,
  2. blood pressure,
  3. platelet function, and
  4. glucose tolerance.

Mechanistically, short term effects on endothelium-dependent vasodilation

  • following the consumption of flavanol-rich foods, as well as purified flavanols,
  • have been linked to an increased nitric oxide bioactivity.

The critical biological target(s) for flavanols have yet to be identified (13), but we are beginning to see over the horizon.

Nutritional sciences

Nutrition sciences apply

  1. transcriptomics,
  2. proteomics and
  3. metabolomics

to molecularly assess nutritional adaptations.

Transcriptomics can generate a

  • holistic overview on molecular changes to dietary interventions.

Proteomics is most challenging because of the higher complexity of proteomes as compared to transcriptomes and metabolomes. However, it delivers

  • not only markers but also
  • targets of intervention, such as
  • enzymes or transporters, and
  • it is the platform of choice for discovering bioactive food proteins and peptides.

Metabolomics is a tool for metabolic characterization of individuals and

  • can deliver metabolic endpoints possibly related to health or disease.

Omics in nutrition should be deployed in an integrated fashion to elucidate biomarkers

  • for defining an individual’s susceptibility to diet in nutritional interventions and
  • for assessing food ingredient efficacy (14).

The more elaborate tools offered by metabolomics opened the door to exploring an active role played by adipose tissue that is affected by diet, race, sex, and probably age and activity. When the multifactorial is brought into play, and the effect of changes in diet and activities studied we leave the study of metabolomics and enter the world of ―metabonomics‖. Adiponectin and adipokines arrive (15-22). We shall discuss ―adiposity‖ later.

Potential Applications of Metabolomics

Either individually or grouped as a profile, metabolites are detected by either

  • nuclear magnetic resonance spectroscopy or mass spectrometry.

There is potential for a multitude of uses of metabolome research, including

  1. the early detection and diagnosis of cancer and as
  2. both a predictive and pharmacodynamic marker of drug effect.

However, the knowledge regarding metabolomics, its technical challenges, and clinical applications is unappreciated

  • even though when used as a translational research tool,
  • it can provide a link between the laboratory and clinic.

Precise numbers of human metabolites is unknown, with estimates ranging from the thousands to tens of thousands. Metabolomics is a term that encompasses several types of analyses, including

(a) metabolic fingerprinting, which measures a subset of the whole profile with little differentiation or quantitation of metabolites;

(b) metabolic profiling, the quantitative study of a group of metabolites, known or unknown, within or associated with a particular metabolic pathway; and

(c) target isotope-based analysis, which focuses on a particular segment of the metabolome by analyzing

  • only a few selected metabolites that comprise a specific biochemical pathway.

 

Dynamic Construct of the –Omics

Dynamic Construct of the –Omics

 

Dynamic Construct of the –Omics

 

 

Iron metabolism – Anemia

Hepcidin is a key hormone governing mammalian iron homeostasis and may be directly or indirectly involved in the development of most iron deficiency/overload and inflammation-induced anemia. The anemia of chronic disease (ACD) is characterized by macrophage iron retention induced by cytokines and hepcidin regulation. Hepcidin controls cellular iron efflux on binding to the iron export protein ferroportin. While patients present with both ACD and iron deficiency anemia (ACD/IDA), the latter results from chronic blood loss. Iron retention during inflammation occurs in macrophages and the spleen, but not in the liver. In ACD, serum hepcidin concentrations are elevated, which is related to reduced duodenal and macrophage expression of ferroportin. Individuals with ACD/IDA have significantly lower hepcidin levels than ACD subjects. ACD/IDA patients, in contrast to ACD subjects, were able to absorb dietary iron from the gut and to mobilize iron from macrophages. Hepcidin elevation may affect iron transport in ACD and ACD/IDA and it is more responsive to iron demand with IDA than to inflammation. Hepcidin determination may aid in selecting appropriate therapy for these patients (23).

There is correlation between serum hepcidin, iron and inflammatory indicators associated with anemia of chronic disease (ACD), ACD, ACD concomitant iron-deficiency anemia (ACD/IDA), pure IDA and acute inflammation (AcI) patients. Hepcidin levels in anemia types were statistically different, from high to low: ACD, AcI > ACD/IDA > the control > IDA. Serum ferritin levels were significantly increased in ACD and AcI patients but were decreased significantly in ACD/IDA and IDA. Elevated serum EPO concentrations were found in ACD, ACD/IDA and IDA patients but not in AcI patients and the controls. A positive correlation exists between hepcidin and IL-6 levels only in ACD/IDA, AcI and the control groups. A positive correlation between hepcidin and ferritin was marked in the control group, while a negative correlation between hepcidin and ferritin was noted in IDA. The significant negative correlation between hepcidin expression and reticulocyte count was marked in both ACD/IDA and IDA groups. If the hepcidin role in pathogenesis of ACD, ACD/IDA and IDA, it could be a potential marker for detection and differentiation of these anemias (24).

Cancer

Because cancer cells are known to possess a highly unique metabolic phenotype, development of specific biomarkers in oncology is possible and might be used in identifying fingerprints, profiles, or signatures to detect the presence of cancer, determine prognosis, and/or assess the pharmacodynamic effects of therapy (25).

HDM2, a negative regulator of the tumor suppressor p53, is over-expressed in many cancers that retain wild-type p53. Consequently, the effectiveness of chemotherapies that induce p53 might be limited, and inhibitors of the HDM2–p53 interaction are being sought as tumor-selective drugs. A binding site within HDM2 has been dentified which can be blocked with peptides inducing p53 transcriptional activity. A recent report demonstrates the principle using drug-like small molecules that target HDM2 (26).

Obesity, CRP, interleukins, and chronic inflammatory disease

Elevated CRP levels and clinically raised CRP levels were present in 27.6% and 6.7% of the population, respectively. Both overweight (body mass index [BMI], 25-29.9 kg/m2) and obese (BMI, 30 kg/m2) persons were more likely to have elevated CRP levels than their normal-weight counterparts (BMI, <25 kg/m2). After adjusting for potential confounders, the odds ratio (OR) for elevated CRP was 2.13 for obese men and 6.21 for obese women. In addition, BMI was associated with clinically raised CRP levels in women, with an OR of 4.76 (95% CI, 3.42-6.61) for obese women. Waist-to-hip ratio was positively associated with both elevated and clinically raised CRP levels, independent of BMI. Restricting the analyses to young adults (aged 17-39 years) and excluding smokers, persons with inflammatory disease, cardiovascular disease, or diabetes mellitus and estrogen users did not change the main findings (27).

A study of C-reactive protein and interleukin-6 with measures of obesity and of chronic infection as their putative determinants related levels of C-reactive protein and interleukin-6 to markers of the insulin resistance syndrome and of endothelial dysfunction. Levels of C-reactive protein were significantly related to those of interleukin-6 (r=0.37, P<0.0005) and tumor necrosis factor-a (r=0.46, P<0.0001), and concentrations of C-reactive protein were related to insulin resistance as calculated from the homoeostasis model and to markers of endothelial dysfunction (plasma levels of von Willebrand factor, tissue plasminogen activator, and cellular fibronectin). A mean standard deviation score of levels of acute phase markers correlated closely with a similar score of insulin resistance syndrome variables (r=0.59, P<0.00005) and the data suggested that adipose tissue is an important determinant of a low level, chronic inflammatory state as reflected by levels of interleukin-6, tumor necrosis factor-a, and C-reactive protein (28).

A number of other studies have indicated the inflammatory ties of visceral obesity to adipose tissue metabolic profiles, suggesting a role in ―metabolic syndrome‖. There is now a concept of altered liver metabolism in ―non-alcoholic‖ fatty liver disease (NAFLD) progressing from steatosis to steatohepatitis (NASH) (31,32).

These unifying concepts were incomprehensible 50 years ago. It was only known that insulin is anabolic and that insulin deficiency (or resistance) would have consequences in the point of entry into the citric acid cycle, which generates 16 ATPs. In fat catabolism, triglycerides are hydrolyzed to break them into fatty acids and glycerol. In the liver the glycerol can be converted into glucose via dihydroxyacetone phosphate and glyceraldehyde-3-phosphate by way of gluconeogenesis. In the case of this cycle there is a tie in with both catabolism and anabolism.

 

TCA_reactions

TCA_reactions

 http://www.newworldencyclopedia.org/entry/Image:TCA_reactions.gif

 

For bypass of the Pyruvate Kinase reaction of Glycolysis, cleavage of 2 ~P bonds is required. The free energy change associated with cleavage of one ~P bond of ATP is insufficient to drive synthesis of phosphoenolpyruvate (PEP), since PEP has a higher negative G of phosphate hydrolysis than ATP.

The two enzymes that catalyze the reactions for bypass of the Pyruvate Kinase reaction are the following:

(a) Pyruvate Carboxylase (Gluconeogenesis) catalyzes:

pyruvate + HCO3 + ATP — oxaloacetate + ADP + Pi

(b) PEP Carboxykinase (Gluconeogenesis) catalyzes:

oxaloacetate + GTP — phosphoenolpyruvate + GDP + CO2

The concept of anomalies in the pathways with respect to diabetes was sketchy then, and there was much to be filled in. This has been substantially done, and is by no means complete. However, one can see how this comes into play with diabetic ketoacidosis accompanied by gluconeogenesis and in severe injury or sepsis with peripheral proteolysis to provide gluconeogenic precursors. The reprioritization of liver synthetic processes is also brought into play with the conundrum of protein-energy malnutrition.

The picture began to be filled in with the improvements in technology that emerged at the end of the 1980s with the ability to profile tissue and body fluids by NMR and by MS. There was already a good inkling of a relationship of type 2 diabetes to major indicators of CVD (29,30). And a long suspected relationship between obesity and type 2 diabetes was evident. But how did it tie together?

End Stage Renal Disease and Cardiovascular Risk

Mortality is markedly elevated in patients with end-stage renal disease. The leading cause of death is cardiovascular disease.

As renal function declines,

  • the prevalence of both malnutrition and cardiovascular disease increase.

Malnutrition and vascular disease correlate with the levels of

  • markers of inflammation in patients treated with dialysis and in those not yet on dialysis.

The causes of inflammation are likely to be multifactorial. CRP levels are associated with cardio-vascular risk in the general population.

The changes in endothelial cell function,

  • in plasma proteins, and
  • in lpiids in inflammation

are likely to be atherogenic.

That cardiovascular risk is inversely correlated with serum cholesterol in dialysis patients, suggests that

  • hyperlipidemia plays a minor role in the incidence of cardiovascular disease.

Hypoalbuminemia, ascribed to malnutrition, has been one of the most powerful risk factors that predict all-cause and cardiovascular mortality in dialysis patients. The presence of inflammation, as evidenced by increased levels of specific cytokines (interleukin-6 and tumor necrosis factor a) or acute-phase proteins (C-reactive protein and serum amyloid A), however, has been found to be associated with vascular disease in the general population as well as in dialysis patients. Patients have

  • loss of muscle mass and changes in plasma composition—decreases in serum albumin, prealbumin, and transferrin levels, also associated with malnutrition.

Inflammation alters

  • lipoprotein structure and function as well as
  • endothelial structure and function

to favor atherogenesis and increases

  • the concentration of atherogenic proteins in serum.

In addition, proinflammatory compounds, such as

  • advanced glycation end products, accumulate in renal failure, and
  • defense mechanisms against oxidative injury are reduced,

contributing to inflammation and to its effect on the vascular endothelium (33,34).

Endogenous copper can play an important role in postischemic reperfusion injury, a condition associated with endothelial cell activation and increased interleukin 8 (IL-8) production. Excessive endothelial IL-8 secreted during trauma, major surgery, and sepsis may contribute to the development of systemic inflammatory response syndrome (SIRS), adult respiratory distress syndrome (ARDS), and multiple organ failure (MOF). No previous reports have indicated that copper has a direct role in stimulating human endothelial IL-8 secretion. Copper did not stimulate secretion of other cytokines. Cu(II) appeared to be the primary copper ion responsible for the observed increase in IL-8 because a specific high-affinity Cu(II)-binding peptide, d-Asp-d-Ala-d-Hisd-Lys (d-DAHK), completely abolished this effect in a dose-dependent manner. These results suggest that Cu(II) may induce endothelial IL-8 by a mechanism independent of known Cu(I) generation of reactive oxygen species (35).

Blood coagulation plays a key role among numerous mediating systems that are activated in inflammation. Receptors of the PAR family serve as sensors of serine proteinases of the blood clotting system in the target cells involved in inflammation. Activation of PAR_1 by thrombin and of PAR_2 by factor Xa leads to a rapid expression and exposure on the membrane of endothelial cells of both adhesive proteins that mediate an acute inflammatory reaction and of the tissue factor that initiates the blood coagulation cascade. Other receptors that can modulate responses of the cells activated by proteinases through PAR receptors are also involved in the association of coagulation and inflammation together with the receptors of the PAR family. The presence of PAR receptors on mast cells is responsible for their reactivity to thrombin and factor Xa , essential to the inflammation and blood clotting processes (36).

The understanding of regulation of the inflammatory process in chronic inflammatory diseases is advancing.

Evidence consistently indicates that T-cells play a key role in initiating and perpetuating inflammation, not only via the production of soluble mediators but also via cell/cell contact interactions with a variety of cell types through membrane receptors and their ligands. Signalling through CD40 and CD40 ligand is a versatile pathway that is potently involved in all these processes. Many inflammatory genes relevant to atherosclerosis are influenced by the transcriptional regulator nuclear factor κ B (NFκB). In these events T-cells become activated by dendritic cells or inflammatory cytokines, and these T-cells activate, in turn, monocytes / macrophages, endothelial cells, smooth muscle cells and fibroblasts to produce pro-inflammatory cytokines, chemokines, the coagulation cascade in vivo, and finally matrix metalloproteinases, responsible for tissue destruction. Moreover, CD40 ligand at inflammatory sites stimulates fibroblasts and tissue monocyte/macrophage production of VEGF, leading to angiogenesis, which promotes and maintains the chronic inflammatory process.

NFκB plays a pivotal role in co-ordinating the expression of genes involved in the immune and inflammatory response, evoking tumor necrosis factor α (TNFα), chemokines such as monocyte chemoattractant protein-1 (MCP-1) and interleukin (IL)-8, matrix metalloproteinase enzymes (MMP), and genes involved in cell survival. A complex array of mechanisms, including T cell activation, leukocyte extravasation, tissue factor expression, MMP expression and activation, as well induction of cytokines and chemokines, implicated in atherosclerosis, are regulated by NFκB.

Expression of NFκB in the atherosclerotic milieu may have a number of potentially harmful consequences. IL-1 activates NFκB upregulating expression of MMP-1, -3, and -9. Oxidized LDL increases macrophage MMP-9, associated with increased nuclear binding of NFκB and AP-1. Expression of tissue factor, initiating the coagulation cascade, is regulated by NFκB. In atherosclerotic plaque cells, tissue factor antigen and activity were inhibited following over-expression of IκBα and dominant-negative IKK-2, but not by dominant negative IKK-1 or NIK. Tis supports the concept that activation of the ―canonical‖ pathway upregulates pro-thrombotic mediators involved in disease. Many of the cytokines and chemokines which have been detected in human atherosclerotic plaques are also regulated by NFκB. Over-expression of IκBα inhibits release of TNFα, IL-1, IL-6, and IL-8 in macrophages stimulated with LPS and CD40 ligand (CD40L). This report describes how NFκB activation upregulates major pro-inflammatory and pro-thrombotic mediators of atherosclerosis (37-41).

This review is both focused and comprehensive. The details of evolving methods are avoided in order to build the argument that a very rapid expansion of discovery has been evolving depicting disease, disease mechanisms, disease associations, metabolic biomarkers, study of effects of diet and diet modification, and opportunities for targeted drug development. The extent of future success will depend on the duration and strength of the developed interventions, and possibly the avoidance of dead end interventions that are unexpectedly bypassed. I anticipate the prospects for the interplay between genomics, metabolomics, metabonomics, and personalized medicine may be realized for several of the most common conditions worldwide within a few decades (42-44).

References

  1. Trujillo E, Davis C, Milner J. Nutrigenomics, proteomics, metabolomics, and the practice of dietetics. J Am Diet Assoc. 2006;106(3):403-13.
  2. Kussmann M, Raymond F, Affolter M. OMICS-driven biomarker discovery in nutrition and health. J Biotechnol. 2006;124(4):758-87.
  3. (Zeisel SH. Nutrigenomics and metabolomics will change clinical nutrition and public health practice: insights from studies on dietary requirements for choline. Am J Clin Nutr. 2007;86(3):542-8.
  4. García-Cañas V, Simó C, León C, Cifuentes A. Advances in Nutrigenomics research: novel and future analytical approaches to investigate the biological activity of natural compounds and food functions. J Pharm Biomed Anal. 2010;51(2):290-304.
  5. Kussmann M, Blum S. OMICS-derived targets for inflammatory gut disorders: opportunities for the development of nutrition related biomarkers. Endocr Metab Immune Disord Drug Targets. 2007;7(4):271-87.
  6. Ovesná J, Slabý O, Toussaint O, Kodícek M, et al. High throughput ‘omics’ approaches to assess the effects of phytochemicals in human health studies. Br J Nutr. 2008;99 E Suppl 1:ES127-34.
  7. Workshop Report: ―The 2010 Project‖. Chory J, Ecker JR, Briggs S, et al. A Blueprint for Understanding How Plants Are Built and How to Improve Them. Plant Physiology 2000;123:423–425, http://www.plantphysiol.org.
  8. Stover PJ, Caudill MA. Genetic and epigenetic contributions to human nutrition and health: managing genome-diet interactions. J Am Diet Assoc. 2008 Sep;108(9):1480-7.
  9. Kussmann M, Panchaud A, Affolter M.. Proteomics in nutrition: status quo and outlook for biomarkers and bioactives. J Proteome Res. 201;9(10):4876-87.
  10. Wolfram Weckwerth. Metabolomics in Systems Biology. Annual Review of Plant Biology 2003; 54: 669-689.
  11. Kussmann M, Affolter M, Nagy K, Holst B, Fay LB. Mass spectrometry in nutrition: understanding dietary health effects at the molecular level. Mass Spectrom Rev. 2007;26(6):727-50.
  12. Grainger DJ. Megavariate Statistics meets High Data-density Analytical Methods: The Future of Medical Diagnostics? IRTL Reviews 2003;1:1-6.
  13. Heiss; C, Keen CL, Kelm M. Flavanols and Cardiovascular Disease Prevention. European Heart Journal 2010;31(21):2583-2592.
  14. Kussmann M, Rezzi S, Daniel H. Profiling techniques in nutrition and health research. Curr Opin Biotechnol. 2008;19 (2):83-99.
  15. Ohashi N, Ito C, Fujikawa R, Yamamoto H, et al. The impact of visceral adipose tissue and high-molecular weight adiponectin on cardia-ankle vascular index in asymptomatic Japanese subjects. Metabolism 2009; 58:1023-9. [CAVI and VAT and HMW adiponectin levels];
  1. Zha JM, Di WJ, Zhu T, Xie T, et al. Comparison of gene transcription between subcutaneous and visceral adipose tissue in chinese adults. Endocr J 2009;56:934-44. [TLR4 signaling, 11 beta-HSD1 and GR levels in VAT];
  2. Albert L, Girola A, Gilardini L, Conti A, et al. Type 2 diabetes and metabolic syndrome are associated with increased expression of 11 beta-hydroxysteroid dehydrogenase 1 in obese subjects. Int J Obesity (Lond) 2007;31:1826-31;
  3. Fabbrini E, Markos F, Mohammed BS, Pietka T, et al. Intrahepatic fat, not visceral fat, is linked with metabolic complications of obesity. PNAS 2009;106:15430-5;
  4. Tong J, Boyko EJ, Utzschneider KM, McNeely MJ, et al. Intraabdominal fat accumulation predicts the development of the metabolic syndrome in non-diabetic Japanese-Americans. Diabetologia 2007;50:1156-60;
  5. Kim K, Valentine RJ, Shin Y, Gong K. Association of visceral adiposity and exercise participation with C- reactive protein, insulin resistance, and endothelial dysfunction in Korean healthy adults. Metabolism 2008;57:1181-9. [(VAT-EC exhibits a marked angiogenic and proinflammatory state];
  6. Villaret A, Galitzky J, Decaunes P, Exteve D, et al. Adipose tisue endothelial cells from obese human subjects: differences among depots in angiogenic, metabolic, and inflammatory gene expression and cellular senescence. Diabetes 2010;59:2755-63;
  7. van Dijk -, Feskens EJ, Bos MB, Hoelen DW, et al. A saturated fatty acid-rich diet induces an obesity-linked proinflammatory gene expression profile in adipose tissue of subjects at risk of metabolic syndrome. Am J Clin Nutr 2009;90:1656-64.[MUFA in LDL lowering].
  8. Theurl I, Aigner E, Theurl M, Nairz M, et al. Regulation of iron homeostasis in anemia of chronic disease and iron deficiency anemia: diagnostic and therapeutic implications. Blood. 2009;113(21):5277-86
  9. Cheng PP, Jiao XY, Wang XH, Lin JH, Cai YM. Hepcidin expression in anemia of chronic disease and concomitant iron-deficiency anemia. Clin Exp Med. 2010 May 25. [Epub ahead of print].
  10. Spratlin JL, Serkova NJ, and Eckhardt SG. Clinical Applications of Metabolomics in Oncology: A Review. Clin Cancer Res. 2009 ;15; 15(2): 431–440.
  11. Fischer PM, Lane DP. Small molecule inhibitors of thep53 suppressor HDM2: have protein-protein interactions come of age as drug targets? Trends in Pharm Sci 2004;25(7):343-346.
  12. Visser M, Bouter LM, McQuillan GM, Wener HM. Elevated C-Reactive Protein Levels in Overweight and Obese Adults. JAMA. 1999;282:2131-2135.
  13. Yudkin JS, Stehouwer CDA, Emeis JJ, Coppack SW. C-Reactive Protein in Healthy Subjects: Associations With Obesity, Insulin Resistance, and Endothelial Dysfunction : A Potential Role for Cytokines Originating From Adipose Tissue? Arterioscler. Thromb. Vasc. Biol. 1999; 19:972-978.
  14. Visvikis-Siest S, Siest G. The STANISLAS cohort: a 10-year followup of supposed healthy families. Gene-environment interactions, reference values and evaluation of biomarkers in prevention of cardiovascular diseases. Clin Chem Lab Med 2008;46:733-47.
  15. Schmidt MI, Duncan BB. Diabesity: an inflammatory metabolic condition. Clin Chem Lab Med 2003;41:1120-1130.
  16. Fenkci S, Rota S, Sabir N, Akdag B. Ultrasonographic and biochemical evaluation of visceral obesity in obese women with non-alcoholic fatty liver disease. Eur J Med Res 2007;12:68-73. (VAT, HOMA)
  17. Lee JW, Lee HR, Shim JY, Im JA, et al. Viscerally obese women with normal body weight have greater brachial-ankle pulse wave velocity than non viscerally obese women with excessive body weight. Clin Endocrinol (Oxf) 2007;66:572-8. [visceral obesity – high trigly, high baPWV, greater SFA and thigh SFA].
  18. Kaysen GE. The Microinflammatory State in Uremia: Causes and Potential Consequences. J Am Soc Nephrol 2001;12:1549–1557.
  19. Kaysen GE. Role of Inflammation and Its Treatment in ESRD Patients. Blood Purif 2002;20:70–80.
  20. Bar-Or D, Thomas GW, Yukl RL, Rael LT, et al. Copper stimulates the synthesis and release of interleukin-8 in human endothelial cells: a possible early role in systemic inflammatory responses. Shock 2003;20(2):154–158.
  21. Dugina TN, Kiseleva EV, Chistov IV, Umarova BA, and Strukova SM. Receptors of the PAR Family as a Link between Blood Coagulation and Inflammation. Biochemistry (Moscow), 2002; 67(1):65-74. [Translated from Biokhimiya 2002;67(1):77-87].
  22. Monaco C, Andreakos E, Kiriakidis S, Feldmann M, and and Ewa Paleolog. T-Cell-Mediated Signalling in Immune, Inflammatory and Angiogenic Processes: The Cascade of Events Leading to Inflammatory Diseases. Current Drug Targets – Inflammation & Allergy, 2004, 3, 35-42.
  23. Monaco C, Grosjean J, and Paleolog E. The role of the NFκB pathway in atherosclerosis. [E-mail: e.paleolog@imperial.ac.uk]
  24. Libby P, Ridker PM, and Maseri A. Inflammation and atherosclerosis. Circulation 2002;105:1135-43.
  25. Karin M, Yamamoto Y, Wang QM. The IKK NF-kappa B system: a treasure trove for drug development. Nat Rev Drug Discov 2004;3:17-26.
  26. Karin M, Ben-Neriah Y. Phosphorylation meets ubiquitination: the control of NF-[kappa]B activity. Annu Rev Immunol 2000;18:621-63.
  27. Lee DY, Bowen BP, and Northen TR. Mass spectrometry–based metabolomics, analysis of metabolite-protein interactions, and imaging. BioTechniques 2010;49:557-565.
  28. Faca V, Krasnoselsky A, and Hanash S. Innovative proteomic approaches for cancer biomarker discove.
  29. Sharp, P, and MIT faculty. ‘Convergence’ offers potential for revolutionary advance in biomedicine. The Third Revolution: Convergence of the Life Sciences, Physical Sciences and Engineering. White paper. Reported in Biotechnology Jan 5, 2011. [Convergence is a new paradigm that can yield critical advances in a broad array of sectors]

 

Read Full Post »

Use of Subtyping for Presurgical Breast Cancer Treatment Use

Reporter, Reblog: Larry H Bernstein, MD, FCAP

 

 

More Accurate Identification of Molecular Subgroups May Better Guide Neo-adjuvant Treatment of Breast Cancer

By Susan Reckling
Posted: 8/19/2014 12:43:52 PM
Last Updated: 8/19/2014 12:43:52 PM

Key Points:
  • Although accurate classification of breast tumors by molecular subtype may guide the appropriate selection of therapy, conventional assessment methods lack standardization.
  • In the Neoadjuvant Breast Registry Symphony Trial of more than 400 women with breast cancer, standard assessment methods were compared with a novel 80-gene classifier known as BluePrint in combination with MammaPrint.
  • BluePrint molecular subtyping reclassified nearly one-fourth of tumors, with more responsive patients reassigned to the HER2 and basal categories and less responsive patients reassigned to the luminal category.

BluePrint in combination with MammaPrint molecular subtyping reclassified more than 20% of breast cancer patients into a different subgroup compared with conventional assessment, according to the results of the prospective Neoadjuvant Breast Registry Symphony Trial (NBRST). In Annals of Surgical Oncology, Whitworth et al reported that this reclassification of patients led to an improved distribution of response rates and a more accurate picture of which patients were likely to respond (or not respond) to neoadjuvant chemotherapy for breast cancer.

Selection of the appropriate therapy for a woman with breast cancer can be guided by accurate classification of the tumor by molecular subtype. Currently, however, conventional assessment methods such as immunohistochemistry and fluorescence in situ hybridization (FISH) lack standardization and the interpretation of test results differs among laboratories.

Thus, investigators have turned to other potentially more effective approaches to molecular subtyping. BluePrint, a novel molecular profile, is a multigene classifier, determining the mRNA levels of 80 genes. In combination with MammaPrint (risk stratification by multigene assays), BluePrint can classify patients with breast cancer into three subtypes based on functional molecular pathways: luminal (A or B), HER2, and basal.

Study Details

In the NBRST study, the investigators attempted to predict chemosensitivity in women with histologically proven breast cancer with the 80-gene BluePrint functional subtype profile vs conventional subtyping. Chemosensitivity was defined as pathologic complete response or the absence of invasive carcinoma in both the breast and axilla at microscopic examination of the resected specimen.

More than 400 women with breast cancer who had started or were scheduled to start neoadjuvant chemotherapy or hormone therapy took part in the multicenter NBRST study. All of them had definitive surgical resection. The age of study participants ranged from 22 to 80 years, with a median age of 52 years. Most of the patients (85%) had T2 or T3 tumors.

Patients who had undergone an excisional biopsy or axillary dissection or who had confirmed distant metastases were excluded from the study. Also, those who had received prior chemotherapy, radiotherapy, or endocrine therapy for breast cancer were ineligible for study participation.

Microarray analysis for the 80-gene BluePrint subtype and the 70-gene MammaPrint profiles was conducted at Agendia Laboratory, which was blinded to both clinical and pathologic data. BluePrint and MammaPrint analysis categorized the study patients as follows: 59 (14%) were luminal A, 153 (36%) were luminal B, 74 (17%) were HER2, and 140 (33%) were basal.

Reclassification to Different Molecular Subgroup

In total, 22% (94 of 426 patients) were reclassified in a different BluePrint/MammaPrint molecular subgroup compared with conventional subtyping. For instance, 37 of 211 patients (18%) of conventionally determined hormone receptor–positive/HER2-negative patients were reassigned by BluePrint as basal (35) or HER2-positive (2). In addition, 53 of 123 conventionally determined HER2-positive patients (43%) were reclassified as luminal (36) or basal (17).

As for response rates to neoadjuvant chemotherapy, the investigators reported an overall pathologic complete response rate of 25% (99 of 403 patients). Six percent of patients with luminal breast tumors had a pathologic complete response rate (2% for luminal A, 7% for luminal B).

More than half of the 74 patients with BluePrint-determined HER2-positive tumors had a pathologic complete response, which the investigators noted was significantly superior (P = .047) to the 38% of conventionally assigned HER2-positive patients.

Clinical Implications

Use of the multigene classifier BluePrint may assist oncologists in accurately identifying which patients with breast cancer may benefit from neoadjuvant chemotherapy and which ones are less likely to do so. According to the investigators, there are potential clinical implications for two particular groups of reassigned patients via BluePrint molecular subtyping: (1) those who were conventionally assigned as HER2-positive but not classified as such by BluePrint, and (2) those who were considered to have hormone receptor–positive/HER2-negative disease via conventional assessment but were reclassified to basal disease by BluePrint.

“This reclassification of patients leads to an improved distribution of response rates in the different subgroups of patients: a lower pathologic complete response rate for BluePrint luminal patients compared with [immumohistochemistry]/FISH-defined conventional luminal patients, with more responsive patients reassigned to the HER2 and basal categories,” concluded the investigators.

Pat Whitworth, MD, of the Department of Surgery, Nashville Breast Center, Nashville, Tennessee, is the corresponding author of the article in Annals of Surgical Oncology.

Lisette Stork-Sloots, MSc, and Femke A. de Snoo, MD, PhD, are employees of Agendia NV, Amsterdam, The Netherlands. The other authors disclosed no potential conflicts of interest.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.

Read Full Post »

The multi-step transfer of phosphate bond and hydrogen exchange energy

Curator: Larry H. Bernstein, MD, FCAP, Leaders in Pharmaceutical Intelligence

In this subtext of the series we expand on a tie between respiration and glycolysis, and the functioning of the mitochondrion to discover the key role played by oxidative phosphorylation, “acetyl coenzyme A, and electron transport.  This was crucial to understanding cellular energetics, which explains the high energy of fatty acid catabolism from stored adipose tissue, and the criticality of the multi-step sequence of reactions in energy transfer.

This portion considerably provides a response to the TWO points made by Jose EDS Rosallis:

  1. Just at the beginning, when phosphorylation of proteins is presented, I assume you must mention that some proteins are activated by phosphorylation. This is fundamental in order to present self –organization reflex upon fast regulatory mechanisms. This poiny needs further clarification, but he makes important observations here.
  • Even from an historical point of view. The first observation arrived from a sample due to be studied on the following day of glycogen synthetase. It was unintended left overnight out of the refrigerator. The result was it had changed from active form of the previous day to a non-active form.

The story could have being finished here, if the researcher did not decide to spent this day increasing substrate levels (it could be a simple case of denaturation of proteins that changes its conformation despite the same order of amino acids). He kept on trying and found restoration of maximal activity.

  • This assay was repeated with glycogen phosphorylase and the result was the opposite it increases its activity.

This led to the discovery of cAMP activated protein kinase and the assembly of a very complex system in the glycogen granule that is not a simple carbohydrate polymer. Instead

  • it has several proteins assembled and preserves the capacity to receive from a single event (rise in cAMP) two opposing signals with maximal efficiency,
  • stops glycogen synthesis, as long as levels of glucose 6 phosphate are low and
  • increases glycogen phosphorylation as long as AMP levels are high).

I did everything I was able to do by the end of 1970 in order to repeat these assays with

  • PK I, PKII and PKIII of M. Rouxii and Sutherland route to cAMP failed in this case.

I ask Leloir to suggest to my chief (SP) the idea of AA, AB, BB subunits as was observed in lactic dehydrogenase (tetramer)
(Nathan O. Kaplan discovery) indicating this as his idea. The reason was my “chief” (SP) more than once,  said to me: “Leave these great ideas for the Houssay, Leloir etc…We must do our career with small things. ” However, as she also had a faulty ability for recollection she also used to arrive some time later, with the very same idea but in that case, as her idea.

[This reminds me of when I was studying the emergence of lactic dehysrogenase isoenzyme patterns in the developing eye lens of cattle, I raised reservations about Elliott Vessells challenge to Nathan Kaplan, but that not being my primary problem, my brilliant mentor (H.M.), a very young full professor of anatomy said – leave that to NOK.}

Leloir, said to me: I will not offer your interpretation to her as mine. I think it is not phosphorylation, however I think it is

  • glycosylation that explains the changes in the isoenzymes with the same molecular weight preserved.

This dialogue explains why during the Schroedinger’s “What is life?” reading with him he asked me if from biochemist in exile, to biochemist I expressed all of my thoughts to him. Since I had considered that Schrödinger did not confront Darlington & Haldane for being in exile. This may explain why Leloir could have answered a bad telephone call from P. Boyer, Editor of The Enzymes in a way that suggests the the pattern could be of covalent changes over a protein. Our FEBS and Eur J. Biochemistry papers on pyruvate kinase of M. Rouxii is wrongly quoted in this way on his review about pyruvate kinase of
that year(1971).

  1. show in detail with different colors what carbons belongs to CoA a huge molecule, in comparison with the single two carbons of acetate that will produce the enormous jump in energy yield in comparison with anaerobic glycolysis. The idea is how much must have being spent in DNA sequences to build that molecule in order to use only two atoms of carbon. Very limited aspects of biology could be explained in this way. In case we follow an alternative way of thinking, it becomes clearer that proteins were made more stable by interaction with other molecules (great and small). Afterwards, it rather easy to understand how the stability of protein-RNA complexes where transmitted to RNA (vibrational +solvational reactivity stability pair of conformational energy). Latter, millions of years, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.

The outline of what I am presenting in series is as follows:

  1. Signaling and Signaling Pathways
    http://pharmaceuticalintelligence.com/2014/08/12/signaling-and-signaling-pathways/
  1. Signaling transduction tutorial.
    http://pharmaceuticalintelligence.com/2014/08/12/signaling-transduction-tutorial/
  1. Carbohydrate metabolism
    http://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/

3.1  Selected References to Signaling and Metabolic Pathways in Leaders in Pharmaceutical Intelligence

http://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/

  1. Lipid metabolism

4.1  Studies of respiration lead to Acetyl CoA

http://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/

4.2 The multi-step transfer of phosphate bond and hydrogen exchange energy

  1. Protein synthesis and degradation
  2. Subcellular structure
  3. Impairments in pathological states: endocrine disorders; stress hypermetabolism; cancer.

Oxidation-Reduction Reactions

Rachel Casiday, Carolyn Herman, and Regina Frey
Department of Chemistry, Washington University
St. Louis, MO 63130

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/cytochromes.html

 

OX-Phos steps

OX-Phos steps

http://s1.hubimg.com/u/6583902_f496.jpg

 

Key Concepts:

  • ATP as Free-Energy Currency in the Body
  • Coupled Reactions
    • Standard Free-Energy Change for Coupled Reactions
    • ATP Dephosphorylation Coupled to Nonspontaneous Reactions
    • Coupled Reactions to Generate ATP
  • Structure and Function of the Mitochondria
  • Oxidation-Reduction Reactions in the Electron-Transport Chain
    • Electron-Carrier Proteins (NOTE: This section includes a separate link and an animation.)
    • Relationship Between Reduction Potentials and Free Energy
  • Proton Gradient as Means of Coupling Oxidative and Phosphorylation Components of Oxidative Phosphorylation
  • ATP Synthetase Uses Energy From Proton Gradient to Generate ATP

Every day, we build bones, move muscles, eat food, think, and perform many other activities with our bodies. All of these activities are based upon chemical reactions. However, most of these reactions are not spontaneous (i.e., they are accompanied by a positive change in free energy, DG>0) and do not occur without some other source of free energy. Hence, the body needs some sort of “free-energy currency,” (Figure 1) a molecule that can store and release free energy when it is needed to power a given biochemical reaction.

The four questions:

  1. How does the body “spend” free-energy currency to make a nonspontaneous reaction spontaneous? The answer, which is based on thermodynamics, is to use coupled reactions.
  2. How is food used to produce the reducing agents (NADH and FADH2) that can regenerate the free-energy currency? The answer, from biology, is found in glycolysis and the citric-acid cycle.
  3. How are the reducing agents (NADH and FADH2) able to generate the free-energy currency molecule (ATP)? Once again, coupled reactions are key.
  4. What mechanism does the body use to couple the reducing agent reactions and the generation of ATP? ATP is synthesized primarily by a two-step process consisting of an electron-transport chain and a proton gradient.  This process is based on electrochemistry and equilibrium, as well as thermodynamics.

The free-energy change (DG) for the net reaction is given by the sum of the free-energy changes for the individual reactions.  The phospholipids that form cell membranes are formed from glycerol with a phosphate group and two fatty-acid chains attached.This step actually consists of two reactions:

(1) the phosphorylation of glycerol, and

(2) the dephosphorylation of ATP (the free-energy-currency molecule). The reactions may be added as shown in Equations 2-4, below:

      Glycerol + HPO42- –>  (Glycerol-3-Phosphate)2- + H2O DGo= +9.2 kJ
(nonspontaneous)
(2)
+      ATP4- + H2O –>       ADP3- + HPO42- + H+ DGo30.5 kJ
(spontaneous)
(3)
     Glycerol + ATP4- –> (Glycerol-3-Phosphate)2- +ADP3- + H+ DGo21.3 kJ
(spontaneous)
(4)
   

ATP is the most important “free-energy-currency” molecule in living organisms (see Figure 2, below). Adenosine triphosphate (ATP) is a useful free-energy currency because the dephosphorylation reaction is very spontaneous; i.e., it releases a large amount of free energy (30.5 kJ/mol). Thus, the dephosphorylation reaction of ATP to ADP and inorganic phosphate (Equation 3) is often coupled with nonspontaneous reactions (e.g., Equation 2) to drive them forward. The body’s use of ATP as a free-energy currency is a very effective strategy to cause vital nonspontaneous reactions to occur.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP.jpg

structure of ATP

structure of ATP

This is the two-dimensional (ChemDraw) structure of ATP, adenosine triphosphate. The removal of one phosphate group (green) from ATP requires the breaking of a bond (blue) and results in a large release of free energy. Removal of this phosphate group (green) results in ADP, adenosine diphosphate.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP.jpg

flowchart of food energy

flowchart of food energy

This flowchart shows that the energy used by the body for its many activities ultimately comes from the chemical energy in our food. The chemical energy in our food is converted to reducing agents (NADH and FADH2). These reducing agents are then used to make ATP. ATP stores chemical energy, so that it is available to the body in a readily accessible form.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/flowchart1.jpg

Glycolysis   Glucose + 2 HPO42- + 2 ADP3- + 2 NAD+ –>
2 Pyruvate + 2 ATP4- + 2 NADH + 2 H+ + 2 H2O
(5)
Intermediate Step   2(Pyruvate + Coenzyme A + NAD+ –>
Acetyl CoA + CO2 + NADH)
(6)
Citric-Acid Cycle 2(Acetyl CoA + 3 NAD++ FAD + GDP3-
+ HPO42- + 2H2O –> 2 CO2 + 3 NADH + FADH2
+ GTP4- + 2H+ + Coenzyme A)
(7)

The structures of the important molecules in Equations 5-7 are shown in Table 1, below.

How is Food Used to Make the Reducing Agents Needed for the Production of ATP?

To make ATP, energy must be absorbed. This energy is supplied by the food we eat, and then used to synthsize two reducing agents, NADH and FADH2 that are needed to produce ATP. One of the principal energy-yielding nutrients in our diet is glucose (see structure in Table 1 in the blue box below), a simple six-carbon sugar that can be broken down by the body. When the chemical bonds in glucose are broken, free energy is released. The complete breakdown of glucose into CO2 occurs in two processes: glycolysis and the citric-acid cycle. The reactions for these two processes are shown in the blue box below.

pyruvate

pyruvate

  Pyruvate

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/pyruvate.jpg

acetylCoA

acetylCoA

Acetyl CoA

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/acetylCoA.jpg

NADH

NADH

NADH

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/acetylCoA.jpg

 

FADH2

FADH2

FADH2

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/FADH2.jpg

two-dimensional representations of several important molecules in Equations 5-7.

As seen in Equations 5-7 in the blue box, glycolysis and the citric-acid cycle produce a net total of only four ATP or GTP molecules (GTP is an energy-currency molecule similar to ATP) per glucose molecule. This yield isfar below the amount needed by the body for normal functioning, and in fact is far below the actual ATP yield for glucose in aerobic organisms (organisms that use molecular oxygen). For each glucose molecule the body processes, the body actually gains approximately 30 ATP molecules! (See Figure 4, below.)  So, how does the body generate ATP?

The process that accounts for the high ATP yield is known as oxidative phosphorylation. A quick examination of Equations 5-7 shows that glycolysis and the citric-acid cycle generate other products besides ATP and GTP, namely NADH and FADH2 (blue). These products are molecules that are oxidized (i.e., give up electrons) spontaneously. The body uses these reducing agents (NADH and FADH2) in an oxidation-reduction reaction .  As you will see later in this tutorial, it is the free energy from these redox reactions that is used to drive the production of ATP.

flowchart - making of ATP

flowchart – making of ATP

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/flowchart2.jpg

This flowchart shows the major steps involved in breaking down glucose from the diet and converting its chemical energy to the chemical energy in the phosphate bonds of ATP, in aerobic (oxygen-using) organisms. Note: In this flowchart, red denotes a source of carbon atoms (originally from glucose),green denotes energy-currency molecules, and blue denotes the reducing agents that can be oxidized spontaneously.

In the discussion above, we see that glucose by itself generates only a tiny amount of ATP. However, during the breakdown of glucose, a large amount of NADH and FADHis produced; it is these reducing agents that dramatically increase the amount of ATP produced. How does this work?

How are the reducing agents (NADH and FADH2) able to generate the free-energy currency molecule (ATP)?

As discussed in an earlier section about coupling reactions, ATP is used as free-energy currency by coupling its (spontaneous) dephosphorylation (Equation 3) with a (nonspontaneous) biochemical reaction to give a net release of free energy (i.e., a net spontaneous reaction). Coupled reactions are also used to generate ATP by phosphorylating ADP. The nonspontaneous reaction of joining ADP to inorganic phosphate to make ATP (Equation 8, below, and Figure 2, above) is coupled to the oxidation reaction of NADH or FADH(Equation 9, below). (Recall, NADH and FADH2 are produced in glycolysis and the citric-acid cycle as described in the blue box). For simplicity, we shall henceforth discuss only the oxidation of NADH; FADH2 follows a very similar oxidation pathway.

The oxidation reaction for NADH has a larger, but negative, DG than the positive DG required for the formation of ATP from ADP and phosphate. This set of coupled reactions is so important that it has been given a special name: oxidative phosphorylation. This name emphasizes the fact that an oxidation (of NADH) reaction (Equation 9 and Figure 5, below) is being coupled to a phosphorylation (of ADP) reaction (Equation 8, below, and Figure 2, above). In addition, we must consider the reduction reaction (gaining of electrons) that accompanies the oxidation of NADH. (Oxidation reactions are always accompanied by reduction reactions, because an electron given up by one group must be accepted by another group.) In this case, molecular oxygen (O2) is the electron acceptor, and the oxygen is reduced to water (Equation 10, below) .

The individual reactions of interest for oxidative phosphorylation are:

Phosphorylation

ADP3- + HPO42- + H+ –>
ATP4- + H2O

DGo= +30.5 kJ
(nonspontaneous)
(8)
oxidation

NADH –> NAD+ + H+ +  2e

DGo158.2 Kj
(spontaneous)
(9)
reduction

1/2 O2 + 2H+ + 2e –> H2O

DGo61.9 kJ
(spontaneous)

                                                                       (10)                                    

The net reaction is obtained by summing the coupled reactions, as shown in Equation 11, below.

ADP3- + HPO42- + NADH + 1/2 O2 + 2H+ –>
ATP4- + NAD+ + 2 H2O
DGo= -189.6 kJ
(spontaneous)
(11)

The molecular changes that occur upon oxidation of NADH are shown:

NAD+_NADH

NAD+_NADH

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/NAD+_NADH.jpg

This is a two-dimensional (ChemDraw) representation showing the change that occurs when NADH is oxidized to NAD+. “R” represents the part of the structure that is shown in black in the drawing of NADH in Table 1, and does not change during the oxidation half-reaction. The molecular changes that occur upon oxidation are shown in red.

In this tutorial, we have seen that nonspontaneous reactions in the body occur by coupling them with a very spontaneous reaction (usually the ATP reaction shown in Equation 3). We have just seen that ATP is produced by coupling the phosphorylation reaction with NADH oxidation (a very spontaneous reaction). But we have not yet answered the question: by what mechanism are these reactions coupled?

Coupling Reactions in Biological Systems

Every day your body carries out many nonspontaneous reactions. As discussed earlier, if a nonspontaneous reaction is coupled to a spontaneous reaction, as long as the sum of the free energies for the two reactions is negative, the coupled reactions will occur spontaneously. How is this coupling achieved in the body? Living systems couple reactions in several ways, but the most common method of coupling reactions is to carry out both reactions on the same enzyme. Consider again the phosphorylation of glycerol (Equations 2-4). Glycerol is phosphorylated by the enzyme glycerol kinase, which is found in your liver. The product of glycerol phosporylation, glycerol-3-phosphate (Equation 2), is used in the synthesis of phospholipids.

Glycerol kinase is a large protein comprised of about 500 amino acids. X-ray crystallography of the protein shows us that there is a deep groove or cleft in the protein where glycerol and ATP attach (see Figure 6, below). Because the enzyme holds the ATP and the glycerol in place, the phosphate can be transferred directly from the ATP to glycerol. Instead of two separate reactions where ATP loses a phosphate (Equation 3) and glycerol picks up a phosphate (Equation 2), the enzyme allows the phosphate to move directly from ATP to glycerol (Equation 4).

The coupling in oxidative phosphorylation uses a more complicated (and amazing!) mechanism, but the end result is the same: the reactions are linked together, the net free energy for the linked reactions is negative, and, therefore, the linked reactions are spontaneous.

glyckin

glyckin

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/glyckin.jpg

This is a schematic representation of ATP and glycerol bound (attached) to glycerol kinase. The enzyme glycerol kinase is a dimer (consists of two identical subuits). There is a deep cleft between the subunits where ATP and glycerol bind. Since the ATP and phosphate are physically so close together when they are bound to the enzyme, the phosphate can be transferred directly from ATP to glycerol. Hence, the processes of ATP losing a phosphate (spontaneous) and glycerol gaining a phosphate (nonspontaneous) are linked together as one spontaneous process

Questions on ATP: The Body’s Free-Energy Currency (How Free-Energy Currency Works)

  • Biological systems involve many molecules containing phosphate groups, such as ATP. Although ATP is the most commonly used free-energy currency, any of these phosphorylated molecules could, in theory, be used as free-energy currency. The standard free-energy change (DGo) for the dephosphorylation (removal of a phosphate group) of several biological compounds is given below:
Acetyl phosphate DGo = -47.3 kJ/mol
Adenosine triphosphate (ATP) DGo = -30.5 kJ/mol
Glucose-6-phosphate DGo = -13.8 kJ/mol
Phosphoenolpyruvate (PEP) DGo = -61.9 kJ/mol
Phosphocreatine DGo = -43.1 kJ/mol

Neglecting any differences in difficulty synthesizing or accessing these molecules by biological systems, rank the molecules in order of their efficiency as a free-energy currency (i.e., the amount of nonspontaneous reactions enabled per phosphate removed from a molecule of free-energy currency) from the most efficient to the least efficient.

  • What, if any, changes are there in the shape of the ring as NADH is oxidized to NAD+(see Figure 5)? (Hint: Consider which atoms lie in the same plane in each structure.)

Mechanism of Coupling the Oxidative-Phosphorylation Reactions

In order to couple the redox and phosphorylation reactions needed for ATP synthesis in the body, there must be some mechanism linking the reactions together. In cells, this is accomplished through an elegant proton-pumping system that occurs inside special double-membrane-bound organelles (specialized cellular components) known as mitochondria. A number of proteins are required to maintain this proton-pumping system and catalyze the oxidative and phosphorylation reactions.

Synthesis of ATP (Equation 8) is coupled with the oxidation of NADH (Equation 9) and the reduction of O2 (Equation 10). There are three key steps in this process:

  1. Electrons are transferred from NADH, through a series of electron carriers, to O2. The electron carriers are proteins embedded in the inner mitochondrial membrane. (More detail about the structure of the mitochondria is presented in the next section.) (See Figure 7a.)
  2. Transfer of electrons by these carriers generates a proton (H+) gradient across the inner mitochondrial membrane. (See Figure 7b.)
  3. When Hspontaneously diffuses back across the inner mitochondrial membrane, ATP is synthesized. The large positive free energy of ATP synthesis is overcome by the even larger negative free energy associated with proton flow down the concentration gradient. (See Figure 7c.)

These steps are outlined below.

  1. Electron Transport (Oxidation-Reduction Reactions) Through a Series of Proteins in the Inner Membrane of the Mitochondria
e_transfer

e_transfer

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/e_transfer.jpg

Generation of H+(Proton) Concentration Gradient Across the Inner Mitochondrial Membrane During the Electron-Transport Process (via a Proton Pump)

. Generation of H+ (Proton) Concentration Gradient Across the Inner Mitochondrial Membrane

. Generation of H+ (Proton) Concentration Gradient Across the Inner Mitochondrial Membrane

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/gradient.jpg

Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+Back to the Matrix Inside the Inner Mitochondrial Membrane

. Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+

. Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP_produced.jpg

The three major steps in oxidative phosphorylation are

(a) oxidation-reduction reactions involving electron transfers between specialized proteins embedded in the inner mitochondrial membrane; 

(b) the generation of a proton (H+) gradient across the inner mitochondrial membrane (which occurs simultaneously with step (a)); and 

(c) the synthesis of ATP using energy from the spontaneous diffusion of electrons down the proton gradient generated in step (b).

Note: Steps (a) and (b) show cytochrome oxidase, the final electron-carrier protein in the electron-transport chain described above. When this protein accepts an electron (green) from another protein in the electron-transport chain, an Fe(III) ion in the center of a heme group (purple) embedded in the protein is reduced to Fe(II). The coordinates for the protein were determined using x-ray crystallography, and the image was rendered using SwissPDB Viewer and POV-Ray (see References).

Cells use a proton-pumping system made up of proteins inside the mitochondria to generate ATP. Before we examine the details of ATP synthesis, we shall step back and look at the big picture by exploring the structure and function of the mitochondria, where oxidative phosphorylation occurs.

Structure and Function of the Mitochondria

mitochondria

mitochondria

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/mitochondria.jpg

This is a schematic diagram showing the membranes of the mitochondrion. The purple shapes on the inner membrane represent proteins, which are described in the section below. An enlargement of the boxed portion of the inner membrane in this figure is shown in Figure.

The mitochondrial membranes are crucial for this organelle’s role in oxidative phosphorylation. As shown in Figure 8, mitochondria have two membranes, an inner and an outer membrane. The outer membrane ispermeable to most small molecules and ions, because it contains large protein channels called porins. The inner membrane is impermeable to most ions and polar molecules. The inner membrane is the site of oxidative phosphorylation. Although the membrane is mostly impermeable, it contains special H+ (proton) channels and pumps that enable the coupling of the redox reaction involving NADH and O2 (Equations 9-10) to the phosphorylation reaction of ADP (Equation 8), as described below (“Oxidation-Reduction Reactions and Proton Pumping in Oxidative Phosphorylation”). (Recall the discussion of protein channels in the “Maintaining the Body’s Chemistry: Dialysis in the Kidneys” Tutorial .)

As shown in Figure 8, inside the inner membrane is a space known as the matrix; the space between the two membranes is known as the intermembrane space. The matrix side of the inner membrane has a negative electrical charge relative to the intermembrane space due to an H+ gradient set up by the redox reaction (Equations 9 and 10). This charge difference is used to provide free energy (G) for the phosphorylation reaction (Equation 8).

Oxidation-Reduction Reactions and Proton Pumping in Oxidative Phosphorylation

Phosphorylation of ADP (Equation 8) is coupled to the oxidation-reduction reaction of NADH and O2 (Equations 9 and 10). Electrons are not transferred directly from NADH to O2, but rather pass through a series of intermediate electron carriers in the inner membrane of the mitochondrion. Why? This allows something very important to occur: the pumping of protons across the inner membrane of the mitochondrion. As we shall see, it is this proton pumping that is ultimately responsible for coupling the oxidation-reduction reaction to ATP synthesis.

Two major types of mitochondrial proteins (see Figure 9, below) are required for oxidative phosphorylation to occur. Both classes of proteins are located in the inner mitochondrial membrane.

  1. The electron carriers (NADH-Q reductase, ubiquinone (Q), cytochrome reductase, cytochrome c, and cytochrome oxidase shown in shades of purple in Figure 9 below) transport electrons in a stepwise fashion from NADH to O2.  Three of these carriers (NADH-Q reductase, cytochrome reductase, and cytochrome oxidase) are also proton pumps, and simultaneously pump H+ ions (protons) from the matrix to the intermembrane space. (Proton movement from one side of the membrane to the other is shown as blue arrows in Figure 9, below.) The protons that are pumped across the membrane complete the redox reaction (Equations 9 and 10). The creation of a proton gradient across the membrane is one way of storing free energy.
  2. ATP synthetase (shown in red in Figure 9 below) allows H+ ions to diffuse back into the matrix and uses the free energy released to synthesize ATP from ADP and HPO42-. The ATP synthetase is essential for the phosphorylation to occur (Equation 8). (Proton movement from one side of the membrane to the other is shown as blue arrows in Figure 9, below.)

The electron carriers can be divided into three protein complexes (NADH-Q reductase (1), cytochrome reductase (3), and cytochrome oxidase (5)) that pump protons from the matrix to the intermembrane space, and two mobile carriers (ubiquinone (2) and cytochrome c (4)) that transfer electrons between the three proton-pumping complexes. (Gold numbers refer to the labels on each protein in Figure 9, below.) Because electrons move from one carrier to another until they are finally transferred to O2, the electron carriers (shown in Figure 9,below) are said to form an electron-transport chain.

Figure  below, is a schematic representation of the proteins involved in oxidative phosphorylation. To see an animation of oxidative phosphorylation, click on “View the Movie.”

Proteins of inner space

Proteins of inner space

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/Proteins.jpg

This is a schematic diagram illustrating the transfer of electrons from NADH, through the electron carriers in the electron transport chain, to molecular oxygen. Please click on the pink button below to view a QuickTime animation of the functions of the proteins embedded in the inner mitochondrial membrane that are necessary for oxidative phosphorylation. Click the blue button below to download QuickTime 4.0 to view the movie.

NADH-Q reductase (1), cytochrome reductase (3) , and cytochrome oxidase (5) are electron carriers as well as proton pumps, using the energy gained from each electron-transfer step to move protons (H+) against a concentration gradient, from the matrix to the intermembrane space.Ubiquinone (Q) (2) and cytochrome c (Cyt C) (4) are mobile electron carriers. (Ubiquinone is not actually a protein.) All of the electron carriers are shown in purple, with lighter shades representing increasingly higher reduction potentials. Together, these electron carriers form a “chain” to transport electrons from NADH to O2. The path of the electrons is shown with the green dotted line.

ATP synthetase (red) has two components: a proton channel (allowing diffusion of protons down a concentration gradient, from the intermembrane space to the matrix), and a catalytic component to catalyze the formation of ATP.

For a more complete description of each step in oxidative phosphorylation (indicated by the gold numbers), click here.

view movie

view movie

http://www.apple.com/quicktime/

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/movie.jpg

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/QuickTime.jpg

Click here for a brief description of each of the electron carriers in the electron-transport chain. It is important to note that, although NADH donates two electrons and O2 ultimately accepts four electrons, each of the carriers can only transfer one electron at a time. Hence, there are several points along the chain where electrons can be collected and dispersed. For the sake of simplicity, these points are not described in this tutorial.

In the section above, we see that the oxidation-reduction process is a series of electron transfers that occurs spontaneously and produces a proton gradient. Why are the electron tranfers from one electron carrier to the next spontaneous?

What causes electrons to be transferred down the electron-transport chain?

As seen in Table 2, below, and Figure 7a, in these carriers, the species being oxidized or reduced is Fe, which is found either in a iron-sulfur (Fe-S) group or in a heme group. (Recall the heme group from the Chem 151 tutorial “Hemoglobin and the Heme Group: Metal Complexes in the Blood“.) The iron in these groups is alternately oxidized and reduced between Fe(II) (reduced) or Fe(III) (oxidized) states.

Table 2 shows that the electrons are transferred through the electron-transport chain because of the difference in the reduction potential of the electron carriers. As explained in the green box below, the higher the electrical potential (e) of a reduction half reaction is, the greater the tendency is for the species to accept an electron. Hence, in the electron-transport chain, electrons are transferred spontaneously from carriers whose reduction results in a small electrical potential change to carriers whose reduction results in an increasingly larger electrical potential change.

Reduction Potentials and Relationship to Free Energy

An oxidation-reduction reaction consists of an oxidation half reaction and a reduction half reaction. Every half reaction has an electrical potential (e). By convention, all half reactions are written as reductions, and the electrical potential for an oxidation half-reaction is equal in magnitude, but opposite in sign, to the electrical potential for the corresponding reduction (i.e., the opposite reaction). The electrical potential for an oxidation-reduction reaction is calculated by

erxn = eoxidation + ereduction (12)

For example, for the overall reaction of the oxidation of NADH paired with the reduction of O2, the potential can be calculated as shown below.

Reduction Potentials ereduction
NAD+ + 2H+ + 2e –> NADH + H+ -0.32 V
(1/2) O2 + 2H+ + 2e –> H2O +0.82 V

The overall reaction is

NADH + H–> NAD+ + 2H+ + 2e eoxidation = 0.32 V
(1/2) O2 + 2H+ + 2e –> H2O ereduction = 0.82 V
net: NADH + (1/2)O2 + H+ –>
H2O + NAD+
erxn = 1.14 V

The electrical potential (erxn) is related to the free energy (DG) by the following equation:

DG= -nFerxn (13)

where n is the number of electrons transferred (in moles, from the balanced equation), and F is the Faraday constant (96,485 Coulombs/mole). (Using this equation, DG is given in Joules; one Joule = 1 Volt x 1 Coulomb.)

Hence the overall reaction for the oxidation of NADH paired with the reduction of O2 has a negative change in free energy (DG =-220 kJ); i.e., it is spontaneous. Thus, the higher the electrical potential of a reduction half reaction, the greater the tendency for the species to accept an electron.

Just as in the box above, the electrical potential for the overall reaction (electron transfer) between two electron carriers is the sum of the potentials for the two half reactions. As long as the potential for the overall reaction is positive the reaction is spontaneous. Hence, from Table 2 below, we see that cytochrome c1 (part of the cytochrome reductase complex, #3 in Figure 9) can spontaneously transfer an electron to cytochrome c (#4 in Figure 9). The net reaction is given by Equation 16, below.

reduced cytochrome c–> oxidized cytochrome c+ e eoxidation = – .220 V (14)
oxidized cytochrome c + e –> reduced cytochrome c ereduction = .250 V (15)
NET: reduced cyt c1 + oxidized cyt c –>
oxidized cyt c+ reduced cyt c
erxn = 0.030 V (16) Spontaneous

We can also see from Table 2 that cytochrome c1 cannot spontaneously transfer an electron to cytochrome b (Equation 19):

reduced cyt c–> oxidized cyt c+ e eoxidation = – .220 V (17)
oxidized cyt b + e –> reduced cyt b ereduction = – 0.34 V (18)
NET: reduced cyt c1 + oxidized cyt c –>
oxidized cyt c+ reduced cyt c
erxn = – 0.56 V (19) NOT Spontaneous

Table 2 lists the reduction potentials for each of the cytochrome proteins (i.e., the last three steps in the electron-transport chain before the electrons are accepted by O2) involved in the electron-transport chain. Note that each electron transfer is to a cytochrome with a higher reduction potential than the previous cytochrome. As described in the box above and seen in Equations 14-19, an increase in potential leads to a decrease in DG (Equation 13), and thus the transfer of electrons through the chain is spontaneous.

Complex Name Half Reaction Reduction Potential
Cytochrome reductase

(also known as cytochrome b-c1 complex)

(3 in Figure 9)

Cytochrome b (Fe(III) center)
+ e –>
Cytochrome b (Fe(II) center)
-0.34 V
(at pH 7, T=30oC)
Cytochrome c1 (Fe(III) center)
+ e– –>
Cytochrome c1 (Fe(II) center)
+0.220 V
(at pH 7, T=30oC)
Cytochrome c

(4 in Figure 9)

Cytochrome c (Fe(III) center)
+ e– –>
Cytochrome c (Fe(II) center)
+0.250 V
(at pH 7, T=30oC)
Cytochrome oxidase

(5 in Figure 9)

Cytochrome oxidase
( Fe(III) center) + e– –>
Cytochrome oxidase
(Fe(II) center)
+0.285 V
(at pH 7.4, T=25oC)
Table 2

To view the cytochrome molecules interactively using RASMOL, please click on the name of the complex to download the pdb file.

Hence, the electron-transport chain (which works because of the difference in reduction potentials) leads to a large concentration gradient for H+. As we shall see below, this huge concentration gradient leads to the production of ATP.

Questions on Electron Carriers: Steps in the Electron-Transport Chain; Reduction Potentials and Relationship to Free Energy

  • Briefly, explain why electrons travel from NADH-Q reductase, to ubiquinone (Q), to cytochrome reductase, rather than in the opposite direction.
  • One result of the transfer of electrons from NADH-Q reductase down the electron transport chain is that the concentration of protons (H+ ions) in the intermembrane space is increased.  Could cells move protons (H+ ions) from the matrix to the intermembrane space without transporting electrons?  Why or why not?

 ATP Synthetase: Production of ATP

We have seen that the electron-transport chain generates a large proton gradient across the inner mitochondrial membrane. But recall that the ultimate goal of oxidative phosphorylation is to generate ATP to supply readily-available free energy for the body. How does this occur? In addition to the electron-carrier proteins embedded in the inner mitochondrial membrane, a special protein called ATP synthetase (Figure 9, the red-colored protein) is also embedded in this membrane. ATP synthetase uses the proton gradient created by the electron-transport chain to drive the phosphorylation reaction that generates ATP (Figure 7c).

ATP synthetase is a protein consisting of two important segments: a transmembrane proton channel, and a catalytic component located inside the matrix. The proton-channel segment allows H+ ions to diffuse from the intermembrane space, where the concentration is high, to the matrix, where the concentration is low. Recall from the Kidney Dialysis tutorial that particles spontaneously diffuse from areas of high concentration to areas of low concentration. Thus, since the diffusion of protons through the channel component of ATP synthetase is spontaneous, this process is accompanied by a negative change in free energy (i.e., free energy is released). The catalytic component of ATP synthetase has a site where ADP can enter. Then, using the free energy released by the spontaneous diffusion of protons through the channel segment, a bond is formed between the ADP and a free phosphate group, creating an ATP molecule. The ATP is then released from the reaction site, and a new ADP molecule can enter in order to be phosphorylated.

Questions on ATP Synthetase: Production of ATP

  • A scientist has created a phospholipid-bilayer membrane containing ATP-synthetase proteins. Instead of a proton gradient, this scientist has created a large Cs+ gradient (many Cs+ ions on the side of the membrane without the catalytic unit, and few Cs+ ions on the side of the membrane with the catalytic unit). Would you expect the ATP-synthetase proteins in this membrane to be able to generate ATP, given an abundant supply of ADP and phosphate? Briefly, explain your answer. (HINT: Draw on your knowledge of the structure of protein channels to predict what effect replacing H+ ions with Cs+ ions would have.)
  • Certain toxins allow H+ ions to move freely across the inner mitochondrial membrane (i.e., without needing to pass through the channel in ATP synthetase). What effect do you expect these toxins to have on the production of ATP? Briefly, explain your answer.

Summary

In this tutorial, we have learned that the ability of the body to perform daily activities is dependent on thermodynamic, equilibrium, and electrochemical concepts.   These activities, which are typically based on nonspontaneous chemical reactions, are performed by using free-energy currency. The common free-energy currency is ATP, which is a molecule that easily dephosphorylates (loses a phosphate group) and releases a large amount of free energy. In the body, the nonspontaneous reactions are coupled to this very spontaneous dephosphorylation reaction, thereby making the overall reaction spontaneous (DG < 0). As the coupled reactions occur (i.e., as the body performs daily activities), ATP is consumed and the body regenerates ATP by using energy from the food we eat (Figure 3). As seen in Figure 4, the breakdown of glucose (glycolysis) obtained from the food we eat cannot by itself generate the large amount of ATP that is needed for metabolic energy by the body. However, glycolysis and the subsequent step, the citric-acid cycle, produce two easily oxidized molecules: NADH and FADH2. These redox molecules are used in an oxidative-phosphorylation process to produce the majority of the ATP that the body uses. This oxidative-phosphorylation process consists of two steps: the oxidation of NADH (or FADH2) and the phosphorylation reaction which regenerates ATP. Oxidative phosphorylation occurs in the mitochondria, and the two reactions (oxidation of NADH or FADHand phosphorylation to generate ATP) are coupled by a proton gradient across the inner membrane of the mitochondria (Figure 9). As seen in Figures 7 and 9, the oxidation of NADH occurs by electron transport through a series of protein complexes located in the inner membrane of the mitochondria. This electron transport is very spontaneous and creates the proton gradient that is necessary to then drive the phosphorylation reaction that generates the ATP. Hence, oxidative-phosphorylation demonstrates that free energy can be easily transferred by proton gradients. Oxidative-phosphorylation is the primary means of generating free-energy currency for aerobic organisms, and as such is one of the most important subjects in the study of bioenergetics (the study of energy and its chemical changes in the biological world).

Additional Link:

  • This fun description of oxidative phosphorylation by Dr. E.J.Oakeley contains step-by-step animated illustrations of the redox reactions involved, as well as a quiz to test your understanding of the material.

References:

Alberts, B. et al. In Molecular Biology of the Cell, 3rd ed., Garland Publishing, Inc.: New York, 1994, pp. 653-684.

Becker, W.M. and Deamer, D.W. In The World of the Cell, 2nd ed., The Benjamin/Cummings Publishing Co., Inc.: Redwood City, CA, 1991, pp. 291-307.

Fasman, G.D. In Handbook of Biochemistry and Molecular Biology, 3rd ed., CRC Press, Inc.: Cleveland, OH, 1976, Vol. I (Physical and Chemical Data), pp. 132-137.

Guex, N. and Peitsch, M.C. Electrophoresis, 1997, 18, 2714-2723. (SwissPDB Viewer) URL: http://www.expasy.ch/spdbv/mainpage.htm.

Moa, C., Ozer, Z., Zhou, M. and Uckun, F. X-Ray Structure of Glycerol Kinase Complexed with an ATP Analog Implies a Novel Mechanism for the ATP-Dependent Gylcerol Phosphorylation by Glycerol Kinase.Biochemical and Biophysical Reaearch Communications. 1999, 259, 640-644.

Persistence of Vision Ray Tracer (POV-Ray). URL: http://www.povray.org.

Stryer, L. In Biochemistry, 4th. ed., W.H. Freeman and Co.: New York, 1995, pp. 490, 509, 513, 529-557.

Zubay, G. Biochemistry, 3rd. ed., Wm. C. Brown Publishers: Dubuque, IA, 1983, p. 42.

Acknowledgements:

The authors thank Dewey Holten (Washington University in St. Louis) for many helpful suggestions in the writing of this tutorial.

The development of this tutorial was supported by a grant from the Howard Hughes Medical Institute, through the Undergraduate Biological Sciences Education program, Grant HHMI# 71199-502008 to Washington University.

Copyright 1999, Washington University, All Rights Reserved.

 

 

 

Read Full Post »

Studies of Respiration Lead to Acetyl CoA

Curator: Larry H. Bernstein, MD, FCAP

In this series of discussions it has become clear that the studies of carbohydrate metabolism were highlighted by Meyerhof’s work on the glycolytic pathway, and the further elucidation of a tie between Warburg’s studies of impaired respiration for malignant aerobic cells relying on glycolysis, comparanle to Pasteur’s observations 60 years earlier by for yeast.   The mitochondrion was unknown at the time, and it took many years to discover the key role played by oxidative phosphorylation and Fritz Lipmann’s discovery of “acetyl coenzyme A, and the later explanation of electron transport.  This was crucial to understanding cellular energetics, which explains the high energy of fatty acid catabolism from stored adipose tissue.  I shall here embark on a journey to trace these important connected developments.

  1. Signaling and signaling pathways
  2. Signaling transduction tutorial.
  3. Carbohydrate metabolism

3.1  Selected References to Signaling and Metabolic Pathways in Leaders in Pharmaceutical Intelligence

  1. Lipid metabolism

4.1  Studies of respiration lead to Acetyl CoA

  1. Protein synthesis and degradation
  2. Subcellular structure
  3. Impairments in pathological states: endocrine disorders; stress hypermetabolism; cancer.

Phosphorylation

In some reactions, the purpose of phosphorylation is to “activate” or “volatize” a molecule, increasing its energy so it is able to participate in a subsequent reaction with a negative free-energy change. All kinases require a divalent metal ion such as Mg2+ or Mn2+ to be present, which stabilizes the high-energy bonds of the donor molecule (usually ATP or ATP derivative) and allows phosphorylation to occur.This is a major focus of this discussion.

In other reactions, phosphorylation of a protein substrate can inhibit its activity (as when AKT phosphorylates the enzyme GSK-3). When src is phosphorylated on a particular tyrosine, it folds on itself, and thus masks its own kinase domain, and is thus turned “off”. In still other reactions, phosphorylation of a protein causes it to be bound to other proteins which have “recognition domains” for a phosphorylated tyrosine, serine, or threonine motif. In the late 1990s it was recognized that phosphorylation of some proteins causes them to be degraded by the ATP-dependent ubiquitin/proteasome pathway. This is all that needs to be said at this time about proteins.

 

Oxidative Phosphorylation

ATP is the molecule that supplies energy to metabolism. Almost all aerobic organisms carry out oxidative phosphorylation. This pathway is probably so pervasive because it is a highly efficient way of releasing energy, compared to alternative fermentation processes such as anaerobic glycolysis.

During oxidative phosphorylation, electrons are transferred from electron donors to electron acceptors such as oxygen, in redox reactions. These redox reactions release energy, which is used to form ATP. In eukaryotes, these redox reactions are carried out by a series of protein complexes within the cell’s intermembrane wall mitochondria, whereas, in prokaryotes, these proteins are located in the cells’ intermembrane space.

O t to  W a r b u r g
Nobel Lecture, December 10, 1931

The oxygen-transferring ferment of respiration

The effects of iron are very great, and it follows that oxidation and reduction of the ferment iron must occur extremely rapidly. In fact, almost every molecule of oxygen that comes into contact with an atom of ferment iron reacts with it.  Complex-bound bivalent iron in compounds reacts, in vitro as well as in the cell, with molecular oxygen. tt is not yet possible to reduce in vitro trivalent iron with the cell fuel: it is always necessary to add a substance of unknown composition, a ferment, that activates the combustible material for the attack of the iron. It must, therefore, be concluded that activation of the combustible substance in the breathing cell precedes the attack of the ferment iron; this corresponds with “hydrogen activation” as postulated in the theory of Wieland and Thunberg. According to the results of a joint research with W. Christian, this is a cleavage comparable with those known as fermentation.

It is possible that the interplay of splitting ferment and oxygen-transferring ferment does not fully explain the mechanism of cellular respiration; that the iron that reacts with the molecular oxygen does not directly oxidize the activated combustible substances, but that it exerts its effects indirectly through still other iron compounds – the three non-auto-oxidizable cell hemes of MacMunn, which occur in living cells according to the spectroscopic observations of MacMunn and Keilin, and which are reduced in the cell under exclusion of oxygen. It is still not possible to answer the question whether the MacMunn hemes form part of the normal respiratory cycle, i.e., whether respiration is not a simple iron catalysis but a four-fold one. The available spectroscopic observations are also consistent with the view that the MacMunn hemes in the cell are only reduced when the concentration of activated combustible substance is physiologically above normal. This will suffice to indicate that oxygen transfer by the iron of the oxygen transferring ferment is not the whole story of respiration. Respiration requires not only oxygen-transferring ferment and combustible substance, but oxygen-transferring ferment and the living cell.

Inhibition of cellular respiration by prussic acid was discovered some 50 years ago by Claude Bernard, and has interested both chemists and biologists ever since. It takes place as the result of a reaction between the prussic acid and the oxygen-transferring ferment iron, that is, with the ferment iron in trivalent form. [In the prussic acid reaction] the oxidizing OH-group of the trivalent ferment-iron is replaced by the non-oxidizing CN-group, thus bringing transfer of oxygen to a standstill. Prussic acid inhibits reduction of the ferment iron. Inhibition of respiration by carbon monoxide was discovered only a few years ago. [Given] the initial reaction in respiration, then, in the presence of carbon monoxide, the competing reaction will also occur and, varying with the pressures of the carbon monoxide and of the oxygen, more or less of the ferment iron will be removed from the catalytic process on account of fixation of carbon monoxide to the ferment iron. Unlike prussic acid, therefore, carbon monoxide affects the bivalent iron of the ferment. Carbon monoxide inhibits oxidation of the ferment iron.

Thus inhibition of respiration by carbon monoxide, unlike that by prussic acid, depends upon the partial pressure of oxygen. The toxic action of prussic acid in the human subject is based on its inhibitory action on cellular respiration. The toxic effect of carbon monoxide on man has nothing to do with inhibition of cellular respiration by carbon monoxide but is based on the reaction of carbon monoxide with blood iron. For, the effect of carbon monoxide on blood iron occurs at pressures of carbon monoxide far from the level at which cellular respiration would be inhibited.

If carbon monoxide is added to the oxygen in which living cells breathe, respiration ceases, as has already been mentioned, but if exposure to ultraviolet or visible light is administered, respiration recurs. By alternate illumination and darkness it is possible to cause respiration and cessation of respiration in living, breathing cells in mixtures of carbon monoxide and oxygen. In the dark, the iron of the oxygen-transferring ferment becomes bound to carbon monoxide, whereas in the light the carbon monoxide is split off from the iron which is, thus, liberated for oxygen transfer. This fact was discovered in 1926 in collaboration with Fritz Kubowitz. Photochemical dissociation of iron carbonyl compounds was discovered in 1891 by Mond and Langer, by exposing iron pentacarbonyl. This reaction is specific for carbonyl compounds of iron, most of which appear to dissociate in the presence of light, e.g., carbon-monoxide hemoglobin (John Haldane, 1897) carbon-monoxide hemochromogen (Anson and Mirsky, 1925), carbon-monoxide pyridine hemochromogen (H. A. Krebs, 1928), and carbon-monoxide ferrocysteine (W. Cremer, 1929).

When the photochemical dissociation of iron carbonyl compounds is measured quantitatively (we followed hereby Emil Warburg’s photochemical experiments), by using monochromatic light and comparing the amount of light energy absorbed with the amount of carbon monoxide set free, it is found that Einstein’s law of photochemical equivalence is very exactly fulfilled. The number of FeCO-groups set free is equal to the number of light quanta absorbed, and this is independent of the wavelength employed.

Photochemical dissociation of iron carbonyl compounds can be used to determine the absorption spectrum of a catalytic oxygen-transferring iron compound. One combines the catalyst in the dark with carbon monoxide, and so abolishes the oxygen-transferring power of the iron. If then this is exposed to monochromatic light of various wavelengths and of measured quantum intensity, and the effect of light W measured the increase in the rate of catalysis – it is found that the effects of the light are proportional to the quanta absorbed. The arrangement becomes very simple if the catalyst is present, as is usually the case, in infinitesimally low concentration in the exposed system. Then the thickness of the layers related to the amount of absorption of light can be considered to be infinitely thin, the number of quanta absorbed is proportional to the number of quanta supplied by irradiation.

In collaboration with Erwin Negelein, this principle was employed to measure the relative absorption spectrum of the oxygen-transferring respiratory ferment. The respiration of living cells was inhibited by carbon monoxide which was mixed with the oxygen. We then irradiated with monochromatic light of various wavelengths and of measured quantum intensity, and [measured] the increase of respiration together with the relative absorption spectrum. Only practically colorless cells are suitable for this type of experiment, [which requires] a layer infinitely thin with regard to light absorption.

Imagine living cells whose respiration is inhibited by carbon monoxide. If these are irradiated, respiration does not increase suddenly from the dark to the light-value, but there is a definite, although very short, interval until the combination of carbon monoxide with the ferment is broken down by the light. Even without calculation, it is obvious that the rate of increase in the effect of light must be related to the depth of colour of the ferment. If the ferment absorbs strongly, the -monoxide compound will be rapidly broken down, and vice versa.

The time of increase of the action of light can be measured. The time taken for a given intensity of light to cause dissociation of approximately half the carbon-monoxide compound of the ferment can be measured and, from this time, and from the effective intensity of light, the absolute absorption coefficient of the ferment for every wavelength can be calculated. The absorption capacity of the ferment, measured in accordance with this principle, was found to be of the same order as the power of light absorption of our strongest pigments. If one imagines a ferment solution of molar concentration, a layer of 2 x 10-6 cm thickness would weaken the blue mercury line 436 µµ up by half. The fact that the ferment in spite of this cannot be seen in the cells is due to its low concentration.

Monochromators and color filters were used to isolate the lines from these sources of light. If the absorption coefficient is entered as a function of the wavelength, the absorption spectrum of the carbon-monoxide compound of the ferment is obtained. The principal absorption-band or y-band lies in the blue.
This is the spectrum of a heme compound, according to the position of the bands, the intensity state of the bands, and the absolute magnitude of the absorption coefficients.

It appeared essential to have a control to ascertain whether heme as an oxidation catalyst of carbon monoxide and prussic acid really behaves like the ferment. If cysteine is dissolved in water containing pyridine, and a trace of heme is added, and this is shaken with air, the cysteine is catalytically oxidized by the oxygen-transferring power of the heme. According to Krebs, the catalysis is inhibited by carbon monoxide in the dark, but the inhibition ceases when the mixture is illuminated. Prussic acid too acts on this model on cellular respiration, inasmuch as it combines with the trivalent heme and inhibits its reduction. Just as in life, inhibition by carbon monoxide is dependent on the oxygen pressure, while inhibition by prussic acid is independent of the oxygen pressure.

In conjunction with Negelein, this model was also used to test the ferment experiments quantitatively. Heme catalysis in the model was inhibited by carbon monoxide in the dark. Then monochromatic light of known quantum intensity was used to irradiate it, and the absorption spectrum of the catalyst calculated from the effect of the light which was known from direct measurements on the pure substance. The calculation gave the absorption spectrum of the heme that had been added as a catalyst, and so the method was verified as a technique for the determination of the ferment spectrum, both the calculation and the measurement method.

The positions of the principal band and a-band of the ferment are:

Principal band            α-band

433 µµ                    590 µµ

These will be referred to as the “ferment bands” because the ferment was the first for which they were determined. Hemes are the complex iron compounds of the porphyrins, in which two valencies of the iron are bound to nitrogen. The porphyrins, of which Hans Fischer determined the chemical structure, are tetrapyrrole compounds in which the four pyrrole nuclei are held together by four interposed methane groups in the cr-position. Green, red, and mixed shades of hemes are known. If magnesium is replaced by iron in chlorophyll, green hemes are obtained. Their color is due to a strong band in the red which is already recognized in chlorophyll. The ferment does not absorb in the red and cannot, therefore, be a green heme. Red hemes are the usual hemes in blood pigment and in its related substances, such as mesoheme and deuteroheme. Coproheme is also a red heme which is an iron compound of the coproporphyrin that H. Fischer recognized in the body. Other red hemes are 20 µµ further from the red than the ferment bands. It follows that the ferment is not a red heme.

The pheoporphyrins are closely related to blood pigment but, as H. Fischer showed, pheoporphyrin a is simply mesoporphyrin in which the one propionic acid has been oxidized so that ring closure with the porphyrin nucleus is made possible. Pheoporphyrin a is a reduction product of chlorophyll a or an oxidation product of blood pigment, and connects together, in an amazingly simple manner, the principal pigments of the organic world the blood pigment and the leaf pigment.

Chlorophyll b has, in general, bands of longer wavelength than chlorophyll a, and for this reason,

  1. Christian and I applied Fischer’s reduction method to it. In this way we obtained pheoheme b, which, when linked with protein, corresponds with the ferment in respect to the position of the principal band. The principal band of the carbon-monoxide compound of pheohemoglobin b is 435 µµ.
  2. However, while the principal band of pheohemoglobin corresponds with the ferment bands within the permitted limits, the α-band shifts so far beyond them because it lies too near the red. It is, nevertheless, interesting that
  3. when ‘chlorophyll b is reduced, one obtains a pheoporphyrin of which the heme of all the pheohemes that have been demonstrated up to the present time is the most like the ferment.

 

Still nearer the ferment in its spectrum, is a heme occurring in Nature. This is

  • spirographis heme, which has been isolated from chlorocruorin, the blood pigment of the bristle-worm Spirographis,

in collaboration with Negelein and Haas, the bands of spirographis heme, coupled to globin, are :

  • carbon-monoxyhemoglobin of spirographis:  principle band, 434 µµ; α-band, 594 µµ.

Spirographis heme differs from the red hemes by the surplus or ketone oxygen-atom, and is classified as pheoheme. Like Fischer’s pheohemes, spirographis heme is intermediate between chlorophyll and blood pigment in respect of

  • the degree of oxidation of the side-chains.

The two hemes with a spectrum most like that of the ferment – pheoheme b and spirographis heme – possess a remarkable property. If they are dissolved in dilute sodium-hydroxide solution, in the form of ferrous compounds,

  • the absorption bands slowly wander towards the blue, near the bands of blood heme. In this way,
  • mixed-color hemes have been converted into red hemes.

On acidification, the change reverts: the <<blood bands>> disappear and

  • the ferment bands appear.

This experiment shows that

  1. oxidation of the side-chains does not suffice to give rise to the ferment bands, but
  2. some process of the type of anhydride formation must also occur.

The unique intermediate status of the ferment-like hemes demonstrated by these simple experiments suggests

  1. the suspicion that blood pigment and leaf pigments have both arisen from the ferment –
  2. blood pigments by reduction, and leaf pigment by oxidation.
  • For evidently, the ferment existed earlier than hemoglobin and chlorophyll.

The investigations on the oxygen-transferring ferment have been supported from the start by the Notgemeinschaft der deutschen Wissenschaft and the Rockefeller Foundation, without whose help they could not have been carried out. I have to thank both organizations here.

A L B E R T S Z E N T- GY Ö R G Y I      Nobel Lecture, December 11, 1937

Oxidation, energy transfer, and vitamins

A living cell requires energy not only for all its functions, but also

  • for the maintenance of its structure.
  • The source of this energy is the sun’s radiation.

Energy from the sun’s rays is trapped by green plants, and

  • converted into a bound form, invested in a chemical reaction.

When sunlight falls on green-plants, they liberate oxygen from carbon dioxide, and

  1. store up carbon, bound to the elements of water, as carbohydrate.

The radiant energy is now locked up in this carbohydrate molecule. This molecule is our food. When energy is required,

  • the carbohydrate is again combined with oxygen to form carbon dioxide, oxidized, and energy released.

Investigations during the last few decades have brought hydrogen instead of carbon, and instead of CO2 water, the mother of all life, into the foreground. It is becoming increasingly probable that

  1. radiant energy is used primarily to break water down into its elements,
  2. while CO2, serves only to fix the elusive hydrogen thus released.

While this concept of energy fixation was still being developed, the importance of hydrogen in the reversal of this process, whereby energy is liberated by oxidation, had already been confirmed by H. Wieland’s experiments.

Our body really only knowns one fuel, hydrogen. The foodstuff, carbohydrate, is essentially a packet of hydrogen, a hydrogen supplier, a hydrogen donor, and the main event during its combustion is

  • the splitting off of hydrogen.

So the combustion of hydrogen is

  • the real energy-supplying reaction;

To the elucidation of reaction (6), which seems so simple, I have devoted all my energy for the last fifteen years.

When I first ventured into this territory, the foundations had already been laid by the two pioneers H. Wieland and
O. Warburg, and Wieland’s teaching had been applied by Th. Thunberg to the realm of animal physiology.Wieland and Thunberg showed, with regard to foodstuffs, how

  1. the first step in oxidation is the “activation” of hydrogen, whereby
  2. the bonds linking it to the food molecule are loosened, and
  3. hydrogen prepared for splitting off.

But at the same time oxygen is also, as Warburg showed,

  • activated for the reaction by an enzyme.
  • the hydrogen-activating enzymes are called dehydrases or dehydrogenases.

Warburg called his oxygen-activating catalyst, “respiratory enzyme”.These concepts of Wieland and Warburg were apparently contradictory, and

  1. my first task was to show that the two processes are complementary to one another, and that
  2. in muscle cells activated oxygen oxidizes activated hydrogen.

This picture was enriched by the English worker D. Keilin, who showed that

  • activated oxygen does not oxidize activated hydrogen directly, but
  • that a dye, cytochrome, is interposed between them.

In keeping with this function, the “respiratory enzyme” is now also called “cytochrome oxidase”.

About ten years ago, when I tried to construct this system of respiration artificially and added together the respiratory enzyme with cytochrome and some foodstuff together with its dehydrogenase, I could justifiably expect that this system would use up oxygen and oxidize the food. But the system remained inactive. I found that

  • the dehydrogenation of certain donors is linked to the presence of a co-enzyme.

Analysis of this co-enzyme showed it to be a nucleotide, identical with v. Euler’s co-zymase, which H. v. Euler and R. Nilsson had already shown to accelerate the process of dehydration. As a result of Warburg’s investigations,this co-dehydrogenase has recently come very much into the foreground. Warburg showed that

  • it contains a pyridine base, and that it accepts hydrogen directly
    [pyridine nucleotide, triphosphopyridine nucleotide, TPN]

from food when the latter is dehydrogenated. It is therefore, the primary H-acceptor.

While working on the isolation of the co-enzyme with Banga, I found a remarkable dye, which showed clearly by its reversible oxidation that it, too, played a part in the respiration. We called this new dye cytoflav. Later Warburg showed that

  • this substance exercised its function in combination with a protein.

He called this protein complex of the dye, “yellow enzyme”. R. Kuhn, to whom we owe the structural analysis of the dye, called the dye lactoflavin and, with Györgyi and Wagner-Jauregg, showed it to be identical with vitamin B,.But the respiratory system stayed inactive even

  • after the addition of both these new components, codehydrogenase and yellow enzyme.

The C4-dicarboxylic acids and their activators which Thunberg discovered are

  • interposed between cytochrome and the activation of hydrogen as intermediate hydrogen-carriers.

In the case of carbohydrate, hydrogen from the food is first taken up by oxaloacetic acid, which

  • is reacted with the cytoplasmic malic dehydrogenase (and pyridine nucleotide –
    reduced DPN[H])
    , and thereby activated.

By taking up two hydrogen atoms, oxaloacetic acid is changed into malic acid.

  • OAA + NADH – (MDH) – malate + NAD+ + H+

This malic acid now passes on the H-atoms, and thus reverts to oxaloacetic acid,

  • which can again take up new H-atoms.

Malate + NAD+ + H+ — MDH – OAA + NADH

The H-atoms released by malic acid are taken up by fumaric acid, which is similarly

  • activated by the so-called succinic dehydrogenase.

The uptake of two H-atoms

  • converts the fumarate to succinate, to succinic acid.

The two H-atoms of succinic acid are then

  • oxidized away by the cytochrome.

Finally the cytochrome is oxidized by the respiratory enzyme, and

  • the respiratory enzyme by oxygen.

The function of the C4-dicarboxylic acids is not to be pictured as consisting of a certain amount of C4-dicarboxylic acid in the cell which is alternately oxidized and reduced. Fig. 2 corresponds more to the real situation. The protoplasmic surface, which is represented by the semi-circle, has single molecules of oxaloacetate and fumarate attached to it as prosthetic groups. These fused, activated dicarboxylic molecules then temporarily bind the hydrogen from the food. The co-dehydrogenases and the yellow enzymes also take part in this system. I have attempted to add them in at the right place.

This diagram, which will probably still undergo many more modifications, states that the “foodstuff” – H-donor – starts by

  1. passing its hydrogen, which has been activated by dehydrase, to the co-dehydrogenase.
  2. The coenzyme passes it to the oxaloacetic acid*.
  3. The malic acid then passes it on again to a co-enzyme,
  4. which passes the hydrogen to the yellow enzyme.
  5. The yellow enzyme passes the hydrogen to the fumarate.
  6. The succinate so produced is then oxidized by cytochrome,
  7. the cytochrome by respiratory enzyme,
  8. the respiratory enzyme by oxygen.

So the reaction 2H + O – H2O, which seems such a simple one,

  • breaks down into a long series of separate reactions.

With each new step, with each transfer between substances,

  • the hydrogen loses some of its energy,
  • finally combining with oxygen in its lowest-energy compound.

So each hydrogen atom is gradually oxidized in a long series of reactions, and

  • its energy released in stages.

This oxidation of hydrogen in stages seems to be one of the basic principles of biological oxidation. The reason for it is probably mainly that

  • the cell would not be able to harness and transfer to other processes
  • the large amount of energy which would be released by direct oxidation.

The cell needs small change if it is to be able to

  • pay for its functions without losing too much in the process.

So it oxidizes the H-atom by stages, converting the large banknote into small change.

About half of all plants – contain a polyphenol, generally a pyrocatechol derivative, together with an enzyme, polyphenoloxidase, which oxidizes polyphenol with the help of oxygen. The current interpretation of the mode of action of this oxidase was a confused one. I succeeded in showing that the situation was simply this, that

the oxidase oxidizes the polyphenol to quinone with oxygen.

  • In the intact plant the quinone is reduced back again
  • with hydrogen made available from the foodstuff.

Phenol therefore acts as a hydrogen-carrier between oxygen and the H-donor, and we are here again faced with a probably still imperfectly understood system for

  • the stepwise combustion of hydrogen.

——————————————————————————————————————————–

Vitamin C

If benzidine is added to a peroxide in the presence of peroxidase, a deep-blue color appears immediately, which is caused by the oxidation of the benzidine. This reaction does not occur without peroxidase. I simply used some juice which had been squeezed from these plants instead of a purified peroxidase, and added benzidine and peroxide, and the blue pigment appeared, after a small delay of about a second. Analysis of this delay showed that it was due to the presence of a powerful reducing substance, which reduced the oxidized benzidine again, until it had itself been used up. Thanks to the invitation from F. G. Hopkins and the help of the Rockefeller Foundation, I was able ten years ago to transfer my workshop to Cambridge, where for the first time I was able to pay more serious attention to chemistry. Soon I succeeded in isolating the substance in question from adrenals and various plants, and in showing that it corresponded to the formula C6H8O6 and was related to the carbohydrates. This last circumstance induced me to apply to Prof. W. N. Haworth, who immediately recognized the chemical interest of the substance and asked me for a larger quantity to permit analysis of its structure.

The Mayo Foundation and Prof. Kendall came to my help on a large scale, and made it possible for me to work, regardless of expense, on the material from large American slaughter-houses. The result of a year’s

work-was 25 g of a crystalline substance, which was given the name “hexuronic acid”. I shared this amount of the substance with Prof. Haworth. He undertook to investigate the exact structural formula of the substance. I used the other half of my preparation to gain a deeper understanding of the substance’s function. The substance could not replace the adrenals, but caused the disappearance of pigmentation in patients with Addison’s disease.

In 1930 I settled down in my own country at the University of Szeged. I also received a first-rate young American collaborator, J. L. Svirbely, who had experience in vitamin research, but besides this experience brought only the conviction that my hexuronic acid was not identical with vitamin C. In the autumn of 1931 our first experiments were completed, and showed unmistakably that hexuronic acid was power- fully anti-scorbutic, and that the anti-scorbutic acitvity of plant juices corresponded to their hexuronic acid content. We did not publish our results till the following year after repeating our experiments. At this time Tillmans was already directing attention to the connection between the reducing strength and the vitamin activity of plant juices. At the same time King and Waugh also reported crystals obtained from lemon juice, which were active anti-scorbutically and resembled our hexuronic acid.

My town, Szeged, is the centre of the Hungarian paprika industry. Since this fruit travels badly, I had not had the chance of trying it earlier. The sight of this healthy fruit inspired me one evening with a last hope, and that same night investigation revealed that this fruit represented an unbelievably rich source of hexuronic acid, which, with Haworth, I re-baptized ascorbic acid. I also had the privilege of providing my two prize-winning colleagues P. Karrer and W. N. Haworth with abundant material, and making its structural analysis possible for them. I myself produced with Varga the mono-acetone derivative of ascorbic acid, which forms magnificent crystals; from which, after repeated dissolving and recrystallization, ascorbic acid can be separated again with undiminished activity. This was the first proof that ascorbic acid was identical with vitamin C.
————————————————————————————————————————————-

Returning to the processes of oxidation, I now tried to analyse further the system of respiration in plants, in which ascorbic acid and peroxidase played an important part. I had already found in Rochester that the peroxidase plants contain an enzyme which reversibly oxidizes ascorbic acid with two valencies in the presence of oxygen. Further analysis showed that here again a system of respiration was in question, in which hydrogen was oxidized by stages. I would like, in the interests of brevity, to summarize the end result of these experiments, which I carried out with St. Huszák. Ascorbic acid oxidase oxidizes the acid with oxygen to reversible dehydroascorbic acid, whereby the oxygen unites with the two labile H-atoms from the acid to form hydrogen peroxide. This peroxide reacts with peroxidase and oxidizes a second molecule of ascorbic acid. Both these molecules of dehydro-ascorbic acid again take up hydrogen from the foodstuff, possibly by means of SH-groups. But peroxidase does not oxidize ascorbic acid directly. Another substance is interposed between the two, which belongs to the large group of yellow, water-soluble phenol-benzol-r-pyran plant dyes (flavone, flavonol, flavanone). Here the peroxidase oxidizes the phenol group to the quinone, which then oxidizes the ascorbic acid directly, taking up both its H-atoms.

At the time that I had just detected the rich vitamin content of the paprika, I was asked by a colleague of mine for pure vitamin C. This colleague himself suffered from a serious haemorrhagic diathesis. Since I still did not have enough of this crystalline substance at my disposal then, I sent him paprikas. My colleague was cured. But later we tried in vain to obtain the same therapeutic effect with pure vitamin C. Guided by my earlier studies into the peroxidase system, I investigated with my friend St. Rusznyák and his collaborators Armentano and Bentsáth the effect of the other link in the chain, the flavones. Certain members of this group of substances, the flavanone hesperidin (Fig. 5) and the formerly unknown eriodictyolglycoside, a mixture of which we had isolated from lemons and named citrin, now had the same therapeutic effect as paprika itself.

H U G O T H E O R E L L          Nobel Lecture, December 12, 1955

The nature and mode of action of oxidation enzymes

 

Practically all chemical reactions in living nature are started and directed in their course by enzymes. This being the case, Man has of course since time immemorial seen examples of what we now call enzymatic reactions, e.g. fermentation and decay. It would thus be possible to trace the history of enzymes back to the ancient Greeks, or still further for that matter. But it would be rather pointless, since to observe a phenomenon is not the same thing as to explain it. It is more correct to say that our knowledge of enzymes is essentially a product of twentieth-century research.

Jöns Jacob Berzelius, wrote in his yearbook in 1835: “…The catalytic force appears actually to consist thought herein that through their mere presence, and not through their affinity, bodies are able to arouse affinities which at this temperature are slumbering…”  Enzymes are the catalyzers of the biological world, and Berzelius’ description of catalytic force is surprisingly far-sighted…  if one could once understand the mechanism it would doubtless prove that the forces of ordinary chemistry would suffice to explain also these as yet mysterious reactions.

The year 1926 was a memorable one. The German chemist Richard Willstitter gave a lecture then in Deutsche Chemische Gesellschaft, in which he summarized the experiences gained in his attempts over many years to produce pure enzymes. Willstätter drew the conclusion that the enzymes did not belong to any known class of chemical substances, and that the effects of the enzymes derived from a new natural force, thus taking the view that 90 years earlier Berzelius thought to be improbable. That same year, through an irony of fate, the American researcher J. B. Sumner published a work in which he claimed to have crystallized in pure form an enzyme, urease. In the ensuing years J. H. Northrop and his collaborators crystallized out a further three enzyme preparations, pepsin, trypsin, and chymotrypsin, like urease, hydrolytic enzymes that split linkages by introducing water. If these discoveries had been undisputed from the outset it would probably not have been 20 years before Sumner, together with Northrop and Stanley, received a Nobel Prize.

When in 1933 I went on a Rockefeller fellowship to Otto Warburg’s institute in Berlin, Warburg and Christian had in the previous year produced a yellow-coloured preparation of an oxidation enzyme from yeast. The yellow colour was of particular interest: it faded away on reduction and returned on oxidation with e.g. oxygen, so that it was evident that the yellow pigment had to do with the actual enzymatic process of oxido-reduction. It was possible to free the yellow pigment from the high-molecular carrier substance, whose nature was still unknown, for example by treatment with acid methyl alcohol, whereupon the enzyme effect disappeared. Through simultaneous works by Warburg in Berlin, Kuhn in Heidelberg and Karrer in Zurich the constitution of the yellow pigment (lactoflavin, later riboflavin or vitamin B,) was determined. It was here for the first time possible to localize the enzymatic effect to a definite atomic constellation: hydrogen freed from the substrate (hexose monophosphate) is, with the aid of a special enzyme system (TPN-Zwischenferment) whose nature was elucidated somewhat later, placed on the nitrogen atoms of the flavin (1) and (10), giving rise to the colourless leucoflavin. This is reoxidized by oxygen, hydrogen peroxide being formed, and may afterwards be reduced again, and so forth. This cyclic process then continues until the entire amount of substrate has been deprived of two hydrogen atoms and been transformed into phosphogluconic acid; and a corresponding amount of hydrogen peroxide has been formed. At the end of the process the yellow enzyme is still there in unchanged form, and has thus apparently, as Berzelius expressed himself, aroused a chemical affinity through its mere presence.

The polysaccharides, which constituted 80-90% of the entire weight, were completely removed, together with some inactive colourless proteins. After fractionated precipitations with ammonium sulphate I produced a crystalline preparation which on ultracentrifuging and electrophoresis appeared homogeneous. The enzyme was a protein with the molecular weight 75,000 and strongly yellow-colored by the flavin part. The result of the Flavin analysis was 1 mol flavin per 1 mol protein. With dialysis against diluted hydrochloric acid at low temperature the yellow pigment was separated from the protein, which then became colorless. In the enzyme test the flavin part and the protein separately were inactive, but if the flavin part and the protein were mixed at approximately neutral reaction the enzyme effect returned, and the original effect came back when one mixed them in the molecular proportions 1 : 1. That in this connection a combination between the pigment and the protein came about was obvious, moreover, for other reasons: the green-yellow colour of the flavin part changed to pure yellow,and its strong. yellow fluorescence disappeared with linking to the protein.

In my electrophoretic experiments lactoflavin behaved as a neutral body, while the pigment part separated from the yellow enzyme moved rapidly towards the anode and was thus an acid. An analysis for phosphorus showed 1 P per mol flavin, and when after a time (1934) I succeeded in isolating the natural pigment component this proved to be a lactoflavin phosphoric acid ester, thus a kind of nucleotide, and it was obvious that the phosphoric acid served to link the pigment part to the protein. I will now show some simple experiments with the yellow enzyme, its colored part, which we now generally refer to as FMN (flavin mononucleotide), and the colorless enzyme protein.

  • The ferment-solution is pure yellow, the FMN-solution green-yellow,owing to the 1st that the light-absorption band in the blue of the free FMN is displaced somewhat in the long-wave direction on being linked with the protein component. A reducing agent (Na2S2O4) is now added to the one cuvette, it is indifferent which. The colour disappears in consequence of the formation of leucoflavin. Oxygen-gas is bubbled through the solution: the colour comes back as soon as the excess of reducing agent has been consumed. The experiment demonstrates the reaction cycle of the yellow enzyme: reduction through hydrogen from the substrate side, reoxidation with oxygen-gas.
  • A flask containing FMN-solution so diluted that its yellow color is not descernible to the eye is placed on a lamp giving long-wave ultraviolet light. The solution gives a strong, yellow fluorescence which disappears on reduction and returns on bubbling with oxygen-gas.
  • Two flasks are placed on the fluorescence lamp. The one contains a diluted solution of the free protein in phosphate buffer (pH 7), the other phosphate buffer alone. An equal amount of FMN-solution is dripped into each flask. In the flask with protein the fluorescence is at once extinguished,

but in the flask with buffer-solution alone it remains. The experiment demonstrates the resynthesis of  yellow enzyme, and since the fluorescence is extinguished by the protein, one may draw the conclusion that some group in the protein is in this connection linked to the imino-group NH(3) of the flavin, which according to Kuhn must be free for the fluorescence to appear.

The significance of these investigations on the yellow enzyme may be summarized

as follows.

  1. The reversible splitting of the yellow enzyme to apo-enzyme + coenzyme in the simple molecular relation 1 : 1 proved that we had here to do with a pure enzyme; the experiments would have been incomprehensible if the enzyme itself had been only an impurity.
  2. This enzyme was thus demonstrably a protein. In the sequel all the enzymes which have been isolated have proved to be proteins.
  3. The first coenzyme, FMN, was isolated and found to be a vitamin phosphoric acid ester. This has since proved to be something occurring widely in nature: the vitamins nicotinic acid amide, thiamine and pyridoxine form in an analogous way nucleotide-like coenzymes, which like the nucleic acids

themselves combine reversibly with proteins.

During the past 20 years a large number of flavoproteins with various enzyme effects have been produced. Instead of FMN many of them contain a dinucleotide, FAD, which consists of FMN + adenylic acid.

We constructed a very sensitive apparatus to record changes in the intensity of the fluorescence, and were thus able to follow the rapidity with which the fluorescence diminishes when FMN and protein are combined, or increases when they are split. Under suitable conditions the speed of combination is very high. Thanks to the great sensitivity of the fluorescent method my Norwegian collaborator Agnar Nygaard and I were able to make accurate determinations of the speed-constant simply by working in extremely diluted solutions, where the speed of combination is low because an FMN molecule so seldom happens to collide with a protein-molecule. We then varied the degree of acidity, ionic milieu and temperature, and we treated the protein with a large number of different reagents which affect in a known way different groups in proteins. In this way we succeeded with a rather high degree of certainty in ascertaining that phosphoric acid in FMN is linked to primary amino-groups in the protein, and the imino-group (3) in FMN to the phenolic hydroxyl group in a tyrosine residue, whereby the fluorescence is extinguished.

We still do not quite understand how through its linkage to the coenzyme the enzyme-protein “activates” the latter to a rapid absorption and giving off of hydrogen. But something we do know. The so-called oxido-reduction potential of the enzyme is in any case of great importance, and it is determined by a simple relation to the dissociation constants for the oxidized and for the reduced coenzyme-enzyme complex. The dissociation constants are in their turn functions of the velocity constants for the combination between coenzyme and enzyme and for the reverse process, and these velocity constants we have been able to determine both in the yellow ferment and in a number of enzyme systems. Without going into any details I may mention that the linkage of coenzyme to enzyme was found to have surprisingly big effects upon the potential of the former.

Alcohol dehydrogenase

 

Alcohol dehydrogenases occur in both the animal and the vegetable kingdoms, e.g. in liver, in yeast, and in peas. They are colourless proteins which together with DPN may either oxidize alcohol to aldehyde, as occurs chiefly in the liver, or conversely reduce aldehyde to alcohol, as occurs in yeast.

The yeast enzyme was crystallized by Negelein & Wulf (1936) in Warburg’s institute, the liver enzyme (from horse liver) by Bonnichsen & Wassén at our institute in Stockholm in 1948. These two enzymes have come to play a certain general rôle in biochemistry on account of the fact that it has been possible to investigate their kinetics more accurately than is the case with other enzyme systems. The liver enzyme especially, we have on repeated occasions studied with particular thoroughness, since especially favourable experimental conditions here presented themselves. For all reactions with DPN-system it is possible to follow the reaction DPN+ + 2H =+ DPNH + H+ spectrophotometrically, since DPNH has an absorption-band in the more long-wave ultraviolet region, at 340 rnp, and thousands of such experiments have been performed all over the world. A couple of years ago, moreover, we began to apply our fluorescence method, which is based on the fact that DPNH but not DPN fluoresces, even if considerably more weakly than the flavins. Asregards the liver enzyme there is a further effect, which proved extremely useful for certain spectrophotometrical determinations of reaction speeds; together with Bonnichsen I found in 1950 that the 340 rnp band of the reduced coenzyme was displaced, on combination with liver alcohol dehydrogenase, to 325 rnp, and together with Britton Chance we were thus able with the help of his extremely refined rapid spectrophotometric methods to determine the velocity constant for this very rapid reaction. This reaction belongs to the 3 bost problem involving the enzyme, the coenzyme, and the substrate, and both the coenzyme and the substrate occur in both oxidized and reduced forms.

It is a curious whim of nature that the same coenzyme which in the yeast makes alcohol by attaching hydrogen to aldehyde also occurs in the liver to remove from alcohol the same hydrogen, so that the alcohol becomes aldehyde again, which is then oxidized further
————————————————————————————————————————————–
Heme proteins

In 1936 we had obtained cytochrome approximately 80% pure, and in 1939 close to 100%.It is a beautiful red, iron-porphyrin-containing protein which functions as a link in the chain of the cell-respiration enzymes, the iron atom now taking up and now giving off an electron, and the iron thus alternating valency between the 3-valent ferri and the 2-valent ferro stages. It is a very pleasant substance to work with, not merely because it is lovely to look at, but also because it is uncommonly stable and durable. From 100 kg horse heart one can produce 3-4 grams of pure cytochrome c. The molecule weighs about 12,000 and contains one mol iron porphyrin per-mol.

Exp. 4. Two cuvettes each contain a solution of ferricytochrome c. The colour is blood-red. To the one are added some grains of sodium hydrosulphite: the color is changed to violet-red (ferrocytochrome). Oxygen is now bubbled through the ferrocytochrome-solution: no visible change occurs. The ferrocyto-chrome can thus not be oxidized by oxygen. A small amount of cytochrome oxidase is now added: the ferricytochrome color returns.

From this experiment we can draw the conclusion that reduced cytochrome c cannot react with molecular oxygen. In a chain of oxidation enzymes it will thus not be able to be next to the oxygen. The incapacity of cytochrome to react with oxygen was a striking fact that required an explanation. Another peculiarity was the extremely firm linkage between the red heme pigment and the protein part; in contradistinction to the majority of other heme protides, the pigment cannot be split off by the addition of acetone acidified with hydrochloric acid. Further, there was a displacement of the light-absorption bands which indicated that the two unsaturated vinyl groups occurring in ordinary protohemin were saturated in the hematin of

the cytochrome. In 1938 we succeeded in showing that the porphyrin part of the cytochrome was linked to the protein by means of two sulphur bridges from cysteine residues in the protein of the porphyrin in such a way that the vinyl groups were saturated and were converted to α-thioether groups. The firmness of the linkage and the displacement of the spectral bands were herewith explained. This was the first time that it had been possible to show the nature of chemical linkages between a “prosthetic” group (in this case iron porphyrin) and the protein part in an enzyme.

The light-absorption bands of the cytochrome showed that it is a so-called hemochromogen, which means that two as a rule nitrogen-containing groups are linked to the iron, in addition to the four pyrrol-nitrogen atoms in the porphyrin. From magnetic measurements that I made at Linus Pauling’s institute in Pasadena and from amino-acid analyses, titration curves and spectrophotometry together with Å. Åkeson it emerged (1941) that the nitrogen-containing, hemochromogen-forming groups in cytochrome c were histidine residues, or to be more specific, their imidazole groups.   Recently we have got a bit farther. Tuppy & Bodo in Vienna began last year with Sanger’s method to elucidate the amino-acid sequence in the hemin-containing peptide fragment that one obtains with the proteolytic breaking down of cytochrome c, and succeeded in determining the sequence of the amino acids nearest the heme. The experiments were continued and supplemented by Tuppy, Paléus & Ehrenberg at our institute in Stockholm with the following result:

The peptide chain 1-12 (“Val”) = the amino acid valine, “Glu” = glutamine,”Lys” = lysine, and so forth) is by means of two cysteine-S-bridges and a linkage histidine-Fe linked to the heme. When in 1954 Linus Pauling delivered his Nobel Lecture in Stockholm he showed a new kind of models for the study of the steric configuration of peptide chains, which as we know may form helices or “pleated sheets” of various kinds. It struck me then that it would be extremely interesting to study the question as to which of these possibilities might be compatible with the sulphur bridges to the hemin part and with the linkage of nitrogen containing groups to the iron. Pauling was kind enough to make me a present of his peptide-model pieces, which I shall show presently. This is thus the second time they figure in a Lecture.

Anders Ehrenberg and I now made a hemin model on the same scale as the peptide pieces and constructed models of hemin peptides with every conceivable variant of hydrogen bonding. It proved that many variants could be definitely excluded on steric grounds, and others were improbable for other reasons. Of the original, at least 20 alternatives, finally only one remained – a left-twisting a-helix with the cysteine residue no. 4 linked to the porphyrin side-chain in 4-position, and cysteine no. 7 to the side-chain in 2-position. The imidazole residue fitted exactly to linkage with the iron atom. The peptide spiral becomes parallel with the plane of the heme disc.

Through calculations on the basis of the known partial specific volume of the cytochrome we now consider it extremely probable that the heme plate in cytochrome c is surrounded by peptide spirals on all sides in such a way that the heme iron is entirely screened off from contact with oxygen; here is the explanation of our experiment in which we were unable to oxidize reduced cytochrome c with oxygen-gas. The oxygen simply cannot get at the iron atom. There is, on the other hand, a possibility for electrons to pass in and out in the iron atom via the imidazole groups.  It strikes us as interesting that even at this stage the special mode of reacting of the cytochrome is beginning to be understood from what we know of its chemical constitution.

F r i t z  L i p m a n n           Nobel Lecture, December 11, 1953

Development of the acetylation problem: a personal account

 

In my development, the recognition of facts and the rationalization of these facts into a unified picture, have interplayed continuously. After my apprenticeship with Otto Meyerhof, a first interest on my own became the phenomenon we call the Pasteur effect, this peculiar depression of the wasteful fermentation in the respiring cell. By looking for a chemical explanation of this economy measure on the cellular level, I was prompted into a study of the mechanism of pyruvic acid oxidation, since it is at the pyruvic stage where respiration branches off from fermentation. For this study I chose as a promising system a relatively simple looking pyruvic acid oxidation enzyme in a certain strain of Lactobacillus delbrueckii1. The decision to explore this particular reaction started me on a rather continuous journey into partly virgin territory to meet with some unexpected discoveries, but also to encounter quite a few nagging disappointments

The most important event during this whole period, I now feel, was the accidental observation that in the L. delbrueckii system, pyruvic acid oxidation was completely dependent on the presence of inorganic phosphate. This observation was made in the course of attempts to replace oxygen by methylene blue. To measure the methylene blue reduction manometrically, I had to switch to a bicarbonate buffer instead of the otherwise routinely used phosphate. In bicarbonate, to my surprise, as shown in Fig. 1, pyruvate oxidation was very slow, but the addition of a little phosphate caused a  remarkable increase in rate. The next figure, Fig. 2, shows the phosphate effect more drastically, using a preparation from which all phosphate was removed by washing with acetate buffer. Then it appeared that the reaction was really fully dependent on phosphate. In spite of such a phosphate dependence, the phosphate balance measured by the ordinary Fiske-Subbarow procedure did not at first indicate any phosphorylative step. Nevertheless, the suspicion remained that phosphate in some manner was entering into the reaction and that a phosphorylated intermediary was formed. As a first approximation, a coupling of this pyruvate

oxidation with adenylic acid phosphorylation was attempted. And, indeed, addition of adenylic acid to the pyruvic oxidation system brought out a net disappearance of inorganic phosphate, accounted for as adenosine triphosphate (Table 11). In parallel with the then just developing fermentation now concluded that the missing link in the reaction chain was acetyl phosphate. In partial confirmation it was shown that a crude preparation of acetyl phosphate, synthesized by the old method of Kämmerer and Carius2

would transfer phosphate to adenylic acid (Table 2). However, it still took quite some time from then on to identify acetyl phosphate definitely as the initial product of the pyruvic oxidation in this system3,4.

At the time when these observations were made, about a dozen years ago, there was, to say the least, a tendency to believe that phosphorylation was rather specifically coupled with the glycolytic reaction. Here, however, we had found a coupling of phosphorylation with a respiratory system. This observation immediately suggested a rather sweeping biochemical significance, of transformations of electron transfer potential, respiratory or fermentative, to phosphate bond energy and therefrom to a wide range of biosynthetic reactions7.

There was a further unusual feature in this pyruvate oxidation system in that the product emerging from the process not only carried an energy-rich phosphoryl radical such as already known, but the acetyl phosphate was even more impressive through its energy-rich acetyl. It rather naturally became a contender for the role of “active” acetate, for the widespread existence of which the isotope experience had already furnished extensive evidence. I became, therefore, quite attracted by the possibility that acetyl phosphate could serve two rather different purposes, either to transfer its phosphoryl group into the phosphate pool, or to supply its active acetyl for biosynthesis of carbon structures. Thus acetyl phosphate should be able to serve as acetyl donor as well as phosphoryl donor, transferring, as shown in Fig. 3, on either side of the oxygen center, such as indicated by Bentley’s early experiments on cleavage7a of acetyl phosphate in H2 18O.

These two novel aspects of the energy problem, namely

(1) the emergence of an energy-rich phosphate bond from a purely respiratory reaction; and

(2) the presumed derivation of a metabolic building-block through this same there towards a general concept of transfer of activated groupings by carrier as the fundamental reaction in biosynthesis8,9.

Although in the related manner the appearance of acetyl phosphate as a metabolic intermediary first

focussed attention to possible mechanisms for the metabolic elaboration of group activation, it soon turned out that the relationship between acetyl phosphate and acetyl transfer was much more complicated than anticipated. reaction, prompted me to propose

  • not only the generalization of the phosphate bond as a versatile energy distributing system,
  • but also to aim there towards a general concept of transfer of activated groupings by carrier as the fundamental reaction in biosynthesis8,9.

Although in the related manner the appearance of acetyl phosphate as a metabolic intermediary first focussed attention to possible mechanisms for the metabolic elaboration of group activation, it soon turned out that the relationship between acetyl phosphate and acetyl transfer was much more complicated than anticipated.

It appeared that as an energy source the particle bound oxidative phosphorylation of the kind observed first by Herman Kalckar14 could be replaced by ATP, as had first been observed with the acetylation of choline in brain preparations by Nachmansohn and his group15,16. Using ATP and acetate as precursors, it was possible to set up a homogeneous particle-free acetylation system obtained by extraction of acetone pigeon liver. In this extract acetyl phosphate was unable to replace the ATP acetate as acetyl precursor.

In spite of this disappointment with acetyl phosphate, our decision to turn to a study of acetylation started then to be rewarding in another way. During these studies we became aware of the participation of a heat-stable factor which disappeared from our enzyme extracts on aging or dialysis. This cofactor was present in boiled extracts of all organs, as well as in microorganisms and yeast. It could not be replaced by any other known cofactor. Therefore, it was suspected that we were dealing with a new coenzyme. From then on, for a number of years, the isolation and identification of this coenzyme became the prominent task of our laboratory. The problem now increased in volume and I had the very good fortune that a group of exceedingly able people were attracted to the laboratory; first Constance Tuttle, then Nathan O. Kaplan and shortly afterwards, G. David Novelli, and then others.

Early data on the replacement of this heat-stable factor by boiled extracts are shown in the next table (Table 3). The pigeon liver acetylation system proved to be a very convenient assay system for the new coenzyme17 since on aging for 4 hours at room temperature, the cofactor was completely autolyzed.

Fortunately, on the other hand, the enzyme responsible for the decomposition of this factor was quite unstable and faded out during the aging, while the acetylation apoenzymes were unaffected.

The next figure, Fig. 4, shows coenzyme A (CoA) assay curves obtained with acetone pigeon liver extract. Finding pig liver a good source for the coenzyme, we set out to collect a reasonably large quantity of a highly purified preparation and then to concentrate on the chemistry with this material. In this analysis we paid particular attention to the possibility of finding in this obviously novel cofactor one of the vitamins.

The subsequence finding of a B-vitamin in the preparation gave us further confidence that we were dealing here with a key substance. We still felt, however, slightly dissatisfied with the proof for pantothenic acid. Therefore, to liberate the chemically rather unstable pantothenic acid from CoA, we made use of observations on enzymatic cleavage of the coenzyme. Two enzyme preparations, intestinal phosphatase and an enzyme in pigeon liver extract, had caused independent inactivation. It then was found that through combined action of these two enzymes, pantothenic acid was liberated18,19.

The two independent enzymatic cleavages indicated early that in CoA existed two independent sites of attachment to the pantothenic acid molecule. One of these obviously was a phosphate link, linking presumably to one of a hydroxyl group in pantothenic acid. The other moiety attached to pantothenic acid, which, cleaved off by liver enzyme, remained unidentified for a long time. In addition to pantothenic acid, our sample of 40 per cent purity had been found to contain about 2 per cent sulfur by elementary analysis and identified by cyanide-nitroprusside test as a potential SH grouping 20,21. Furthermore, the coenzyme preparation contained large amounts of adenylic acid21.

Units Coenzyme

Fig. 4. Concentration-activity curves for coenzyme A preparations of different purity. The arrow indicates the point of 1 unit on the curve. (o) crude coenzyme, 0.25 unit per mg; (x) purified coenzyme, 130 units per mg.

In the subsequent elaboration of the structure, the indications by enzyme analysis for the two sites of attachment to pantothenic acid have been most helpful. The phosphate link was soon identified as a pyrophosphate bridge22; 5-adenylic acid was identified by Novelli23 as enzymatic split product and by Baddiley 24, through chemical cleavage. At the same time, Novelli made observations which indicated the presence of a third phosphate in addition to the pyrophosphate bridge. These indications were confirmed by analysis of a nearly pure preparation which was obtained by Gregoryas from Streptomyces fradiae in collaboration with the research group at the Upjohn Company26.

It was at this period that we started to pay more and more attention to the sulfur in the coenzyme. As shown in Table 5, our purest preparation contained 4.13 per cent sulfur corresponding to one mole per mole of pantothenate. We also found26 that dephosphorylation of CoA yielded a compound containing pantothenic acid and the sulfur carrying moiety, which we suspected as bound through the carboxyl. Through the work of Snell and his group27, the sulfur-containing moiety proved to be attached to pantothenic acid through a link broken by our liver enzyme. It was identified as thioethanolamine by Snell and his group, linked peptidically to pantothenic acid.

Through analysis and synthesis, Baddiley now identified the point of attachment of the phosphate bridge to pantothenic acid in 4-position24 and Novelli et al.28 completed the structure analysis by enzymatic synthesis of “dephospho-CoA” from pantetheine-4’-phosphates and ATP. Furthermore, the attachment of the third phosphate was identified by Kaplan29 to attach in s-position on the ribose of the 5-adenylic acid (while in triphosphopyridine nucleotide it happens to be in 2-position). Therefore, the structure was now

established, as shown in Fig. 5.

Fig. 5. Structure of coenzyme A

 

The metabolic function of CoA


Parallel with this slow but steady elaboration of the structure, all the time we explored intensively metabolic mechanisms in the acetylation field. By use of the enzymatic assay, as shown in Tables 6, 7, 8, and 9, CoA was found present in all living cells, animals, plants and microorganisms17. Furthermore,

the finding that all cellular pantothenic acid could be accounted for by CoA17 made it clear that CoA represented the only functional form of this vitamin. The finding of the vitamin furnished great impetus; nevertheless, a temptation to connect the pantothenic acid with the acetyl transfer function has

blinded us for a long time to other possibilities.

The first attempts to further explore the function of CoA were made with pantothenic acid-deficient cells and tissues. A deficiency of pyruvate oxidation in pantothenic acid-deficient Proteus morganii, an early isolated observation by Dorfman30 and Hills31, now fitted rather well into the picture. We soon became quite interested in this effect, taking it as an indication for participation of CoA in citric acid synthesis. A parallel between CoA levels and pyruvate oxidation in Proteus morganii was demonstrated32. Using panto thenic aciddeficient yeast, Novelli et al.33 demonstrated a CoA-dependence of acetate oxidation (Fig. 5a) and Olson and Kaplan34 found with duck liver a striking parallel between CoA content and pyruvic utilization, which is shown in Fig. 6.

But more important information was being gathered on -the enzymatic level. The first example of a generality of function was obtained by comparing the activation of apoenzymes for choline- and sulfonamide-acetylation respectively, using our highly purified preparations9 of CoA. As shown in Fig. 7, similar activation curves obtained for the two respective enzymes. Through these experiments, the heat-stable factor for choline acetylation that had been found by Nachmansohn and Berman35 and by Feldberg and Mann36 was identified with CoA. The next most significant step toward a generalization of CoA function for acetyl transfer was made by demonstrating its functioning in the enzymatic synthesis of acetoacetate. The CoA effect in acetoacetate synthesis was studied by Morris Soodak37, who obtained for this reaction a reactivation curve quite similar to those for enzymatic acetylation, as shown in Fig. 8.

Soon afterwards Stern and Ochoa38 showed a CoA-dependent citrate synthesis with a pigeon liver fraction similar to the one used by Soodak for acetoacetate synthesis. In our laboratory, Novelli et al. confirmed and extended this observation with extracts of Escherichia coli39.

In the course of this work, which more and more clearly defined the acetyl transfer function of CoA, Novelli once more tried acetyl phosphate. To our surprise and satisfaction, it then appeared, as shown in Table 9, that in Escherichia coli extracts in contrast to the animal tissue, acetyl phosphate was more than twice as active as acetyl donor for citrate synthesis than ATP acetate 39. Acetyl phosphate, therefore, functioned as a patent microbial acetyl donor. Acetyl transfer from acetyl phosphate, like that from ATP-acetate, was CoA-dependent, as shown in Table 9. Furthermore, a small amount of “microbial conversion factor”, as we called it first, primed acetyl phosphate for activity with pigeon liver acetylation systems40, as shown in Table 10.

Eventually the microbial conversion factor was identified by Stadtman et al.40 with the transacetylase first encountered by Stadtman and Barker in extracts of Clostridium kluyveri41 and likewise, although not clearly defined as such, in extracts of Escherichia coli and Clostridium butylicum by Lipmann and Tuttle42. The definition of such a function was based on the work of Doudoroff et al.43 on transglucosidation with sucrose phosphorylase. Their imaginative use of isotope exchange for closer definition of enzyme mechanisms has been most influential. Like glucose-I-phosphate with sucrose phosphorylase, acetyl phosphate with these various microbial preparations equilibrates its phosphate rapidly with the inorganic phosphate of the solution. As in Doudoroff et al. experiments, first a covalent substrate enzyme derivative had been proposed 43. However, then Stadtman et al.40, with the new experience of CoA dependent acetyl transfer, could implicate CoA in this equilibration between acetyl- and inorganic phosphate and thus could define the transacetylase as an enzyme equilibrating acetyl between phosphate and CoA:

In the course of these various observations, it became quite clear that there existed in cellular metabolism an acetyl distribution system centering around CoA as the acetyl carrier which was rather similar to the ATP-centered phosphoryl distribution system. The general pattern of group transfer became recognizable, with donor and acceptor enzymes being connected through the CoA —- acetyl CoA shuttle. A clearer definition of the donor-acceptor enzyme scheme was obtained through acetone fractionation of our standard system for acetylation of sulfonamide into two separate enzyme fractions, which were inactive separately but showed the acetylation effect when combined. A fraction, A-40, separating out with 40 per cent acetone, was shown by Chou44 to contain the donor enzyme responsible for the ATP-CoA-acetate reaction, while with more acetone precipitated, the acceptor function, A-60, the acetoarylamine kinase as we propose to call this type of enzyme. The need for a combination of the two for overall acetyl transfer is shown in Fig. 9. This showed that a separate system was responsible for acetyl CoA formation through interaction of ATP, CoA and acetate (cf. below) and that the overall acetylation was a two-step reaction:

These observations crystallized into the definition of a metabolic acetyl transfer territory as pictured in Fig. 10. This picture had developed from the growing understanding of enzymatic interplay involving metabolic generation of acyl CoA and transfer of the active acyl to various acceptor systems. A most important, then still missing link in the picture was supplied through the brilliant work of Feodor Lynen45 who chemically identified acetyl CoA as the thioester of CoA. Therewith the thioester link was introduced as a new energy-rich bond and this discovery added a very novel facet to our understanding of the mechanisms of metabolic energy transformation.

Enzyme Localization In The  Anaerobic Mitochondria Of Ascaris L Umbricoides

 

Robert S. Rew And Howard J. Saz

From the Department of Biology, University of Notre Dame, Notre Dame, Indiana 46556

 

Mitochondria from the muscle of the parasitic nematode Ascaris lumbricoides   var. suum function anaerobically in electron transport-associated  phosphorylations under physiological conditions. These helminth organelles have been fractionated into inner and outer membrane, matrix, and intermembrane space fractions. The distributions of enzyme systems were determined and compared with corresponding distributions reported in mammalian mitochondria.  Succinate and pyruvate dehydrogenases as well as NADH  oxidase, Mg++-dependent ATPase, adenylate kinase, citrate synthase, and cytochrome c  reductases  were  determined to be distributed  as  in mammalian mitochondria.  In contrast  with  the  mammalian systems, fumarase and NAD-linked “malic” enzyme were isolated primarily from the intermembrane  space fraction of the worm mitochondria. These enzymes required for the anaerobic  energy-generating system in Ascaris and would be expected to give rise to NADH in the intermembrane space.  The need for and possible mechanism of a proton translocation system to obtain energy generation is suggested.                                Downloaded from jcb.rupress.org

                                                                                                                                                      

                          

                               

                               

                               

                               

David Keilin’s Respiratory Chain Concept and its Chemiosmotic Consequences

Peter Mitchell              Nobel Lecture, 8 December, 1978

Glynn Research Institute, Bodmin, Cornwall, U. K.
“for his contribution to the understanding of biological energy transfer through the formulation of the chemiosmotic theory”

Peter D. Mitchell (1920-1992) received the Nobel Prize in 1978 for developing the Chemiosmotic Theory to explain ATP synthesis resulting from membrane-associated electron transport [Ubiquinone and the Proton Pump].

Mitchell is the last of the gentleman scientists. He first proposed the chemiosmotic principle in a 1961 Nature article while he was at the University of Edinburgh. Shortly after that, ill health forced him to move to Cornwall where he renovated an old manor house and converted it into a research laboratory. From then on, he and his research colleague, Jennifer Moyle, continued to work on the chemiosmotic theory while being funded by his private research foundation. [Peter Mitchell: Wikipedia]

The Chemiosmotic Theory was controversial in 1978 and it still has not been fully integrated into some biochemistry textbooks in spite of the fact that it is now proven. The main reason for the resistance is that it overthrows much of traditional biochemistry and introduces a new way of thinking. It is a good example of a “paradigm shift” in biology.

Because he was such a private, and eccentric, scientist there are very few photos of Peter Mitchell or his research laboratory at Glynn House . The best description of him is in his biography Wandering in the Gardens of the Mind: Peter Mitchell and the Making of Glynn by John Prebble, and Bruce Weber. A Nature review by E.C. Slater [Metabolic Gardening] gives some of the flavor and mentions some of the controversy.

Wandering_in_the_Gardens_of_the_Mind_Peter_Mitchell

Wandering_in_the_Gardens_of_the_Mind_Peter_Mitchell

Peter_Mitchell

Peter_Mitchell

http://pharmaceuticalintelligence.com/wp-content/uploads/2014/08/44abe-peter_mitchell.jpg

http://pharmaceuticalintelligence.com/wp-content/uploads/2014/08/77938-wandering_in_the_gardens_of_the_mind_peter_mitchell.jpg

Many scientists believe that the Chemiosmotic Theory was the second greatest contribution to biology in the 2oth century (after the discovery of the structure of DNA). Mitchell had to overcome many critics including Hans Krebs. The case is strong.

In the 1960s, ATP was known to be the energy currency of life, but the mechanism by which ATP was created in the mitochondria was assumed to be by substrate-level phosphorylation. Mitchell’s chemiosmotic hypothesis was the basis for understanding the actual process of oxidative phosphorylation. At the time, the biochemical mechanism of ATP synthesis by oxidative phosphorylation was unknown.

Mitchell realised that the movement of ions across an electrochemical potential difference could provide the energy needed to produce ATP. His hypothesis was derived from information that was well known in the 1960s. He knew that living cells had a membrane potential; interior negative to the environment. The movement of charged ions across a membrane is thus affected by the electrical forces (the attraction of positive to negative charges). Their movement is also affected by thermodynamic forces, the tendency of substances to diffuse from regions of higher concentration. He went on to show that ATP synthesis was coupled to this electrochemical gradient.[11]

His hypothesis was confirmed by the discovery of ATP synthase, a membrane-bound protein that uses the potential energy of the electrochemical gradient to make ATP.

Growth, development and metabolism are some of the central phenomena in the study of biological organisms. The role of energy is fundamental to such biological processes. The ability to harness energy from a variety of metabolic pathways is a property of all living organisms. Life is dependent on energy transformations; living organisms survive because of exchange of energy within and without.

In a living organism, chemical bonds are broken and made as part of the exchange and transformation of energy. Energy is available for work (such as mechanical work) or for other processes (such as chemical synthesis and anabolic processes in growth), when weak bonds are broken and stronger bonds are made. The production of stronger bonds allows release of usable energy.

One of the major triumphs of bioenergetics is Peter D. Mitchell‘s chemiosmotic theory of how protons in aqueous solution function in the production of ATP in cell organelles such as mitochondria.[5] This work earned Mitchell the 1978 Nobel Prize for Chemistry. Other cellular sources of ATP such as glycolysis were understood first, but such processes for direct coupling of enzyme activity to ATP production are not the major source of useful chemical energy in most cells. Chemiosmotic coupling is the major energy producing process in most cells, being utilized in chloroplasts and several single celled organisms in addition to mitochondria.

Cotransport

In August 1960, Robert K. Crane presented for the first time his discovery of the sodium-glucose cotransport as the mechanism for intestinal glucose absorption.[2] Crane’s discovery of cotransport was the first ever proposal of flux coupling in biology and was the most important event concerning carbohydrate absorption in the 20th century.[3][4]

The free energy (ΔG) gained or lost in a reaction can be calculated: ΔG = ΔH – TΔS
where G = Gibbs free energy, H = enthalpy, T = temperature, and S = entropy.

How inositol pyrophosphates control cellular phosphate homeostasis?

Adolfo Saiardi*

Cell Biology Unit, Medical Research Council Laboratory for Molecular Cell Biology, Department of Cell and Developmental Biology,

University College London, Gower Street, London WC1E 6BT, United Kingdom

Advances in Biological Regulation 52 (2012) 351–359

Phosphorus in his phosphate PO43_ configuration is an essential constituent of all life forms. Phosphate diesters are at the core of nucleic acid structure, while phosphate monoester transmits information under the control of protein kinases and phosphatases. Due to these fundamental roles in biology it is not a surprise that phosphate cellular homeostasis is under tight control.

Inositol pyrophosphates are organic molecules with the highest proportion of phosphate groups, and they are capable of regulating many biological processes, possibly by controlling energetic metabolism and adenosine triphosphate (ATP) production.

Furthermore, inositol pyrophosphates influence inorganic polyphosphates (polyP) synthesis. The polymer polyP is solely constituted by phosphate groups and beside other known functions, it also plays a role in buffering cellular free phosphate [Pi] levels, an event that is ultimately necessary to generate ATP and inositol pyrophosphate.

Two distinct classes of proteins the inositol hexakisphosphates kinases (IP6Ks) and the diphosphoinositol pentakisphosphate kinases (PP-IP5Ks or IP7Ks) are capable of synthesizing inositol pyrophosphates.

IP6Ks utilize ATP as a phosphate donor to phosphorylate IP6 to IP7, generation the isomer 5PP-IP5 (Fig. 1A), and inositol pentakisphosphate I(1,3,4,5,6)P5 to PP-IP4 (Saiardi et al., 1999, 2000; Losito et al., 2009). Furthermore, at least in vitro, IP6Ks generate more complex molecules containing two or more pyrophosphate moieties, or even three-phosphate species (Draskovic et al., 2008; Saiardi et al., 2001). Three IP6K isoforms referred to as IP6K1, 2, 3 exist in mammal; however, there is a single IP6K in the yeast Saccharomyces cerevisiae called Kcs1.

The PP-IP5Ks enzymes, synthesize inositol pyrophosphate from IP6, but not from IP5, (Losito et al., 2009) generating the isomer 1PP-IP5. Kinetic studies performed in vitro suggested that IP7, the 5PP-IP5 isomer generated by IP6Ks, is the primary substrate of this new enzyme, and this finding was confirmed in vivo by analysing PP-IP5K null yeast (vip1D) that accumulate the un-metabolized substrate IP7 (Azevedo et al., 2009; Onnebo and Saiardi, 2009). Thus PP-IP5K is responsible for IP8,

isomer 1,5PP2-IP4 synthesis (Fig. 1A). Two PP-IP5K isoforms referred to as PP-IP5Ka and b exist in mammal while a single PP-IP5K called Vip1 is present in S. cerevisiae.

Inositol pyrophosphates are hydrolysed by the diphosphoinositol-polyphosphate phosphohydrolases (DIPPs) (Safrany et al., 1998). Four mammalian enzymes DIPP1,2,3,4 have been identified, while only one DIPP protein exists in S. cerevisiae called Ddp1. These phosphatases are promiscuous enzymes able to hydrolyse inositol pyrophosphate as well as nucleotide analogues, such as diadenosine hexaphosphate (Ap6A) (Caffrey et al., 2000; Fisher et al., 2002). More recently, it has been shown that DIPPs also degrade polyP (Lonetti et al., 2011). Inositol pyrophosphates control the most disparate biological processes, from telomere length to vesicular trafficking. It is conceivable that all these function can be focused on the fact that inositol pyrophosphates are controlling cellular energy metabolism and consequently, ATP production. We have recently, demonstrated that inositol pyrophosphates control glycolysis and mitochondrial oxidative phosphorylation by both inhibiting the glycolytic flux and increasing mitochondrial activity (Szijgyarto et al., 2011).

Another important molecule to briefly introduce is polyP (Fig. 1B). The interested reader is encouraged to read the following comprehensive reviews (Kornberg et al., 1999; Rao et al., 2009). The polyP polymer likely represents a phosphate buffer that is synthesized and degraded in function of the phosphate needs of the cells. Furthermore, it also functions as a chelator of metal ions, thereby regulating cellular cation homeostasis. However, polyP also possesses more classical signalling roles.

In bacteria for example, it influences pathogenicity (Brown and Kornberg, 2008) and in mammalian cells it has been proposed to regulate fibrinolysis and platelet aggregation (Caen and Wu, 2010). In prokaryotes, polyP synthesis is carried out by a family of conserved polyP kinases (PPKs), whereas degradation is mediated by several polyP phosphatases (Rao et al., 2009). In higher eukaryotes polyP synthesis remains poorly characterized.

In humans alteration of phosphate metabolism is implicated in several pathological states. Higher serum phosphate leads to vascular calcification and cardiovascular complications. Although only very small amount of phosphate circulates in the serum, its concentration is tightly regulated and it is independent from dietary phosphorus intake (de Boer et al., 2009). Therefore, it is not surprising that intense research efforts are aimed to elucidate phosphate uptake and metabolism. IP6K2 was initially cloned while searching for a novel mammalian intestinal phosphate transporter that the group of Murer identified as PiUS (Phosphate inorganic Uptake Stimulator) (Norbis et al., 1997). Once transfected into Xenopus oocytes, PiUS stimulated the cellular uptake of radioactive phosphate.

Subsequently, two groups discovered that PiUS was capable of converting IP6 to IP7 and rename it to IP6K2 (Saiardi et al., 1999; Schell et al., 1999). The ability of inositol pyrophosphate to control the uptake of phosphate is an evolutionary conserved feature; in fact, kcs1D yeast with undetectable level of IP7 exhibits a reduced uptake of phosphate from the culture medium (Saiardi et al., 2004).

In mammals, regulation of phosphate homeostasis is not restricted to IP6K2, all three mammalian IP6Ks are likely to play a role. A genome-wide study aimed at identifying genetic variations associated with changes of serum phosphorus concentration identified IP6K3 (Kestenbaum et al., 2010). This human genetic study identified two independent single nucleotide polymorphisms (SNP) at locus 6p21.31, which are localised within the first intron of the IP6K3 gene. Interestingly, this study that analysed more than 16,000 humans identified SNP variant in only seven genes. Three of which, the sodium phosphate cotransporter type IIa, the calcium sensing receptor and the fibroblast growth factor 23, are well known regulators of phosphate homeostasis. These evidences support a role for IP6K3 in controlling serum phosphate levels in humans (Kestenbaum et al., 2010).

 

The hypothesis

 

Although, inositol pyrophosphate may have acquired unique organism-specific functions, the conserved ability of this class of molecules to regulate phosphate metabolism suggests an evolutionary ancient role. In this last paragraph, I will formulate few hypotheses that I hope will stimulate further research aimed at elucidating the biological link between phosphate, inositol pyrophosphates and polyP.

Inositol pyrophosphates regulate the entry of phosphate into the cells (Norbis et al., 1997), suggesting that they could affect phosphate uptake either directly (by stimulating a transporter, for example) or a indirectly by helping ‘fixing’ free phosphates in organic molecules. The cytosolic concentration of free phosphate [Pi] cannot fluctuate widely. Therefore, cellular entry of phosphates and its utilization may well be coupled. For example, the synthesis of polyP may be linked to phosphate entry in the cell. Inositol pyrophosphate control of energy metabolism (Szijgyarto et al., 2011) affects not only ATP levels but it can also alter the entire cellular balance of adenine nucleotides. Given that phosphate transfer reactions mainly use ATP as a vehicle for the phosphate groups, inositol pyrophosphate could affect phosphate metabolism by regulating the adenylate cellular pool. Moreover, it is tempting to speculate the existence of a feedback mechanism that coordinates the metabolic balance between ATP, phosphate and inositol pyrophosphates.

Inositol pyrophosphates could either contribute to the regulation of polyP synthesis, play a role in polyP degradation, or both. The yeast polyP polymerase has been identified with the subunit four (Vtc4) of the vacuolar membrane transporter chaperone (VTC) complex (Hothorn et al., 2009). Interestingly, pyrophosphates (Pi–Pi) dramatically accelerate the polyP polymerase reaction. It would therefore be interesting to determine whether the pyrophosphate moiety of IP7 can stimulate polyP vacuolar synthesis in a similar fashion. Similarly, it would be interesting to analyse the effect of inositol pyrophosphates on controlling the activity of the actin-like DdIPK2 enzyme. It should be noted however, that the existence even in yeast or Dictyostelium of other enzymes able to synthesize different polyP pools cannot be excluded. Thus, we will be able to validate and fully appreciate the role played by inositol pyrophosphates on polyP synthesis only after the identification of higher eukaryotes polyp synthesizing peptide/s.

The most abundant form of organic phosphate on earth is IP6, or phytic acid, a molecule that is highly abundant in plant seeds from which was originally characterised. In plant seeds, IP6 represents a phosphate storage molecule that it is hydrolysed during germination, releasing phosphates and cations. It will be an astonishing twist of event if inositol pyrophosphates were controlling the levels of their own precursor IP6 (Raboy, 2003), although due to the evolutionary conserved ability of inositol pyrophosphate to control phosphate homeostasis we should not be entirely surprised.

Although it is not yet clear how inositol pyrophosphates regulate cellular metabolism, understanding how inositol pyrophosphates influence phosphates homeostasis will help to clarify this important link.

Auesukaree C, Tochio H, Shirakawa M, Kaneko Y, Harashima S. Plc1p, Arg82p, and Kcs1p, enzymes involved in inositol pyrophosphate synthesis, are essential for phosphate regulation and polyphosphate accumulation in Saccharomyces cerevisiae. J Biol Chem 2005;280:25127–33.

Azevedo C, Burton A, Ruiz-Mateos E, Marsh M, Saiardi A. Inositol pyrophosphate mediated pyrophosphorylation of AP3B1 regulates HIV-1 Gag release. Proc Natl Acad Sci U S A 2009;106:21161–6.

Bennett M, Onnebo SM, Azevedo C, Saiardi A. Inositol pyrophosphates: metabolism and signaling. Cell Mol Life Sci 2006;63:552–64.

Boer VM, Crutchfield CA, Bradley PH, Botstein D, Rabinowitz JD. Growth-limiting intracellular metabolites in yeast growing under diverse nutrient limitations. Mol Biol Cell 2010;21:198–211.

Brown MR, Kornberg A. The long and short of it – polyphosphate, PPK and bacterial survival. Trends Biochem Sci 2008;33:284–90.

Burton A, Hu X, Saiardi A. Are inositol pyrophosphates signalling molecules? J Cell Physiol 2009;220:8–15.

Caen J, Wu Q. Hageman factor, platelets and polyphosphates: early history and recent connection. J Thromb Haemost 2010;8:1670–4.

Caffrey JJ, Safrany ST, Yang X, Shears SB. Discovery of molecular and catalytic diversity among human diphosphoinositol polyphosphate phosphohydrolases. An expanding Nudt family. J Biol Chem 2000;275:12730–6.

A Mitochondrial RNAi Screen Defines Cellular Bioenergetic Determinants and Identifies an Adenylate Kinase as a Key Regulator of ATP Levels

Nathan J. Lanning,1 Brendan D. Looyenga,1,2 Audra L. Kauffman,1 Natalie M. Niemi,1 Jessica Sudderth,3

Ralph J. DeBerardinis,3 and Jeffrey P. MacKeigan1,*

Cell Reports   http://dx.doi.org/10.1016/j.celrep.2014.03.065

Altered cellular bioenergetics and mitochondrial function are major features of several diseases, including cancer, diabetes, and neurodegenerative disorders. Given this important link to human health, we sought to define proteins within mitochondria that are critical for maintaining homeostatic ATP levels.

We screened an RNAi library targeting >1,000 nuclear-encoded genes whose protein products localize to the mitochondria in multiple metabolic conditions in order to examine their effects on cellular ATP levels. We identified a mechanism by which electron transport chain (ETC) perturbation under glycolytic conditions increased ATP production through enhanced glycolytic flux, thereby highlighting the cellular potential for metabolic plasticity.

Additionally, we identified a mitochondrial adenylate kinase (AK4) that regulates cellular ATP levels and AMPK signaling and whose expression significantly correlates with glioma patient survival. This study maps the bioenergetic landscape of >1,000 mitochondrial proteins in the context of varied metabolic substrates and begins to link key metabolic genes with clinical outcome.

Comments to be further addressed by JES Roselino

I will add some observations or at least one single observation.
Just at the beginning, when phosphorylation of proteins is presented, I assume you must mention that some proteins are activated by phosphorylation. This is fundamental in order to present self –organization reflex upon fast regulatory mechanisms. Even from an historical point of view. The first observation arrived from a sample due to be studied on the following day of glycogen synthetase. It was unintended left overnight out of the refrigerator. The result was it has changed from active form of the previous day to a non-active form. The story could have being finished here, if the researcher did not decide to spent this day increasing substrate levels (it could be a simple case of denaturation of proteins that changes its conformation despite the same order of amino acids). He kept on trying and found restoration of maximal activity. This assay was repeated with glycogen phosphorylase and the result was the opposite it increases its activity. This lead to the discovery of cAMP activated protein kinase and the assembly of a very complex system in the glycogen granule that is not a simple carbohydrate polymer. Instead it has several proteins assembled and preserves the capacity to receive from a single event (rise in cAMP) two opposing signals with maximal efficiency, stops glycogen synthesis, as long as levels of glucose 6 phosphate are low and increases glycogen phosphorylation as long as AMP levels are high).
I did everything I was able to do by the end of 1970 in order to repeat this assays with PK I, PKII and PKIII of M. Rouxii and Sutherland route to cAMP failed in this case. I ask Leloir to suggest to my chief (SP) the idea of AA, AB, BB subunits as was observed in lactic dehydrogenase (tetramer) indicating this as his idea. The reason was my “chief”(SP) more than once, have said it to me: “Leave these great ideas for the Houssay, Leloir etc…We must do our career with small things.” However, as she also have a faulty ability for recollection she also uses to arrive some time later, with the very same idea but in that case, as her idea.
Leloir, said to me: I will not offer your interpretation to her as mine. I think it is not phosphorylation, however I think it is glycosylation that explains the changes in the isoenzymes with the same molecular weight preserved. This dialogue explains why during “What is life” reading with him he asked me if from biochemist in exile, to biochemist I talked everything to him. Since I have considered that Schrödinger did not have confronted Darlington & Haldane for being in exile. Also, may explain why Leloir could have answered a bad telephone call from P. Boyer, Editor of The Enzymes in a way that suggest the the pattern could be of covalent changes over a protein. Our FEBS and Eur J. Biochemistry papers on pyruvate kinase of M. Rouxii is wrongly quoted in this way on his review about pyruvate kinase of that year(1971).

Another aspect I think you must call attention, in my opinion, is the following, show in detail with different colors what carbons belongs to CoA a huge molecule, in comparison with the single two carbons of acetate that will produce the enormous jump in energy yield in comparison with anaerobic glycolysis. The idea is how much must have being spent in DNA sequences to build that molecule in order to use only two atoms of carbon. Very limited aspects of biology could be explained in this way. In case we follow an alternative way of thinking, it becomes clearer that proteins were made more stable by interaction with other molecules (great and small). Afterwards, it rather easy to understand how the stability of protein-RNA complexes where transmitted to RNA (vibrational +solvational reactivity stability pair of conformational energy). Latter, millions of years, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.

Yours,

JES Roselino

Read Full Post »

Signaling transduction tutorial

Larry H. Bernstein, MD, FCAP, Reporter and Curator
Leaders in Pharmaceutical Intelligence

http://pharmaceuticalintelligence.com/8-10-2014/Signaling transduction tutorial

This portion of the discussion is a series of articles on signaling and signaling pathways. Many of the protein-protein interactions or protein-membrane interactions and associated regulatory features have been referred to previously, but the focus of the discussion or points made were different.  I considered placing this after the discussion of proteins and how they play out their essential role, but this is quite a suitable place for a progression to what follows.  This is introduced by material taken from Wikipedia, which will be followed by a series of mechanisms and examples from the current literature, which give insight into the developments in cell metabolism, with the later goal of separating views introduced by molecular biology and genomics from functional cellular dynamics that are not dependent on the classic view.  The work is vast, and this discussion does not attempt to cover it in great depth.  It is the first in a series.  This discussion, in particular is a tutorial on signaling transduction that was already available, and relevant.  One may note that all of the slides used herein were also used in the previous blog, but in a different construction.  I shall tweak the contents, as I find helpful.

  1. Signaling and signaling pathways
  2. Signaling transduction tutorial.
  3. Carbohydrate metabolism
  4. Lipid metabolism
  5. Protein synthesis and degradation
  6. Subcellular structure
  7. Impairments in pathological states: endocrine disorders; stress hypermetabolism; cancer.

 

Signal Transduction Tutorial

The goal of this tutorial is for you to gain an understanding of how cell signaling occurs in a cell.  Upon completion of the tutorial,

  • you will have a basic understanding signal transduction and
  • the role of phosphorylation in signal transduction.

You will also have detailed knowledge of

  • the role of Tyrosine kinases and
  • G protein-coupled receptors in cell signaling.
  1. Description of Signal Transduction

As living organisms

  • we are constantly receiving and interpreting signals from our environment.

These signals can come

  • in the form of light, heat, odors, touch or sound.

The cells of our bodies are also

  • constantly receiving signals from other cells.

These signals are important to

  • keep cells alive and functioning as well as
  • to stimulate important events such as
  • cell division and differentiation.

Signals are most often chemicals that can be found

  • in the extracellular fluid around cells.

These chemicals can come

  • from distant locations in the body (endocrine signaling by hormones), from
  • nearby cells (paracrine signaling) or can even
  • be secreted by the same cell (autocrine signaling).
intercellular signaling

intercellular signaling

http://www.hartnell.edu/tutorials/biology/images/intercellularsignaling.jpg

Signaling molecules may trigger any number of cellular responses, including

  • changing the metabolism of the cell receiving the signal or
  • result in a change in gene expression (transcription) within the nucleus of the cell or both.

Overview of Cell Signaling

Cell signaling can be divided into 3 stages.

  1. Reception: A cell detects a signaling molecule from the outside of the cell. A signal is detected when the chemical signal (also known as a ligand) binds to a receptor protein on the surface of the cell or inside the cell.
  2. Transduction: When the signaling molecule binds the receptor it changes the receptor protein in some way. This change initiates the process of transduction. Signal transduction is usually a pathway of several steps. Each relay molecule in the signal transduction pathway changes the next molecule in the pathway.
  3. Response: Finally, the signal triggers a specific cellular response.
signal transduction_simple

signal transduction_simple

http://www.hartnell.edu/tutorials/biology/images/signaltransduction_simple.jpg

Reception

Signal Transduction - ligand binds to surface receptor

Signal Transduction – ligand binds to surface receptor

 

 

Membrane receptors function by binding the signal molecule (ligand) and causing the production of a second signal (also known as a second messenger) that then causes a cellular response. These types of receptors transmit information from the extracellular environment to the inside of the cell

  • by changing shape or
conformational-rearrangements

conformational-rearrangements

Enzyme_Model  allosterism

Enzyme_Model allosterism

  • by joining with another protein
  • once a specific ligand binds to it.

Examples of membrane receptors include

  • G Protein-Coupled Receptors and
membrane_receptor_g protein

membrane_receptor_g protein

 

 

 

 

  • Receptor Tyrosine Kinases.
activation of receptor Tyrosine Kinase

activation of receptor Tyrosine Kinase

http://www.hartnell.edu/tutorials/biology/images/membrane_receptor_tk.jpg

Intracellular receptors are found inside the cell, either in the cytopolasm or in the nucleus of the target cell (the cell receiving the signal).

Chemical messengers that are hydrophobic or very small (steroid hormones for example) can

  • pass through the plasma membrane without assistance and
  • bind these intracellular receptors.

Once bound and activated by the signal molecule,

  • the activated receptor can initiate a cellular response, such as a
  • change in gene expression.

Note that this is the first time that change in gene expression is stated.  Is the change in gene expression implication of a change in the genetic information – such as – mutation?  That does not have to be the case in the normal homeostatic case.  This might only be

  • a change in the rate of a transcription or a suppression of expression through RNA.
intracellular_receptor_steroid

intracellular_receptor_steroid

http://www.hartnell.edu/tutorials/biology/images/intracellular_receptor_steroid.jpg

Transduction

Since signaling systems need to be

  • responsive to small concentrations of chemical signals and act quickly,
  • cells often use a multi-step pathway that transmits the signal quickly,
  • while amplifying the signal to numerous molecules at each step.
Signal transduction cascades amplify the signal output

Signal transduction cascades amplify the signal output

Steps in the signal transduction pathway often involve

  • the addition or removal of phosphate groups which results in the activation of proteins.
  • Enzymes that transfer phosphate groups from ATP to a protein are called protein kinases.

Many of the relay molecules in a signal transduction pathway are protein kinases and

  • often act on other protein kinases in the pathway. Often
  • this creates a phosphorylation cascade, where
  • one enzyme phosphorylates another, which then phosphorylates another protein, causing a chain reaction.
phosphorylation-cascade

phosphorylation-cascade

Also important to the phosphorylation cascade are

  • a group of proteins known as protein phosphatases.

Protein phosphatases are enzymes that can rapidly remove phosphate groups from proteins (dephosphorylation) and thus inactivate protein kinases. Protein phosphatases are

  • the “off switch” in the signal transduction pathway.

Phosphorylation Dephosphorylation

 

Turning the signal transduction pathway off when the signal is no longer present is important

  • to ensure that the cellular response is regulated appropriately.

Dephosphorylation also makes protein kinases

  • available for reuse and
  • enables the cell to respond again when another signal is received.

Kinases are not the only tools used by cells in signal transduction. Small, nonprotein, water-soluble molecules or ions called second messengers (the ligand that binds the receptor is the first messenger) can also

  • relay signals received by receptors on the cell surface
  • to target molecules in the cytoplasm or the nucleus.
membrane protein receptor binds with hormone

membrane protein receptor binds with hormone

 

insulin receptor and and insulin receptor signaling pathway (IRS)

insulin receptor and and insulin receptor signaling pathway (IRS)

 

 

binding-proteins-and-bioavailable-25-hydroxyvitamin-d

binding-proteins-and-bioavailable-25-hydroxyvitamin-d

 

 

Examples of second messengers include cyclic AMP (cAMP) and calcium ions.

membrane_receptor_g protein

membrane_receptor_g protein

http://www.hartnell.edu/tutorials/biology/images/membrane_receptor_gprotein.jpg

Response

Cell signaling ultimately leads to the regulation of one or more cellular activities. Regulation of gene expression (turning transcription of specific genes on or off) is a common outcome of cell signaling. A signaling pathway may also

  • regulate the activity of a protein, for example
ion-transporters-and-channels-in-mammalian-choroidal-epithelium

ion-transporters-and-channels-in-mammalian-choroidal-epithelium

Ca(2+) and contraction

Ca(2+) and contraction

 

transepithelial-electrogenic-ion-transport

transepithelial-electrogenic-ion-transport

 

calcium release flux

calcium release flux

 

coupled receptors

 

 

 

 

  1. opening or closing an ion channel in the plasma membrane or
  2. promoting a change in cell metabolism such as catalyzing the breakdown of glycogen.

Signaling pathways can also lead to important cellular events such as

  • cell division or apoptosis (programmed cell death).
ubiquitylation-is-a-multistep-reaction.

ubiquitylation-is-a-multistep-reaction.

 

Involvement of VSMCs apoptosis in fibrous plaque rupture.

Involvement of VSMCs apoptosis in fibrous plaque rupture.

 

 

 

 

 

 

 

 

 

 

 

 

G- Protein-Coupled Receptor

 

membrane_receptor_g protein

membrane_receptor_g protein

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

Signal Transduction Tutorial bDr. Katherine Harris is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

Funded by the U.S. Department of Education, College Cost Reduction and Access (CCRAA) grant award # P031C080096.

http://creativecommons.org/licenses/by-nc-sa/3.0/

  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

Adapt — remix, transform, and build upon the material

hormone + receptor signaling

http://home.earthlink.net/~dayvdanls/SignalTrans.gif

Signal-Transduction-Pathway

http://pi-silico.hkbu.edu.hk/wp-content/uploads/2012/12/Signal-Transduction-Pathway.png

http://upload.wikimedia.org/wikipedia/commons/a/a4/1Signal_Transduction_Pathways_Model.jpg

Akt mTOR pathway

Akt mTOR pathway

http://cc.scu.edu.cn/G2S/eWebEditor/uploadfile/20120810155043970.jpg

Quia – 9AP Chapter 11 – Cell Commun

http://www.quia.com/files/quia/users/lmcgee/membranetransport/cell_communication/reception_transduction_resp.gif

http://cc.scu.edu.cn/G2S/eWebEditor/uploadfile/20120810155043970.jpg

HER2 in Breast Cancer–What Does it Mean?

http://img.medscape.com/fullsize/migrated/editorial/clinupdates/2000/681/tu02.fig2.jpg

Protease signalling: the cutting edge

http://emboj.embopress.org/content/embojnl/31/7/1630/F5.large.jpg

Quia – 9AP Chapter 11 – Cell Commun

http://www.quia.com/files/quia/users/lmcgee/membranetransport/cell_communication/phosphorylation-cascade.gif

 

Signal Transduction in Autism

http://www.mun.ca/biology/desmid/brian/BIOL2060/BIOL2060-14/1403.jpg

The multiple protein-dependent steps in signal transduction

http://www.nature.com/nrm/journal/v1/n2/images/nrm1100_145a_i2.gif

CONVERSING AT THE CELLULAR LEVEL: AN INTRODUCTION TO SIGNAL …

  1. scq.ubc.ca

 

http://www.scq.ubc.ca/wp-content/uploads/2006/07/transduction.gif

 

Biology 1710 > Davis > Flashcards > exam 1 | StudyBlue

  1. studyblue.com

 

http://classconnection.s3.amazonaws.com/602/flashcards/1005602/png/bio101332955375817.png

 

Read Full Post »

Signaling and Signaling Pathways

Curator: Larry H. Bernstein, MD, FCAP

 

http://pharmaceuticalintelligence.com/8-9-2014/Signaling and Signaling Pathways

This portion of the discussion is a series of articles on signaling and signaling pathways. Many of the protein-protein interactions or protein-membrane interactions and associated regulatory features have been referred to previously, but the focus of the discussion or points made were different.  I considered placing this after the discussion of proteins and how they play out their essential role, but this is quite a suitable place for a progression to what follows.  This is introduced by material taken from Wikipedia, which will be followed by a series of mechanisms and examples from the current literature, which give insight into the developments in cell metabolism, with the later goal of separating views introduced by molecular biology and genomics from functional cellular dynamics that are not dependent on the classic view.  The work is vast, and this discussion does not attempt to cover it in great depth.  It is the first in a series.

  1. Signaling and signaling pathways
  2. Signaling transduction tutorial.
  3. Carbohydrate metabolism
  4. Lipid metabolism
  5. Protein synthesis and degradation
  6. Subcellular structure
  7. Impairments in pathological states: endocrine disorders; stress hypermetabolism; cancer.

Signal transduction

(From Wikipedia, the free encyclopedia)
http://en.wikipedia.org/wiki/File:Signal_transduction_publications_graph.jpeg

 

Signal_transduction_pathways.svg

Signal_transduction_pathways.svg

 

Signal transduction occurs when an extracellular signaling[1] molecule activates a specific receptor located on the cell surface or inside the cell. In turn, this receptor triggers a biochemical chain of events inside the cell, creating a response.[2] Depending on the cell, the response alters the cell’s metabolism, shape, gene expression, or ability to divide.[3] The signal can be amplified at any step. Thus, one signaling molecule can cause many responses.[4]

In 1970, Martin Rodbell examined the effects of glucagon on a rat’s liver cell membrane receptor. He noted that guanosine triphosphate disassociated glucagon from this receptor and stimulated the G-protein, which strongly influenced the cell’s metabolism. Thus, he deduced that the G-protein is a transducer that accepts glucagon molecules and affects the cell.[5] For this, he shared the 1994 Nobel Prize in Physiology or Medicine with Alfred G. Gilman.

Signal_transduction_publications_graph

Signal_transduction_publications_graph

The earliest MEDLINE entry for “signal transduction” dates from 1972.[6] Some early articles used the terms signal transmission and sensory transduction.[7][8] In 2007, a total of 48,377 scientific papers—including 11,211 e review papers—were published on the subject. The term first appeared in a paper’s title in 1979.[9][10] Widespread use of the term has been traced to a 1980 review article by Rodbell:[5][11] Research papers focusing on signal transduction first appeared in large numbers in the late 1980s and early 1990s.[12]

Notch-mediated juxtacrine signal between adjacent cells.

Notch-mediated juxtacrine signal between adjacent cells.

Signal transduction involves the binding of extracellular signaling molecules and ligands to cell-surface receptors that trigger events inside the cell. The combination of messenger with receptor causes a change in the conformation of the receptor, known as receptor activation. This activation is always the initial step (the cause) leading to the cell’s ultimate responses (effect) to the messenger. Despite the myriad of these ultimate responses, they are all directly due to changes in particular cell proteins. Intracellular signaling cascades can be started through cell-substratum interactions; examples are the integrin that binds ligands in the extracellular matrix and steroids.[13] Most steroid hormones have receptors within the cytoplasm and act by stimulating the binding of their receptors to the promoter region of steroid-responsive genes.[14] Examples of signaling molecules include the hormone melatonin,[15] the neurotransmitter acetylcholine[16] and the cytokine interferon γ.[17]

Signal transduction cascades amplify the signal output

Signal transduction cascades amplify the signal output

Various environmental stimuli exist that initiate signal transmission processes in multicellular organisms; examples include photons hitting cells in the retina of the eye,[20] and odorants binding to odorant receptors in the nasal epithelium.[21] Certain microbial molecules, such as viral nucleotides and protein antigens, can elicit an immune system response against invading pathogens mediated by signal transduction processes. This may occur independent of signal transduction stimulation by other molecules, as is the case for the toll-like receptor. It may occur with help from stimulatory molecules located at the cell surface of other cells, as with T-cell receptor signaling. Unicellular organisms may respond to environmental stimuli through the activation of signal transduction pathways. For example, slime molds secrete cyclic adenosine monophosphate upon starvation, stimulating individual cells in the immediate environment to aggregate,[22] and yeast cells use mating factors to determine the mating types of other cells and to participate in sexual reproduction.[23] Receptors can be roughly divided into two major classes: intracellular receptors and extracellular receptors.

Extracellular

Extracellular receptors are integral transmembrane proteins and make up most receptors. They span the plasma membrane of the cell, with one part of the receptor on the outside of the cell and the other on the inside. Signal transduction occurs as a result of a ligand binding to the outside; the molecule does not pass through the membrane. This binding stimulates a series of events inside the cell; different types of receptor stimulate different responses and receptors typically respond to only the binding of a specific ligand. Upon binding, the ligand induces a change in the conformation of the inside part of the receptor.[24] These result in either the activation of an enzyme in the receptor or the exposure of a binding site for other intracellular signaling proteins within the cell, eventually propagating the signal through the cytoplasm.

In eukaryotic cells, most intracellular proteins activated by a ligand/receptor interaction possess an enzymatic activity; examples include tyrosine kinase and phosphatases. Some of them create second messengers such as cyclic AMP and IP3, the latter controlling the release of intracellular calcium stores into the cytoplasm. Other activated proteins interact with adaptor proteins that facilitate signalling protein interactions and coordination of signalling complexes necessary to respond to a particular stimulus. Enzymes and adaptor proteins are both responsive to various second messenger molecules.

Many adaptor proteins and enzymes activated as part of signal transduction possess specialized protein domains that bind to specific secondary messenger molecules. For example, calcium ions bind to the EF hand domains of calmodulin, allowing it to bind and activate calmodulin-dependent kinase. PIP3 and other phosphoinositides do the same thing to the Pleckstrin homology domains of proteins such as the kinase protein AKT.

G protein-coupled

G protein-coupled receptors (GPCRs) are a family of integral transmembrane proteins that possess seven transmembrane domains and are linked to a heterotrimeric G protein. Many receptors are in this family, including adrenergic receptors and chemokine receptors.

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

Signal transduction by a GPCR begins with an inactive G protein coupled to the receptor; it exists as a heterotrimer consisting of Gα, Gβ, and Gγ.[25] Once the GPCR recognizes a ligand, the conformation of the receptor changes to activate the G protein, causing Gα to bind a molecule of GTP and dissociate from the other two G-protein subunits. The dissociation exposes sites on the subunits that can interact with other molecules.[26] The activated G protein subunits detach from the receptor and initiate signaling from many downstream effector proteins such as phospholipases and ion channels, the latter permitting the release of second messenger molecules.[27] The total strength of signal amplification by a GPCR is determined by the lifetimes of the ligand-receptor complex and receptor-effector protein complex and the deactivation time of the activated receptor and effectors through intrinsic enzymatic activity.

A study was conducted where a point mutation was inserted into the gene encoding the chemokine receptor CXCR2; mutated cells underwent a malignant transformation due to the expression of CXCR2 in an active conformation despite the absence of chemokine-binding. This meant that chemokine receptors can contribute to cancer development.[28]

Tyrosine and histidine kinase

Receptor tyrosine kinases (RTKs) are transmembrane proteins with an intracellular kinase domain and an extracellular domain that binds ligands; examples include growth factor receptors such as the insulin receptor.[29] To perform signal transduction, RTKs need to form dimers in the plasma membrane;[30] the dimer is stabilized by ligands binding to the receptor. The interaction between the cytoplasmic domains stimulates the autophosphorylation of tyrosines within the domains of the RTKs, causing conformational changes. Subsequent to this, the receptors’ kinase domains are activated, initiating phosphorylation signaling cascades of downstream cytoplasmic molecules that facilitate various cellular processes such as cell differentiation and metabolism.[29]

As is the case with GPCRs, proteins that bind GTP play a major role in signal transduction from the activated RTK into the cell. In this case, the G proteins are members of the Ras, Rho, and Raf families, referred to collectively as small G proteins. They act as molecular switches usually tethered to membranes by isoprenyl groups linked to their carboxyl ends. Upon activation, they assign proteins to specific membrane subdomains where they participate in signaling. Activated RTKs in turn activate small G proteins that activate guanine nucleotide exchange factors such as SOS1. Once activated, these exchange factors can activate more small G proteins, thus amplifying the receptor’s initial signal. The mutation of certain RTK genes, as with that of GPCRs, can result in the expression of receptors that exist in a constitutively activate state; such mutated genes may act as oncogenes.[31]

Histidine-specific protein kinases are structurally distinct from other protein kinases and are found in prokaryotes, fungi, and plants as part of a two-component signal transduction mechanism: a phosphate group from ATP is first added to a histidine residue within the kinase, then transferred to an aspartate residue on a receiver domain on a different protein or the kinase itself, thus activating the aspartate residue.[32]

Integrin

integrin-mediated signal transduction

integrin-mediated signal transduction

An overview of integrin-mediated signal transduction, adapted from Hehlgens et al. (2007).[33]

Integrins are produced by a wide variety of cells; they play a role in cell attachment to other cells and the extracellular matrix and in the transduction of signals from extracellular matrix components such as fibronectin and collagen. Ligand binding to the extracellular domain of integrins changes the protein’s conformation, clustering it at the cell membrane to initiate signal transduction. Integrins lack kinase activity; hence, integrin-mediated signal transduction is achieved through a variety of intracellular protein kinases and adaptor molecules, the main coordinator being integrin-linked kinase.[33] As shown in the picture to the right, cooperative integrin-RTK signalling determines the timing of cellular survival, apoptosis, proliferation, and differentiation.

Important differences exist between integrin-signalling in circulating blood cells and non-circulating cells such as epithelial cells; integrins of circulating cells are normally inactive. For example, cell membrane integrins on circulating leukocytes are maintained in an inactive state to avoid epithelial cell attachment; they are activated only in response to stimuli such as those received at the site of an inflammatory response. In a similar manner, integrins at the cell membrane of circulating platelets are normally kept inactive to avoid thrombosis. Epithelial cells (which are non-circulating) normally have active integrins at their cell membrane, helping maintain their stable adhesion to underlying stromal cells that provide signals to maintain normal functioning.[34]

Toll gate

When activated, toll-like receptors (TLRs) take adapter molecules within the cytoplasm of cells in order to propagate a signal. Four adaptor molecules are known to be involved in signaling, which are Myd88, TIRAP, TRIF, and TRAM.[35][36][37] These adapters activate other intracellular molecules such as IRAK1, IRAK4, TBK1[disambiguation needed], and IKKi that amplify the signal, eventually leading to the induction or suppression of genes that cause certain responses. Thousands of genes are activated by TLR signaling, implying that this method constitutes an important gateway for gene modulation.

Ligand-gated ion channel

A ligand-gated ion channel, upon binding with a ligand, changes conformation to open a channel in the cell membrane through which ions relaying signals can pass. An example of this mechanism is found in the receiving cell of a neural synapse. The influx of ions that occurs in response to the opening of these channels induces action potentials, such as those that travel along nerves, by depolarizing the membrane of post-synaptic cells, resulting in the opening of voltage-gated ion channels.

An example of an ion allowed into the cell during a ligand-gated ion channel opening is Ca2+; it acts as a second messenger initiating signal transduction cascades and altering the physiology of the responding cell. This results in amplification of the synapse response between synaptic cells by remodelling the dendritic spines involved in the synapse.

Ion transporters and channels in mammalian choroidal epithelium

Ion transporters and channels in mammalian choroidal epithelium

 

 

Intracellular

Extracellular receptors are integral transmembrane proteins and make up most receptors. They span the plasma membrane of the cell, with one part of the receptor on the outside of the cell and the other on the inside. Signal transduction occurs as a result of a ligand binding to the outside; the molecule does not pass through the membrane. This binding stimulates a series of events inside the cell; different types of receptor stimulate different responses and receptors typically respond to only the binding of a specific ligand. Upon binding, the ligand induces a change in the conformation of the inside part of the receptor.[24] These result in either the activation of an enzyme in the receptor or the exposure of a binding site for other intracellular signaling proteins within the cell, eventually propagating the signal through the cytoplasm.

Understanding these receptors and identifying their ligands and the resulting signal transduction pathways represent a major conceptual advance

Understanding these receptors and identifying their ligands and the resulting signal transduction pathways represent a major conceptual advance

 

intercellular signaling

intercellular signaling

 

conformational-rearrangements

conformational-rearrangements

 

 

membrane protein receptor binds with hormone

membrane protein receptor binds with hormone

 

 

 

The multiple protein-dependent steps in signal transduction

The multiple protein-dependent steps in signal transduction

In eukaryotic cells, most intracellular proteins activated by a ligand/receptor interaction possess an enzymatic activity; examples include tyrosine kinase and phosphatases. Some of them create second messengers such as cyclic AMP and IP3, the latter controlling the release of intracellular calcium stores into the cytoplasm. Other activated proteins interact with adaptor proteins that facilitate signalling protein interactions and coordination of signalling complexes necessary to respond to a particular stimulus. Enzymes and adaptor proteins are both responsive to various second messenger molecules.

Ca++ exchange

Ca++ exchange

Many adaptor proteins and enzymes activated as part of signal transduction possess specialized protein domains that bind to specific secondary messenger molecules. For example, calcium ions bind to the EF hand domains of calmodulin, allowing it to bind and activate calmodulin-dependent kinase. PIP3 and other phosphoinositides do the same thing to the Pleckstrin homology domains of proteins such as the kinase protein AKT.

G protein-coupled

G protein-coupled receptors (GPCRs) are a family of integral transmembrane proteins that possess seven transmembrane domains and are linked to a heterotrimeric G protein. Many receptors are in this family, including adrenergic receptors and chemokine receptors.

membrane_receptor_g protein

membrane_receptor_g protein

 

intracellular_receptor_steroid

intracellular_receptor_steroid

Signal transduction by a GPCR begins with an inactive G protein coupled to the receptor; it exists as a heterotrimer consisting of Gα, Gβ, and Gγ.[25] Once the GPCR recognizes a ligand, the conformation of the receptor changes to activate the G protein, causing Gα to bind a molecule of GTP and dissociate from the other two G-protein subunits. The dissociation exposes sites on the subunits that can interact with other molecules.[26] The activated G protein subunits detach from the receptor and initiate signaling from many downstream effector proteins such as phospholipases and ion channels, the latter permitting the release of second messenger molecules.[27] The total strength of signal amplification by a GPCR is determined by the lifetimes of the ligand-receptor complex and receptor-effector protein complex and the deactivation time of the activated receptor and effectors through intrinsic enzymatic activity.

A study was conducted where a point mutation was inserted into the gene encoding the chemokine receptor CXCR2; mutated cells underwent a malignant transformation due to the expression of CXCR2 in an active conformation despite the absence of chemokine-binding. This meant that chemokine receptors can contribute to cancer development.[28]

Tyrosine and histidine kinase

Receptor tyrosine kinases (RTKs) are transmembrane proteins with an intracellular kinase domain and an extracellular domain that binds ligands; examples include growth factor receptors such as the insulin receptor.[29] To perform signal transduction, RTKs need to form dimers in the plasma membrane;[30] the dimer is stabilized by ligands binding to the receptor. The interaction between the cytoplasmic domains stimulates the autophosphorylation of tyrosines within the domains of the RTKs, causing conformational changes. Subsequent to this, the receptors’ kinase domains are activated, initiating phosphorylation signaling cascades of downstream cytoplasmic molecules that facilitate various cellular processes such as cell differentiation and metabolism.[29]

insulin-receptor-and-and-insulin-receptor-signaling-pathway-irs

insulin-receptor-and-and-insulin-receptor-signaling-pathway-irs

 

 

 

 

 

 

 

 

receptors-regulators

receptors-regulators

phosphorylation-cascade

phosphorylation-cascade

 

 

 

As is the case with GPCRs, proteins that bind GTP play a major role in signal transduction from the activated RTK into the cell. In this case, the G proteins are members of the Ras, Rho, and Raf families, referred to collectively as small G proteins. They act as molecular switches usually tethered to membranes by isoprenyl groups linked to their carboxyl ends. Upon activation, they assign proteins to specific membrane subdomains where they participate in signaling. Activated RTKs in turn activate small G proteins that activate guanine nucleotide exchange factors such as SOS1. Once activated, these exchange factors can activate more small G proteins, thus amplifying the receptor’s initial signal. The mutation of certain RTK genes, as with that of GPCRs, can result in the expression of receptors that exist in a constitutively activate state; such mutated genes may act as oncogenes.[31]

Histidine-specific protein kinases are structurally distinct from other protein kinases and are found in prokaryotes, fungi, and plants as part of a two-component signal transduction mechanism: a phosphate group from ATP is first added to a histidine residue within the kinase, then transferred to an aspartate residue on a receiver domain on a different protein or the kinase itself, thus activating the aspartate residue.[32]

 

Integrin

integrin-mediated signal transduction

integrin-mediated signal transduction

An overview of integrin-mediated signal transduction, adapted from Hehlgens et al. (2007).[33]

Integrins are produced by a wide variety of cells; they play a role in cell attachment to other cells and the extracellular matrix and in the transduction of signals from extracellular matrix components such as fibronectin and collagen. Ligand binding to the extracellular domain of integrins changes the protein’s conformation, clustering it at the cell membrane to initiate signal transduction. Integrins lack kinase activity; hence, integrin-mediated signal transduction is achieved through a variety of intracellular protein kinases and adaptor molecules, the main coordinator being integrin-linked kinase.[33] As shown in the picture to the right, cooperative integrin-RTK signalling determines the timing of cellular survival, apoptosis, proliferation, and differentiation.

Platelet signaling pathways

Platelet signaling pathways

 

 

 

 

 

 

Protein ubiquitylation

Protein ubiquitylation

ubiquitylation-is-a-multistep-reaction.

ubiquitylation-is-a-multistep-reaction.

 

 

Important differences exist between integrin-signaling in circulating blood cells and non-circulating cells such as epithelial cells; integrins of circulating cells are normally inactive. For example, cell membrane integrins on circulating leukocytes are maintained in an inactive state to avoid epithelial cell attachment; they are activated only in response to stimuli such as those received at the site of an inflammatory response. In a similar manner, integrins at the cell membrane of circulating platelets are normally kept inactive to avoid thrombosis. Epithelial cells (which are non-circulating) normally have active integrins at their cell membrane, helping maintain their stable adhesion to underlying stromal cells that provide signals to maintain normal functioning.[34]

Toll gate

When activated, toll-like receptors (TLRs) take adapter molecules within the cytoplasm of cells in order to propagate a signal. Four adaptor molecules are known to be involved in signaling, which are Myd88, TIRAP, TRIF, and TRAM.[35][36][37] These adapters activate other intracellular molecules such as IRAK1, IRAK4, TBK1[disambiguation needed], and IKKi that amplify the signal, eventually leading to the induction or suppression of genes that cause certain responses. Thousands of genes are activated by TLR signaling, implying that this method constitutes an important gateway for gene modulation.

 

SignalTrans

SignalTrans

 

 

Signal-Transduction-Pathway

 

 

 

 

Ligand-gated ion channel

A ligand-gated ion channel, upon binding with a ligand, changes conformation to open a channel in the cell membrane through which ions relaying signals can pass. An example of this mechanism is found in the receiving cell of a neural synapse. The influx of ions that occurs in response to the opening of these channels induces action potentials, such as those that travel along nerves, by depolarizing the membrane of post-synaptic cells, resulting in the opening of voltage-gated ion channels.

An example of an ion allowed into the cell during a ligand-gated ion channel opening is Ca2+; it acts as a second messenger initiating signal transduction cascades and altering the physiology of the responding cell. This results in amplification of the synapse response between synaptic cells by remodelling the dendritic spines involved in the synapse.

Ion transporters and channels in mammalian choroidal epithelium

Ion transporters and channels in mammalian choroidal epithelium

Intracellular

Intracellular receptors, such as nuclear receptors and cytoplasmic receptors, are soluble proteins localized within their respective areas. The typical ligands for nuclear receptors are lipophilic hormones like the steroid hormones testosterone and progesterone and derivatives of vitamins A and D. To initiate signal transduction, the ligand must pass through the plasma membrane by passive diffusion. On binding with the receptor, the ligands pass through the nuclear membrane into the nucleus, enabling gene transcription and protein production.

 

 

Signal Transduction

Signal Transduction

 

Activated nuclear receptors attach to the DNA at receptor-specific hormone-responsive element (HRE) sequences, located in the promoter region of the genes activated by the hormone-receptor complex. Due to their enabling gene transcription, they are alternatively called inductors of gene expression. All hormones that act by regulation of gene expression have two consequences in their mechanism of action; their effects are produced after a characteristically long period of time and their effects persist for another long period of time, even after their concentration has been reduced to zero, due to a relatively slow turnover of most enzymes and proteins that would either deactivate or terminate ligand binding onto the receptor.

Signal transduction via these receptors involves little proteins, but the details of gene regulation by this method are not well-understood. Nucleic receptors have DNA-binding domains containing zinc fingers and a ligand-binding domain; the zinc fingers stabilize DNA binding by holding its phosphate backbone. DNA sequences that match the receptor are usually hexameric repeats of any kind; the sequences are similar but their orientation and distance differentiate them. The ligand-binding domain is additionally responsible for dimerization of nucleic receptors prior to binding and providing structures for transactivation used for communication with the translational apparatus.

 

signal-transduction-in-protease-signaling-

signal-transduction-in-protease-signaling-

 

protein changes in biological mechanisms

protein changes in biological mechanisms

 

Steroid receptors are a subclass of nuclear receptors located primarily within the cytosol; in the absence of steroids, they cling together in an aporeceptor complex containing chaperone or heatshock proteins (HSPs). The HSPs are necessary to activate the receptor by assisting the protein to fold in a way such that the signal sequence enabling its passage into the nucleus is accessible. Steroid receptors, on the other hand, may be repressive on gene expression when their transactivation domain is hidden; activity can be enhanced by phosphorylation of serine residues at their N-terminal as a result of another signal transduction pathway, a process called crosstalk.

Structure of the N-terminal domain of the yeast Hsp90 chaperone

Structure of the N-terminal domain of the yeast Hsp90 chaperone

Pincer movement of Hsp90 coupled to the ATPase cycle. NTD = N-terminal domain, MD = middle domain, CTD = C-terminal domain.

Pincer movement of Hsp90 coupled to the ATPase cycle. NTD = N-terminal domain, MD = middle domain, CTD = C-terminal domain.

Retinoic acid receptors are another subset of nuclear receptors. They can be activated by an endocrine-synthesized ligand that entered the cell by diffusion, a ligand synthesised from a precursor like retinol brought to the cell through the bloodstream or a completely intracellularly synthesised ligand like prostaglandin. These receptors are located in the nucleus and are not accompanied by HSPs; they repress their gene by binding to their specific DNA sequence when no ligand binds to them, and vice versa.

Certain intracellular receptors of the immune system are cytoplasmic receptors; recently identified NOD-like receptors (NLRs) reside in the cytoplasm of some eukaryotic cells and interact with ligands using a leucine-rich repeat (LRR) motif similar to TLRs. Some of these molecules like NOD2 interact with RIP2 kinase that activates NF-κB signaling, whereas others like NALP3 interact with inflammatory caspases and initiate processing of particular cytokines like interleukin-1β.[38][39]

 

Cell signaling

signaling pathjways map

signaling pathjways map

Cell signalling is part of a complex system of communication that governs basic cellular activities and coordinates cell actions. The ability of cells to perceive and correctly respond to their microenvironment is the basis of development, tissue repair, and immunity as well as normal tissue homeostasis. Errors in cellular information processing are responsible for diseases such as cancer, autoimmunity, and diabetes. By understanding cell signalling, diseases may be treated effectively and, theoretically, artificial tissues may be created.

Traditional work in biology has focused on studying individual parts of cell signaling pathways. Systems biology research helps us to understand the underlying structure of cell signaling networks and how changes in these networks may affect the transmission and flow of information. Such networks are complex systems in their organization and may exhibit a number of emergent properties. Long-range allostery is often a significant component of cell signaling events.[1]

Enzyme_Model allosterism

Enzyme_Model allosterism

Classification

Signaling within, between, and among cells is subdivided into the following classifications:

  • Intracrine signals are produced by the target cell that stay within the target cell.
  • Autocrine signals are produced by the target cell, are secreted, and effect the target cell itself via receptors. Sometimes autocrine cells can target cells close by if they are the same type of cell as the emitting cell. An example of this are immune cells.
  • Juxtacrine signals target adjacent (touching) cells. These signals are transmitted along cell membranes via protein or lipid components integral to the membrane and are capable of affecting either the emitting cell or cells immediately adjacent.
transepithelial-electrogenic-ion-transport

transepithelial-electrogenic-ion-transport

calcium release calmodulin + ER

calcium release calmodulin + ER

 

Ca++ exchange

Ca++ exchange

Paracrine bidirectional cardiac fibroblast-myocyte crosstalk

Paracrine bidirectional cardiac fibroblast-myocyte crosstalk

  • Paracrine signals target cells in the vicinity of the emitting cell. Neurotransmitters represent an example.
  • Endocrine signals target distant cells. Endocrine cells produce hormones that travel through the blood to reach all parts of the body.
Notch-mediated juxtacrine signal between adjacent cells.

Notch-mediated juxtacrine signal between adjacent cells.

 

Notch-mediated juxtacrine signal between adjacent cells.

Some cell–cell communication requires direct cell–cell contact. Some cells can form gap junctions that connect their cytoplasm to the cytoplasm of adjacent cells. In cardiac muscle, gap junctions between adjacent cells allows for action potential propagation from the cardiac pacemaker region of the heart to spread and coordinately cause contraction of the heart.

The notch signaling mechanism is an example of juxtacrine signaling (also known as contact-dependent signaling) in which two adjacent cells must make physical contact in order to communicate. This requirement for direct contact allows for very precise control of cell differentiation during embryonic development. In the worm Caenorhabditis elegans, two cells of the developing gonad each have an equal chance of terminally differentiating or becoming a uterine precursor cell that continues to divide. The choice of which cell continues to divide is controlled by competition of cell surface signals. One cell will happen to produce more of a cell surface protein that activates the Notch receptor on the adjacent cell. This activates a feedback loop or system that reduces Notch expression in the cell that will differentiate and that increases Notch on the surface of the cell that continues as a stem cell.[5]

Many cell signals are carried by molecules that are released by one cell and move to make contact with another cell. Endocrine signals are called hormones. Hormones are produced by endocrine cells and they travel through the blood to reach all parts of the body. Specificity of signaling can be controlled if only some cells can respond to a particular hormone. Paracrine signals such as retinoic acid target only cells in the vicinity of the emitting cell.[6] Neurotransmitters represent another example of a paracrine signal. Some signaling molecules can function as both a hormone and a neurotransmitter. For example, epinephrine and norepinephrine can function as hormones when released from the adrenal gland and are transported to the heart by way of the blood stream. Norepinephrine can also be produced by neurons to function as a neurotransmitter within the brain.[7] Estrogen can be released by the ovary and function as a hormone or act locally via paracrine or autocrine signaling.[8] Active species of oxygen and nitric oxide can also act as cellular messengers. This process is dubbed redox signaling.

Signaling Pathways

Cell Signaling Biology

Michael J. Berridge

Module 2

Cell Signaling Pathways
The nine membrane-bound adenylyl cyclases (AC1–AC9) have a similar domain structure. The single polypeptide has a tandem repeat of six transmembrane domains (TM) with TM1- -TM6 in one repeat and TM7- -TM12 in the other. Each TM cassette is followed by large cytoplasmic domains (C1 and C2), which contain the catalytic regions that convert ATP into cyclic AMP. As shown in the lower panel, the C1 and C2 domains come together to form a heterodimer. The ATP-binding site is located at the interface between these two domains. The soluble AC10 isoform lacks the transmembrane regions, but it retains the C1 and C2 domains that are responsible for catalysis
www.cellsignallingbiology.org  http://www.biochemj.org/csb/002/csb002.pdf

 

Resources:

Elucidate Target-Specific Pathways With a Suite of Cellular Assays

DiscoveRx® offers a comprehensive collection of cell-based pathway indicator assays designed to detect activation or inhibition of complex signal transduction pathways in response to compound treatment. Based on the proven PathHunter® technology, These biosensor cell lines allow you to measure distinct events within a variety of pathways involved in compound toxicity, cholesterol metabolism, antioxidant function, DNA damage and ER stress. In combination with our biosensor cell lines with fast and simple chemiluminescent detection, DiscoveRx Pathway Signaling assays will help you generate cellular pathway selectivity profiles of your compounds without relying on reporter gene assays or complex phenotypic screens. – See more at: http://www.discoverx.com/targets/signaling-pathways?gclid=CPPrxrrli8ACFSdp7AodO2IADQ#sthash.OhK3iKl4.dpuf

  GPCR Targets ,   Kinase Targets ,   Nuclear Receptors ,   Protease Targets ,   Epigenetic Targets ,   Signaling Pathways –  See more at: http://www.discoverx.com/targets#sthash.KjwWEjjx.dpuf

DiscoveRx® offers a comprehensive collection of cell-based pathway indicator assays designed to detect activation or inhibition of complex signal transduction pathways in response to compound treatment. Based on the proven PathHunter® technology, These biosensor cell lines allow you to measure distinct events within a variety of pathways involved in compound toxicity, cholesterol metabolism, antioxidant function, DNA damage and ER stress. – See more at: http://www.discoverx.com/targets/signaling-pathways#sthash.ZTb5UXVO.dpuf

 

 

inhibitors of signal transduction pathway

inhibitors of signal transduction pathway

Inhibitors of MAPK Signaling Pathway

Inhibitors of MAPK Signaling Pathway

 

jak-stat

jak-stat

 

Nrf2 signaling in ARE-mediated coordinated activation of defensive genes

Nrf2 signaling in ARE-mediated coordinated activation of defensive genes

 

Regulation of AMPK

Regulation of AMPK

 

 

metabolic pathways

metabolic pathways

 

On these resource pages you can find signaling pathway diagrams, research overviews, relevant antibody products, publications, and other research resources organized by topic. The pathway diagrams associated with these topics have been assembled by CST scientists and outside experts to provide succinct and current overviews of selected signaling pathways. Please send suggestions for developing new pathways to info@cellsignal.com. Protein nodes in each pathway diagram are linked to specific antibody product information or, optionally, to protein-specific listings in the PhosphoSitePlus® database of post-translational modifications.

http://www.cellsignal.com/common/content/content.jsp?id=science-pathways
http://www.cellsignal.com/common/content/content.jsp?id=pathways-akt-signaling
http://www.cellsignal.com/common/content/content.jsp?id=pathways-mtor-signaling

PI3K / Akt Signaling Overview

 The serine/threonine kinase Akt/PKB exists as three isoforms in mammals. Akt1 has a wide tissue distribution, whereas Akt2 is found predominantly in muscle and fat cells and Akt3 is expressed in testes and brain. Akt regulates multiple biological processes including cell survival, proliferation, growth, and glycogen metabolism. Various growth factors, hormones, and cytokines activate Akt by binding their cognate receptor tyrosine kinase (RTK), cytokine receptor, or GPCR and triggering activation of the lipid kinase PI3K, which generates PIP3 at the plasma membrane. Akt binds PIP3 through its pleckstrin homology (PH) domain, resulting in translocation of Akt to the membrane. Akt is activated through a dual phosphorylation mechanism. PDK1, which is also brought to the membrane through its PH domain, phosphorylates Akt within its activation loop at Thr308. A second phosphorylation at Ser473 within the carboxy terminus is also required for activity and is carried out by the mTOR-rictor complex, mTORC2.

PTEN, a lipid phosphatase that catalyzes the dephosphorylation of PIP3, is a major negative regulator of Akt signaling. Loss of PTEN function has been implicated in many human cancers. Akt activity is also negatively regulated by the phosphatases PP2A and PHLPP, as well as by the chemical modulators wortmannin and LY294002, both of which are inhibitors of PI3K.

Activated Akt phosphorylates a large number of downstream substrates containing the consensus sequence RXRXXS/T. One of its primary functions is to promote cell growth and protein synthesis through regulation of the mTOR signaling pathway. Akt directly phosphorylates and activates mTOR, as well as inhibits the mTOR inhibitor proteins PRAS40 and tuberin (TSC2). Combined, these actions promote cell growth and G1 cell cycle progression through signaling via p70 S6 Kinase and inhibition of 4E-BP1.

Phosphofructokinase mechanism

Phosphofructokinase mechanism

GSK-3 is a primary target of Akt and inhibitory phosphorylation of GSK-3α (Ser21) or GSK-3β (Ser9) has numerous cellular effects such as promoting glycogen metabolism, cell cycle progression, regulation of wnt signaling, and formation of neurofibrillary tangles in Alzheimers disease. Akt promotes cell survival directly by its ability to phosphorylate and inactivate several pro-apoptotic targets, including Bad, Bim, Bax, and the forkhead (FoxO1/3a) transcription factors. Akt also plays an important role in metabolism and insulin signaling. Insulin receptor signaling through Akt promotes Glut4 translocation through activation of AS160 and TBC1D1, resulting in increased glucose uptake. Akt regulates glycolysis through phosphorylation of PFK and hexokinase, and plays a significant role in aerobic glycolysis of cancer cells, also known as the Warburg Effect.

Aberrant Akt signaling is the underlying defect found in several pathologies. Akt is one of the most frequently activated kinases in human cancer as constitutively active Akt can promote unregulated cell proliferation. Abnormalities in Akt2 signaling can result in diabetes due to defects in glucose homeostasis. Akt is also a key player in cardiovascular disease through its role in cardiac growth, angiogenesis, and hypertrophy.

References

  1. Robey RB, Hay N (2009) Is Akt the “Warburg kinase”?-Akt-energy metabolism interactions and oncogenesis. Cancer Biol. 19(1), 25–31.
  2. Zhang S, Yu D (2010) PI(3)king apart PTEN’s role in cancer. Cancer Res. 16(17), 4325–30.
  3. Zoncu R, Efeyan A, Sabatini DM (2011) mTOR: from growth signal integration to cancer, diabetes and ageing. Rev. Mol. Cell Biol. 12(1), 21–35.
  4. Zhang X, Tang N, Hadden TJ, Rishi AK (2011) Akt, FoxO and regulation of apoptosis. Biophys. Acta 1813(11), 1978–86.
  5. Kloet DE, Burgering BM (2011) The PKB/FOXO switch in aging and cancer. Biophys. Acta 1813(11), 1926–37.
  6. Hers I, Vincent EE, Tavars JM (2011) Akt signalling in health and disease. Signal. 23(10), 1515–27.
  7. Wang H, Zhang Q, Wen Q, Zheng Y, Lazarovici P, Philip L, Jiang H, Lin J, Zheng W (2012) Proline-rich Akt substrate of 40kDa (PRAS40): a novel downstream target of PI3k/Akt signaling pathway. Signal. 24(1), 17–24.
  8. Dazert E, Hall MN (2011) mTOR signaling in disease. Opin. Cell Biol. 23(6), 744–55.
  9. Bayley JP, Devilee P (2012) The Warburg effect in 2012. Curr Opin Oncol 24(1), 62–7.

 

mTOR Signaling Pathway

Akt mTOR pathway

Akt mTOR pathway

The mammalian target of rapamycin (mTOR) is an atypical serine/threonine kinase that is present in two distinct complexes. mTOR complex 1 (mTORC1) is composed of mTOR, Raptor, GβL (mLST8), and Deptor and is partially inhibited by rapamycin. mTORC1 integrates multiple signals reflecting the availability of growth factors, nutrients, or energy to promote either cellular growth when conditions are favorable or catabolic processes during stress or when conditions are unfavorable. Growth factors and hormones (e.g. insulin) signal to mTORC1 via Akt, which inactivates TSC2 to prevent inhibition of mTORC1. Alternatively, low ATP levels lead to the AMPK-dependent activation of TSC2 and phosphorylation of raptor to reduce mTORC1 signaling. Amino acid availability is signaled to mTORC1 via a pathway involving the Rag and Ragulator (LAMTOR1-3) proteins. Active mTORC1 has a number of downstream biological effects including translation of mRNA via the phosphorylation of downstream targets (4E-BP1 and p70 S6 Kinase), suppression of autophagy (Atg13, ULK1), ribosome biogenesis, and activation of transcription leading to mitochondrial metabolism or adipogenesis. The mTOR complex 2 (mTORC2) is composed of mTOR, Rictor, GβL, Sin1, PRR5/Protor-1, and Deptor and promotes cellular survival by activating Akt. mTORC2 also regulates cytoskeletal dynamics by activating PKCα and regulates ion transport and growth via SGK1 phosphorylation. Aberrant mTOR signaling is involved in many disease states including cancer, cardiovascular disease, and metabolic disorders.

Selected Reviews:

We would like to thank Carson Thoreen and Prof. David Sabatini, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, for reviewing this diagram. revised November 2012

Protein Folding

 

conformational-rearrangements

conformational-rearrangements

Pincer movement of Hsp90 coupled to the ATPase cycle. NTD = N-terminal domain, MD = middle domain, CTD = C-terminal domain.

Pincer movement of Hsp90 coupled to the ATPase cycle. NTD = N-terminal domain, MD = middle domain, CTD = C-terminal domain.

 

Heat Shock Proteins (HSPs) form seven families (small HSPs (sHSPs), HSP10, 40, 60, 70, 90, and 100) of molecular chaperone proteins that play a central role in the cellular resistance to stress and actin organization. They are involved in the proper folding of proteins and the recognition and refolding of misfolded proteins. HSP expression is induced by a variety of environmental stresses, including heat, hypoxia, nutrient deficiency, free radicals, toxins, ischemia, and UV radiation. HSP27 is a member of the sHSP family. It is phosphorylated at Ser15, Ser78, and Ser82 by MAPKAPK-2 as a result of the activation of the p38 MAP kinase pathway. Phosphorylation and increased concentration of HSP27 has been implicated in actin polymerization and reorganization. HSP70 and HSP90 interact with unfolded proteins to prevent irreversible aggregation and catalyze the refolding of their substrates in an ATP- and co-chaperone-dependent manner. HSP70 has a broad range of substrates including newly synthesized and denatured proteins, while HSP90 tends to have a more limited subset of substrates, most of which are signaling molecules. HSP70 and HSP90 are also essential for the maturation and inactivation of nuclear hormones and other signaling molecules.

References

  1. Stenmark H (2009) Rab GTPases as coordinators of vesicle traffic. Rev. Mol. Cell Biol. 10(8), 513–25.
  2. Horgan CP, McCaffrey MW (2009) The dynamic Rab11-FIPs. Soc. Trans. 37(Pt 5), 1032–6.
  3. Evans CG, Chang L, Gestwicki JE (2010) Heat shock protein 70 (hsp70) as an emerging drug target. Med. Chem. 53(12), 4585–602.
  4. Lanneau D, Wettstein G, Bonniaud P, Garrido C (2010) Heat shock proteins: cell protection through protein triage. ScientificWorldJournal 10, 1543–52.
  5. Ghayour-Mobarhan M, Saber H, Ferns GA (2012) The potential role of heat shock protein 27 in cardiovascular disease. Chim. Acta 413(1-2), 15–24.
  6. Horgan CP, McCaffrey MW (2011) Rab GTPases and microtubule motors. Soc. Trans. 39(5), 1202–6.
  7. Stenmark H (2009) Rab GTPases as coordinators of vesicle traffic. Rev. Mol. Cell Biol. 10(8), 513–25.
  8. Horgan CP, McCaffrey MW (2009) The dynamic Rab11-FIPs. Soc. Trans. 37(Pt 5), 1032–6.
  9. Evans CG, Chang L, Gestwicki JE (2010) Heat shock protein 70 (hsp70) as an emerging drug target. Med. Chem. 53(12), 4585–602.
  10. Lanneau D, Wettstein G, Bonniaud P, Garrido C (2010) Heat shock proteins: cell protection through protein triage. ScientificWorldJournal 10, 1543–52.
  11. Ghayour-Mobarhan M, Saber H, Ferns GA (2012) The potential role of heat shock protein 27 in cardiovascular disease. Chim. Acta 413(1-2), 15–24.
  12. Horgan CP, McCaffrey MW (2011) Rab GTPases and microtubule motors. Soc. Trans. 39(5), 1202–6

– See more at: http://www.cellsignal.com/common/content/content.jsp?id=protein-folding#sthash.xAfeElH1.dpuf

 

 

 

 

 

 

 

 

 

 

Read Full Post »

Pathology Emergence in the 21st Century

Author and Curator: Larry Bernstein, MD, FCAP 

 

This discussion follows the series on DNA and its replication, the code of life, and immediately follows a an up to date survey of RNA, it’s many discovered forms, their function, and the transcription of RNA, intermediate to protein synthesis.  This will comprise a series of articles, including the chemistry, structure, and function of proteins, before turning to the “metabolome”.  This discussion is about the development of the scientific profession of pathology in three notable phases.  The first phase might be considered the gross anatomical discussion developed in Vienna, under Rokitansky, whose greatest student was the Hungarian pathologist, Semelweis, who began the insistence on handwashing prior to visiting the delivery room based on the observation that deliver was safer done by midwifes than by physicians.  This was prior to the great discoveries in microbiology.  The Rokitansky procedure is distinctive in removing organ systems in order to examine postmortem anatomical changes.  His document on the pathology of the organs was monumental.  It was refined by Rudolf Virchow, who removed one organ at a time, but he also had the now developing field of microscopic anatomy (also describing leukemia),

 

chart1  lymphoma_leukemia

 

Rudolph Virchow  1821-1902

Rudolph Virchow 1821-1902

 

 

 

 

 

 

 

that would also be embellished by a generation of histologists who introduced staining techniques.  The most remarkable anatomist to emerge in that period was John Hunter, the Scottish born anatomist and surgeon in London, who taught medical students from England and United States, and who was the physician for Sir David Hume.  When he served in the 30 years war, he pulled wounded soldiers out of the mud to a clearing to care for their wounds.

The 20th century saw the introduction of a new medical school education system under advisement by the Carnegie Commission. [Abraham Flexner Report]  This led to the teaching of basic sciences of anatomy, physiology, pharmacology, pathology, and later genetics and microbiology as prerequisite to clinical teaching.  Moreover, teaching was done by formal systems of disease, which later developed into rotations in Obstetrics and Gynecology, Medicine, and Surgery, Pediatrics, to which endocrinology and neurology and psychiatry were added.  The first great Medical School was actually Johns Hopkins, that paved the groundwork.  Harvard, Cornell, Columbia, and NYU followed, as did the University of California, San Francisco, and the University of Chicago.  This was a period dominated by many important discoveries, and a domination of the Nobel Prizes in Medicine and Physiology, Chemistry and Physics (rivalled only by Germany and United Kingdom).  The Uniqueness of Pathology lies in its placement in the first year of medical school, in preparation for the formal study of medicine, with a foot in both basic sciences and the other in clinical practice.  The pathologist received all tissues removed from surgical procedures, and performed autopsies to determine the cause of death and comorbid conditions.   The development of a substantial knowledge of the kidney gave rise to the specialty of nephrology, and at the same time drew pathologists into a “phase” of molecular biology with the introduction of the electron microscope.  However, the field of immunology developed, and the need for transfusion hastened the emergence of clinical antigen-antibody testing, the crossmatch, blood banking, and later, the emergence of organ transplantation.  In addition, clinical microbiology became a part of clinical pathology, and included fungi and tick-borne diseases, and nemitodes.

We have entered the third phase of pathology with the completion of the human genetic code, and with the development of target pharmaceutical development in the 21st century.

 

Modern Techniques of Molecular Pathology

A look at clinical laboratory science and its expected progress over the next decade

 

Molecular Diagnostics    2013; 22 (2), p 35Promising forecasts project great expectations for medical sciences in the year 2013 and beyond. These predictions follow a decade after completion of the Human Genome Project, and are accompanied by immense breakthroughs in computational and applied mathematics. In my view, they are:

  • Genomic and allied -omics technology
  • Innovation in mathematical classification (complexity)
  • Nanotechnology
  • Synthetic chemistry from physics, organic and inorganic chemistry

It is not my intention to go deeply into the exponential group of these advanced and integrative sciences; rather, I want to raise awareness of an emerging new world that will open to the clinical laboratory scientist, and signal the need in the next generation of laboratory personnel to embrace knowledge domains that will be critical for their careers.

All of these breakthroughs are tied together by a search for personalized and integrative medicine. These breakthroughs will reinvent nutritional and pharmaceutical medicine as well as medical devices and restructure clinical laboratory and imaging applications to cardiology, oncology, radiology and anatomical pathology.

Metabolomics

What does metabolomics and metabolic profiling have to do with this? Metabolomics is the measurement of small molecules that interact with membrane receptors1 that are involved with regulation of genomic transcription and cellular regulation and upregulation or downregulation of metabolic processes essential to health. As well, these small molecules may provide targets for disease treatments, and as they are investigated, also provide further “analytes for diagnosis and, moreover, prediction of short-term or long-term outcomes.”2

As a result, the laboratory will become a more significant factor in measuring health and disease and in guiding health or disease maintenance. As our population has reached increased age limits, the laboratory has been a contributor in the public health sphere, and will have a greater role as a result of:

  • Improved tie in with provision of information to not only the healthcare workers, but also the patient
  • Achieve turnaround times for critical results through better workflow
  • Greater control and access to a next generation of point-of-care technology integrated with the laboratory database, and a restructured electronic health record

Despite the hype about the “big data” revolution, this is achievable in the system proposed because there is a published model to achieve this.2

Familiar Methods

Either individually or grouped as a profile, metabolites are detected by nuclear magnetic resonance spectroscopy or mass spectrometry, providing a basis for uses of ­metabolome findings extended to the early detection and diagnosis of cancer and as both a predictive and pharmacodynamics marker of drug effect. We can expect it to become the link between the laboratory and the clinic. The methods used in genomics are microarrays, and for proteomics they are the already familiar chromatographic principles that species migrate at different rates through a column matrix based on their volatility, or carries out a separation as the molecules differ by their adsorption to and elution from a solid matrix, dependent on the binding to the matrix and solubility in the solvent eluate, modified by pH, ionic concentration, and specific conditions needed for recovery. Powerful mathematical tools are used to analyze the data.3

Cardiovascular Disease

Although coronary thrombosis is the final event in acute coronary syndromes, increasing evidence suggests that inflammation also plays a key role in development of atherosclerosis and its clinical manifestations, such as myocardial infarction, stroke and peripheral vascular disease. The inflammatory component was indicated by epidemiological studies of elevated serum levels of high sensitivity C-reactive protein. That eventually led to the demonstration of a benefit from reduction of CRP in individuals without ­characteristic lipidemia in a major clinical trial, which drew a relationship between diabetes, obesity and disordered inflammatory response in the causation of coronary artery disease, aortic valve and artery disease, carotid artery and peripheral vascular disease.

Cancer

Because cancer cells are known to possess a highly unique metabolic phenotype, development of specific biomarkers in oncology is possible and might be used in identifying fingerprints, profiles or signatures to detect the presence of cancer, determine prognosis and/or assess the pharmacodynamic effects of therapy.4

HDM2, a negative regulator of the tumor suppressor p53, is over-expressed in many cancers that retain wild-type p53. Consequently, the effectiveness of chemotherapies that induce p53 might be limited, and inhibitors of the HDM2-p53 interaction are being sought as tumor-selective drugs.5

Coagulation

Blood coagulation plays a key role among numerous mediating systems activated in inflammation. Receptors of the PAR family serve as sensors of serine proteinases of the blood clotting system in the target cells involved in inflammation. Activation of PAR_1 by thrombin and of PAR_2 by factor Xa leads to a rapid expression and exposure on the membrane of endothelial cells of both adhesive proteins that mediate an acute inflammatory reaction and of the tissue factor that initiates the blood coagulation cascade.

The details of evolving methods are avoided in order to build the argument that a very rapid expansion of discovery has been evolving depicting disease, disease mechanisms, disease associations, metabolic biomarkers, study of effects of diet and diet modification, and opportunities for targeted drug development.

  1. Bernstein is CEO of Triplex Medical Science and CSO of Leaders in Pharmaceutical Intelligence. He has been involved in a collaborative project on reducing the noise that exists in complex data and developing a primary evidence-based classification since retiring from a career in pathology supning four decades.

References

1. Bernstein LH. Metabolomics, metabonomics and functional nutrition: The next step in nutritional metabolism and biotherapeutics. Journal of Pharmacy and Nutrition Sciences, 2012;2.

2. David G, Bernstein LH, Coifman RR. Generating evidence-based interpretation of hematology screens via anomaly characterization. The Open Clinical Chemistry Journal 2011;4: 10-16.

3. Grainger DJ. Megavariate statistics meets high data-density analytical methods: The future of medical diagnostics? IRTL Reviews 2003;1:1-6.

4. Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: A review. Clin Cancer Res 2009;15; 15(2): 431-440.

5. Fischer PM, Lane DP. Small molecule inhibitors of thep53 suppressor HDM2: Have protein-protein interactions come of age as drug targets? Trends in Pharm Sci 2004;25(7):343-346.

Directions for Genomics in Personalized Medicine

Author: Larry H. Bernstein, MD, FCAP

Purpose

This discussion will identify the huge expansion of genomic technology in the search for  biopharmacotherapeutic targets that continue to be explored involving different levels and interacting signaling pathways.   There are several methods of analyzing gene expression that will be discussed. Great primary emphasis required investigation of combinations of mutations expressed in different cancer types.

 

James Watson has proposed a major hypothesis that expresses the need to focus on “central” “driver mutations” that correspond with the regulation of gene expression, cell proliferation, and cell metabolism with a critical rejection of antioxidant benefits.  What hasn’t been know is why drug resistance develops and whether the cellular migration and aerobic glycolysis can be redirected after cell metastasis occurs.  I attempt to bring out the complexities of current efforts.

 

Introduction

  • This discussion is a continuation of a previous discussion on the role of genomics if discovery of therapeutic targets for cancer, each somewhat different, but all related to:
  • The reversal of carcinoma by targeting a key driver of multiple signaling pathways that activate cell proliferation
  • Pinpointing a stage in a multistage process at which tumor progression links to changes in morphology from basal cells to invasive carcinoma with changes in polarity and loss of glandular architecture
  • Reversal of the carcinoma through using a small molecule that either is covalently bound to a nanoparticle delivery system that blocks or reverses tumor development
  • Synthesis of a small molecule that interacts with the translation of the genome either by substitution of a key driver molecule or by blocking at the mRNA stage of translation
  • Blocking more than one signaling pathway that are links to carcinogenesis and cellular proliferation and invasion

Difficulty of the problem

A problem expressed by James Watson is that the investigations that are ongoing

  • are following a pathway that is not driven by attacking the “primary” driver of carcinogenesis.

He uses the Myc gene as an example, as noted in the previous discussion. The problem may be more complicated than he envisions.

  • The most consistent problem in chemotherapy, irrespective of the design and the target has been cancer remission for a short time followed by recurrence, and then
  • switching to another drug, or combination chemotherapy.

It is common to “clean” the field at the time of resection using radiotherapy before chemotherapy.

  • But the goal is understood to be “palliation”, not cure.

This raises a serious issue in the hypothesis posed by Watson. The issue is

  • whether there is a core locus of genetic regulation that is common to carcinogenesis irrespective of tissue metabolic expression.
  • This is supported by the observation that tissue specific expression is lost in cancer cells by de-differentiation.

Historical Perspective

AEROBIC GLYCOLYSIS

In 1967 Otto Warburg published his view in a paper “The prime cause and prevention of cancer”.
There are primary and secondary causes of all diseases

  • plague – primary: plague bacillus
  • plague – secondary: filth, rats, and fleas

Otto Heinrich Warburg (1883-  )

 

 

 

 

 

 

Otto Heinrich Warburg (Photo credit: Wikipedia)

 

cancer, above all diseases,

  • has countless seconday causes
  • primary – replacement of respiration of oxygen in normal body tissue by fermentation of glucose with conversion from obligate aerobic to anaerobic, as in bacterial cells

The cornerstone to understanding cancer is in study of the energetics of life

This thinking came out of decades of work in the Dahlem Institute Kaiser Wilhelm pre WWII and Max Planck Institute after WWII, supported by the Rockefeller Foundation.

  • The oxygen- and hydrogen-transferring enzymes were discovered and isolated.
  • The methods were elegant for that time, using a manometer that improved on the method used by Haldane, that did not allow the leakage of O2 or CO2.
  • The interest was initiated by the increased growth of Sea Urchin eggs after fertization, which turned out to be not comparable to the rapid growth of cancer cells.
  • Warburg used both normal and cancer tissue and measured the utilization of O2. He found
    • that the normal tissue did not accumulate lactic acid.
    • Cancer tissue generated lactic acid
    • the rate of O2 consumption the same as normal tissue, but
    • the rate of lactate formation far exceeded any tissue, except the retina.
    • This was a discovery studied by “Pasteur” 60 years earlier (facultative aerobes), which he called thePasteur effect.
    • Hematopoietic cells of bone marrow develop aerobic glycolysis when exposed to a low oxygen condition.

He then followed on an observation by Otto Meyerhoff (Embden-Myerhoff cycle) that in muscle

  • the consumption of one molecule of oxygen generates two molecules of lactate, but in aerobic glycolysis, the relationship disappears.
  • He expressed the effectiveness of respiration by the ‘Meyerhoff quotient’.
  • He found that cancer cells didn’t have a quotient of ’2′

The role of the allosteric enzyme phosphofructokinase (PFK) not then known, would tie together the glycolytic and gluconeogenic pathways.
He used a heavy metal ion chelator ethylcarbylamine to

  • sever the link and turn on aerobic glycolysis.

The explanation for this was provided years later by the work fleshed out by Lynen, Bucher, Lowry, Racker, and Sols.

  • The rate-limiting enzyme, PFK is regulated by the concentrations of ATP, ADP, and inorganic phosphate. The ethylcarbylamide was an ‘uncoupler’ of oxidative phosphorylation.

Warburg understood that when normal cells switched to aerobic glycolysis

  • it is a re-orientation of normal cell expression.
  • this provides the basis for the inference that neoplastic cells become more like each other than their cell of origin.
  • embryonic cells can be transformed into cancer cells under hypoxic conditions
  • re-exposure to higher oxygen did not cause reversion back to normal cells.

Warburg publically expressed the rejected view in 1954 (at age 83) that restriction of chemical wastes, food additives, and air pollution would substantially reduce cancer rates.

His emphasis on the impairment of respiration was inadequate.

  • the prevailing view today is loss of controlled growth of normal cells in cancer cells.

Otto Warburg: Cell Physiologist, Biochemist, and Eccentric. Hans Krebs, in collaboration with Roswitha Schmid. Clarendon Press, Oxford. 1991.ISBN 0-19-858171-8.

 

A multiphoton fluorescence microscope (MFM) is a specialized optical microscope.

The MFM uses pulsed long-wavelength light to excite fluorophores within the specimen being observed. The fluorophore absorbs the energy from two long-wavelength photons which must arrive simultaneously in order to excite an electron into a higher energy state, from which it can decay, emitting a fluorescence signal. It differs from traditional fluorescence microscopy in which the excitation wavelength is shorter than the emission wavelength, as the summed energies of two long-wavelength exciting photons will produce an emission wavelength shorter than the excitation wavelength.[1]

Multiphoton fluorescence microscopy has similarities to confocal laser scanning microscopy. Both use focused laser beams scanned in a raster pattern to generate images, and both have an optical sectioning effect. Unlike confocal microscopes, multiphoton microscopes do not contain pinhole apertures, which give confocal microscopes their optical sectioning quality. The optical sectioning produced by multiphoton microscopes is a result of the point spread function formed where the pulsed laser beams coincide. The multiphoton point spread function is typically dumbbell-shaped (longer in the x-y plane), compared to the upright rugby-ball shaped point spread function of confocal microscopes. However, in many interesting cases the shape of the spot and its size can be designed to realize specific desired goals.[2]

The longer wavelength, low energy (typically infra-red) excitation lasers of multiphoton microscopes are well-suited to use in imaging live cells as they cause less damage than short-wavelength lasers, so cells may be observed for longer periods with fewer toxic effects. Many researchers are currently working toward better and higher resolution multiphoton imaging developments.

Two-photon excitation microscopy is a fluorescence imaging technique that allows imaging of living tissue up to a very high depth, up to about one millimeter. Being a special variant of the multiphoton fluorescence microscope, it uses red-shifted excitation light which can also excite fluorescent dyes. However, for each excitation, two photons of infrared light are absorbed. Using infrared light minimizes scattering in the tissue. Due to the multiphoton absorption, the background signal is strongly suppressed. Both effects lead to an increased penetration depth for these microscopes. Two-photon excitation can be a superior alternative to confocal microscopy due to its deeper tissue penetration, efficient light detection, and reduced phototoxicity.[1]

Two-photon excitation employs two-photon absorption, a concept first described by Maria Goeppert-Mayer (1906–1972) in her doctoral dissertation in 1931,[2] and first observed in 1961 in a CaF2:Eu2+ crystal using laser excitation by Wolfgang Kaiser.[3] Isaac Abella showed in 1962 in cesium vapor that two-photon excitation of single atoms is possible.[4]

The concept of two-photon excitation is based on the idea that two photons of comparably lower energy than needed for one photon excitation can also excite a fluorophore in one quantum event. Each photon carries approximately half the energy necessary to excite the molecule. An excitation results in the subsequent emission of a fluorescence photon, typically at a higher energy than either of the two excitatory photons. The probability of the near-simultaneous absorption of two photons is extremely low. Therefore a high flux of excitation photons is typically required, usually from a femtosecond laser. The purpose of employing the two-photon effect is that the axial spread of the point-spread-function is substantially lower than for single-photon excitation. As a result, the resolution along the z dimension is improved, allowing for thin optical sections to be cut. In addition, in many interesting cases the shape of the spot and its size can be designed to realize specific desired goals.[5] Two-photon microscopes are less damaging to the sample than a single-photon confocal microscope.

The most commonly used fluorophores have excitation spectra in the 400–500 nm range, whereas the laser used to excite the two-photon fluorescence lies in the ~700–1000 nm (infrared) range. If the fluorophore absorbs two infrared photons simultaneously, it will absorb enough energy to be raised into the excited state. The fluorophore will then emit a single photon with a wavelength that depends on the type of fluorophore used (typically in the visible spectrum). Because two photons are absorbed during the excitation of the fluorophore, the probability for fluorescent emission from the fluorophores increases quadratically with the excitation intensity. Therefore, much more two-photon fluorescence is generated where the laser beam is tightly focused than where it is more diffuse. Effectively, excitation is restricted to the tiny focal volume (~1 femtoliter), resulting in a high degree of rejection of out-of-focus objects. This localization of excitation is the key advantage compared to single-photon excitation microscopes, which need to employ additional elements such as pinholes to reject out-of-focus fluorescence. The fluorescence from the sample is then collected by a high-sensitivity detector, such as a photomultiplier tube. This observed light intensity becomes one pixel in the eventual image; the focal point is scanned throughout a desired region of the sample to form all the pixels of the image. The scan head is typically composed of two mirrors, the angles of which can be rapidly altered with a galvanometer.

.Multiphoton microscopy: a potential “optical biopsy” tool for real-time evaluation of lung tumors without the need for exogenous contrast agents.

 

Jain M1, Narula N, Aggarwal A, Stiles B, Shevchuk MM, Sterling J, Salamoon B, Chandel V, Webb WW, Altorki NK, Mukherjee S.
From the Departments of Urology (Dr Jain), Pathology and Laboratory Medicine (Drs Narula and Shevchuk), Biochemistry (Drs Aggarwal and Mukherjee, Mr Sterling, and Mr Salamoon), Thoracic Surgery (Drs Stiles and Altorki), and Surgery (Mr Chandel), Weill Cornell Medical College, New York, New York; and the School of Applied and Engineering Physics, Cornell University, Ithaca, New York (Dr Webb). Dr Aggarwal is now with the Department of Science, Borough of Manhattan Community College, New York.
Arch Pathol Lab Med. 2014 Aug;138(8):1037-47.   http://dx.doi.org:/10.5858/arpa.2013-0122-OA. Epub 2013 Nov 7

Context.-Multiphoton microscopy (MPM) is an emerging, nonlinear, optical-biopsy technique, which can generate subcellular-resolution images from unprocessed and unstained tissue in real time.

Objective.-To assess the potential of MPM for lung tumor diagnosis. Design.-Fresh sections from tumor and adjacent nonneoplastic lung were imaged with MPM and then compared with corresponding hematoxylin-eosin slides.

Results.-Alveoli, bronchi, blood vessels, pleura, smokers’ macrophages, and lymphocytes were readily identified with MPM in nonneoplastic tissue. Atypical adenomatous hyperplasia (a preinvasive lesion) was identified in tissue adjacent to the tumor in one case. Of the 25 tumor specimens used for blinded pathologic diagnosis, 23 were diagnosable with MPM. Of these 23 cases, all but one adenocarcinoma (15 of 16; 94%) was correctly diagnosed on MPM, along with their histologic patterns. For squamous cell carcinoma, 4 of 7 specimens (57%) were correctly diagnosed. For the remaining 3 squamous cell carcinoma specimens, the solid pattern was correctly diagnosed in 2 additional cases (29%), but it was not possible to distinguish the squamous cell carcinoma from adenocarcinoma. The other squamous cell carcinoma specimen (1 of 7; 14%) was misdiagnosed as adenocarcinoma because of pseudogland formation. Invasive adenocarcinomas with acinar and solid pattern showed statistically significant increases in collagen. Interobserver agreement for collagen quantification (among 3 observers) was 80%.

Conclusions.-Our pilot study provides a proof of principle that MPM can differentiate neoplastic from nonneoplastic lung tissue and identify tumor subtypes. If confirmed in a future, larger study, we foresee real-time intraoperative applications of MPM, using miniaturized instruments for directing lung biopsies, assessing their adequacy for subsequent histopathologic analysis or banking, and evaluating surgical margins in limited lung resections. PMID: 24199831

Lung cancer is the most common cause of cancer-related mortality worldwide in both men and women, with 226 160 new cases and 160 340 deaths estimated in the United States alone in 2012.1 Lung tumors are currently detected on chest radiography and computed tomography imaging, but definitive diagnosis, especially distinguishing the various subtypes of lung cancer, requires cytologic or histopathologic examination. Although considered the gold standard in establishing diagnosis, histopathology requires time-consuming tissue processing and can sometimes require repeat biopsies if the initial specimen was nondiagnostic. To overcome some of the obstacles associated with histopathologic processing, efforts have been made to develop high-resolution “optical biopsy” imaging techniques.

In this proof-of-principle pilot study, we explored the use of multiphoton microscopy (MPM) as a promising, new optical biopsy tool for the detection and diagnosis of lung tumors in real time.

Multiphoton microscopy relies on the simultaneous absorption of 2 or 3 low-energy (near-infrared) photons to cause a nonlinear excitation, equivalent to that created by a single photon of shorter wavelength light. By using 2-photon excitation in the 700- to 800-nm range, MPM enables both in vivo and ex vivo imaging of fresh, unprocessed, and unstained tissue at histologic resolution via generating intrinsic tissue emissions. Intrinsic tissue emission signals used in this study included autofluorescence and second harmonic generation (SHG).2–4

Twenty-five adult subjects diagnosed with lung cancer and undergoing lobectomies at our institution participated in this Institutional Review Board–approved study.

An Olympus FluoView FV1000MPE imaging system (Olympus America, Center Valley, Pennsylvania) was used for all MPM imaging. For detailed description of MPM imaging conditions, please see Supplementary Methods (supplemental digital content for this article is available at www.archivesofpathology.org in the August 2014 table of contents). Briefly, fresh (unprocessed and unstained) specimens were excited using 780 nm light from a tunable titanium-sapphire laser (Mai Tai DeepSee, Spectra-Physics, Irvine, California). Three distinct intrinsic tissue-emission signals were collected using photomultiplier tubes and were then color coded by using MetaMorph (version 7.0, revision 4, Molecular Devices, Sunnyvale, California) as follows: (1) SHG (360–400 nm, color-coded red), a nonlinear scattering signal originating from tissue collagen; (2) short wavelength autofluorescence (420–490 nm, color-coded green), originating in part from reduced nicotinamide adenine dinucleotide and flavin adenine dinucleotide in normal epithelial, neoplastic, and inflammatory cells, and from elastin in the alveolar septa; and (3) long wavelength autofluorescence (550–650 nm, color-coded blue), originating in part from carbon-laden macrophages.

Arch Path MPM

Arch Path MPM

http://www.archivesofpathology.org/na101/home/literatum/publisher/pinnacle/journals/content/arpa/2014/15432165-138.8/arpa.2013-0122-oa/20140721/images/medium/i1543-2165-138-8-1037-f01.gif

http://www.archivesofpathology.org/na101/home/literatum/publisher/pinnacle/journals/content/arpa/2014/15432165-138.8/arpa.2013-0122-oa/20140721/images/large/i1543-2165-138-8-1037-f01.jpeg

Figure 1. Comparative multiphoton microscopy (MPM) and hematoxylin-eosin images of nonneoplastic and smoker lung. A and B, Low-magnification images show lung parenchyma composed of alveoli (arrows) surrounded by pleura (arrowheads). Inset in MPM shows pleura with collagen (red) and elastin (green) components. C and D, High-magnification images show primarily elastin fibers, with some collagen in the septal wall (arrowheads) of the alveoli (arrows). E and F, Low-magnification images show bronchus (*) with cartilage (arrowheads) and a medium-sized blood vessel (arrows). G and H, High-magnification images show columnar lining of the bronchus (arrows) and underlying connective tissue (arrowheads). I and J, Alveoli filled with carbon-laden macrophages (arrows; blue in MPM) and noncarbon-laden macrophages (arrowheads; green in MPM). K and L, A collection of small lymphocytes (arrowheads and inset), along with smoker’s macrophages (arrows). Some loss and thickening of the alveolar septa is shown (I through L) (MPM, original magnifications ×48 [A and E], ×96 [A inset], ×300 [C, G, I, and K], ×600 [K inset]; hematoxylin-eosin, original magnifications ×40 [B, F] and ×200 [D, H, J, and L]).

To assess the diagnostic potential of MPM, a blinded analysis was conducted. The attending pulmonary pathologist and an attending general surgical pathologist first familiarized themselves to the histologic features seen on MPM images in both nonneoplastic and neoplastic (adenocarcinoma and squamous cell carcinoma [SCC]) lung tissue. Because, in our study, multiple images were acquired from different areas of a given tumor, we used some of those images for the training set and did not include them in the blinded test set. Subsequently, test MPM images from all lung tumor specimens were assessed in a blinded fashion and were categorized according to subtype and pattern using the routine histopathologic criteria.9,10 These diagnoses were then correlated with diagnoses made by the same pathologist, based on the corresponding H&E sections prepared from the same specimens.

Visualizing Lung From Smoker With MPM
Using MPM to Identify Invasive and Preinvasive Adenocarcinoma

Figure 2, A through B, shows an example of a lesion with atypical adenomatous hyperplasia, which was an incidental finding on MPM in “tumor-free” lung tissue. It shows the proliferation of atypical pneumocytes, along the preexisting alveolar wall. Gaps between the cells (discontinuous layer of pneumocytes) support the diagnosis of atypical adenomatous hyperplasia. Adenocarcinoma of lung with lepidic-predominant pattern, in contrast, shows continuous proliferation of tumor cells along the alveolar wall.

Arch Path  progression from atypical lesion to invasive adenocarcinoma

Arch Path progression from atypical lesion to invasive adenocarcinoma

Figure 2. Comparative multiphoton microscopy and hematoxylin-eosin images showing progression from atypical lesion to various patterns of invasive adenocarcinoma of lung. A and B, Images of atypical adenomatous hyperplasia shows a focus of pneumocyte proliferation (cuboidal cells with gaps between them) along the alveolar wall (arrows and insets). C and D, Images of adenocarcinoma of lung with lepidic-predominant pattern (arrows) and a few clusters of free-floating tumor cells (arrowheads). E and F, Images of adenocarcinoma of lung with acinar-predominant pattern (arrows). G and H, Images showing solid pattern (arrows) with suggestion of gland formation (arrowheads). I and J, Images showing papillary pattern (papillae with fibrovascular core; arrows). K and L, Images showing micropapillary pattern, with complete destruction of normal lung parenchyma. The airspace shows small papillary clusters of tumor cells (arrows) lacking true fibrovascular cores (multiphoton microscopy, original magnifications ×300 [A, C, E, G, I, and K] and ×600 [inset]; hematoxylin-eosin, originalmagnifications ×200 [B, D, F, H, J, and L] ×400 [inset]).

Arch Path SCC

Arch Path SCC

http://www.archivesofpathology.org/na101/home/literatum/publisher/pinnacle/journals/content/arpa/2014/15432165-138.8/arpa.2013-0122-oa/20140721/images/medium/i1543-2165-138-8-1037-f02.gif

http://www.archivesofpathology.org/na101/home/literatum/publisher/pinnacle/journals/content/arpa/2014/15432165-138.8/arpa.2013-0122-oa/20140721/images/large/i1543-2165-138-8-1037-f02.jpeg

Adenocarcinoma with a papillary-predominant pattern shows clear papillary projections composed of cuboidal to columnar cells, which line collagen-rich fibrovascular cores (Figure 2, I and J). Micropapillary adenocarcinoma, on the other hand, shows small papillary clusters of malignant cells within the airspace, with no true fibrovascular cores (Figure 2, K and L).

Using MPM to Identify SCC of Lung

Figure 3 shows high-magnification images acquired from the tumor mass in subjects with a diagnosis of SCC. Figure 3, A and B, shows sheets of malignant cells with a complete loss of the normal architecture of the lung parenchyma. These cells are seen as arranged in a pavementlike fashion with a high nuclear to cytoplasmic ratio (indicating a high-grade tumor). Pavementlike arrangement of cells is a characteristic of SCC that helps to differentiate it from the solid-predominant pattern of adenocarcinoma. We also observed an increased amount of stroma and mononuclear inflammatory cells surrounding the tumor (Figure 3, A, inset). These mononuclear inflammatory cells were confirmed as lymphocytes on H&E. This case was correctly diagnosed as SCC on MPM. Figure 3, C and D, shows images from the tumor mass of another subject, also diagnosed with SCC on H&E. However, it was misdiagnosed as adenocarcinoma on MPM, primarily because the central necrosis in the tumor nest was interpreted as gland formation. When the images were reanalyzed with knowledge of the SCC diagnosis, the pavementlike arrangement of cells was identified. We thus expect that, with a larger sample of SCC specimens and more experience, our ability to correctly diagnose SCC will improve significantly. Also, in the future, any specimen with a likely SCC diagnosis will be imaged over a larger area by using image tiling and by taking multiple stacks from various areas in the lesion, so as not to be misled by occasional, spurious, histologic features.

Figure 3. Comparative multiphoton microscopy and hematoxylin-eosin images of squamous cell carcinoma of the lung. A and B, Images of squamous cell carcinoma (SCC) of the lung showing sheets of malignant cells with high nuclear to cytoplasmic ratio (arrows), surrounded by lymphocytes (arrowheads) interspersed in collagen bundles (inset). C and D, Images of SCC of the lung showing pavementlike arrangement of the cells (arrows). Also shown is a nest of squamous cells with focal necrosis (arrowheads) forming pseudoglands, leading to a misdiagnosis of adenocarcinoma (multiphoton microscopy, original magnifications ×300 [A and C] and ×600 [inset]; hematoxylin-eosin, original magnifications ×200 [B and D]).

http://www.archivesofpathology.org/na101/home/literatum/publisher/pinnacle/journals/content/arpa/2014/15432165-138.8/arpa.2013-0122-oa/20140721/images/medium/i1543-2165-138-8-1037-f03.gif

http://www.archivesofpathology.org/na101/home/literatum/publisher/pinnacle/journals/content/arpa/2014/15432165-138.8/arpa.2013-0122-oa/20140721/images/large/i1543-2165-138-8-1037-f03.jpeg

Using MPM to Assess the Degree of Collagen in Lung Carcinoma

Previous studies have reported the amount of collagen as a prognostic factor in small, peripheral lung adenocarcinomas5,6,8 and SCCs.7 In our study, we categorized adenocarcinomas into 3 groups:

(1) well-differentiated, adenocarcinomas with lepidic-predominant patterns;

(2) moderately differentiated, invasive adenocarcinomas with acinar-predominant patterns; and

(3) poorly differentiated, adenocarcinomas with solid-predominant patterns.

A well-preserved lung architecture (Figure 4, A through C), with slight alveolar septal thickening from collagen deposition, was seen in low-magnification images of a well-differentiated adenocarcinoma (with a lepidic-predominant pattern). In contrast, invasive adenocarcinomas with both acinar-predominant (Figure 4, D through F) and solid-predominant (Figure 4, G through I) patterns showed increases in collagen content.

Our proof-of-principle pilot study indicates the potential utility of MPM for differentiating nonneoplastic from neoplastic lung tissue in fresh, ex vivo specimens without the use of exogenous contrast agents. Furthermore, study pathologists successfully identified the histologic subtypes of tumor and recognized inflammatory cells, such as lymphocytes and smoker macrophages. We also performed collagen quantification in adenocarcinomas and demonstrated its correlation with the degree of differentiation.

Indeed, the diagnostic potential of MPM for differentiating malignant from benign/inflammatory lesions has been previously investigated in multiple organ systems in both animal models and human tissues.3,12–19 Specifically, normal and diseased lung have been investigated in both small animals and in ex vivo human tissue using MPM.20–26 However, most studies focused on extracellular matrix remodeling associated with lung pathologies.22,23,25,26 To date, few studies have explored the potential of MPM for differentiating benign lesions from neoplastic ones.

. Our study is the first, to our knowledge, to present not only a detailed histology of normal human lung tissue obtained with MPM but also to show the histologic features that can be used to identify a variety of inflammatory, preneoplastic, and neoplastic lesions, in accordance with World Health Organization10 and International Association for the Study of Lung Cancer9 criteria. Furthermore, we demonstrated the ability of a pulmonary pathologist and a general surgical pathologist to differentiate between lesion subtypes in a blinded fashion with high reliability

 

 

The Human Genome Project

j.craigventer

 

 

 

 

 

 

 

 

 

 

 

J. Craig Venter (Photo credit: Wikipedia)

The Human Genome Project, driven by Francis Collins at NIH, and by Craig Venterat the Institute for Genome Research (TIGR) had parallel projects to map the human chromosome, completed in 2003. It originally aimed to map the nucleotides contained in a human haploid reference genome (more than three billion). TIGR was the first complete genomic sequencing of a free living organism, Haemophilus influenzae, in 1995. This used a shotgun sequencing technique pioneered earlier, but which had never been used for a whole bacterium.
Venter broke away from the HGP and started Celera in 1998 because of resistance to the shotgun sequency method, and his team completed the genome sequence in three years – seven years’ less time than the HGP timetable (using the gene of Dr. Venter). TIGR eventually sequenced and analyzed more than 50 microbial genomes. Its bioinformatics group developed

  • pioneering software algorithms that were used to analyze these genomes,
  • including the automatic gene finder GLIMMER and
  • the sequence alignment program MUMmer.

In 2002, Venter created and personally funded the J. Craig Venter Institute (JCVI) Joint Technology Center (JTC), which specialized in high throughput sequencing.  The JTC, in the top ranks of scientific institutions worldwide, sequenced nearly 100 million base pairs of DNA per day for its affiliated institutions (JCVI) .

He received his his Ph.D. degree in physiology and pharmacology from the University of California, San Diego in 1975 under biochemist Nathan O. Kaplan. A full professor at the State University of New York at Buffalo, he joined the National Institutes of Health in 1984. There he learned of a technique for rapidly identifying all of the mRNAs present in a cell and began to use it to identify human brain genes. The short cDNA sequence fragments discovered by this method are calledexpressed sequence tags (ESTs), a name coined by Anthony Kerlavage at TIGR.
Venter believed that shotgun sequencing was the fastest and most effective way to get useful human genome data. There was a belief that shotgun sequencing was less accurate than the clone-by-clone method chosen by the HGP, but the technique became widely accepted by the scientific community and is still the de facto standard used today.

References

Shreeve, James (2004). The Genome War: How Craig Venter Tried to Capture the Code of Life and Save the World. Knopf. ISBN 0375406298.
Sulston, John (2002). The Common Thread: A Story of Science, Politics, Ethics and the Human Genome. Joseph Henry Press. ISBN 0309084091.
“The Human Genome Project Race”. Center for Biomolecular Science & Engineering,UC Santa Cruz. Retrieved 20 March 2012.
Venter, J. Craig (2007). A Life Decoded: My Genome: My Life. Viking Adult. ISBN 0670063584.

Use of a Fluorophor Probe

An article has been discussed by Dr.  Tilda Barilya on use of a sensitive fluorescent probe in the near IR spectrum at > 700 nm to identify malignant ovarian cells in-vivo in abdominal exploration

  • by tagging an overexpressed FR-α (folate-FITA)

The author makes the point that:

  • In ovarian cancer, the FR-α appears to constitute a good target because it is overexpressed in 90–95% of malignant tumors, especially serous carcinomas.
  • Targeting ligand, folate, is attractive as it is nontoxic, inexpensive and relatively easily conjugated to a fluorescent dye to create a tumor-specific fluorescent contrast agent.
  • The report is identified as “ the first in-human proof-of-principle of the use of intraoperative tumor-specific fluorescence imaging in staging and debulking surgery for ovarian cancer using the systemically administered targeted fluorescent agent folate-FITC.”

While this does invoke possibilities for prognosis, the decision to perform the surgery,

  • whether laparoscopic or open, is late in the discovery process. However,

it does suggest the possibility that the discovery and the treatment might be combined

  • if the biomarker itself had the fluorescence to identify the overexpression, but
  • it also is combined with a tag to block the overexpession. This hypothetical possibility is now expressed below.

http://pharmaceuticalintelligence.com/2013/01/19/ovarian-cancer-and-fluorescence-guided-surgery-a-report/

Gene Editing

Aviva Lev-Ari, PhD, RD

Aviva Lev-Ari, PhD, RD

 

 

 

 

 

 

Dr. Aviva Lev-Ari reports that a new technique developed at MIT Broad Institute and the Rockefeller University can edit DNA in precise locations

taken from Science News titled Editing Genome With High Precision: New Method to Insert Multiple Genes in Specific Locations, Delete Defective Genes”.

Using this system, scientists can alter

  • several genome sites simultaneously and
  • can achieve much greater control over where new genes are inserted

According to Feng Zhang, this is an improvement beyond splicing the gene in specific locations and

insertion of complexes difficult to assemble known as

transcription activator-like effector nucleases (TALENs).

  • The researchers create DNA-editing complexes
  • using naturally occurring bacterial protein-RNA systems
  • that recognize and snip viral DNA, including
  • a nuclease called Cas9 bound to short RNA sequences.
  • they target specific locations in the genome, and
  • when they encounter a match, Cas9 cuts the DNA.

This approach can be used either to

  • disrupt the function of a gene or
  • to replace it with a new one.
  • To replace the gene, a DNA template for the new gene has to be copied into the genome after the DNA is cut. The method is also very precise –
  • if there is a single base-pair difference between the RNA targeting sequence and the genome sequence, Cas9 is not activated.

In its first iteration, it appears comparable in efficiency to what

zinc finger nucleases and TALENs have to offer.

The research team has deposited the necessary genetic components with a nonprofit called Addgene, and they have also created a website with tips and tools for using this new technique.

The above story is reprinted from materials provided by Massachusetts Institute of Technology.

The original article was written by Anne Trafton. Le Cong, F. Ann Ran, David Cox, Shuailiang Lin, Robert Barretto, Naomi Habib, Patrick D. Hsu, Xuebing Wu, Wenyan Jiang, Luciano Marraffini, and Feng Zhang.

Multiplex Genome Engineering Using CRISPR/Cas Systems.
Science, 3 January 2013   DOI: 10.1126/science.1231143.
http://Science.comEditing genome with high precision: New method to insert multiple genes in specific locations, delete defective genes. ScienceDaily. Retrieved January 20, 2013, from http://www.sciencedaily.com­ /releases/2013/01/130103143205.htm?  goback=%2Egde_4346921_member_205356312.

Dr. Lev-Ari also reports on a study of early detection of breast cancer in “Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment“, by Dr. Rotem Karni and PhD student Vered Ben Hur at the Institute for Medical Research Israel-Canada of the Hebrew University.
http://pharmaceuticalintelligence.com/2013/01/17/mechanism-involved-in-breast-cancer-cell-growth-function-in-early-detection-treatment/
These researchers have discovered a new mechanism by which breast cancer cells switch on their aggressive cancerous behavior. The discovery provides a valuable marker for the early diagnosis and follow-up treatment of malignant growths.
The method they use is

  • RNA splicing and insertion.
  • The information needed for the production of a mature protein is encoded in segments called exons .
  • In the splicing process, the non-coding segments of the RNA (introns) are spliced from the pre-mRNA and
  • the exons are joined together.

Alternative splicing is when a specific ”scene” (or exon) is either inserted or deleted from the movie (mRNA), thus changing its meaning.

  • Over 90 percent of the genes in our genome undergo alternative splicing of one or more of their exons, and
  • the resulting changes in the proteins encoded by these different mRNAs are required for normal function.
  • the normal process of alternative splicing is altered in cancer, and
  • ”bad” protein forms are generated that aid cancer cell proliferation and survival.

The researchers reported in online Cell Reports that breast cancer cells

  • change the alternative splicing of an important enzyme, calledS6K1, which is
  • a protein involved in the transmission of information into the cell.
  • when this happens, breast cancer cells start to produce shorter versions of this enzyme and
  • these shorter versions transmit signals ordering the cells to grow, proliferate, survive and invade other tissues (otherwise proliferation is suppressed)

The application to biotherapeutics would be to ”reverse” the alternative splicing of S6K1 in cancer cells back to the normal situation as a novel anti-cancer therapy.

 

Imaging Mass Cytometry

This literature review shows how researchers used CyTOF mass cytometry to obtain spatial resolution of cell samples.

The authors used mass cytometry to measure heterogeneity in breast cancer tumors using FFPE breast cancer samples. [© Kheng Guan Toh – Fotolia.com]

The integration of mass spectrometry (specifically laser ablation of the sample in combination with inductively coupled plasma mass spectrometry) with flow cytometry instrumentation along with sensitive and rare earth metal labels

  • has enabled multiplexing of up to 32 cell markers (see Assay Drug Dev Technol 2011;9:567 commentary “Flow cytometry goes atomic;” CyTOF system sold by Fluidigm, formerly DVS Sciences).

This process employs typical immunocytochemistry techniques, but the antibodies are tagged with rare earth metal isotopes (predominately lanthanides)

  • that act as specific reporters of cellular proteins.

Comparison of these rare earth–labeled antibodies to typical fluorescent antibody labels supports that

  • these labels do not affect the specificity or sensitivity of the antibodies. In this article the authors* extend this method to obtain spatial resolution of cell samples.

 

mass cytometry to measure heterogeneity in breast cancer tumors using FFPE

mass cytometry to measure heterogeneity in breast cancer tumors using FFPE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Workflow of imaging mass cytometry.

 

 

is correlated with the position of the laser spot as it is scanned across the sample with 1 μm resolution.In the method, the signals from the rare earth reporters following laser ablation of the sample

The limit of detection is determined to be ∼500 molecules. The data can then be plotted based on the position of each ion spot for each rare earth reporter, and these images

  • are then overlaid to create a high-dimensional image that can be analyzed (Figure).

Measurement of a 0.5 mm × 0.5 mm area at 1 μm resolution takes ∼5 h. The system is capable of measuring 100 analytes simultaneously, but only 32 rare earth metal chelates are currently available. The authors applied this method

  • to measure heterogeneity in breast cancer tumors using formalin-fixed, paraffin-embedded (FFPE) breast cancer samples.

A total of 21 FFPE samples were analyzed using 32-plex imaging mass cytometry covering cell markers and phosphoproteins. Differences in expression even within the same tumor sample were noted, and

  • the subpopulations branch points often contained markers used for patient classification. Some exceptions occurred; for example,
  • Her2 was detected and confirmed in one triple-negative case.

This high-dimensional imaging should increase our understanding of tumor biology and pathologies.

*Abstract from Nature Methods 2014, Vol. 11: 403–406

Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer

  • allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions.

To gain spatial information, we have coupled

  • immunohistochemical and immunocytochemical methods
  • with high-resolution laser ablation to CyTOF mass cytometry.

This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution;

  • with the availability of additional isotopes, measurement of over 100 markers will be possible.

We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell–cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry

  • complements existing imaging approaches.

It will enable basic studies of tissue heterogeneity and function and

  • support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.

 

Preventing a cellular identity crisis 

http://news.sciencemag.org/sites/default/files/styles/thumb_article_l/public/sn-criticalgenesH.jpg?itok=0wmD-o4a

 

Staying true.

 

neural progenitor cells keep their identities

neural progenitor cells keep their identities

 

 

 

 

 

Researchers have discovered a molecular signature in the genome that might help cells like these neural progenitor cells keep their identities throughout their lives.

By Mitch Leslie 31 July 2014

Cells rely on different ways to establish who they are and what they do.  A novel mechanism

  • marks the identities of different kinds of cells in the human body—
  • and prevents them from transforming into another type altogether.

Scientists learned decades ago to read the basic genetic code by which cells convert a string of DNA bases into a protein’s amino acids. But for more than 10 years,

  • they’ve been trying to crack what’s known as the histone code, a more complex cipher embedded within organisms’ genomes.

Histones are the proteins that DNA coils around in chromosomes. Chemically tweaking histones in a variety of ways can

  • adjust the activity of genes, turning them up or down. For example,
  • cells shut off genes by attaching three methyl groups to a specific spot on a histone type known as H3.
  • affixing three methyl groups to another H3 location, a modification known as H3K4me3, has a different effect.

Cells typically add the H3K4me3 tags to histones in small sections of the genome, but researchers noticed that

  • sometimes the tag can sprawl across much larger areas,
  • modifying broad swaths of histones.

To find out whether these large blocks of histones carrying H3K4me3 tags convey a message in the histone code, molecular geneticist Anne Brunet of Stanford University in Palo Alto, California, and colleagues

  • traced their occurrence in more than 20 different cell types.

They found that the longest stretches pinpoint different sets of genes in different types of cells. As a result, the researchers realized

  • they could discriminate liver cells from, say, muscle cells or kidney cells
  • based only on the chromosomal locations of the largest H3K4me3 blocks. In addition,
  • they noticed that these stretches tended to mark genes that are crucial for a cell type’s function or
  • that help make it distinct. In embryonic stem cells, for instance,
  • they occur on genes that control the cells’ capacity to specialize.

The researchers further demonstrated that the labels mark cell identity genes by

  • using a technique called RNA interference (RNAi) in adult neural progenitor cells,
  • which can morph into any cell type in the brain.

As the researchers revealed online today in Cell,

  • they applied RNAi to dial down the genes that carried large blocks of H3K4me3 tags and
  • found that it impaired the cells’ ability to reproduce and to spawn neurons. However,
  • the progenitor cells could still divide normally if the researchers quieted genes that had only short sections of H3K4me3 tags or none at all.

The presence of long stretches of H3K4me3 markers

  • might help cells keep their identities for life.
  • “we’ve discovered a new signature,” Brunet says.

Although many other scientists have studied H3K4me3 tags, “the concept that this one mark

  • can distinguish all these cell types
  • the discovery could allow quick identification of cell types,
  • which would be useful in situations such as cancer diagnosis.

Posted in Biology

 

Gene Deletion Slows Aging and Reduces Cancer Risk

gene deletion

gene deletion

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source: © Gernot Krautberger – Fotolia.com

Scientists at the Wistar Institute say they have discovered that

  • mice lacking a specific protein live longer lives with fewer age-related illnesses.

The mice that lack the TRAP-1 protein, demonstrated less age-related tissue degeneration, obesity, and spontaneous tumor formation when compared to normal mice. Their findings could change how scientists view the metabolic networks within cells. In healthy cells,

TRAP-1 is an important regulator of metabolism and

  •  regulates energy production in mitochondria, organelles that generate chemically useful energy for the cell.

In the mitochondria of cancer cells, TRAP-1 is universally overproduced.

The Wistar team’s report (“Deletion of the Mitochondrial Chaperone TRAP-1 Uncovers Global Reprogramming of Metabolic Networks”), which appears in Cell Reports, shows how ”

  • knockout” mice bred to lack the TRAP-1 protein compensate for this loss by switching to alternative cellular mechanisms for making energy.

“We see this astounding change in TRAP-1 knockout mice, where they show fewer signs of aging and are less likely to develop cancers,” said Dario C. Altieri. M.D.,  director of the Wistar Institute’s National Cancer Institute-designated Cancer Center. “Our findings provide

  • an unexpected explanation for how TRAP-1 and related proteins regulate metabolism within our cells.
  • we didn’t expect to see  healthier mice with fewer tumors.

Dr. Altieri and his colleagues created the TRAP-1 knockout mice as part of their

  • ongoing investigation into the drug Gamitrinib, which targets the protein in the mitochondria of tumor cells.

TRAP-1 is a member of the heat shock 90 (HSP90) protein family, which are

  • chaperone proteins that guide the physical formation of other proteins and
  • serve a regulatory function within mitochondria.
  • Tumors use HSP90 proteins like TRAP-1 to help survive therapeutic attack.

In tumors,

  • the loss of TRAP-1 is devastating, triggering a host of catastrophic defects, including
  • metabolic problems that ultimately result in in death of the tumor cells,, BUT
  • Mice that lack TRAP-1 from the start have three weeks in the womb to compensate for the loss of the protein.”

In the knockout mice, the loss of TRAP-1

  1. causes mitochondrial proteins to misfold, which then
  2. triggers a compensatory response that causes cells to consume more oxygen and metabolize more sugar. which
  3. causes mitochondria in knockout mice to produce deregulated levels of ATP,
  4. the chemical used as an energy source to power all the everyday molecular reactions that allow a cell to function.

This increased mitochondrial activity actually creates a moderate boost in oxidative stress (free radical damage) and the associated DNA damage. While DNA damage may seem counterproductive to longevity and good health,

  • the low level of DNA damage actually reduces cell proliferation—slowing growth down to allow the cell’s natural repair mechanisms to take effect.

“TRAP-1−/− mice are viable and showed reduced incidence of age-associated pathologies including – obesity, inflammatory tissue degeneration, dysplasia, and spontaneous tumor formation,- accompanied by

  • global upregulation of oxidative phosphorylation and glycolysis transcriptomes, causing

 

 

  1. deregulated mitochondrial respiration,
  2. oxidative stress,
  3. impaired cell proliferation, and
  4. a switch to glycolytic metabolism in vivo.

These data identify TRAP-1 as

  1. a central regulator of mitochondrial bioenergetics, and
  2. this pathway could contribute to metabolic rewiring in tumors.”

“Our findings strengthen the case

  • for targeting HSP90 in tumor cells, but it
  • may have implications for metabolism and longevity,” explained Dr. Altieri.

GEN News 8-1-2014

 

The role of the Wnt signaling pathway in cancer stem cells: prospects for drug development

Yong-Mi Kim, Michael Kahn
1Children’s Hospital Los Angeles, Division of Hematology and Oncology, Department of Pediatrics and Pathology, 2Department of Biochemistry and Molecular Biology, Keck School of Medicine of University of Southern California, 3Norris Comprehensive Cancer Research Center, University of Southern California, Los Angeles, CA, USA

Research and Reports in Biochemistry July 2014; 4:1—12
http://dx.doi.org/10.2147/RRBC.S53823

Abstract: Cancer stem cells (CSCs), also known as tumor initiating cells are now considered to be

  • the root cause of most if not all cancers, evading treatment and giving rise to disease relapse.

They have become a central focus in new drug development.

  1. Prospective identification,
  2. understanding the key pathways that maintain CSCs, and
  3. being able to target CSCs, particularly
  • if the normal stem cell population could be spared, could offer an incredible therapeutic advantage.

The Wnt signaling cascade is critically important in stem cell biology, both

  • in homeostatic maintenance of tissues and organs through their respective somatic stem cells and
  • in the CSC/tumor initiating cell population.

Aberrant Wnt signaling is associated with a wide array of tumor types. Therefore, the ability to

  • safely target the Wnt signaling pathway offers enormous promise to target CSCs. However,
  • just like the sword of Damocles, significant risks and concerns regarding targeting such a critical pathway in normal stem cell maintenance and tissue homeostasis remain ever present.

With this in mind, we review recent efforts in modulating the Wnt signaling cascade and critically analyze therapeutic approaches at various stages of development.

Keywords: beta-catenin, CBP, p300, wnt inhibition

 

 

A*STAR Scientists Pinpoint Genetic Changes that Spell Cancer: Fruit flies light the way for scientists to uncover genetic changes.

With a new approach, researchers may rapidly distinguish the range of

  • genetic changes that are causally linked to cancer (i.e.“driver” mutations)
  • versus those with limited impact on cancer progression.

This study published in the prestigious journal Genes & Development could pave the way

  • to design more targeted treatment against different cancer types, based on
  • the specific cancer-linked mutations present in the patient,
  • an advance in the development of personalized medicine.

Signaling pathways involved in tumour formation are conserved from fruit flies to humans. In fact, about 75 percent of known human disease genes have a recognizable match in the genome of fruit flies.
Leveraging on their genetic similarities, Dr Hector Herranz, a post-doctorate from the Dr. Stephen Cohen’s team developed an innovative strategy to genetically screen the whole fly genome for “cooperating” cancer genes.

  • These genes appear to have little or no impact on cancer.
  • However, they cooperate with other cancer genes, so that
  • the combination causes aggressive cancer, which
  • neither would cause alone.

In this study, the team was specifically looking for genes that

  • could cooperate withEGFR “driver” mutation,
  • a genetic change commonly associated with breast and lung cancers in humans.
  • SOCS5 (reported in this paper) is one of the several new “cooperating” cancer genes to be identified.

Already, there are indications that levels of SOCS5 expression are

  • reduced in breast cancer, and
  • patients with low levels of SOCS5 have poor prognosis.”

The IMCB team is preparing to explore the use of SOCS5 as a biomarker in diagnosis for cancer.

 

Probing What Fuels Cancer

http://genes&development.com

‘Altered cellular metabolism is a hallmark of cancer,’ says Dr Patrick Pollard, in the Nuffield Department of Clinical Medicine at Oxford.

Most cancer cells get the energy they need predominantly through

  • a high rate of glycolysis – allowing cancer cells deal with the low oxygen levels that tend to be present in a tumour. But
  • whether dysfunctional metabolism causes cancer, as Warburg believed, or is something that happens afterwards is a different question.
  • In the meantime, gene studies rapidly progressed and indicated that genetic changes occur in cancer.

DNA mutations spring up all the time in the body’s cells, but

  • most are quickly repaired.
  • Alternatively the cell might shut down or be killed off (apoptosis) before any damage is caused. However, the repair machinery is not perfect.
  • If changes occur that bypass parts of the repair machinery or sabotage it,
  • the cell can escape the body’s normal controls on growth and
  • DNA changes can begin to accumulate as the cell becomes cancerous.

Patrick believes certain changes in cells can’t always be accounted for by ‘genetics.’
He is now collaborating with Professor Tomoyoshi Soga’s large lab at Keio University in Japan, which has been at the forefront of developing the technology for metabolomics research over the past couple of decades.

The Japanese lab’s ability to

  • screen samples for thousands of compounds and metabolites at once, and
  • the access to tumour material and cell and animal models of disease
  • enables them to probe the metabolic changes that occur in cancer.

There is reason to believe that

  • dysfunctional cell metabolism is important in cancer.
  • genes with metabolic functions are associated with some cancers
  • changes in the function of a metabolic enzyme have been implicated in the development of gliomas.

These results have led to the idea that

  • some metabolic compounds, or metabolites, when they accumulate in cells, can cause changes to metabolic processes and set cells off on a path towards cancer.

Patrick Pollard and colleagues have now published a perspective article in the journal Frontiers in Molecular and Cellular Oncology that proposes

  • fumarate as such an ‘oncometabolite’. Fumarate is a standard compound involved in cellular metabolism.

The researchers summarize evidence that shows how

  • accumulation of fumarate when an enzyme goes wrong affects various biological pathways in the cell.
  • It shifts the balance of metabolic processes and disrupts the cell in ways that could favour development of cancer.

Patrick and colleagues write in their latest article that the shift in focus of cancer research to include cancer cell metabolism ‘has highlighted how woefully ignorant we are about the complexities and interrelationships of cellular metabolic pathways’.

 

NATURE GENETICS | BRIEF COMMUNICATION

 

Recurrent SMARCA4 mutations in small cell carcinoma of the ovary

Nature Genetics (2014); 46: 424–426    http://dx.doi.org:/10.1038/ng.2922

 

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, highly aggressive form of ovarian cancer primarily diagnosed in young women. We identified

  • inactivating biallelic SMARCA4 mutations in 100% of the 12 SCCOHT tumors examined.

Protein studies confirmed loss of SMARCA4 expression, suggesting a key role for the SWI/SNF chromatin-remodeling complex in SCCOHT.

At a glance

Figures

Figure 1: SMARCA4 mutations in SCCOHT and TCGA samples.close

SMARCA4 mutations in SCCOHT and TCGA samples.close

SMARCA4 mutations in SCCOHT and TCGA samples.close

 

 

 

 

 

 

 

 

 

 

 

 

(a) Domain structure of the SMARCA4 protein (UniProt, SMCA4_HUMAN) overlaid with the alterations identified in 11 of the 12 SCCOHT cases in this study (case numbers in parentheses; case 103 with exon deletion is not shown). SNF2_N, SNF…

http://www.nature.com/ng/journal/v46/n5/carousel/ng.2922-F1.jpg

Figure 2: Analyses of the splice-site mutation in case 102.close

Analyses of the splice-site mutation in case 102.close

Analyses of the splice-site mutation in case 102.close

 

 

 

 

 

 

 

 

 

 

(a) Immunoblotting with antibody to the N terminus of SMARCA4. A high-grade serous ovarian cancer cell line (PEO4) and frozen tumor samples from two individuals with high-grade serous ovarian cancer (HGOC) were used as positive control…

http://www.nature.com/ng/journal/v46/n5/carousel/ng.2922-F2.jpg

 

References

 

  1. Estel, R., Hackethal, A., Kalder, M. & Munstedt, K.  Gynecol. Obstet.284, 1277–1282(2011).
  2. Young, R.H., Oliva, E. & Scully, R.E.  J. Surg. Pathol.18, 1102–1116 (1994).
  3. Harrison, M.L. et al Oncol.100, 233–238 (2006).
  4. Seidman, J.D.  Oncol.59, 283–287 (1995).
  5. Pautier, P. et al Oncol.18, 1985–1989 (2007).
  6. McCluggage, W.G.  Anat. Pathol.11, 288–296 (2004).
  7. Hendricks, K.B., Shanahan, F. & Lees, E.  Cell. Biol.24, 362–376 (2004).
  8. Napolitano, M.A. et al Cell Sci.120, 2904–2911 (2007).
  9. Kupryjańczyk, J. et al J. Pathol.64, 238–246 (2013).
  10. Longy, M., Toulouse, C., Mage, P., Chauvergne, J. & Trojani, M.  Med. Genet.33, 333–335(1996).
  11. McDonald, J.M. et al Pediatr. Surg.47, 588–592 (2012).
  12. Reisman, D., Glaros, S. & Thompson, E.A. Oncogene28, 1653–1668 (2009).
  13. Wilson, B.G. & Roberts, C.W.  Rev. Cancer11, 481–492 (2011).
  14. Oike, T. et alCancer Res.73, 5508–5518 (2013).
  15. Guan, B., Wang, T.L. & Shih, I.-M. Cancer Res.71, 6718–6727 (2011).
  16. Won, H.H., Scott, S.N., Brannon, A.R., Shah, R.H. & Berger, M.F.  Vis. Exp.80, e50710(2013).
  17. Wagle, N. et alCancer Discov.2, 82–93 (2012).
  18. Li, H. & Durbin, R. Bioinformatics25, 1754–1760 (2009).
  19. DePristo, M.A. et al Genet.43, 491–498 (2011).
  20. Cibulskis, K. et al Biotechnol.31, 213–219 (2013).
  21. Robinson, J.T. et al Biotechnol.29, 24–26 (2011).
  22. Cerami, E. et alCancer Discov.2, 401–404 (2012).
  23. Gao, J. et al Signal.6, pl1 (2013).

Download references

Primary authors

 

These authors contributed equally to this work.

Petar Jelinic & Jennifer J Mueller, Department of Surgery

Petar Jelinic, Jennifer J Mueller, Narciso Olvera, Fanny Dao & Douglas A Levine
Department of Surgery

Sasinya N Scott, Ronak Shah, Robert A Soslow & Michael F Berger
Department of Pathology,

JianJiong Gao & Nikolaus Schultz
Computational Biology Program,

Mithat Gonen  Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Corresponding author

Douglas A Levine

Supplementary information

 

Supplementary Figures

  1. Supplementary Figure 1: Sequence analyses forSMARCA4 in SCCOHT cases. (715 KB)

Next-generation sequence coverage demonstrating identified variants (top panels) and validation through Sanger sequencing (bottom panels).

  1. Supplementary Figure 2:SMARCA4 gene expression across TCGA tumors for cases with available mutation and RNA-seq data (RSEM). (366 KB)

A correlation is seen between inactivating SMARCA4 mutations and decreased gene expression across various solid tumors. A two-sided Student’s t test was used to compare samples with non-missense mutations and other samples without mutations or with only missense mutations. For all TCGA samples, the mean RNA-seq RSEM (2,050, s.d. of 1,760) was less in samples with non-missense mutations than in other samples without mutations or with only missense mutations (3,724, s.d. of 1,692; P = 8.7 × 10−4). For TCGA lung adenocarcinoma samples, the mean RNA-seq RSEM (601, s.d. of 370) was less in samples with non-missense mutations than in other samples without mutations or with only missense mutations (3,330, s.d. of 1,524; P = 2 × 10−8).

  1. Supplementary Figure 3: Immunohistochemistry for SMARCA4 in SCCOHT cases. (566 KB)

High-grade serous ovarian carcinoma is used as a positive control. Case numbers are indicated in each panel. Immunohistochemistry results are provided in Supplementary Table 1. Note the intense staining of blood vessels and stromal cell nuclei as internal controls.

  1. Supplementary Figure 4: Analysis of homozygous deletion in case 103. (109 KB)

Next-generation sequence coverage demonstrating that exons 25 and 26 are deleted. An electropherogram from Sanger sequencing of cDNA validating that the deletion retains an ORF from exon 24 to exon 27 (Panel A). One-step RT-PCR confirms that tumor tissue yields a single band with primers that span exons 24 and 27 (Panel B; *, nonspecific band). One-step RT-PCR with primers targeting regions upstream and downstream from the deletion site show equal expression, demonstrating continuation of transcription downstream from the deletion (Panel C).

  1. Supplementary Figure 5: Analysis of splice-site variant in case 102. (90 KB)

One-step RT-PCR confirms that the exon-intron band is preferentially expressed over the exon-exon band in tumor tissue (Panel A). One-step RT-PCR with primers targeting regions upstream and downstream from the mutation site show equal expression, demonstrating continuation of transcription downstream from the mutation (Panel B). Immunoblots are shown in Figure 2b. The exon-exon primers detected weaker bands, reflecting loss of expression in tumor tissues compared with normal tissues in cases with splice-site mutations. The exon-intron primers demonstrated equivalent to greater expression of the retained intron in the tumor tissues. As SMARCA4 introns may be retained in non-cancer tissues, some intronic expression is expected in normal tissues. These data taken together indicate preferential intronic expression, as expected, in cDNA sequenced from tumor samples with splice-site mutations.

  1. Supplementary Figure 6: Sequence analyses forSMARCA4 in the H1299 cell line. (134 KB)

An electropherogram from Sanger sequencing of genomic DNA validating a 69-nt deletion in the ORF of this control cell line that results in loss of protein expression, as shown inFigure 2b.

  1. Supplementary Figure 7: SMARCA4 effect on cell proliferation. (131 KB)

SMARCA4 overexpression in H1299 cells. Representative immunoblot from three biologic replicates demonstrates a correlation between increased SMARCA4 and p21 expression (Panel A). Cell growth assessment in H1299 cells overexpressing SMARCA4. Mean cell counts from three biologic replicates (Panel B). Representative immunoblot confirmed SMARCA4 knockdown in 293T cells using shRNA. As a control, shNTC (Non-Targeting Control) was used (Panel C). XTT proliferation assay in 293T cells depleted of SMARCA4. Means represent three independent experiments (Panel D).

  1. Supplementary Figure 8: Overall survival among lung adenocarcinoma TCGA cases based on inactivatingSMARCA4  (174 KB)

Median overall survival was 11.6 months among 6 patients with inactivating SMARCA4mutations compared with 44.6 months for 197 patients without inactivating mutations.

  1. Supplementary Figure 9: Histopathological features of an SCCOHT. (979 KB)

The typical histopathological features of SCCOHT, including a combination of small neoplastic cells forming a pseudofollicular space and larger rhabdoid cells, are visible in a sample obtained from 1 of 12 tumors that were subjected to target capture and massively parallel DNA sequencing (hematoxylin and eosin).

PDF files

  1. Supplementary Text and Figures (5,357 KB)

Supplementary Figures 1–9 and Supplementary Tables 1–5

 

 

CRISPR-Cas9 Foundational Technology originated at UC, Berkeley & UCSF, Broad Institute is developing Biotech Applications — Intellectual Property emerging as Legal Potential Dispute

Curator and Reporter: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/06/18/crispr-cas9-foundational-technology-originated-at-uc-berkeley-ucsf-broad-institute-is-developing-biotech-applications-intellectual-property-emerging-as-legal-potential-dispute/

 

CRISPR-Cas9 Foundational Technology – The definition of “Prior Art” is at a very high stack, June 2014.

On 6/16/2014 Dr. Aviva Lev-Ari published the following two articles:

Lecture Contents delivered at Koch Institute for Integrative Cancer Research, Summer Symposium 2014: RNA Biology, Cancer and Therapeutic Implications, June 13, 2014 @MIT
http://pharmaceuticalintelligence.com/2014/06/16/lecture-contents-delivered-at-koch-institute-for-integrative-cancer-research-summer-symposium-2014-rna-biology-cancer-and-therapeutic-implications-june-13-2014-mit/
Prediction of the Winner RNA Technology, the FRONTIER of SCIENCE on RNA Biology, Cancer and Therapeutics & The Start Up Landscape in Boston
http://pharmaceuticalintelligence.com/2014/06/16/prediction-of-the-winner-rna-technology-the-frontier-of-science-on-rna-biology-cancer-and-therapeutics-the-start-up-landscape-in-boston/

 

Other related articles on CRISPR-Cas9 Technology published on this Open Access Online Scientific Journal include the following:

2:15 – 2:45, 6/13/2014, Jennifer Doudna “The biology of CRISPRs: from genome defense to genetic engineering”

http://pharmaceuticalintelligence.com/2014/06/13/215-245-6132014-jennifer-doudna-the-biology-of-crisprs-from-genome-defense-to-genetic-engineering/

 

Ribozymes and RNA Machines – Work of Jennifer A. Doudna

http://pharmaceuticalintelligence.com/2013/04/15/ribozymes-and-rna-machines-work-of-jennifer-a-doudna/

 

CRISPR @MIT – Genome Surgery

http://pharmaceuticalintelligence.com/2014/04/21/crispr-mit-genome-surgery/


Gene Therapy and the Genetic Study of Disease: @Berkeley and @UCSF – New DNA-editing technology spawns bold UC initiative as Crispr Goes Global

http://pharmaceuticalintelligence.com/2014/03/27/gene-therapy-and-the-genetic-study-of-disease-berkeley-and-ucsf-new-dna-editing-technology-spawns-bold-uc-initiative-as-crispr-goes-global/

Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

http://pharmaceuticalintelligence.com/2013/03/28/diagnosing-diseases-gene-therapy-precision-genome-editing-and-cost-effective-microrna-profiling/

An expanded-DNA Biology from Scripps Research Institute: Beyond A-T and C-G: Applications for new Medicines and Nanotechnology

http://pharmaceuticalintelligence.com/2014/05/11/an-expanded-dna-biology-from-scripps-research-institute-beyond-a-t-and-c-g-applications-for-new-medicines-and-nanotechnology/

 

Evaluate your Cas9 Gene Editing Vectors: CRISPR/Cas Mediated Genome Engineering – Is your CRISPR gRNA optimized for your cell lines?

http://pharmaceuticalintelligence.com/2014/03/25/evaluate-your-cas9-gene-editing-vectors-crisprcas-mediated-genome-engineering-is-your-crispr-grna-optimized-for-your-cell-lines/

 

2:15 – 2:45, 6/13/2014,  Jennifer Doudna  “The biology of CRISPRs: from genome defense to genetic engineering” 

http://pharmaceuticalintelligence.com/2014/06/13/215-245-6132014-jennifer-doudna-the-biology-of-crisprs-from-genome-defense-to-genetic-engineering/

About CRISPR “this technology will revolutionize biology in the same way PCR did,” Rudolf Jaenisch introducing Jennifer Doudna

Top CRISPR Related Publications

http://blog.appliedstemcell.com/top-crispr-related-publications/

Capturing key concepts of Prof.  Jennifer Doudna’s Lecture @ KI Symposium:

  • acquired immunity in bacteria
  • three steps:
  1. adaptation
  2. biogenesis
  3. interference

 

 

Big Pharma is using its venture cash to outsource early R&D to biotech

July 31, 2014 | By John Carroll

Analysts at Silicon Valley Bank have been crunching the numbers on biotech investing, and they have found that

  • a group of busy corporate venture arms has fundamentally changed the landscape for startups and
  • the entire field of early-stage drug development–
  • with some big implications for the current crop of industry upstarts.

Over the past two years corporate venture funding for biotech companies has surged back to 2008 levels, the bank’s analysts conclude, and

  • it now adds up to a much larger portion of the total amount of investment cash that’s available to biotechs.

Last year these corporate financing arms accounted for slightly

  • more than a third of all the cash that flowed into biotech, according to SVB. And
  • the corporate VCs have a big appetite for investing in early-stage rounds.
Courtesy of Silicon Valley Bank

Courtesy of Silicon Valley Bank

 

 

 

 

 

 

 

 

 

 

 

 

Courtesy of Silicon Valley Bank

“I think we’ve reached a healthy level of funding in the sector right now,” says Jon Norris, the author of the report and managing director at Silicon Valley Bank. He adds that

  • with the IPO window still open to biotechs, a lot of early- or mid-stage companies are now choosing to jump through
  • to the public market rather than make a deal with pharma.

The IPO alternative has also made it possible to drive up the value of biotech assets, which now command record payments.

But that’s a trend that can’t run forever.

“This can’t run out too much longer into 2015,” says Norris. “I can see it starting to close.”

As the window shuts, he adds, you can expect to see the number of M&A deals rise. And the biggest biotech deals will likely be worth more, as big exits–defined as deals with an upfront of $75 million or more–jumped to an average record high of $549 million last year, a 10% spike over 2012.

Courtesy of Silicon Valley Bank 2

Courtesy of Silicon Valley Bank 2

 

 

 

 

 

 

 

 

 

 

 

 

 

Courtesy of Silicon Valley Bank

In its analysis, Silicon Valley Bank concludes that the early-stage investment gamble now

  • amounts to a strategic move by the top Big Pharma companies to outsource a considerable portion of their early-stage R&D work,
  • priming the cash pump directly through their own venture arms as well as by investing in many of the new venture funds filling up with risk capital. And
  • the change-up is likely to continue to drive partnering as well as Big Pharma
  • forges a new round of development pacts and M&A deals with their venture colleagues involved in biotech.

“We’ve all seen over the last few years the pullback in overall R&D spending by pharma and biotech,” says Norris. “There’s a tendency for these (pharma) folks to outsource their innovation.”

Not surprisingly, experimental

  • cancer drugs are attracting the bulk of Big Pharma’s attention and corporate cash, followed by
  • platform technologies that generate new leads, metabolics, ophthalmology, cardiovascular, CNS, dermatology, GI and inflammation, says SVB.

The leading corporate venture investors in the industry include Novartis ($NVS),Astellas, Pfizer ($PFE), S.R. One ($GSK), Amgen ($AMGN) and J&J Development Corp. ($JNJ). And nearly 90% of top corporate investment deals are directed at Series A or B rounds. More than half of these new investments, says SVB, were in preclinical or Phase I companies.

 

 

Extensive Promoter-Centered Chromatin Interactions Provide a Topological Basis for Transcription Regulation
(Li G, Ruan X, Auerbach RK, Sandhu KS, et al.) Cell 2012; 148(1-2): 84-98. http://cell.com

http://FrontiersMolecularCellularOncology.com

 

Using genome-wide Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET),
mapped long-range chromatin interactions associated with RNA polymerase II in human cells
uncovered widespread promoter-centered intragenic, extragenic, and intergenic interactions.

  • These interactions further aggregated into higher-order clusters
  • proximal and distal genes were engaged through promoter-promoter interactions.
  • most genes with promoter-promoter interactions were active and transcribed cooperatively
  • some interacting promoters could influence each other implying combinatorial complexity of transcriptional controls.

Comparative analyses of different cell lines showed that

  • cell-specific chromatin interactions could provide structural frameworks for cell-specific transcription,
  • and suggested significant enrichment of enhancer-promoter interactions for cell-specific functions.
  • genetically-identified disease-associated noncoding elements were spatially engaged with corresponding genes through long-range interactions.

Overall, our study provides insights into transcription regulation by

  • three-dimensional chromatin interactions for both housekeeping and
  • cell-specific genes in human cells.

 

New Nucleoporin: Regulator of Transcriptional Repression and Beyond.

NJ Sarma and K Willis
Nucleus 2012; 3(6): 1–8;     http://Nucleus.com © 2012 Landes Bioscience

 

Transcriptional regulation is a complex process that requires the integrated action of many multi-protein complexes.
The way in which a living cell coordinates the action of these complexes in time and space is still poorly understood.

  • nuclear pores, well known for their role in 3′ processing and export of transcripts, also participate in the control of transcriptional initiation.
  • nuclear pores interface with the well-described machinery that regulates initiation.

This work led to the discovery that

  • specific nucleoporins are required for binding of the repressor protein Mig1 to its site in target promoters.
  • Nuclear pores are involved in repressing, as well as activating, transcription.

Here we discuss in detail the main models explaining our result and consider what each implies about the roles that nuclear pores play in the regulation of gene expression.

 

Computational Design of Targeted Inhibitors of Polo-Like Kinase 1 ( lk1).

(KS Jani and DS Dalafave) Bioinformatics and Biology Insights 2012:6 23–31.
http://dx.doi.org:/10.4137/BBI.S8971

Computational design of small molecule putative inhibitors of Polo-like kinase 1 (Plk1) is presented. Plk1, which regulates the cell cycle, is often over expressed in cancers.

  • Down regulation of Plk1 has been shown to inhibit tumor progression.
  • Most kinase inhibitors interact with the ATP binding site on Plk1, which is highly conserved.
  • This makes the development of Plk1-specific inhibitors challenging, since different kinases have similar ATP sites.

However, Plk1 also contains a unique region called the polo-box domain (PBD), which is absent from other kinases.

  • the PBD site was used as a target for designed Plk1 putative inhibitors.
  • Common structural features of several experimentally known Plk1 ligands were first identified.
  • The findings were used to design small molecules that specifically bonded Plk1.
  • Drug likeness and possible toxicities of the molecules were investigated.
  • Molecules with no implied toxicities and optimal drug likeness values were used for docking studies.
  • Several molecules were identified that made stable complexes only with Plk1 and LYN kinases, but not with other kinases.
  • One molecule was found to bind exclusively the PBD site of Plk1.

Possible utilization of the designed molecules in drugs against cancers with over expressed Plk1 is discussed.

Conclusions

The previous discussions reviewed the status of an evolving personalized medicine multicentered and worldwide enterprise.  It is also clear from these reports that the search for targeted drugs matched to a cancer profile or signature has identified several approaches that show great promise.

  • We know considerably  more about metabolic pathways and linked changes in transcription that occur in neoplastic development.
  • There are several methods used to do highly accurate  insertions in gene sequences that are linked to specific metabolic changes, and
  • some may have significant implications for therapeutics, if
    • the link is a change that is associated with a driver mutation
    • the link can be identified by a fluorescent or other probe
    • the link is tied to a mRNA or peptide product that is a biomarker measured in the circulation
  • We have probes to genetic links to the control of many and interacting signaling pathways.
  • We know more about transcription through mRNA.
  • We are closer to the possibility that metabolic substrates, like ‘fumarate’ (a key intermediate in the TCA cycle), may provide a means to reverse regulate the neoplastic cells.
  • We may also find metabolic channels that drive the cells from proliferation to apoptosis or normal activity.

Summary

This discussion identified the huge expansion of genomic technology in the investigation of biopharmacotherapeutic targets that have been identified involving different levels and interacting signaling pathways.   There are several methods of analyzing gene expression, and a primary emphasis is given to combinations of mutations expressed in different cancer types.  There is a major hypothesis that expresses the need to focus on “central” “driver mutations” that correspond with the regulation of gene expression, cell proliferation, and cell metabolism.  What hasn’t been know is why drug resistance develops and whether the cellular migration and aerobic glycolysis can be redirected after cell metastasis occurs.

 

mutation in the matched nucleotides

mutation in the matched nucleotides

 

 

 

 

 

.

A slight mutation in the matched nucleotides can lead to chromosomal aberrations and unintentional genetic rearrangement. (Photo credit: Wikipedia)

 

Phosphofructokinase mechanism

Phosphofructokinase mechanism

 

 

 

 

 

Deutsch: Regulation der Phosphofructokinase (Photo credit: Wikipedia)

Additional Related articles

 

 

Universal Language: The Pistoia Alliance Takes on Indescribable Biology

 

By Aaron Krol

July 18, 2014 | The Pistoia Alliance, founded after a meeting between members of Pfizer, AstraZeneca, Novartis and GlaxoSmithKline, has come to resemble a United Nations of the life sciences industry. Now in its fifth year, the Alliance’s membership has grown to include nearly all the largest pharma companies (Eli Lilly is the only holdout in the top ten) plus a huge assortment of publishers, IT vendors, small biotechs and academic groups. It makes for a complicated network of business partners and competitors, but they do have some basic needs in common. In particular, the Pistoia Alliance exists to build IT architectures that serve the precompetitive stages of research and development.

“The key to the Pistoia Alliance is that, as time has gone by, most companies have figured out that you can’t go it alone,” says Sergio Rotstein, the Director of Research Business Technologies at Pfizer and a member of the Alliance’s board of directors. “Even the tightest of companies has opened up its walls quite a bit to collaboration… The idea of me asking my buddy from Merck, how did you solve that problem, and by the way would you mind giving me the solution — ten years ago, that would have gotten me laughed out of the room.”

The Pistoia Alliance has previously sponsored new methods for querying databases and the scientific literature, and a more effective algorithm for compressing and sharing genetic sequencing data. Over the past year, another Pistoia project, HELM, has entered the public domain after gradual development by an assortment of Alliance members. An open source language and set of editing tools for working with large biomolecules, HELM has already become a foundational part of research in at least three large pharmaceutical companies.

At the Bio-IT World Best Practices Awards this April, the HELM project won the Pistoia Alliance a top prize in the category of Informatics. These awards recognize advances in information technology and good management strategies at all levels of the biomedical industry. While the Best Practices Awards always seek to highlight programs that could be widely replicated, Bio-IT World rarely has the opportunity to single out a project that has been adopted so quickly across so many organizations as the Pistoia Alliance’s efforts around HELM.

A Loss for Words

HELM addresses a problem at the root level of drug discovery. Pharmaceutical and biotech companies are looking at increasingly complex molecules in the search for new therapeutics, testing out RNA- and peptide-based compounds that tap directly into cellular pathways. The trouble is that these large molecules, which are often hybrids of RNA, amino acids and other chemical structures, are difficult to concisely describe, even when their structures are perfectly known. They are too large and ungainly to represent atom-by-atom, but not uniform enough to be reduced to nucleotides and peptide chains.

“There have been a number of ways to represent small molecules,” says Rotstein. “That’s been the bread and butter of a number of companies for a long time, and that’s the realm of cheminformatics. And there’s been a lot of methodology for dealing with sequence-based entities, like genes and proteins, which is the realm of bioinformatics. The issue is that the types of molecules that we are targeting fall in between these two.”

This isn’t just a semantic issue; not having a standard language for biomolecules has practical consequences. It’s hard to register these molecules in databases, and even harder to conduct searches for them or share their structures with collaborators. The problem has recently come to a head, as growing knowledge of interlocking cellular systems has led researchers to therapies that increasingly resemble the body’s own tangled biology. “It follows the natural progression of science itself,” says Rotstein. “The application of peptides with unnatural amino acids, and the area of antibody -drug conjugates, has been growing a great deal over the past few years. A lot of the companies that traditionally worked in the small molecule space, nowadays are looking for a diverse portfolio.”

In 2008, Rotstein was part of an oligonucleotide unit at Pfizer that set out to build a new language to describe the compounds it was working with. The language would be similar to the small molecule notation SMILES (the Simplified Molecular-Input Line-Entry System), which renders a chemical structure as a continuous string of characters, while using symbols from the ASCII alphabet to resolve properties like where bonds occur and how molecules branch. Instead of using atoms as the smallest units in the chain, however, much larger groups — monomers like nucleotides and amino acids — would receive short, unique IDs that could be strung together into polymers. The amino acid cysteine, for instance, could be represented simply as “C.” New monomers would be registered with new IDs in a central database, and every ID would be linked to a complete description in small molecule notation.

oligonucleotide conplex

oligonucleotide conplex

 

 

A complex oligonucleotide peptide conjugate, featuring amino acids, RNA, and other chemical structures. The molecule is rendered as both a monomer graph, and in HELM notation. Reproduced from the Journal of Chemical Information and Modeling with permission of the author

The language was called HELM, the Hierarchical Editing Language for Macromolecules: “hierarchical” because strings of monomers are built into simple polymers, which in turn are joined into complex polymers. HELM was easy to use and unambiguous, and was soon adopted in many more departments at Pfizer. For the first time, it was possible to quickly enter a new macromolecule in Pfizer’s registry, check for uniqueness, and receive a corporate ID to take the project forward.

A Living Language

At the same time that Rotstein’s team was developing HELM at Pfizer, other pharma companies and informatics vendors were struggling with the same problem. The software provider Accelrys (now BIOVIA), for instance, had modified the Molfile chemical table format to deal with hybrid macromolecules, in a system the company called the Self-Contained Sequence Representation (SCSR). There was a danger of proliferating standards, which would not only create redundant work at each company writing its own language, but also threaten the ability of these organizations to share information with each other.

Meanwhile, a member survey at the Pistoia Alliance flagged the representation of complex biomolecules as one of the industry’s top three non-competitive problems. Since Pfizer had already published a paper on HELM and built a software toolkit around the system, the company volunteered to make the entire program open source and continue its development with other members of the Alliance.

“We saw an opportunity for Pfizer,” says Rotstein. “If this did indeed become a standard, and the open source tools continued to evolve through contributions of the whole community, that would help us too.” All told, 24 companies sent volunteers to work on HELM, untangling the code from Pfizer’s internal systems, making it public, and extending the tools that serve the language.

The entire HELM project is now available on GitHub, and uses the permissive MIT open source license, which gives anyone the right to download and modify the code without requiring any contribution back to the project. That should encourage vendors to build commercial software on top of HELM, helping to foster compatibility across the industry.

The basic HELM toolkit includes search functions and uniqueness checks, as well as the HELM Editor, a platform for drawing chemical structures. The HELM Editor lets users plug in or draw monomers, then move up the scale to polymers made from those building blocks. It can be used simply as a translation tool, taking existing structures and giving them names in HELM notation, but Rotstein says it would also be a preferred platform for making new molecules from scratch.

 

HELM photo of siRNA

HELM photo of siRNA

 

 

 

A screenshot from the HELM Editor, showing a siRNA molecule under construction. Image credit: Pistoia Alliance

Since HELM was released to the public last year, development has continued at various partner organizations. Roche was one of the first adopters, and has been relentlessly adding functionality to the toolkit. “Roche created a custom antibody-drawing capability on top of the HELM Editor, and it’s truly phenomenal,” says Rotstein. “They are now putting the finishing touches on that, and as soon as they’re done, they are pushing it right back out into the open source.”

He adds that Pfizer plans to start using Roche’s antibody drawing tool itself. “That tool alone will probably return our entire investment on externalizing HELM.”

Most recently, this month the Pistoia Alliance released Exchangeable HEL M, another big push for interoperability. While some basic monomers, like the natural amino acids and nucleotides, have universal IDs in HELM, most monomer IDs are unique to each user, stored in an internal database. That’s a necessary feature to make HELM flexible to the needs of every user, but it means that most molecules only make sense in the context of the databases against which they were designed.

Exchangeable HELM provides a file format that includes both the larger HELM sequence of a macromolecule, and separately, the chemical structure of each monomer inside it. That makes it easy for collaborators — say, a large pharma company and a CRO hired for a specific project — to send molecular structures back and forth. Exchangeable HELM also offers a tool to “translate” between databases, if two organizations have different internal IDs for the same monomer.

The Lingua Franca

So far, Pfizer, Roche, and Lundbeck are the largest drug companies to switch their systems over to HELM, and Rotstein says a “robust pipeline of other companies” is preparing to adopt the language. Meanwhile, vendors that serve the drug industry are preparing for a widespread change. NextMove Software and ChemAxon are both working in HELM, and even BIOVIA, which plans to continue using SCSR internally, has made its systems compatible with HELM to more easily share large molecules with clients and partners.

The adoption of HELM will be buoyed by public resources in the life sciences that are turning to the language as the obvious choice for representing complex molecules. One big supporter is the European Bioinformatics Institute, whose ubiquitous ChEMBL database of chemical compounds will include HELM notations in its next release.

Increasingly, says Rotstein, the Pistoia Alliance is speaking of a HELM ecosystem. “We want to have content providers that have structures in HELM format. We want vendors whose software can read and write HELM. We want companies that use HELM as their standard, we want CROs that can use HELM to exchange information with those companies, and next on our list are downstream things like scientific journals and regulatory agencies.” Large publishers and regulators would be especially important adopters, because they are such frequent and public ports of call for companies sending macromolecular structures outside their walls. If the FDA or Nature Publishing Group began accepting HELM structures, it could be a major convenience when applying for clinical trials and publications. “It would be much easier to just send a file that says, ‘here’s exactly what my structure is,’” says Rotstein, “rather than having to verbally explain the structure.”

Having HELM in place as a widely-shared language could also benefit other Pistoia Alliance projects. For example, the Controlled Substance Compliant Services Project is currently building a database of compounds that are regulated or restricted in various countries around the world, so companies can quickly refer to the local legislation affecting compounds they want to work with. If large biomolecules are subject to regulations, HELM would be a convenient way to make those policies searchable.

Like other Pistoia Alliance initiatives, HELM is designed to run smoothly in the background. Defining the structure of macromolecules in a standard format, is not a process that should offer any company an edge in drug discovery, but a basic feature at the foundation of the life sciences. In an ideal world, says Rotstein, “this should be a non-issue. The ability to represent these molecules, and get them in and out of our system so we can store them, search them, and run calculations on them, should be trivial.”

 

Pathology Practiced Todat

How doctors group non Hodgkin lymphomas

There are many different types of non Hodgkin lymphoma. Doctors estimate that there are more than 60 subtypes. Understanding how the  different types of NHL are grouped, or classified, can be difficult. A variety of systems for classifying lymphomas have been used over the years. The latest is the World Health Organisation classification of 2008. We give a simple description of the groups on this page.

The pathologist will examine the cells to see

Grade of NHL

Doctors put non Hodgkin lymphomas into 2 groups depending on how quickly they are likely to grow and spread

  • Low grade (indolent) – these tend to grow very slowly
  • High grade (aggressive) – these tend to grow more quickly

The different grades of non Hodgkin lymphoma are treated in slightly different ways.

Type of white blood cell

One way of classifying NHL is by the type of white blood cells (lymphocytes) affected – B cells or T cells. Most people with NHL have B cell lymphomas.

What the lymphoma cells look like

Your doctor will be able to give your type of non Hodgkin lymphoma a name depending on the appearance of the lymphoma cells. These names are quite complicated. But they are useful to doctors because the different types can behave differently. Different treatments are used for the different types. So knowing the type helps the doctor know how to treat them. In the laboratory a pathologist looks at the cells to see if they are

  • Large or small
  • Grouped together in structures called follicles (follicular type) or spread out (diffuse type)

Low grade non Hodgkin lymphomas tend to have small cells that are grouped together.

Low grade (slowly growing) NHL

Low grade lymphomas tend to grow very slowly. Doctors call them indolent lymphomas. They include

Small lymphocytic lymphoma

Small lymphocytic lymphoma is also called chronic lymphocytic leukaemia (CLL). It makes up about 6 out of 100 lymphomas in the UK (6%). In theory, lymphoma is an illness that starts in the lymph nodes and leukaemia is an illness of the blood. But leukaemia and lymphoma have many similarities and often affect the body in similar ways. Chronic lymphocytic leukaemia is the term used for this condition if many of the abnormal cells are in the blood. Doctors call it small lymphocytic lymphoma when the disease involves the lymph nodes in particular.

The B-cell lymphomas are types of lymphoma affecting B cells. Lymphomas are “blood cancers” in the lymph glands. They develop more frequently in older adults and in immunocompromised individuals.

B-cell lymphomas include both Hodgkin’s lymphomas and most non-Hodgkins lymphomas. They are often divided into indolent (slow-growing) lymphomas and aggressive lymphomas. Indolent lymphomas respond rapidly to treatment and are kept under control (in remission) with long-term survival of many years, but are not cured. Aggressive lymphomas usually require intensive treatments, but have good prospects for a permanent cure.[1]

Prognosis and treatment depends on the specific type of lymphoma as well as the stage and grade. Treatment includes radiation and chemotherapy. Early-stage indolent B-cell lymphomas can often be treated with radiation alone, with long-term non-reoccurrence. Early-stage aggressive disease is treated with chemotherapy and often radiation, with a 70-90% cure rate.[1] Late-stage indolent lymphomas are sometimes left untreated and monitored until they progress. Late-stage aggressive disease is treated with chemotherapy, with cure rates of over 70%.[1]

Small cell Lymphocytic lymphoma (overlaps with Chronic lymphocytic leukemia)

Indolent NHL. These types of lymphoma grow very slowly. As a result, people with indolent NHL may not need to start treatment when it is first diagnosed. They are followed closely, and treatment is only started when they develop symptoms or the disease begins to change; this is called watchful waiting. When indolent lymphoma is located only in one area (called localized disease, stages I and II; see the Stages section), radiation therapy may eliminate the NHL.

 

Subtyping

In addition to determining if the NHL is indolent or aggressive and whether it is B-cell, T-cell, of NK-cell lymphoma, it is very important to determine the subtype of NHL because each subtype can behave differently and may require different treatments. There are about 35 subtypes of NHL.

Small lymphocytic lymphoma. This type of lymphoma is very closely related to a disease called B-cell chronic lymphocytic leukemia (CLL), and about 5% of people with NHL have this subtype. It is considered an indolent lymphoma. Patients with small lymphocytic lymphoma may receive a combination of chemotherapy, monoclonal antibodies, and/or radiation therapy, or they may be followed closely with watchful waiting.

Lymphoma – B cell neoplasms

Non-Hodgkin Lymphoma

Cytogenetics
Reviewer: Nikhil Sangle, M.D., University of Utah and ARUP Laboratories (see Reviewers page)
Revised: 17 February 2011, last major update February 2011
Copyright: (c) 2001-2011, PathologyOutlines.com, Inc.

List of translocations
==============================================

Relatively common translocations are listed below
See each topic for more complete lists:

● t(1;14)(p32;q11): SCL (tal-1) and T cell receptor delta/alpha; preT ALL (15-30%)
● deletion of 11q23: CLL (10-20%)
● t(11;14)(q13;q32): bcl1/PRAD1 and IgH; mantle cell lymphoma (90%), B cell prolymphocytic leukemia (20%), myeloma (3%)
● Trisomy 12: B-CLL (30%)
● deletion 13q14: B -CLL (25-50%)
● t(14;19)(q32;q13): IgH and bcl3; B-CLL
● t(16;22);(q23;q11): cmaf and Ig lambda; multiple myeloma
● Trisomy 18: common in marginal zone lymphoma, MALT type

 

Lymphoma – B cell neoplasms

B cell lymphoma subtypes

Chronic lymphocytic leukemia – features that differ from SLL
Reviewer: Nikhil Sangle, M.D., University of Utah and ARUP Laboratories (see Reviewers page)
Revised: 20 September 2012, last major update February 2011
Copyright: (c) 2001-2012, PathologyOutlines.com, Inc

 

Terminology
==============================================

  • Leukemic disorder of CD5+ CD23+ tumor cells, usually B cell, that are small, round, low grade, with soccer ball appearance

Terminology
==============================================

  • Called CLL/chronic lymphocytic leukemia if leukemic involvement at diagnosis (5K or more of monoclonal B cell lymphocytosis per microliter)
    ● Less than 5K per microliter is termed monoclonal B lymphocytosis or possibly low stage CLL
    ● CLL with increased prolymphocytes (CLL/prolymphocytic leukemia): 10-55% prolymphocytes
    ● Prolymphocytic leukemia: >55% prolymphocytes

 

Lymphoma – B cell neoplasms

B cell lymphoma subtypes

Small lymphocytic lymphoma
Reviewer: Nikhil Sangle, M.D., University of Utah and ARUP Laboratories (see Reviewers page)
Revised: 6 February 2012, last major update February 2011
Copyright: (c) 2001-2012, PathologyOutlines.com, Inc

 

Definition
==============================================

  • Common low grade B cell lymphoma with pseudofollicles composed of mature lymphocytes resembling soccer balls in peripheral blood; cells are CD5+, CD23+

Clinical features
==============================================

  • Usually older patients (median age 60 years), 2/3 male, with disease in bone marrow, lymph nodes, spleen, liver
    ● Often presents with leukemia, although patients may be asymptomatic
    ● SLL may progress to blood (leukemic) involvement, but if so, there is usually less leukocytosis than cases with initial diagnosis of CLL
    ● Almost all cases are B cell origin
    ● Associated with hypogammaglobulinemia, monoclonal immunoglobulin spikes in some, infections; also autoantibodies to red blood cells and platelets causing hemolytic anemia and thrombocytopenia
    ● Median survival 4-6 years; indolent unless it transforms

Poor prognostic factors
==============================================

  • 17p deletions, 11q22-23 deletion, non-mutated immunoglobulin genes, aberrant expression of CD2, CD7, CD10, CD13, CD33 or CD34 (Am J Clin Pathol 2003;119:824)

 

 

In 2013, the North American market was valued at $128.9 million and accounted for the largest share of the global digital pathology market, followed by Europe and Asia. The North American market is expected to grow at a healthy growth rate over the next five years. This high growth can be attributed to the favorable reimbursement scenario in the U.S. and the use of digital pathology to improve the quality of cancer diagnosis in Canada. However, lack of FDA approvals for digital pathology to be used for primary diagnosis acts as a major barrier for the North American market.

 

Other posts related to this discussion were published on this Open Source  Online Scientific Journal from Leaders in Pharmaceutical Business  Intelligence:

Big Data in Genomic Medicine, LHB
http://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/

A New Therapy for Melanoma, LHB
http://pharmaceuticalintelligence.com/2012/09/15/a-new-therapy-for-melanoma/

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair,  S Saha
http://pharmaceuticalintelligence.com/2012/12/04/brca1-a-tumour-suppressor-in-breast-and-ovarian-cancer-functions-in-transcription-ubiquitination-and-dna-repair/

Judging ‘Tumor response’-there is more food for thought,  R Saxena
http://pharmaceuticalintelligence.com/2012/12/04/judging-the-tumor-response-there-is-more-food-for-thought/

Computational Genomics Center: New Unification of Computational Technologies at Stanford, A. Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/03/computational-genomics-center-new-unification-of-computational-technologies-at-stanford/

Ovarian Cancer and fluorescence-guided surgery: A report, T.  Barliya
http://pharmaceuticalintelligence.com/2013/01/19/ovarian-cancer-and-fluorescence-guided-surgery-a-report/

Personalized medicine gearing up to tackle cancer ,  R. Saxena
http://pharmaceuticalintelligence.com/2013/01/07/personalized-medicine-gearing-up-to-tackle-cancer/

Exploring the role of vitamin C in Cancer therapy,   R. Saxena
http://pharmaceuticalintelligence.com/2013/01/15/exploring-the-role-of-vitamin-c-in-cancer-therapy/

Differentiation Therapy – Epigenetics Tackles Solid Tumors,    SJ Williams
http://pharmaceuticalintelligence.com/2013/01/03/differentiation-therapy-epigenetics-tackles-solid-tumors/

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment,   A. Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/17/mechanism-involved-in-breast-cancer-cell-growth-function-in-early-detection-treatment/

Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS),  A. Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/01/personalized-medicine-cancer-cell-biology-and-minimally-invasive-surgery-mis/

Role of Primary Cilia in Ovarian Cancer,  A. Awan
http://pharmaceuticalintelligence.com/2013/01/15/role-of-primary-cilia-in-ovarian-cancer-2/

The Molecular Pathology of Breast Cancer Progression,  T. Bailiya`
http://pharmaceuticalintelligence.com/2013/01/10/the-molecular-pathology-of-breast-cancer-progression/

Stanniocalcin: A Cancer Biomarker,   A. Awan
http://pharmaceuticalintelligence.com/2012/12/25/stanniocalcin-a-cancer-biomarker/

Nanotechnology, personalized medicine and DNA sequencing,  T. Barliya
http://pharmaceuticalintelligence.com/2013/01/09/nanotechnology-personalized-medicine-and-dna-sequencing/

Gastric Cancer: Whole-genome reconstruction and mutational signatures,  A. Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/gastric-cancer-whole-genome-reconstruction-and-mutational-signatures-2/

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1, A. Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/paradigm-shift-in-human-genomics-predictive-biomarkers-and-personalized-medicine-part-1/

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2,  A. Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/leaders-in-genome-sequencing-of-genetic-mutations-for-therapeutic-drug-selection-in-cancer-personalized-treatment-part-2/

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3, A. Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/personalized-medicine-an-institute-profile-coriell-institute-for-medical-research-part-3/

The Consumer Market for Personal DNA Sequencing: Part 4, A. Lev-Ari

http://pharmaceuticalintelligence.com/2013/01/13/consumer-market-for-personal-dna-sequencing-part-4/

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @http://pharmaceuticalintelligence.com   A. Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/7000/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”  A Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/14/gsk-for-personalized-medicine-using-cancer-drugs-needs-alacris-systems-biology-model-to-determine-the-in-silico-effect-of-the-inhibitor-in-its-virtual-clinical-trial/

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors,  S. Saha
http://pharmaceuticalintelligence.com/2012/11/19/recurrent-somatic-mutations-in-chromatin-remodeling-and-ubiquitin-ligase-complex-genes-in-serous-endometrial-tumors/

Metabolic drivers in aggressive brain tumors,  pkandala
http://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/

Personalized medicine-based cure for cancer might not be far away, R. Saxena
http://pharmaceuticalintelligence.com/2012/11/20/personalized-medicine-based-cure-for-cancer-might-not-be-far-away/

Response to Multiple Cancer Drugs through Regulation of TGF-β Receptor Signaling: a MED12 Control, A. Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/21/response-to-multiple-cancer-drugs-through-regulation-of-tgf-%CE%B2-receptor-signaling-a-med12-control/

Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence,  A. Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/24/human-variome-project-encyclopedic-catalog-of-sequence-variants-indexed-to-the-human-genome-sequence/

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition,  SJ Williams
http://pharmaceuticalintelligence.com/2012/11/30/histone-deacetylase-inhibitors-induce-epithelial-to-mesenchymal-transition-in-prostate-cancer-cells/

Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?, R. Saxena
http://pharmaceuticalintelligence.com/2012/12/13/imaging-and-targeting-the-tumor-predicting-tumor-response-where-we-stand/

Nanotechnology: Detecting and Treating metastatic cancer in the lymph node, T. Barliya
http://pharmaceuticalintelligence.com/2012/12/19/nanotechnology-detecting-and-treating-metastatic-cancer-in-the-lymph-node/

Heroes in Medical Research: Barnett Rosenberg and the Discovery of Cisplatin, SJ Williams
http://pharmaceuticalintelligence.com/2013/01/12/heroes-in-medical-research-barnett-rosenberg-and-the-discovery-of-cisplatin/

Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics,  A. Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/10/inspiration-from-dr-maureen-cronins-achievements-in-applying-genomic-sequencing-to-cancer-diagnostics/

The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953,      A. Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/09/the-cancer-establishments-examined-by-james-watson-co-discover-of-dna-wcrick-41953/

Nanotech Therapy for Breast Cancer. T. Barlyia
http://pharmaceuticalintelligence.com/2012/12/09/naotech-therapy-for-breast-cancer/

Dasatinib in Combination With Other Drugs for Advanced, Recurrent Ovarian Cancer,  pkandala
http://pharmaceuticalintelligence.com/2012/12/08/dasatinib-in-combination-with-other-drugs-for-advanced-recurrent-ovarian-cancer/

Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker, pkandala
http://pharmaceuticalintelligence.com/2012/12/08/squeezing-ovarian-cancer-cells-to-predict-metastatic-potential-cell-stiffness-as-possible-biomarker/

Hypothesis – following on James Watson,  LHB

http://pharmaceuticalintelligence.com/2013/01/27/novel-cancer-h…ts-are-harmful/

Otto Warburg, A Giant of Modern Cellular Biology, LHB
http://pharmaceuticalintelligence.com/2012/11/02/otto-warburg-a-giant-of-modern-cellular-biology/

Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?  LHB
http://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/

Remembering a Great Scientist among Mentors,  LHB
http://pharmaceuticalintelligence.com/2013/01/26/remembering-a-great-scientist-among-mentors/

Portrait of a great scientist and mentor: Nathan Oram Kaplan,   LHB
http://pharmaceuticalintelligence.com/2013/01/26/portrait-of-a-great-scientist-and-mentor-nathan-oram-kaplan/

Predicting Tumor Response, Progression, and Time to Recurrence, LHB
http://pharmaceuticalintelligence.com/2012/12/20/predicting-tumor-response-progression-and-time-to-recurrence/

Directions for genomics in personalized medicine,   LHB
http://pharmaceuticalintelligence.com/2013/01/27/directions-for-genomics-in-personalized-medicine/

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,  Sjwilliams
http://pharmaceuticalintelligence.com/2012/10/31/how-mobile-elements-in-junk-dna-prote-cacner-part1-transposon-mediated-tumorigenesis/

Novel Cancer Hypothesis Suggests Antioxidants Are Harmful, LHB
http://pharmaceuticalintelligence.com/2013/01/27/novel-cancer-hypothesis-suggests-antioxidants-are-harmful/

Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation,  LHB
http://pharmaceuticalintelligence.com/2012/09/26/mitochondria-origin-from-oxygen-free-environment-role-in-aerobic-glycolysis-metabolic-adaptation/

Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets, LHB
http://pharmaceuticalintelligence.com/2012/10/22/advances-in-separations-technology-for-the-omics-and-clarification-of-therapeutic-targets/

Cancer Innovations from across the Web, LHB
http://pharmaceuticalintelligence.com/2012/11/02/cancer-innovations-from-across-the-web/

Mitochondrial Damage and Repair under Oxidative Stress, LHB
http://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/

Mitochondria: More than just the “powerhouse of the cell” R. Saxena
http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/

Mitochondria and Cancer: An overview of mechanisms, R. Saxena
http://pharmaceuticalintelligence.com/2012/09/01/mitochondria-and-cancer-an-overview/

Mitochondrial fission and fusion: potential therapeutic targets?  R. Saxena
http://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/

Mitochondrial mutation analysis might be “1-step” away, R. Saxena
http://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/

β Integrin emerges as an important player in mitochondrial dysfunction associated Gastric Cancer,       R. Saxena
http://pharmaceuticalintelligence.com/2012/09/10/%CE%B2-integrin-emerges-as-an-important-player-in-mitochondrial-dysfunction-associated-gastric-cancer/

mRNA interference with cancer expression, LHB
http://pharmaceuticalintelligence.com/2012/10/26/mrna-interference-with-cancer-expression/

What can we expect of tumor therapeutic response?  LHB
http://pharmaceuticalintelligence.com/2012/12/05/what-can-we-expect-of-tumor-therapeutic-response/

Expanding the Genetic Alphabet and linking the genome to the metabolome, LHB
http://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/

Breast Cancer, drug resistance, and biopharmaceutical targets, LHB
http://pharmaceuticalintelligence.com/2012/09/18/breast-cancer-drug-resistance-and-biopharmaceutical-targets/

Breast Cancer: Genomic Profiling to Predict Survival: Combination of Histopathology and Gene Expression Analysis, A. Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/breast-cancer-genomic-profiling-to-predict-survival-combination-of-histopathology-and-gene-expression-analysis/

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis,   LHB
http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/

Identification of Biomarkers that are Related to the Actin Cytoskeleton, LHB
http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/

Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function, LHB
http://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-a-concomitant-influence-on-mitochondrial-function/

Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology,  A. Lev-Ari http://pharmaceuticalintelligence.com/2012/08/22/genomic-analysis-fluidigm-technology-in-the-life-science-and-agricultural-biotechnology/

Nanotechnology: Detecting and Treating metastatic cancer in the lymph node, T. Barliya
http://pharmaceuticalintelligence.com/2012/12/19/nanotechnology-detecting-and-treating-metastatic-cancer-in-the-lymph-node/

 

Reporter: Aviva Lev-Ari, PhD, RN 

Crohn’s disease driven by inflammation – not genetics, reports study   Aug. 15, 2012

Inflammation — not genetic susceptibility —

  • drives the growth of intestinal bacteria and invasive E. coli linked to Crohn’s disease (CD), reports a new Cornell study.

Scientists have long wondered about the role of bacteria in CD. Recent studies have shown marked changes in the composition of the intestinal bacteria in people

  • with CD, leading researchers to ask: Are microbial abnormalities a direct consequence of genetic abnormalities linked to Crohn’s and precede and initiate inflammation, or does intestinal inflammation bring on the bugs?

This study also reports that a common therapy directed against intestinal inflammation decreases dysbiosis. In addition, the study found that

  • the lack of a receptor that helps recruit T cells, which are needed for cell-mediated immunity, to the gut also decreases inflammation and dysbiosis, offering a new option for therapeutic intervention.Inflammation, in fact,
  • drives microbial imbalances (dysbiosis) and

the proliferation of a specific type of E. coli that is adherent, invasive and found in the ileum, reported Cornell researchers July 31 in PLoS (7[7]).

CD is a chronic debilitating inflammatory bowel disease that involves a complex interaction of

  • host genes,
  • the immune system,
  • the intestinal microbiome and
  • the environment.

To mirror the complex nature of the disease, Simpson’s team designed a study that

  • incorporated inflammatory triggers related to relapse of CD and ileal inflammation.

The team focused on ileal dysbiosis, which is prevalent in 70 percent of CD cases and

  • used a variety of contemporary techniques to generate a comprehensive picture of the composition and spatial distribution of the ileal microbiome.

Particular attention was paid to pinpointing

  1. the number,
  2. pathotype and
  3. location

of E. coli associated with intestinal inflammation in people, dogs and mice.

The findings demonstrate that

  • inflammation drives ileal dysbiosis and proliferation of CD-associated adherent invasive E. coli.
  1. the host genotype and therapeutically blocking inflammation both impact the onset and extent of ileal dysbiosis.

The investigation leveraged the knowledge and resources of researchers in the labs of Erik Denker, Dwight Bowman and Sean McDonough labs. Building on findings in patients with Crohn’s disease evaluated by Dr. Ellen Scherl’s group at Weill Cornell Medical College, this collaboration shed new light on this debilitating disease.

This work was supported by NewYork-Presbyterian Hospital/Weill Cornell Medical Center, the Jill Roberts Center for Inflammatory Bowel Disease and the National Institutes of Health.

http://www.news.cornell.edu/stories/Aug12/Inflammation.html 

 

Functional Proteomics Related to Energy Metabolism of Synaptosomes

from iTRAQ-Based Quantitative Proteomics Analysis Revealed Alterations of Carbohydrate Metabolism Pathways and Mitochondrial Proteins in a Male Sterile Cybrid Pummelo

Bei-Bei Zheng †, Yan-Ni Fang †, Zhi-Yong Pan †, Li Sun †, Xiu-Xin Deng †, Jude W. Grosser ‡, andWen-Wu Guo *

 Key Laboratory of Horticultural Plant Biology (Ministry of Education), Huazhong Agricultural University, Wuhan 430070, China
 Citrus Research and Education Center, University of Florida, 700 Experiment Station Road, Lake Alfred, Florida 33850, United States

  1. Proteome Res., May 13, 201413(6), pp 2998–3015 http://dx.doi.org:/10.1021/pr500126g

 

Plant Biochemistry

Comprehensive and quantitative proteomic information on citrus floral bud is significant for understanding

  • male sterility of the cybrid pummelo (G1+HBP) with nuclear genome of HBP and foreign mitochondrial genome of G1.

Scanning electron microscopy and transmission electron microscopy analyses of the anthers showed that

  • the development of pollen wall in G1+HBP was severely defective with a lack of exine and sporopollenin formation.

Proteomic analysis was used to identify the differentially expressed proteins between male sterile G1+HBP and fertile type (HBP)

  • with the aim to clarify their potential roles in another development and male sterility.

On the basis of iTRAQ quantitative proteomics, we identified 2235 high-confidence protein groups, 666 of which showed

  • differentially expressed profiles in one or more stages.

Proteins up- or down-regulated in G1+HBP were mainly involved in

  1. carbohydrate and energy metabolism (e.g., pyruvate dehydrogenase, isocitrate dehydrogenase, ATP synthase, and malate dehydrogenase),
  2. nucleotide binding (RNA-binding proteins),
  3. protein synthesis and degradation (e.g., ribosome proteins and proteasome subunits).

Additionally, the proteins located in mitochondria also showed changed expression patterns. These findings provide a valuable inventory of proteins involved in floral bud development and contribute to elucidate the mechanism of cytoplasmic male sterility in the cybrid pummelo.

Keywords: cybridmale sterilitymitochondriaproteometranscriptomeprimary metabolites

 

BIMSB Proteomics / Metabolomics

Overview

Within the past decades biochemical data of single processes, metabolic and signaling pathways were collected and advances in technology

  • led to improvements of sensitivity and resolution of bioanalytical techniques.

These achievements build the bases for the so called ‘genome wide biochemistry’. High throughput techniques are the tool for large scale ‘-omics’ studies

  • allowing the obtainment of a nearly complete picture of a determinate cell state, concerning its metabolites, proteins and transcripts.

However, a single level study of a living organism cannot give a complete understanding of the mechanisms regulating biological functions.
The integration of transcriptomics, proteomics and metabolomics data with existing knowledge allows connecting biological processes which were treated as independent so far. In this context the aim of our group is

  1. to apply metabolomics and proteomics techniques for absolute quantification and
  2. to analyze turnover rates of proteins and metabolites using stable isotopes. In addition,
  3. the development of data analysis workflows and integrative strategies are in the focus of our interest.

The central metabolism is the principal source of energy and building blocks for cell growth and survival. It is highly flexible and adjusted to the physiological program of the cell, organ and organism. In a healthy state

  • cellular metabolism is tightly regulated to guarantee physiological function but also efficient usage of available recourses.

Metabolic dys-regulations are cause or response to many diseases. An impaired metabolic activity can lead to

  • the loss of the physiological activity, cell damage or inefficient substrate usage. However,
  • the underlying mechanisms leading to metabolic dys-functions are not well understood.

The regulation of metabolism is complex, because

  • it acts at all biological layers – transcriptional, translational and post-translational.

Thus the metabolic activity of a cell, organ or organism inherits the information of regulatory layers in a multidimensional manner. I guess only the use of integrative mathematical approaches will enable us to decode such complex information.

In this regard, decoding the metabolic composition of biofluids e.g. blood serum

  • may allow to determine a systems status, to identify diseases, predict drug responsiveness and to follow the success of medical treatments. This is a step towards personalized medicine.

http://www.mdc-berlin.de/20902775/en/research/core_facilities/cf_massspectromety_bimsb

 

Coordination of bacterial proteome with metabolism by cyclic AMP signaling

Conghui You, Hiroyuki Okano, Sheng Hui, Zhongge Zhang, Minsu Kim, et al.

Nature  (15 August 2013);  500, 301–306  http://dx.doi.org:/10.1038/nature12446

 

The cyclic AMP (cAMP)-dependent catabolite repression effect in Escherichia coli is among the most intensely studied regulatory processes in biology. However,

  • the physiological function(s) of cAMP signalling and its molecular triggers remain elusive.

Here we use a quantitative physiological approach to show that

  • cAMP signalling tightly coordinates the expression of catabolic proteins with biosynthetic and ribosomal proteins,
  • in accordance with the cellular metabolic needs during exponential growth.

The expression of carbon catabolic genes increased linearly

  • with decreasing growth rates upon limitation of carbon influx,
  • but decreased linearly with decreasing growth rate upon limitation of nitrogen or sulphur influx.

In contrast, the expression of biosynthetic genes showed the opposite linear growth-rate dependence as the catabolic genes. A coarse-grained mathematical model provides a quantitative framework for understanding and predicting

  • gene expression responses to catabolic and anabolic limitations.

A scheme of integral feedback control featuring the inhibition of cAMP signalling by metabolic precursors is proposed and validated. These results reveal a key physiological role of

  • cAMP-dependent catabolite repression: to ensure that proteomic resources are spent on distinct metabolic sectors as needed
  • in different nutrient environments.

Our findings underscore the power of quantitative physiology in unravelling the underlying functions of complex molecular signalling networks.

 

Exosomes from bone marrow mesenchymal stem cells contain a microRNA that promotes dormancy in metastatic breast cancer cells

Makiko Ono1, Nobuyoshi Kosaka1, Naoomi Tominaga1, Yusuke Yoshioka1, Fumitaka Takeshita1,  et al.
Sci. Signal., July 2014;  7(332),  p. ra63    http://dx.doi.org:/10.1126/scisignal.2005231

Breast cancer patients often develop metastatic disease years after resection of the primary tumor. The patients are asymptomatic because the disseminated cells appear to become dormant and are undetectable. Because the proliferation of these cells is slowed, dormant cells are often unresponsive to traditional chemotherapies that exploit the rapid cell cycling of most cancer cells. We generated a bone marrow–metastatic human breast cancer cell line (BM2) by tracking and isolating fluorescent-labeled MDA-MB-231 cells that disseminated to the bone marrow in mice. Coculturing BM2 cells with bone marrow mesenchymal stem cells (BM-MSCs) isolated from human donors revealed that BM-MSCs suppressed the proliferation of BM2 cells, decreased the abundance of stem cell–like surface markers, inhibited their invasion through Matrigel Transwells, and decreased their sensitivity to docetaxel, a common chemotherapy agent. Acquisition of these dormant phenotypes in BM2 cells was also observed by culturing the cells in BM-MSC–conditioned medium or with exosomes isolated from BM-MSC cultures, which were taken up by BM2 cells. Among various microRNAs (miRNAs) increased in BM-MSC–derived exosomes compared with those from adult fibroblasts, overexpression of miR-23b in BM2 cells induced dormant phenotypes through the suppression of a target gene, MARCKS, which encodes a protein that promotes cell cycling and motility. Metastatic breast cancer cells in patient bone marrow had increased miR-23b and decreasedMARCKS expression. Together, these findings suggest that exosomal transfer of miRNAs from the bone marrow may promote breast cancer cell dormancy in a metastatic niche.

Citation:

  1. Ono, N. Kosaka, N. Tominaga, Y. Yoshioka, F. Takeshita, R. Takahashi, M. Yoshida, H. Tsuda, K. Tamura, and T. Ochiya, Exosomes from bone marrow mesenchymal stem cells contain a microRNA that promotes dormancy in metastatic breast cancer cells. Sci. Signal.7, ra63 (2014).


Poly(ADP-ribose) polymerase-dependent energy depletion occurs through inhibition of glycolysis.

Andrabi SA1Umanah GK2Chang C3Stevens DA4Karuppagounder SS2Gagné JP5Poirier GG5Dawson VL6Dawson TM7.

Proc Natl Acad Sci U S A. 2014 Jul 15; 111(28):10209-14. http://dx.doi.org:/10.1073/pnas.1405158111

 

Excessive poly(ADP-ribose) (PAR) polymerase-1 (PARP-1) activation kills cells via a cell-death process designated “parthanatos” in which PAR induces the mitochondrial release and nuclear translocation of apoptosis-inducing factor to initiate chromatinolysis and cell death. Accompanying the formation of PAR are the reduction of cellular NAD(+) and energetic collapse, which have been thought to be caused by the consumption of cellular NAD(+) by PARP-1. Here we show that the bioenergetic collapse following PARP-1 activation is not dependent on NAD(+) depletion. Instead PARP-1 activation initiates glycolytic defects via PAR-dependent inhibition of hexokinase, which precedes the NAD(+) depletion in N-methyl-N-nitroso-N-nitroguanidine (MNNG)-treated cortical neurons. Mitochondrial defects are observed shortly after PARP-1 activation and are mediated largely through defective glycolysis, because supplementation of the mitochondrial substrates pyruvate and glutamine reverse the PARP-1-mediated mitochondrial dysfunction. Depleting neurons of NAD(+) with FK866, a highly specific noncompetitive inhibitor of nicotinamide phosphoribosyltransferase, does not alter glycolysis or mitochondrial function. Hexokinase, the first regulatory enzyme to initiate glycolysis by converting glucose to glucose-6-phosphate, contains a strong PAR-binding motif. PAR binds to hexokinase and inhibits hexokinase activity in MNNG-treated cortical neurons. Preventing PAR formation with PAR glycohydrolase prevents the PAR-dependent inhibition of hexokinase. These results indicate that bioenergetic collapse induced by overactivation of PARP-1 is caused by PAR-dependent inhibition of glycolysis through inhibition of hexokinase.

PMID:24987120     PMCID: PMC4104885   [Available on 2015/1/15]

 

Aim24 stabilizes respiratory chain supercomplexes and is required for efficient respiration

Deckers M1Balleininger M1Vukotic M1Römpler K1Bareth B1Juris L1Dudek J2.

FEBS Lett. 2014 Jun 10. pii: S0014-5793(14)00458-X. http://dx.doi.org:/10.1016/j.febslet.2014.06.006

 

The mitochondrial respiratory chain is essential for the conversion of energy derived from the oxidation of metabolites into the membrane potential, which drives the synthesis of ATP. The electron transporting complexes bc1 complex and the cytochrome c oxidase assemble into large supercomplexes, allowing efficient energy transduction. Currently, we have only limited information about what determines the structure of the supercomplex. Here, we characterize Aim24 in baker’s yeast as a protein, which is integrated in the mitochondrial inner membrane and is required for the structural integrity of the supercomplex. Deletion of AIM24 strongly affects activity of the respiratory chain and induces a growth defect on non-fermentable medium. Our data indicate that Aim24 has a function in stabilizing the respiratory chain supercomplexes.    PMID: 24928273

KEYWORDS: Aim24; Membrane protein; Metabolism; Mitochondria; Respiration; Supercomplex

 

 

 

Read Full Post »

« Newer Posts - Older Posts »