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Archive for the ‘Technology Transfer: Biotech and Pharmaceutical’ Category

Milestones in Physiology & Discoveries in Medicine and Genomics: Request for Book Review Writing on Amazon.com


physiology-cover-seriese-vol-3individualsaddlebrown-page2

Milestones in Physiology

Discoveries in Medicine, Genomics and Therapeutics

Patient-centric Perspective 

http://www.amazon.com/dp/B019VH97LU 

2015

 

 

Author, Curator and Editor

Larry H Bernstein, MD, FCAP

Chief Scientific Officer

Leaders in Pharmaceutical Business Intelligence

Larry.bernstein@gmail.com

Preface

Introduction 

Chapter 1: Evolution of the Foundation for Diagnostics and Pharmaceuticals Industries

1.1  Outline of Medical Discoveries between 1880 and 1980

1.2 The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century

1.3 The Classification of Microbiota

1.4 Selected Contributions to Chemistry from 1880 to 1980

1.5 The Evolution of Clinical Chemistry in the 20th Century

1.6 Milestones in the Evolution of Diagnostics in the US HealthCare System: 1920s to Pre-Genomics

 

Chapter 2. The search for the evolution of function of proteins, enzymes and metal catalysts in life processes

2.1 The life and work of Allan Wilson
2.2  The  evolution of myoglobin and hemoglobin
2.3  More complexity in proteins evolution
2.4  Life on earth is traced to oxygen binding
2.5  The colors of life function
2.6  The colors of respiration and electron transport
2.7  Highlights of a green evolution

 

Chapter 3. Evolution of New Relationships in Neuroendocrine States
3.1 Pituitary endocrine axis
3.2 Thyroid function
3.3 Sex hormones
3.4 Adrenal Cortex
3.5 Pancreatic Islets
3.6 Parathyroids
3.7 Gastointestinal hormones
3.8 Endocrine action on midbrain
3.9 Neural activity regulating endocrine response

3.10 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

 

Chapter 4.  Problems of the Circulation, Altitude, and Immunity

4.1 Innervation of Heart and Heart Rate
4.2 Action of hormones on the circulation
4.3 Allogeneic Transfusion Reactions
4.4 Graft-versus Host reaction
4.5 Unique problems of perinatal period
4.6. High altitude sickness
4.7 Deep water adaptation
4.8 Heart-Lung-and Kidney
4.9 Acute Lung Injury

4.10 Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics

 

Chapter 5. Problems of Diets and Lifestyle Changes

5.1 Anorexia nervosa
5.2 Voluntary and Involuntary S-insufficiency
5.3 Diarrheas – bacterial and nonbacterial
5.4 Gluten-free diets
5.5 Diet and cholesterol
5.6 Diet and Type 2 diabetes mellitus
5.7 Diet and exercise
5.8 Anxiety and quality of Life
5.9 Nutritional Supplements

 

Chapter 6. Advances in Genomics, Therapeutics and Pharmacogenomics

6.1 Natural Products Chemistry

6.2 The Challenge of Antimicrobial Resistance

6.3 Viruses, Vaccines and immunotherapy

6.4 Genomics and Metabolomics Advances in Cancer

6.5 Proteomics – Protein Interaction

6.6 Pharmacogenomics

6.7 Biomarker Guided Therapy

6.8 The Emergence of a Pharmaceutical Industry in the 20th Century: Diagnostics Industry and Drug Development in the Genomics Era: Mid 80s to Present

6.09 The Union of Biomarkers and Drug Development

6.10 Proteomics and Biomarker Discovery

6.11 Epigenomics and Companion Diagnostics

 

Chapter  7

Integration of Physiology, Genomics and Pharmacotherapy

7.1 Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension

7.2 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

7.3 Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

7.4 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

7.5 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

7.6 Imaging Biomarker for Arterial Stiffness: Pathways in Pharmacotherapy for Hypertension and Hypercholesterolemia Management

7.7 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic drugs and Cholinesterase Inhibitors

7.8 Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

7.9 Preserved vs Reduced Ejection Fraction: Available and Needed Therapies

7.10 Biosimilars: Intellectual Property Creation and Protection by Pioneer and by

7.11 Demonstrate Biosimilarity: New FDA Biosimilar Guidelines

 

Chapter 7.  Biopharma Today

8.1 A Great University engaged in Drug Discovery: University of Pittsburgh

8.2 Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

8.3 Predicting Tumor Response, Progression, and Time to Recurrence

8.4 Targeting Untargetable Proto-Oncogenes

8.5 Innovation: Drug Discovery, Medical Devices and Digital Health

8.6 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

8.7 Nanotechnology and Ocular Drug Delivery: Part I

8.8 Transdermal drug delivery (TDD) system and nanotechnology: Part II

8.9 The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

8.10 Natural Drug Target Discovery and Translational Medicine in Human Microbiome

8.11 From Genomics of Microorganisms to Translational Medicine

8.12 Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad

 

Chapter 9. BioPharma – Future Trends

9.1 Artificial Intelligence Versus the Scientist: Who Will Win?

9.2 The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

9.3 The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

9.4 Heroes in Medical Research: The Postdoctoral Fellow

9.5 NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

9.6 1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

9.7 Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

9.8 Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science

9.9 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

9.10 The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research

Epilogue

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cvd-series-a-volume-iv-cover

Series A: e-Books on Cardiovascular Diseases

Series A Content Consultant: Justin D Pearlman, MD, PhD, FACC

VOLUME FOUR

Regenerative and Translational Medicine

The Therapeutic Promise for

Cardiovascular Diseases

  • on Amazon since 12/26/2015

http://www.amazon.com/dp/B019UM909A

 

by  

Larry H Bernstein, MD, FCAP, Senior Editor, Author and Curator

and

Aviva Lev-Ari, PhD, RN, Editor and Curator

 

Part One:

Cardiovascular Diseases,Translational Medicine (TM) and Post TM

Introduction to Part 1: Cardiovascular Diseases,Translational Medicine (TM) and Post TM

Chapter 1: Translational Medicine Concepts

1.0 Post-Translational Modification of Proteins

1.1 Identifying Translational Science within the Triangle of Biomedicine

1.2 State of Cardiology on Wall Stress, Ventricular Workload and Myocardial Contractile Reserve: Aspects of Translational Medicine (TM)

1.3 Risk of Bias in Translational Science

1.4 Biosimilars: Intellectual Property Creation and Protection by Pioneer and by Biosimilar Manufacturers

Chapter 2: Causes and the Etiology of Cardiovascular Diseases: Translational Approaches for Cardiothoracic Medicine

2.1 Genomics

2.1.1 Genomics-Based Classification

2.1.2  Targeting Untargetable Proto-Oncogenes

2.1.3  Searchable Genome for Drug Development

2.1.4 Zebrafish Study Tool

2.1.5  International Human Genome Sequencing Consortium (2004) Finishing the euchromatic sequence of the human genome.

2.2  Proteomics

2.2.1 The Role of Tight Junction Proteins in Water and Electrolyte Transport

2.2.2 Selective Ion Conduction

2.2.3 Translational Research on the Mechanism of Water and Electrolyte Movements into the Cell

2.2.4 Inhibition of the Cardiomyocyte-Specific Kinase TNNI3K ­ Oxidative Stress

2.2.5 Oxidized Calcium Calmodulin Kinase and Atrial Fibrillation

2.2.6 S-Nitrosylation in Cardiac Ischemia and Acute Coronary Syndrome

2.2.7 Acetylation and Deacetylation

2.2.8 Nitric Oxide Synthase Inhibitors (NOS-I) 

2.3 Cardiac and Vascular Signaling

2.3.1 The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

2.3.2 Leptin Signaling in Mediating the Cardiac Hypertrophy associated with Obesity

2.3.3 Triggering of Plaque Disruption and Arterial Thrombosis

2.3.4 Sensors and Signaling in Oxidative Stress

2.3.5 Resistance to Receptor of Tyrosine Kinase

2.3.6  S-nitrosylation signaling in cell biology.

2.4  Platelet Endothelial Interaction

2.4.1 Platelets in Translational Research ­ 1

2.4.2 Platelets in Translational Research ­ 2: Discovery of Potential Anti-platelet Targets

2.4.3 The Final Considerations of the Role of Platelets and Platelet Endothelial Reactions in Atherosclerosis and Novel Treatments

2.4.4 Endothelial Function and Cardiovascular Disease
Larry H Bernstein, MD, FCAP

2.5 Post-translational modifications (PTMs)

2.5.1 Post-Translational Modifications

2.5.2.  Analysis of S-nitrosylated Proteins

2.5.3  Mechanisms of Disease: Signal Transduction: Akt Phosphorylates HK-II at Thr-473 and Increases Mitochondrial HK-II Association to Protect Cardiomyocytes

2.5.4  Acetylation and Deacetylation of non-Histone Proteins

2.5.5  Study Finds Low Methylation Regions Prone to Structural Mutation

2.6 Epigenetics and lncRNAs

2.6.1 The Magic of the Pandora’s Box : Epigenetics and Stemness with Long non-coding RNAs (lincRNA)

2.6.2 The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

2.6.3 Long Noncoding RNA Network regulates PTEN Transcription

2.6.4 How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis.

2.6.5 Transposon-mediated Gene Therapy improves Pulmonary Hemodynamics and attenuates Right Ventricular Hypertrophy: eNOS gene therapy reduces Pulmonary vascular remodeling and Arterial wall hyperplasia

2.6.6 Junk DNA codes for valuable miRNAs: non-coding DNA controls Diabetes

2.6.7 Targeted Nucleases

2.6.8 Late Onset of Alzheimer’s Disease and One-carbon Metabolism
Dr. Sudipta Saha

2.6.9 Amyloidosis with Cardiomyopathy

2.6.10 Long non-coding RNAs: Molecular Regulators of Cell Fate

2.7 Metabolomics

2.7.1 Expanding the Genetic Alphabet and Linking the Genome to the Metabolome

2.7.2 How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia

2.7.3 A Second Look at the Transthyretin Nutrition Inflammatory Conundrum

2.7.4 Transthyretin and Lean Body Mass in Stable and Stressed State

2.7.5 Hyperhomocysteinemia interaction with Protein C and Increased Thrombotic Risk

2.7.6 Telling NO to Cardiac Risk

2.8 Mitochondria and Oxidative Stress

2.8.1 Reversal of Cardiac Mitochondrial Dysfunction

2.8.2 Calcium Signaling, Cardiac Mitochondria and Metabolic Syndrome

2.8.3. Mitochondrial Dysfunction and Cardiac Disorders

2.8.4 Mitochondrial Metabolism and Cardiac Function

2.8.5 Mitochondria and Cardiovascular Disease: A Tribute to Richard Bing

2.8.6 MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix Identified

2.8.7 Mitochondrial Dynamics and Cardiovascular Diseases

2.8.8 Mitochondrial Damage and Repair under Oxidative Stress

2.8.9 Nitric Oxide has a Ubiquitous Role in the Regulation of Glycolysis -with a Concomitant Influence on Mitochondrial Function

2.8.10 Mitochondrial Mechanisms of Disease in Diabetes Mellitus

2.8.11 Mitochondria Dysfunction and Cardiovascular Disease – Mitochondria: More than just the “Powerhouse of the Cell”

Chapter 3: Risks and Biomarkers for Diagnosis and Prognosis in Translational Cardiothoracic Medicine

3.1 Biomarkers. Diagnosis and Management: Biomarkers. Present and Future.

3.2 Landscape of Cardiac Biomarkers for Improved Clinical Utilization

3.3 Achieving Automation in Serology: A New Frontier in Best

3.4 Accurate Identification and Treatment of Emergent Cardiac Events

3.5 Prognostic Marker Importance of Troponin I in Acute Decompensated Heart Failure (ADHF)

3.6 High-Sensitivity Cardiac Troponin Assays Preparing the United States for High-Sensitivity Cardiac Troponin Assays

3.7 Voices from the Cleveland Clinic On Circulating apoA1: A Biomarker for a Proatherogenic Process in the Artery Wall

3.8 Triggering of Plaque Disruption and Arterial Thrombosis

3.9 Relationship between Adiposity and High Fructose Intake Revealed

3.10 The Cardio-Renal Syndrome (CRS) in Heart Failure (HF)

3.11 Aneuploidy and Carcinogenesis

3.12 “Sudden Cardiac Death,” SudD is in Ferrer inCode’s Suite of Cardiovascular Genetic Tests to be Commercialized in the US

Chapter 4: Therapeutic Aspects in Translational Cardiothoracic Medicine

4.1 Molecular and Cellular Cardiology

4.1.1 αllbβ3 Antagonists As An Example of Translational Medicine Therapeutics

4.1.2 Three-Dimensional Fibroblast Matrix Improves Left Ventricular Function post MI

4.1.3 Biomaterials Technology: Models of Tissue Engineering for Reperfusion and Implantable Devices for Revascularization

4.1.4 CELLWAVE Randomized Clinical Trial: Modest improvement in LVEF at 4 months ­ “Shock wave­facilitated intracoronary administration of BMCs” vs “Shock wave treatment alone”

4.1.5 Prostacyclin and Nitric Oxide: Adventures in vascular biology –  a tale of two mediators

4.1.6 Cardiac Contractility & Myocardium Performance: Ventricular Arrhythmiasand Non-ischemic Heart Failure – Therapeutic Implications for Cardiomyocyte Ryanopathy

4.1.7 Publications on Heart Failure by Prof. William Gregory Stevenson, M.D., BWH

4.2 Interventional Cardiology and Cardiac Surgery – Mechanical Circulatory Support and Vascular Repair

4.2.1 Mechanical Circulatory Support System, LVAD, RVAD, Biventricular as a Bridge to Heart Transplantation or as “Destination Therapy”: Options for Patients in Advanced Heart Failure

4.2.2 Heart Transplantation: NHLBI’s Ten Year Strategic Research Plan to Achieving Evidence-based Outcomes

4.2.3 Improved Results for Treatment of Persistent type 2 Endoleak after Endovascular Aneurysm Repair: Onyx Glue Embolization

4.2.4 Carotid Endarterectomy (CEA) vs. Carotid Artery Stenting (CAS): Comparison of CMMS high-risk criteria on the Outcomes after Surgery: Analysis of the Society for Vascular Surgery (SVS) Vascular Registry Data

4.2.5 Effect of Hospital Characteristics on Outcomes of Endovascular Repair of Descending Aortic Aneurysms in US Medicare Population

4.2.6 Hypertension and Vascular Compliance: 2013 Thought Frontier – An Arterial Elasticity Focus

4.2.7 Preventive Medicine Philosophy: Excercise vs. Drug, IF More of the First THEN Less of the Second

4.2.8 Cardio-oncology and Onco-Cardiology Programs: Treatments for Cancer Patients with a History of Cardiovascular Disease

Summary to Part One

 

Part Two:

Cardiovascular Diseases and Regenerative Medicine

Introduction to Part Two

Chapter 1: Stem Cells in Cardiovascular Diseases

1.1 Regeneration: Cardiac System (cardiomyogenesis) and Vasculature (angiogenesis)

1.2 Notable Contributions to Regenerative Cardiology by Richard T. Lee (Lee’s Lab, Part I)

1.3 Contributions to Cardiomyocyte Interactions and Signaling (Lee’s Lab, Part II)

1.4 Jmjd3 and Cardiovascular Differentiation of Embryonic Stem Cells

1.5 Stem Cell Therapy for Coronary Artery Disease (CAD)

1.6 Intracoronary Transplantation of Progenitor Cells after Acute MI

1.7 Progenitor Cell Transplant for MI and Cardiogenesis (Part 1)

1.8 Source of Stem Cells to Ameliorate Damage Myocardium (Part 2)

1.9 Neoangiogenic Effect of Grafting an Acellular 3-Dimensional Collagen Scaffold Onto Myocardium (Part 3)

1.10 Transplantation of Modified Human Adipose Derived Stromal Cells Expressing VEGF165

1.11 Three-Dimensional Fibroblast Matrix Improves Left Ventricular Function Post MI

Chapter 2: Regenerative Cell and Molecular Biology

2.1 Circulating Endothelial Progenitors Cells (cEPCs) as Biomarkers

2.2 Stem Cell Research — The Frontier at the Technion in Israel

2.3 Blood vessel-generating stem cells discovered

2.4 Heart Renewal by pre-existing Cardiomyocytes: Source of New Heart Cell Growth Discovered

2.5 The Heart: Vasculature Protection – A Concept-based Pharmacological Therapy including THYMOSIN

2.6 Innovations in Bio instrumentation for Measurement of Circulating Progenetor Endothelial Cells in Human Blood.

2.7 Endothelial Differentiation and Morphogenesis of Cardiac Precursor

Chapter 3: Therapeutics Levels In Molecular Cardiology

3.1 Secrets of Your Cells: Discovering Your Body’s Inner Intelligence (Sounds True, on sale May 1, 2013) by Sondra Barrett

3.2 Human Embryonic-Derived Cardiac Progenitor Cells for Myocardial Repair

3.3 Repair using iPPCs or Stem Cells

3.3.1 Reprogramming cell in Tissue Repair

3.3.2 Heart patients’ skin cells turned into healthy heart muscle cells

3.4 Arteriogenesis and Cardiac Repair: Two Biomaterials – Injectable Thymosin beta4 and Myocardial Matrix Hydrogel 

3.5 Cardiovascular Outcomes: Function of circulating Endothelial Progenitor Cells (cEPCs): Exploring Pharmaco-therapy targeted at Endogenous Augmentation of cEPCs

3.6 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

Chapter 4: Research Proposals for Endogenous Augmentation of circulating Endothelial Progenitor Cells (cEPCs)

4.1 Peroxisome proliferator-activated receptor (PPAR-gamma) Receptors Activation: PPARγ transrepression for Angiogenesis in Cardiovascular Disease and PPARγ transactivation for Treatment of Diabetes

4.2 Clinical Trials Results for Endothelin System: Pathophysiological role in Chronic Heart Failure, Acute Coronary Syndromes and MI – Marker of Disease Severity or Genetic Determination?

4.3 Endothelin Receptors in Cardiovascular Diseases: The Role of eNOS Stimulation

4.4 Inhibition of ET-1, ETA and ETA-ETB, Induction of NO production, stimulation of eNOS and Treatment Regime with PPAR-gamma agonists (TZD): cEPCs Endogenous Augmentation for Cardiovascular Risk Reduction – A Bibliography

4.5 Positioning a Therapeutic Concept for Endogenous Augmentation of cEPCs — Therapeutic Indications for Macrovascular Disease: Coronary, Cerebrovascular and Peripheral

4.6 Endothelial Dysfunction, Diminished Availability of cEPCs, Increasing CVD Risk for Macrovascular Disease – Therapeutic Potential of cEPCs

4.7 Vascular Medicine and Biology: CLASSIFICATION OF FAST ACTING THERAPY FOR PATIENTS AT HIGH RISK FOR MACROVASCULAR EVENTS Macrovascular Disease – Therapeutic Potential of cEPCs

4.8 Cardiovascular Disease (CVD) and the Role of agent alternatives in endothelial Nitric Oxide Synthase (eNOS) Activation and Nitric Oxide Production

4.9 Resident-cell-based Therapy in Human Ischaemic Heart Disease: Evolution in the PROMISE of Thymosin beta4 for Cardiac Repair

4.10 Macrovascular Disease – Therapeutic Potential of cEPCs: Reduction Methods for CV Risk

4.11 Bystolic’s generic Nebivolol – positive effect on circulating Endothelial Proginetor Cells endogenous augmentation

4.12 Heart Vasculature – Regeneration and Protection of Coronary Artery Endothelium and Smooth Muscle: A Concept-based Pharmacological Therapy of a Combination Three Drug Regimen including THYMOSIN

Summary to Part Two

Epilogue to Volume Four

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https://www.youtube.com/v/fyYT8wozccw?fs=1&hl=fr_FR

Nursing School Doesn’t Have to be so DAMN Hard! CPP=MAP-ICP Normal range should be greater than 70 mmHg How to calculate, regulate, and manage CPP or cerebra…

Sourced through Scoop.it from: www.youtube.com

See on Scoop.itCardiovascular and vascular imaging

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Ubiquitin researchers win Nobel

Larry H. Bernstein, MD, FCAP, Curator

 

Ciechanover, Hershko, and Rose awarded for discovery of ubiquitin-mediated proteolysis

http://www.the-scientist.com/?articles.view/articleNo/23111/title/Ubiquitin-researchers-win-Nobel/

Nature Cell Biology 2, E171 (2000) http://dx.doi.org:/10.1038/35036412

The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry for 2004 “for the discovery of ubiquitin-mediated protein degradation” jointly to

Aaron Ciechanover
Technion – Israel Institute of Technology, Haifa, Israel,

Avram Hershko
Technion – Israel Institute of Technology, Haifa, Israel and

Irwin Rose
University of California, Irvine, USA

 

Proteins labelled for destruction

Proteins build up all living things: plants, animals and therefore us humans. In the past few decades biochemistry has come a long way towards explaining how the cell produces all its various proteins. But as to thebreaking down of proteins, not so many researchers were interested. Aaron Ciechanover, Avram Hershko and Irwin Rose went against the stream and at the beginning of the 1980s discovered one of the cell’s most important cyclical processes, regulated protein degradation. For this, they are being rewarded with this year’s Nobel Prize in Chemistry.

Aaron Ciechanover, Avram Hershko and Irwin Rose have brought us to realise that the cell functions as a highly-efficient checking station where proteins are built up and broken down at a furious rate. The degradation is not indiscriminate but takes place through a process that is controlled in detail so that the proteins to be broken down at any given moment are given a molecular label, a ‘kiss of death’, to be dramatic. The labelled proteins are then fed into the cells’ “waste disposers”, the so called proteasomes, where they are chopped into small pieces and destroyed.

 

Avram Hershko is an Israeli biochemist and winner of the 2004 Nobel Prize for Chemistry.

Hershko (born December 31, 1937) was born as Hersko Ferenc in Karcag, Hungary. In 1950, Hershko and his family emigrated from Hungary to Israel, where he adopted the name Avram. Hershko received his M.D. and Ph.D. from the Hadassah Medical School of the Hebrew University. In 1965-67, Hershko worked as a physician in the Israel Defense Forces.

In 1969-72, Hershko was a postdoctoral fellow with the late Dr. Gordon Tomkins at the University of California, San Francisco.

In 1987, Hershko was awarded the Weizmann Prize for Sciences, an honor given to top Israeli scientists. In 1994, he won the Israeli Prize for his contributions to Israeli society through biochemistry and medicine.

In 2004, Hershko was awarded the Nobel Prize in Chemistry “for the discovery of ubiquitin-mediated protein degradation.”

Ciechanover was born in Haifa, a year before the establishment of Israel. He is the son of Bluma (Lubashevsky), a teacher of English, and Yitzhak Ciechanover, an office worker.[1] His family were Jewish immigrants from Poland before World War II.

He earned a master’s degree in science in 1971 and graduated from Hadassah Medical School in Jerusalem in 1974. He received his doctorate in biochemistry in 1981 from the Technion – Israel Institute of Technology in Haifa before conducting postdoctoral research in the laboratory of Harvey Lodish at the Whitehead Instituteat MIT from 1981-1984. He is currently a Technion Distinguished Research Professor in the Ruth and Bruce Rappaport Faculty of Medicine and Research Institute at the Technion.

Ciechanover is a member of the Israel Academy of Sciences and Humanities, the Pontifical Academy of Sciences, and is a foreign associate of the United States National Academy of Sciences.

As one of Israel’s first Nobel Laureates in Science, he is honored in playing a central role in the history of Israel and in the history of the Technion – Israel Institute of Technology

 

  • Ciechanover, A., Hod, Y. and Hershko, A. (1978). A Heat-stable Polypeptide Component of an ATP-dependent Proteolytic System from Reticulocytes. Biochem. Biophys. Res. Commun. 81, 1100–1105.
  • Ciechanover, A., Heller, H., Elias, S., Haas, A.L. and Hershko, A. (1980). ATP-dependent Conjugation of Reticulocyte Proteins with the Polypeptide Required for Protein Degradation. Proc. Natl. Acad. Sci. USA 77, 1365–1368.
  • Hershko, A. and Ciechanover, A. (1982). Mechanisms of intracellular protein breakdown. Annu. Rev. Biochem. 51, 335–364.

Interview Transcript

Transcript from an interview with the 2004 Nobel Laureates in Chemistry Aaron Ciechanover, Avram Hershko and Irwin Rose, on 9 December 2004. Interviewer is Joanna Rose, science writer.

Aaron Ciechanover, Avram Hershko and Irwin Rose during the interview

Dr Ciechanover, Dr Hershko and Dr Rose, my congratulations to the Nobel Prize and welcome to this interview. I know that you two started as medical doctors but you are in science now, and you get the prize for scientific research. How come you left medicine?

Avram Hershko: Well, I started out as a medical student, I wanted to be a doctor. And during my medical studies I studied biochemistry. That was one of the subjects that every medical student studies, so I liked it very much. I liked, you know, the whole concept of biochemistry, of looking for chemical processes in cells, so we had, we could take off one year from the studies to spend in research in the lab. I also found a very good teacher, Jacob Mager, and I wanted to spend it with him, so I did. That’s how I got involved in biochemistry. Afterwards, I finished my medical studies but already, I, after that one year, I knew that I will go to biochemistry and not to practical medicine. That’s how I started. So, it’s, it’s, like all things in life, it starts by some kind of accident or so, that was the accident, I met a subject during my studies that I liked.

And a good teacher.

Avram Hershko: And a very good teacher.

Was it also a topic, an issue that you were interested in?

Avram Hershko: No, no, not yet, not yet. Mager was interested in many subjects so that was … Actually, I continued with him after my army service as a doctor, and during the course of a couple of years I evolved in four completely different subjects, protein, synthesis, purine metabolism, and a certain disease called glucose-6-phosphate dehydrogenase deficiency, because he was interested in many things, so that gave me a very good background, a very, very, you know, very good basic background.

What about you, Dr Ciechanover?

I fell in love with biochemistry …

Aaron Ciechanover: Surely you can repeat the story verbatim. The same very story, I started in the same medical school, and after four years I decided to try and taste, I fell in love with biochemistry, too.

Like ten years later.

Aaron Ciechanover: Exactly ten years later, and I also decided to taste it, and at that time at medical school they let students take one year off for medical studies, try some research, so I went into biochemistry, same very story, different mentor. And, a wonderful mentor, and I studied lipids.

Also.

Aaron Ciechanover: Not proteins at all, and then exactly, made a decision, that that’s it. But I had, because of obligations to serve in Israel in the military as a physician. I completed my medical studies, went to serve in the army, but meanwhile, in between, I was looking already for a future mentor, in biochemistry, and Avram was at the time abroad, in the University of California in San Francisco, and I got rave recommendation, that he is a great teacher and a great biochemist, and I wrote him, and he was ready to accept me, and there started this story. More or less.

So did you go to the States?

Aaron Ciechanover: No, no, he came here. He returned to /- – -/ fellow, he started a new department in Haifa, which was a new medical school, I joined him, not initially on this project, on a different one because I still had to serve in the army. It’s a little bit complicated date-wise, but basically it’s the same very story, mentorship, the same footstep, without knowing where I am going.

You will never know.

Aaron Ciechanover: I never know, but it’s basically, ten years later the same very footsteps.

Oh, that’s funny. What about you, Dr Rose? How did you get …

Irwin Rose: I have an anomalist’s story. It doesn’t, there is no precedent for this. We moved from the east coast to the town of Spokane, Washington, when I was about 13 years old, and I did not adapt very well to the, to the style of the place, and I spent most of my time in the public library. And I enjoyed the company of the Journal of Biological Chemistry, because it was the book shaped thing, in those days, you know, it was the small journal …

Avram Hershko: At the age of 13?

Irwin Rose: No, you know, like a couple of years, you know, I was very unpopular with the other students, and so I read the Journal of Biological … the small, the small Journal of Biological Chemistry, and I found an article I thought I understood. And I read it and I thought I understood it to the point where I could make some suggestions as to how it would be, the experiment might work, and then I was very satisfied with that, and then I … I didn’t spend much time in science at that point. Went into the navy, got out of the navy, tried to go to the University of California at Berkeley, but due to the failure to find the bulletin board announcing the laboratory time of organic chemistry, I couldn’t do my organic chemistry there.

So I said OK, I’ll be a biochemist …

So I went back to the State College of Washington and there I was influenced, I would say, by the embryology teacher, who was a very strong personality in terms of academic research, he tried to encourage his students. Then I went to the University of Chicago and there was a big shock to learn all the new kinds of things that they were teaching there, in organic chemistry and that sort of stuff, and very attractive concepts, and things began to come together in my mind as to how chemistry worked and how I might be able to exploit some of the early kinds of techniques that were being used in organic chemistry into biochemistry, which was something I was attracted to, due to my reading of the Journal of Biological Chemistry. So at that point I signed up, there was a big gymnasium, and people were signing people up for which major you were going to go into. So I said OK, I’ll be a biochemist.

So I entered into the department of biochemistry, never saw the chairman of biochemistry because he was the appointed ambassador to Britain for the United States. So I floated around in the department of biochemistry and learned some interesting things, and then I began to … I never wanted to work with a mentor, because I always wanted to have my own reputation and be free to do what I wanted to do. So I worked with the weakest people in the department. Don’t make that public. No, I don’t mention the names, but … so I did that sort of thing and that way I came to learn some more independence, and once in a while I did a good experiment, and so I had more confidence that I could do research, and so that’s how it got started.

Avram Hershko: Can I mention the story that you did your PhD or eight counts per minute or …

Irwin Rose: Oh yes, well, in those days people weren’t counting, people counted on planchettes. And you …

Avram Hershko: Puckered.

Irwin Rose: Well, it could be, depends on they were flat.

Avram Hershko: You dried them, didn’t you?

Irwin Rose: Yes, you dried them out, depending … yes, that’s right. You had to dry them out, it depends on what the compound was, but if it was trillium you had to get an infinitely thin layer so that you wouldn’t get self-absorption.

Avram Hershko: It’s common, self-absorption on a planchette.

Irwin Rose: Did you guys do that, too?

Aaron Ciechanover: Yeah, yeah, yeah.

Avram Hershko: We had a counter with only three /- – -/ so we moved it like that …

Irwin Rose: Oh yeah, yeah.

Avram Hershko: … it was a big excitement.

Irwin Rose: So I wasn’t that primitive. You were doing these things in Israel, an advanced state.

Aaron Ciechanover: You came to our country.

Irwin Rose: I did. I came to Israel. But anyway, yes, so we did those things. And even if you had eight counts above background, if there were eight, there were eight. That’s right. So you could do some experiments. That’s how it worked out.

So how did you meet together?

Avram Hershko: Well, that’s another story. I got interested in protein degradation during my post-doc fellowship in San Francisco, and when I came back to Israel I continued with that, and at that time it was a very obscure field, you know. People, there were all kinds of, not too many people were interested in it. Those that were interested were not very good. So I looked for somebody, and so my first time I think I came up and I looked for somebody to spend a sabbatical with. I couldn’t find anybody that attracted me. So then I met Ernie at a meeting in 1976, one year before, before my sabbatical was due. And do you remember, we met in the breakfast, so I said can I, just began to talk …

Irwin Rose: It’s alright, I forgot.

… it turned out that he was interested in protein degradation. And that was a secret …

Avram Hershko: … breakfast table, so I knew who he was, he was very well known for his work on enzyme mechanism. That I knew, but then I asked him what are you interested in, in other things? So it turned out that he was interested in protein degradation. And that was a secret, it was a secret because he never published anything on it, and I asked him how come you never published anything, and so he said there is nothing worth publishing on protein degradation. So that’s what he said.

Irwin Rose: Yeah, that was my opinion. Well, because I hadn’t done anything, you don’t say it right.

Avram Hershko: OK. Well, that’s how I remember it. And anyhow, I liked that attitude very much, and asked, I asked him can I spend my sabbatical with you? And he said yes, so that’s how it started, and then Aaron, the same year he started his PhD with me, and after my sabbatical the following, the summer after my sabbatical, Aaron joined us, and then he joined us for a couple of summers afterwards, so that’s how, that’s how the whole connection started.

But how come you pick up an obscure field in science, to work on?

Irwin Rose: Well, I’ll tell you, because when I first worked at Yale, the guy who had a lab next to me had made the original observation that there was a protein, there was an energy dependent on protein breakdown. Now, nobody believed him, but he had made some pretty strong observations that if you …

Avram Hershko: Here, we could mention names.

Irwin Rose: Yes, Melvin Simpson. He made these important observations.

Aaron Ciechanover: He hardly believed himself, because when you go into discussion on the paper, you kind of come to a convoluted argument whether it’s a direct requirement or indirect. We can do the conclusion that it’s indirect.

When was it?

Avram Hershko: 1953, so …

Irwin Rose: So I didn’t read the paper, but I had this man in the laboratory next to me and he said, he made this observation and I got very interested in it. And worked on it for, on sabbatical, and when I went to England and when I went to Israel I got mice from Mager, it turned out the same guy, but he wasn’t there at the time, and … but I never found an energy dependence on the protein breakdown. And it turns out later on that a fellow named Art Haas who had been a post doc with me, made the observation that if you’re not careful when you break cells, there’s a lysosomal enzyme that degrades the ubiquitin. So I never would have found it, you know. Somebody else had to make the observation that you could make a self-resistent that … that would show an ATP dependence on protein breakdown. It was not for me, but I did work on it earlier, and that’s the, that’s why I told you that I’d never made any important observations.

But you three work together. How does it work, to do things together?

Irwin Rose: I don’t do anything.

You do nothing? Who is the worker?

Avram Hershko: Well, that’s, first of all, that’s not true. I remember that you made some ubiquitin preparation …

Irwin Rose: I did.

Avram Hershko: Yes, and it fell on the floor, and then you collected it up from the floor … yeah, yeah. That first step is to boil the extra, because ubiquitin is heat stable, so you boiled it but then it fell on the floor, but you picked it up and it was good, yeah.

Irwin Rose: It was good, nothing could destroy it.

Irwin Rose: It was a licence only enzyme.

Aaron Ciechanover: The /- – -/ can take it, but not the floor.

Avram Hershko: But, yeah, but when I came to his lab we already had his first step, which was the fractionation, well, the reticulocyte cell-free system system was actually established in the laboratory of somebody else, Alfred Goldberg in Harvard, but they didn’t …

Aaron Ciechanover: /Inaudible./

Avram Hershko: No, no, but, yeah, but he made it first, he made it first.

Aaron Ciechanover: The first publication was from Harvard, no doubt.

Avram Hershko: But then he didn’t progress, but then he didn’t do what he should have done, which is fractionation. It’s hard to purify right away, but ATP dependent enzyme, he never found it. And what we did was fractionation and constitution, so we already had this first step of separating it into two, two fractions, fraction one and fraction two.

… we didn’t really understand that it’s binding …

So during these two years between the beginning of ’77 when I write to your lab and December of ’79, when we made the breakthrough in your lab, we purified the component from fraction one, we found it a heat stable protein, and then you had a part in that, you also boiled ubiquitin, and then in Haifa we found that it gets … when we labelled it with iodine and we found it gets bound to proteins and ATP dependent reaction, but we didn’t really understand that it’s binding, its co-herent binding the substate until that summer in 1971 in the laboratory of Rose where you invited me, together with Aaron who was then my graduate student in /- – -/ who was there. 1979, 1979. So that is when, when the discovery that ubiquitin …

Irwin Rose: Shall I tell the story about the ubiquitin?

Avram Hershko: Yes. I think I have finished. So then, that’s how I remember it, and how …

Irwin Rose: OK, well, here they had a heat stable factor that was required, and they made the observation that the ubiquitin went on to proteins. And so one of my post docs went to a post doc of another student, of another faculty member at the Fox Chase Cancer Centre, and said, there was a conversation, and do you know of any examples of a protein covalently linked to a protein? And this post doctoral fellow said yes, there is in the nucleus, a protein called ubiquitin that’s covalently linked to histone. And so they rushed to look at the amino acid composition of that so-called ubiquitin, and they compared it to the amino acid composition which you had published, I guess …

Aaron Ciechanover: No, not yet.

Irwin Rose: Not yet published.

Aaron Ciechanover: But in the end it was published back to back with JBC.

Irwin Rose: No, no, no. But how did they know the conversation …

Aaron Ciechanover: No, because they knew, the end story is that the Wilkinson paper came back to back with ours on the /- – -/.

Avram Hershko: OK. Let’s not go into the detail.

Irwin Rose: Well, for some reason or other, they found confidence…

Avram Hershko: They knew that I published that.

Irwin Rose: Really, and I was not a leak.

Avram Hershko: No, no, you were not.

Aaron Ciechanover: No, he was in the lab, he was free and did this. We didn’t hide anything.

Irwin Rose: OK, you’re getting the inside story here. Now, wait a second.

I have a statement from your colleague. “At first nobody cared about your work, and those that knew something about it, they didn’t believe it.” Was it so …?

Irwin Rose: Who said that?

Avram Hershko: That was, that was Fred Goldberg, yeah.

Aaron Ciechanover: Let’s not mention names.

Avram Hershko: Oh! No, we didn’t mention names.

That citation is right.

Aaron Ciechanover: I’ll tell you, I’ll tell you a funny story. I left the lab in ’81, basically after my PhD was completed I submitted it and I went to Harvard, I went to MIT to do a post doc fellow, and Harvard carried out weekly seminars. And in this weekly seminar, one of the founders in the field of proteolysis, one of the originally, not the founder, but it doesn’t matter. A famous scientist in the field presented the weekly seminar at Harvard. I knew of him because he was our competitor for many years, and I went to hear the seminar, so I crossed the river by the bus, I took the shuttle bus that goes /- – -/ and I was sitting in the very back bench. And this was probably about two weeks before you came to visit, it was the very beginning of my, do you remember when I met you, I came to the airport to pick you up.

Avram Hershko: Yeah, yeah.

Aaron Ciechanover: And then, near me, was sitting a very famous scientist that I only later realised that his name is Arthur Dee, a very famous scientist, and after this presentation of the professor, this was only ’81 when we had like eight or nine papers already in the literature with a huge amount of information there. And he was a protein researcher and he raised his hand, I remember very well, and the other guy, when we were both  /- – -/ he said, you know, I have a question to ask you. There is a fellow in Haifa by the name of Hershko, and another one with a very complicated Polish name that I cannot even pronounce, that published a series of papers on a small protein that is attached to other proteins and marks them for degradation, can you comment on it? And he basically dismissed it as an artefact.

… it adds to our benefit, because they left us alone for seven successive years …

And I don’t, I don’t criticise him, all I’m telling you it was symbolic for me enough for after eight papers in the literature, this was the spirit in the field from people who worked in the field, and there were very few. As a matter of fact, it adds to our benefit, because they left us alone for seven successive years, even after I left the lab to work out basically the entire system. The next scientist to join the field was a scientist at MIT, Alex Varshavsky, who joined in ’84, ’83, but published in ’84, and given I was there and collaborated, so for seven successive years they let us lay the entire stone down in the literature so I don’t criticise him, actually I appreciate him tremendously for letting us do it. You know, in retrospect.

But I wonder, how do you survive as a scientist when nobody believes you somehow? Nobody’s interested. You become kind of non-visible.

Irwin Rose: You’re making observations, and the observations get published, so the observations are true. Whether anybody will say that belongs to a big story like it turns out to be is not predictable, but so you don’t make claims like that. You say that this is very interesting and so on and so on and so on, and you keep following it up, and it doesn’t necessarily become the centre of attention yet, until you build a big enough story. I think that’s the way it works.

We all survive because funding for research was generous in those days, you know. It’s been less generous now, and we have a peer review system which is more critical and so I think you have to, you have to add successively to the picture you’re trying to portray. It’s not sufficient to just provide data. So I think that’s part of it. But I agree that it’s important to be left alone for a sufficient amount of time in order to be able to do it, and not feel that you’re in the middle of a big activity already, so you know, you need to do that sort of thing.

So do you think you would get support today for such work, which was kind of apart?

Avram Hershko: Well, I hope the fund /- – -/ look up your website and will hear these things. Because it’s … yeah, Joe Goldstein, you know, a Nobel Laureate and a good one, wrote a nice article about this year’s Lasker Award, in which he compared science to a sculpture by this British sculptor who had his stone, it was a huge stone of two and a half ton, on which another stone, and another stone, and another stone, and at the end is a little stone, so he said that in science there are big stones and small stones. The important science is the opposite. When you have a little stone, and on top of it you put a bigger stone and then a bigger stone. If you throw out a big stone at the beginning so there’s a lot of publicity sometimes nothing comes out of it, and the scientist, to find his little stone, on which the other stones can be built. So I recommend to read his article.

Now you find the small stones, Dr Rose, in your kitchen, as I understand it. You have a small laboratory there?

Irwin Rose: You want to talk about my kitchen?

Yeah. Your laboratory, I would say.

Irwin Rose: Well, when I retired from Fox Chase I took my spectrophotometer and a lot of my chemicals, based on a sort of suggestion of Dr … his recommendation. So I took all my chemicals and my spectrophotometer and my constant temperature bath and so forth with me to Irvine, and when the person whose laboratory I was sitting decided to retire, I had to do something with the spectrophotometer and so I found a place in my kitchen for it. And this was very convenient because it saved me a lot of time. I didn’t have to go to work every day and if I had a little experiment to do I could do it in my kitchen. So that was very good, although I’ve got a lot of chemicals that I have no use for and I’d like to take them back.

Aaron Ciechanover: Send them over, send them over.

Irwin Rose: I’ll send them over. I’ll get a box.

Avram Hershko: But I worry that you don’t have an ice machine. You need an ice machine.

Irwin Rose: No, I don’t have an ice machine. But I have a freezer and I can make ice cubes and I can break them up.

It’s kind of worrying, in science. So you can work when everything’s /- – -/ ?

Irwin Rose: Yeah, that’s right, exactly.

So are you the kind of scientists that work all day and all night long, kind of nerd scientists?

So that’s my recommendation, do not retire. Do not retire fellas. …

Irwin Rose: I think we all work all day and all night long. I do. I don’t have any hobbies, you know, I’m very embarrassed when people ask me what are my hobbies, I don’t have any hobbies. I mean, it’s just enough to keep up with the things I’m trying to solve. You know, I used to work on little puzzles and so on and so forth. Each puzzle requires attention and, so you get an idea. You get your ideas at different times. Sometimes your wife makes a statement and you say: aha, maybe you’re right. And so you go off to your kitchen, and do a little experiment, so you try to, that’s the way you make progress, if you continue these things. So that’s my recommendation, do not retire. Do not retire fellas.

Avram Hershko: I won’t.

Aaron Ciechanover: I’m never going to.

You worked together in the beginning, you were the graduate student of Dr Hershko, how was it to separate from each other?

Aaron Ciechanover: Well, it’s the nature of science, I think, because you know, you graduate, you go your post doctorate fellowship, and Avram was gracious enough to bring me back, but now is independent and that’s the entire idea, if you bring a young scientist back, you give him a bench, start up funds, and then you tell him now in five years, come back in five years, and show the committees that you worked for something. So actually, you know, it would be unnatural if we would have continued to work together. So, each of us is independent. Now we’re in the same institute and that’s the whole idea of children that grow up, students that become their own, scientists on their own, I think that’s the way.

Do you compete with each other?

Avram Hershko: No, there is enough to do in the ubiquitin field, we don’t feel that we had to compete. There are different aspects of the ubiquitin field. I am working on cell cycle and he works on …

Aaron Ciechanover: /- – -/. Completely different.

How is it to live in a small country with big problems and to get funds for science?

Avram Hershko: It is not easy, it is not easy. You have to know the daily tension which is of course distractive. The funds are small, some funds for science are small. Graduate students have to go to serve in the army and things like that, so it’s more difficult than elsewhere, but it’s possible, it’s possible.

And now everybody’s happy. About the Nobel Prize. So thank you very much for sharing your thoughts with us, and being with us.

 

See a Video of the Interview
26 min.

 

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To cite this page
MLA style: “Transcript from an interview with the 2004 Nobel Laureates in Chemistry Aaron Ciechanover, Avram Hershko and Irwin Rose, on 9 December 2004”. Nobelprize.org. Nobel Media AB 2014. Web. 5 Sep 2015. <http://www.nobelprize.org/nobel_prizes/chemistry/laureates/2004/ciechanover-hershko-rose-interview-transcript.html>

 

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TEL AVIV, Israel, March 24, 2015 /PRNewswire/ —

 

The Tel Aviv Stock Exchange (TASE) continues to implement the recommendations of the

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(Logo:

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Photo:

http://photos.prnewswire.com/prnh/20130117/588933

 

Photo:http://photos.prnewswire.com/prnh/20130117/588933

http://photoarchive.ap.org/

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Henan Provincial Government of China and APCEO, we are honored to invite you as VIP guest to attend The 3rd World Emerging Industries Summit.
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Dates:   Apr. 20 -22, 2015
Venue:   JW Marriott Hotel Zhengzhou (5-star), Henan province, China
Theme:  Emerging Industries, the new power to world economic growth
VIP Treatment for you (ZHENGZHOU,HENAN,CHNIA, Apr. 20-22, 2015):
Free participation, Free hotel and accommodation.
Basic Agenda & Main Activities:
1. Opening Ceremony
2. Keynote Session
3. Meeting High-Rank Government Officials
4. High-level Forum on Specialized International Cooperation
5. The 9th China Henan International Investment & Trade Fair
(More than 100,000 audiences and more than 5,000 exhibitors)
6. Key Cooperation Projects Signing Ceremony
7. A trip to Airport Economic Comprehensive Experimentation Zone
We are looking forward to meeting you at this important event!
Yours sincerely
Xie Fuzhan
Governor
Henan Provincial Government of China
Zheng Xiongwei
Global Executive Chairman of APCEO

 

 


Shenyang as the Financial Gateway to Northeast China

Co-hosted with the Province of Liaoning and the Municipal Government of Shenyang, this event will provide attendees with high-level networking opportunities together with insight into the region’s economic roadmap.

Known as the ‘Gateway to Northeast China’, Shenyang has witnessed remarkable growth over the last decade. Some of its major sources of investments have come from Hong Kong, Korea, Japan, US, Taiwan, Singapore and Germany. With the development of a Far East Free Trade Zone in Shenyang, the region is well positioned to further benefit from China’s increasing economic ties with Russia, Korea and Mongolia.

Join over 500 international and local business executives, industrialists, investors, bankers, economists and policymakers to make new connections in this region and to discover the opportunities and incentives for doing business in Shenyang.

View the latest agenda: (English) l (Chinese)

Topics and themes:
– Gateway to Northeast China: Economic and Investment Opportunities
– Financing Chinese Enterprise Growth
– Shenyang Far East Free Trade Zone
– The Urban Development of Shenyang
– Private Banking and Wealth Management for Northeast China
– The Development of the Digital Economy in China

Speakers include:
– CHEN Zhenggao, Governor, The People’s Government of Liaoning Province
– PAN Liguo, Mayor, Shenyang Municipal People’s Government

– Martin Lee-Warner, Senior Adviser, Raiffeisen Bank International
– Florian Schmied, Chairman, European Union Chamber of Commerce in China, Shenyang
Chapter
– Karen Chen
, President, Head of China Wealth Management, UBS (China)
– HUANG Fan, Director, Head of Private Wealth Management, Deutsche Bank (China)
– Timothy Lo
, Managing Director, CIC Banque Privée
– Angel Wu, Regional Head of Products & Solutions Asia & Middle East, ABN Amro Bank
– Gina Rivera, China – Business Development Manager, Koehler Group
– Edward Tse, Chief Executive Officer, Gao Feng Advisory Company
– Senior representative, Standard Chartered
– Senior representative, MasterCard

Attendance to the summit is free and by-invitation-only. As an attendee, you will be further invited to one-on-one meetings and site visits, including a tour to the state-of-the-art BMW Brilliance auto assembly plant.

Read Full Post »


Classification of Microbiota –

An Overview of Clinical Microbiology, Classification, and Antimicrobial Resistance

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

Classification of Microbiota

Introduction to Overview of Microbiology

This is a contribution to a series of pieces on the history of biochemistry, molecular biology, physiology and medicine in the 20th century.  Here I describe the common microbial organisms encountered in the clinical laboratory, the method of their collection, plating, culture and identification, and antibiotic sensitivity testing and resistant strains.

I may begin with the recognition that there are common strains in the environment that are not pathogenic, and there are pathogenic bacteria.
In addition, there are bacteria that coexist in the body habitat under specific conditions so that we are able to map the types expected to location, such as, skin, mouth and nasal cavities, the colon, the vagina and urinary system.  Meningitides occur as a result of extension from the nasal cavity to the brain.  When bacteria invade the circulation, it is referred to as septicemia, and the bacteria can cause valvular heart damage.

Bacteriology can be traced to origins in the 19th century.  The clinical features of localized infection are classically referred to as redness, heat, a raised lesion (pustule), and exudate (serous or purulent – watery or cellular).  This not only holds for a focal lesion (as skin), but also for pneumonia, urinary infection, and genital. It may be accompanied by cough, or bloody cough and wheezing, or by an unclear urine. In the case of septicemia, there is fever, and there may be seizures or delirium.

Collection and handling of specimens

Specimens are collected by sterile technique by a nurse or physician and sent to a lab as a swab, or as a blood specimen.  In the case of a febrile illness, blood cultures may be obtained from opposite arms, and another an hour later.  This is related to the possible cyclical seeding of bacteria into the circulation.  If the specimen is collected from a site of infection, a swab may be put onto a glass slide for gram staining.  The specimen collected is sent to the laboratory.

We may consider syphilis and tuberculosis special cases that I’ll set aside.  I shall not go into virology either, although I may referred to smallpox, influenza, polio, HIV under epidemic.  The first step in identification is the Gram stain, developed in the 19th century.  Organisms of the skin are Gram positive and appear blue on staining.  They are cocci, or circular, organized in characteristic clusters (staphylococcus, streptococcus) or in pairs (diplococci, eg. Pneumococcus), and if from the intestine (enterococcus).  If they are elongated rods, they might be coliform.  If they stain red, they are Gram negative.  Gram negative rods are coliform, and are enterobacteriaceae. Meningococci are Gram negative cocci.  So we have certain information about these organisms before we plate them for growth.

Laboratory growth characteristics

The specimen is applied to an agar plate with a metal rod applicator, or perhaps onto more than one agar plate.  The agar plate contains a growth media or a growth inhibitor that is more favorable to certain species than to others.  The bacteria are grown at 37o C in an incubator and colonies develop that are white or nonwhite, and they are smooth or wrinkled.  The appearance of the colonies is characteristic for certain strains.  If there is no contamination, all of the colonies look the same.  The next step is to:

  • Gram stain from a colony
  • Transfer samples from the colony to a series of growth media that identify presence or absence of specific nutrient requirements for growth (which is presumed from the prior findings).

In addition, the colony samples are grown on an agar to which is applied antibiotic tabs.  The tabs either allow or repress growth.  It wa some 50 years ago that the infectious disease physician and microbiologist Abraham Braude would culture the bacteria on agar plates that had a gradient of antibiotic to check for concentration that would inhibit growth.

Principles of Diagnosis (Extracts)

By John A. Washington

The clinical presentation of an infectious disease reflects the interaction between the host and the microorganism. This interaction is affected by the host immune status and microbial virulence factors. Signs and symptoms vary according to the site and severity of infection. Diagnosis requires a composite of information, including history, physical examination, radiographic findings, and laboratory data.

Microbiologic Examination

Direct Examination and Techniques: Direct examination of specimens reveals gross pathology. Microscopy may identify microorganisms. Immunofluorescence, immuno-peroxidase staining, and other immunoassays may detect specific microbial antigens. Genetic probes identify genus- or species-specific DNA or RNA sequences.

Culture: Isolation of infectious agents frequently requires specialized media. Nonselective (noninhibitory) media permit the growth of many microorganisms. Selective media contain inhibitory substances that permit the isolation of specific types of microorganisms.

Microbial Identification: Colony and cellular morphology may permit preliminary identification. Growth characteristics under various conditions, utilization of carbohydrates and other substrates, enzymatic activity, immunoassays, and genetic probes are also used.

Serodiagnosis: A high or rising titer of specific IgG antibodies or the presence of specific IgM antibodies may suggest or confirm a diagnosis.

Antimicrobial Susceptibility: Microorganisms, particularly bacteria, are tested in vitro to determine whether they are susceptible to antimicrobial agents.

Diagnostic medical microbiology is the discipline that identifies etiologic agents of disease. The job of the clinical microbiology laboratory is to test specimens from patients for microorganisms that are, or may be, a cause of the illness and to provide information (when appropriate) about the in vitro activity of antimicrobial drugs against the microorganisms identified (Fig. 1).

Laboratory procedures used in confirming a clinical diagnosis of infectious disease with a bacterial etiology

http://www.ncbi.nlm.nih.gov/books/NBK8014/bin/ch10f1.jpg

A variety of microscopic, immunologic, and hybridization techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

From: Chapter 10, Principles of Diagnosis
Medical Microbiology. 4th edition.
Baron S, editor.
Galveston (TX): University of Texas Medical Branch at Galveston; 1996.

For immunologic detection of microbial antigens, latex particle agglutination, coagglutination, and enzyme-linked immunosorbent assay (ELISA) are the most frequently used techniques in the clinical laboratory. Antibody to a specific antigen is bound to latex particles or to a heat-killed and treated protein A-rich strain of Staphylococcus aureus to produce agglutination (Fig. 10-2). There are several approaches to ELISA; the one most frequently used for the detection of microbial antigens uses an antigen-specific antibody that is fixed to a solid phase, which may be a latex or metal bead or the inside surface of a well in a plastic tray. Antigen present in the specimen binds to the antibody as inFig. 10-2. The test is then completed by adding a second antigen-specific antibody bound to an enzyme that can react with a substrate to produce a colored product. The initial antigen antibody complex forms in a manner similar to that shown inFigure 10-2. When the enzyme-conjugated antibody is added, it binds to previously unbound antigenic sites, and the antigen is, in effect, sandwiched between the solid phase and the enzyme-conjugated antibody. The reaction is completed by adding the enzyme substrate.

agglutination test ch10f2

agglutination test ch10f2

Figure 2 Agglutination test in which inert particles (latex beads or heat-killed S aureus Cowan 1 strain with protein A) are coated with antibody to any of a variety of antigens and then used to detect the antigen in specimens or in isolated bacteria

http://www.ncbi.nlm.nih.gov/books/NBK8014/bin/ch10f2.jpg

Genetic probes are based on the detection of unique nucleotide sequences with the DNA or RNA of a microorganism. Once such a unique nucleotide sequence, which may represent a portion of a virulence gene or of chromosomal DNA, is found, it is isolated and inserted into a cloning vector (plasmid), which is then transformed into Escherichia coli to produce multiple copies of the probe. The sequence is then reisolated from plasmids and labeled with an isotope or substrate for diagnostic use. Hybridization of the sequence with a complementary sequence of DNA or RNA follows cleavage of the double-stranded DNA of the microorganism in the specimen.

The use of molecular technology in the diagnoses of infectious diseases has been further enhanced by the introduction of gene amplication techniques, such as the polymerase chain reaction (PCR) in which DNA polymerase is able to copy a strand of DNA by elongating complementary strands of DNA that have been initiated from a pair of closely spaced oligonucleotide primers. This approach has had major applications in the detection of infections due to microorganisms that are difficult to culture (e.g. the human immunodeficiency virus) or that have not as yet been successfully cultured (e.g. the Whipple’s disease bacillus).

Solid media, although somewhat less sensitive than liquid media, provide isolated colonies that can be quantified if necessary and identified. Some genera and species can be recognized on the basis of their colony morphologies.

In some instances one can take advantage of differential carbohydrate fermentation capabilities of microorganisms by incorporating one or more carbohydrates in the medium along with a suitable pH indicator. Such media are called differential media (e.g., eosin methylene blue or MacConkey agar) and are commonly used to isolate enteric bacilli. Different genera of the Enterobacteriaceae can then be presumptively identified by the color as well as the morphology of colonies.

Culture media can also be made selective by incorporating compounds such as antimicrobial agents that inhibit the indigenous flora while permitting growth of specific microorganisms resistant to these inhibitors. One such example is Thayer-Martin medium, which is used to isolate Neisseria gonorrhoeae. This medium contains vancomycin to inhibit Gram-positive bacteria, colistin to inhibit most Gram-negative bacilli, trimethoprim-sulfamethoxazole to inhibit Proteus species and other species that are not inhibited by colistin and anisomycin to inhibit fungi. The pathogenic Neisseria species, N gonorrhoeae and N meningitidis, are ordinarily resistant to the concentrations of these antimicrobial agents in the medium.

Infection of the bladder (cystitis) or kidney (pyelone-phritis) is usually accompanied by bacteriuria of about ≥ 104 CFU/ml. For this reason, quantitative cultures (Fig. 10-3) of urine must always be performed. For most other specimens a semiquantitative streak method (Fig. 10-3) over the agar surface is sufficient. For quantitative cultures, a specific volume of specimen is spread over the agar surface and the number of colonies per milliliter is estimated.

Identification of bacteria (including mycobacteria) is based on growth characteristics (such as the time required for growth to appear or the atmosphere in which growth occurs), colony and microscopic morphology, and biochemical, physiologic, and, in some instances, antigenic or nucleotide sequence characteristics. The selection and number of tests for bacterial identification depend upon the category of bacteria present (aerobic versus anaerobic, Gram-positive versus Gram-negative, cocci versus bacilli) and the expertise of the microbiologist examining the culture. Gram-positive cocci that grow in air with or without added CO2 may be identified by a relatively small number of tests. The identification of most Gram-negative bacilli is far more complex and often requires panels of 20 tests for determining biochemical and physiologic characteristics.

Antimicrobial susceptibility tests are performed by either disk diffusion or a dilution method. In the former, a standardized suspension of a particular microorganism is inoculated onto an agar surface to which paper disks containing various antimicrobial agents are applied. Following overnight incubation, any zone diameters of inhibition about the disks are measured. An alternative method is to dilute on a log2 scale each antimicrobial agent in broth to provide a range of concentrations and to inoculate each tube or, if a microplate is used, each well containing the antimicrobial agent in broth with a standardized suspension of the microorganism to be tested. The lowest concentration of antimicrobial agent that inhibits the growth of the microorganism is the minimal inhibitory concentration.

Classification Principles

This Week’s Citation Classic®_______ Sneath P H A & Sokal R R.
Numerical taxonomy: the principles and practice of
numerical classification. San Francisco: Freeman, 1973. 573 p.
[Medical Research Council Microbial Systematics Unit, Univ. Leicester, England
and Dept. Ecology and Evolution, State Univ. New York, Stony Brook, NY]
Numerical taxonomy establishes classification
of organisms based on their similarities. It utilizes
many equally weighted characters and employs
clustering and similar algorithms to yield
objective groupings. It can beextended to give
phylogenetic or diagnostic systems and can be
applied to many other fields of endeavour.

Mathematical Foundations of Computer Science 1998
Lecture Notes in Computer Science Volume 1450, 1998, pp 474-482
Date: 28 May 2006
Positive Turing and truth-table completeness for NEXP are incomparable 1998
Levke Bentzien

The truth-table method [matrix method] is one of the decision procedures for sentence logic (q.v., §3.2). The method is based on the fact that the truth value of a compound formula of sentence logic, construed as a truth-function, is determined by the truth values of its arguments (cf. “Sentence logic” §2.2). To decide whether a formula A is a tautology or not, we list all possible combinations of truth values to the variables in A: A is a tautology if it takes the value truth under each assignment.

Using ideas introduced by Buhrman et al. ([2], [3]) to separate various completeness notions for NEXP = NTIME (2poly), positive Turing complete sets for NEXP are studied. In contrast to many-one completeness and bounded truth-table completeness with norm 1 which are known to coincide on NEXP ([3]), whence any such set for NEXP is positive Turing complete, we give sets A and B such that

A is ≤ bT(2) P -complete but not ≤ posT P -complete for NEXP

B is ≤ posT P -complete but not ≤ tt P -complete for NEXP. These results come close to optimality since a further strengthening of (1), as was done by Buhrman in [1] for EXP = DTIME(2poly), seems to require the assumption NEXP = co-NEXP.

Computability and Models
The University Series in Mathematics 2003, pp 1-10
Truth-Table Complete Computably Enumerable Sets
Marat M. Arslanov

We prove a truth-table completeness criterion for computably enumerable sets.
The authors research was partially supported by Russian Foundation of Basic Research, Project 99-01-00830, and RFBR-INTAS, Project 97-91-71991.

TRUTH TABLE CLASSIFICATION AND IDENTIFICATION*
EUGENE W. RYPKA
Department of Microbiology, Lovelace Foundation for Medical Education and Research,
Albuquerque, N.M. 87108, U.S.A.
Space life sciences 1971-12-1; 3(2): pp 135-156
http://dx.doi.org:/10.1007/BF00927988
(Received 15 July, 1971)
Abstract. A logical basis for classification is that elements grouped together and higher categories of elements should have a high degree of similarity with the provision that all groups and categories be disjoint to some degree. A methodology has been developed for constructing classifications automatically that gives
nearly instantaneous correlations of character patterns of organisms with time and clusters with apparent similarity. This means that automatic numerical identification will always construct schemes from which disjoint answers can be obtained if test sensitivities for characters are correct. Unidentified organisms are recycled through continuous classification with reconstruction of identification schemes. This process is
cyclic and self-correcting. The method also accumulates and analyzes data which updates and presents a more accurate biological picture.

Syndromic classification: A process for amplifying information using S-clustering

Eugene W. Rypka, PHD

http://dx.doi.org:/10.1016/S0899-9007(96)00315-2

Optimal classification/Rypka < Optimal classification>

Contents

1 Rypka’s Method

1.1 Equations

1.2 Examples

2 Notes and References

Rypka’s Method

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the code name of the data in the upper left most cell. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting appropriate characteristics to universally define the class of elements may be the most difficult part for the classifier of utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Computational Example
Bounded Class Data

bounded class data

Bounded Class Dimensions

G = 28 – 28 elements – i = 0…G-1[1]

C = 10 – 10 characteristics or attributes – j = 0…C-1

V = 5 – 5 valued logic – l = 0…V-1

Order of Elements

order of elements

Count multisets

count multisets

Squared multiset Counts

squared multiset counts

Separatory Values

separatory values

T=

max(T) = 309 = S8 = highest initial separatory value

Notes

Mathcad’s ORIGIN function applies to all arrays such that if more than one array is being used and one array requires a zero origin then the other arrays must use a zero origin with all variables being adapted as well.

Rypka’s Method Edit

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the title of the attributes in the upper left most cell. Normally the file name of the data is given the title of the element class. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting characteristics which all elements share may be the most difficult part of creating a database which can utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Syndromic Classification: A Process for Amplifying Information Using S-Clustering

Eugene W. Rypka, PhD
University of New Mexico, Albuquerque, New Mexico, USA
Statistics Editor: Marcello Pagano, PhD
Harvard School of Public Health, Boston, Massachusetts, USA
Nutrition 1996; 12(11/12): 827-829

In a previous issue of Nutrition, Drs. Bernstein and Pleban’ use the method of S-clustering to aid in nutritional classification of patients directly on-line. Classification of this type is called primary or syndromic classification.* It is created by a process called separatory (S-) clustering (E. Rypka, unpublished observations). The authors use S-clustering in Table I.  S-clustering extracts features (analytes, variables) from endogenous data that amplify or maximize structural information to create classes of patients (pathophysiologic events) which are the most disjointed or separable. S-clustering differs from other classificatory methods because it finds in a database a theoretic- or more- number of variables with the required variety that map closest to an ideal, theoretic, or structural information standard. In Table I of their article, Bernstein and Pleban’ indicate there would have to be 3 ’ = 243 rows to show all possible patterns. In Table II of this article, I have used a 33 = 27 row truth table to convey the notion of mapping amplified information to an ideal, theoretic standard using just the first three columns. Variables are scaled for use in S-clustering.

A Survey of Binary Similarity and Distance Measures
Seung-Seok Choi, Sung-Hyuk Cha, Charles C. Tappert
SYSTEMICS, CYBERNETICS AND INFORMATICS 2010; 8(1): 43-48
The binary feature vector is one of the most common
representations of patterns and measuring similarity and
distance measures play a critical role in many problems
such as clustering, classification, etc. Ever since Jaccard
proposed a similarity measure to classify ecological
species in 1901, numerous binary similarity and distance
measures have been proposed in various fields. Applying
appropriate measures results in more accurate data
analysis. Notwithstanding, few comprehensive surveys
on binary measures have been conducted. Hence we
collected 76 binary similarity and distance measures used
over the last century and reveal their correlations through
the hierarchical clustering technique.

This paper is organized as follows. Section 2 describes
the definitions of 76 binary similarity and dissimilarity
measures. Section 3 discusses the grouping of those
measures using hierarchical clustering. Section 4
concludes this work.

Historically, all the binary measures observed above have
had a meaningful performance in their respective fields.
The binary similarity coefficients proposed by Peirce,
Yule, and Pearson in 1900s contributes to the evolution
of the various correlation based binary similarity
measures. The Jaccard coefficient proposed at 1901 is
still widely used in the various fields such as ecology and
biology. The discussion of inclusion or exclusion of
negative matches was actively arisen by Sokal & Sneath
in during 1960s and by Goodman & Kruskal in 1970s.

Polyphasic Taxonomy of the Genus Vibrio: Numerical Taxonomy of Vibrio cholerae, Vibrio
parahaemolyticus, and Related Vibrio Species
R. R. COLWELL
JOURNAL OF BACTERIOLOGY, Oct. 1970;  104(1): 410-433
A set of 86 bacterial cultures, including 30 strains of Vibrio cholerae, 35 strains of
V. parahaemolyticus, and 21 representative strains of Pseudomonas, Spirillum,
Achromobacter, Arthrobacter, and marine Vibrio species were tested for a total of 200
characteristics. Morphological, physiological, and biochemical characteristics were
included in the analysis. Overall deoxyribonucleic acid (DNA) base compositions
and ultrastructure, under the electron microscope, were also examined. The taxonomic
data were analyzed by computer by using numerical taxonomy programs
designed to sort and cluster strains related phenetically. The V. cholerae strains
formed an homogeneous cluster, sharing overall S values of >75%. Two strains,
V. cholerae NCTC 30 and NCTC 8042, did not fall into the V. cholerae species
group when tested by the hypothetical median organism calculation. No separation
of “classic” V. cholerae, El Tor vibrios, and nonagglutinable vibrios was observed.
These all fell into a single, relatively homogeneous, V. cholerae species cluster.
PJ. parahaemolyticus strains, excepting 5144, 5146, and 5162, designated members
of the species V. alginolyticus, clustered at S >80%. Characteristics uniformly
present in all the Vibrio species examined are given, as are also characteristics and
frequency of occurrence for V. cholerae and V. parahaemolyticus. The clusters formed
in the numerical taxonomy analyses revealed similar overall DNA base compositions,
with the range for the Vibrio species of 40 to 48% guanine plus cytosine. Generic
level of relationship of V. cholerae and V. parahaemolyticus is considered
dubious. Intra- and intergroup relationships obtained from the numerical taxonomy
studies showed highly significant correlation with DNA/DNA reassociation data.

A Numerical Classification of the Genus Bacillus
By FERGUS G . PRIEST, MICHAEL GOODFELLOW AND CAROLE TODD
Journal of General Microbiology (1988), 134, 1847-1882.

Three hundred and sixty-eight strains of aerobic, endospore-forming bacteria which included type and reference cultures of Bacillus and environmental isolates were studied. Overall similarities of these strains for 118 unit characters were determined by the SSMS,, and Dp coefficients and clustering achieved using the UPGMA algorithm. Test error was within acceptable limits. Six cluster-groups were defined at 70% SSM which corresponded to 69% Sp and 48-57% SJ.G roupings obtained with the three coefficients were generally similar but there were some changes in the definition and membership of cluster-groups and clusters, particularly with the SJ coefficient. The Bacillus strains were distributed among 31 major (4 or more strains), 18 minor (2 or 3 strains) and 30 single-member clusters at the 83% SsMle vel. Most of these clusters can be regarded as taxospecies. The heterogeneity of several species, including Bacillus breuis, B. circulans, B. coagulans, B. megateriun, B . sphaericus and B . stearothermophilus, has been indicated  and the species status of several taxa of hitherto uncertain validity confirmed. Thus on the basis of the numerical phenetic and appropriate (published) molecular genetic data, it is proposed
that the following names be recognized; BacillusJlexus (Batchelor) nom. rev., Bacillus fusiformis (Smith et al.) comb. nov., Bacillus kaustophilus (Prickett) nom. rev., Bacilluspsychrosaccharolyticus (Larkin & Stokes) nom. rev. and Bacillus simplex (Gottheil) nom. rev. Other phenetically well-defined taxospecies included ‘ B. aneurinolyticus’, ‘B. apiarius’, ‘B. cascainensis’, ‘B. thiaminolyticus’ and three clusters of environmental isolates related to B . firmus and previously described as ‘B. firmus-B. lentus intermediates’. Future developments in the light of the numerical phenetic data are discussed.

Numerical Classification of Bacteria
Part II. * Computer Analysis of Coryneform Bacteria (2)
Comparison of Group-Formations Obtained on Two
Different Methods of Scoring Data
By Eitaro MASUOan d Toshio NAKAGAWA
[Agr. Biol. Chem., 1969; 33(8): 1124-1133.
Sixty three organisms selected from 12 genera of bacteria were subjected to numerical analysis. The purpose of this work is to examine the relationships among 38 coryneform bacteria included in the test organisms by two coding methods-Sneath’s and Lockhart’s systems-, and to compare the results with conventional classification. In both cases of codification, five groups and one or two single item(s) were found in the resultant classifications. Different codings brought, however, a few distinct differences in some groups , especially in a group of sporogenic bacilli or lactic-acid bacteria. So far as the present work concerns, the result obtained on Lockhart’s coding rather than that obtained on Sneath’s coding resembled the conventional classification. The taxonomic positions of corynebacteria were quite different from those of the conventional classification, regardless
of which coding method was applied.
Though animal corynebacteria have conventionally been considered to occupy the
taxonomic position neighboring to genera Arthrobacter and Cellulornonas and regarded to be the nucleus of so-called “coryneform bacteria,’ the present work showed that many of the corynebacteria are akin to certain mycobacteria rather than to the organisms belonging to the above two genera.

Numerical Classification of Bacteria
Part III. Computer Analysis of “Coryneform Bacteria” (3)
Classification Based on DNA Base Compositions
By EitaroM ASUaOnd ToshioN AKAGAWA
Agr. Biol. Chem., 1969; 33(11): 1570-1576
It has been known that the base compositions of deoxyribonucleic acids (DNA) are
quite different from organism to organism. A pertinent example of this diversity is
found in bacterial species. The base compositions of DNA isolated from a wide variety
of bacteria are distributed in a range from 25 to 75 GC mole-percent (100x(G+C)/
(A+T+G+C)).1) The usefulness of the information of DNA base composition for
the taxonomy of bacteria has been emphasized by several authors. Lee et al.,” Sueoka,” and Freese) have speculated on the evolutionary significance of microbial DNA base composition. They pointed out that closely related microorganisms generally showed similar base compositions of DNA, and suggested that phylogenetic relationship should be reflected in the GC content.
In the present paper are compared the results of numerical classifications of 45
bacteria based on the two different similarity matrices: One representing the overall
similarities of phenotypic properties, the other representing the similarities of GC contents.

Advanced computational algorithms for microbial community analysis using massive 16S rRNA
sequence data
Y Sun, Y Cai, V Mai, W Farmerie, F Yu, J Li and S Goodison
Nucleic Acids Research, 2010; 38(22): e205
http://dx.doi.org:/10.1093/nar/gkq872

With the aid of next-generation sequencing technology, researchers can now obtain millions of microbial signature sequences for diverse applications ranging from human epidemiological studies to global ocean surveys. The development of advanced computational strategies to maximally extract pertinent information from massive nucleotide data has become a major focus of the bioinformatics community. Here, we describe a novel analytical strategy including discriminant and topology analyses that enables researchers to deeply investigate the hidden world of microbial communities, far beyond basic microbial diversity estimation. We demonstrate the utility of our
approach through a computational study performed on a previously published massive human gut 16S rRNA data set. The application of discriminant and
topology analyses enabled us to derive quantitative disease-associated microbial signatures and describe microbial community structure in far more detail than previously achievable. Our approach provides rigorous statistical tools for sequence based studies aimed at elucidating associations between known or unknown organisms and a variety of physiological or environmental conditions.

What is Drug Resistance?

Antimicrobial resistance is the ability of microbes, such as bacteria, viruses, parasites, or fungi, to grow in the presence of a chemical (drug) that would normally kill it or limit its growth.

Diagram showing the difference between non-resistant bacteria and drug resistant bacteria.

Credit: NIAID

DrugResistance difference between non-resistant bacteria and drug resistant bacteria

DrugResistance difference between non-resistant bacteria and drug resistant bacteria

http://www.niaid.nih.gov/SiteCollectionImages/topics/antimicrobialresistance/1whatIsDrugResistance.gif

Diagram showing the difference between non-resistant bacteria and drug resistant bacteria. Non-resistant bacteria multiply, and upon drug treatment, the bacteria die. Drug resistant bacteria multiply as well, but upon drug treatment, the bacteria continue to spread.

Between 5 and 10 percent of all hospital patients develop an infection. About 90,000 of these patients die each year as a result of their infection, up from 13,300 patient deaths in 1992.

According to the Centers for Disease Control and Prevention (April 2011), antibiotic resistance in the United States costs an estimated $20 billion a year in excess health care costs, $35 million in other societal costs and more than 8 million additional days that people spend in the hospital.

Resistance to Antibiotics: Are We in the Post-Antibiotic Era?

Alfonso J. Alanis
Archives of Medical Research 36 (2005) 697–705
http://dx.doi.org:/10.1016/j.arcmed.2005.06.009

Serious infections caused by bacteria that have become resistant to commonly used antibiotics have become a major global healthcare problem in the 21st century. They not only are more severe and require longer and more complex treatments, but they are also significantly more expensive to diagnose and to treat. Antibiotic resistance, initially a problem of the hospital setting associated with an increased number of hospital acquired infections usually in critically ill and immunosuppressed patients, has now extended into the community causing severe infections difficult to diagnose and treat. The molecular mechanisms by which bacteria have become resistant to antibiotics are diverse and complex. Bacteria have developed resistance to all different classes of antibiotics discovered to date. The most frequent type of resistance is acquired and transmitted horizontally via the conjugation of a plasmid. In recent times new mechanisms of resistance have resulted in the simultaneous development of resistance to several antibiotic classes creating very dangerous multidrug-resistant (MDR) bacterial strains, some also known as ‘‘superbugs’’. The indiscriminate and inappropriate use of antibiotics in outpatient clinics, hospitalized patients and in the food industry is the single largest factor leading to antibiotic resistance. The pharmaceutical industry, large academic institutions or the government are not investing the necessary resources to produce the next generation of newer safe and effective antimicrobial drugs. In many cases, large pharmaceutical companies have terminated their anti-infective research programs altogether due to economic reasons. The potential negative consequences of all these events are relevant because they put society at risk for the spread of potentially serious MDR bacterial infections.

Targeting the Human Macrophage with Combinations of Drugs and Inhibitors of Ca2+ and K+ Transport to Enhance the Killing of Intracellular Multi-Drug Resistant M. tuberculosis (MDR-TB) – a Novel, Patentable Approach to Limit the Emergence of XDR-TB

Marta Martins
Recent Patents on Anti-Infective Drug Discovery, 2011, 6, 000-000

The emergence of resistance in Tuberculosis has become a serious problem for the control of this disease. For that reason, new therapeutic strategies that can be implemented in the clinical setting are urgently needed. The design of new compounds active against mycobacteria must take into account that Tuberculosis is mainly an intracellular infection of the alveolar macrophage and therefore must maintain activity within the host cells. An alternative therapeutic approach will be described in this review, focusing on the activation of the phagocytic cell and the subsequent killing of the internalized bacteria. This approach explores the combined use of antibiotics and phenothiazines, or Ca2+ and K+ flux inhibitors, in the infected macrophage. Targeting the infected macrophage and not the internalized bacteria could overcome the problem of bacterial multi-drug resistance. This will potentially eliminate the appearance of new multi-drug resistant tuberculosis (MDR-TB) cases and subsequently prevent the emergence of extensively-drug resistant tuberculosis (XDR-TB). Patents resulting from this novel and innovative approach could be extremely valuable if they can be implemented in the clinical setting. Other patents will also be discussed such as the treatment of TB using immunomodulator compounds (for example: betaglycans).

Six Epigenetic Faces of Streptococcus

Kevin Mayer
http://www.genengnews.com/gen-news-highlights/six-epigenetic-faces-of-streptococcus/81250430/

Medical illustration of Streptococcus pneumonia. [CDC]

Streptococcus pneumonia

Streptococcus pneumonia

It appears that S. pneumoniae has even more personalities, each associated with a different proclivity toward invasive, life-threatening disease. In fact, any of six personalities may emerge depending on the action of a single genetic switch.

To uncover the switch, an international team of scientists conducted a study in genomics, but they looked beyond nucleotide polymorphisms or accessory regions as possible phenotype-shifting mechanisms. Instead, they focused on the potential of restriction-modification (RM) systems to mediate gene regulation via epigenetic changes.

Scientists representing the University of Leicester, Griffith University’s Institute for Glycomics, theUniversity of Adelaide, and Pacific Biosciences realized that the S. pneumoniae genome contains two Type I, three Type II, and one Type IV RM systems. Of these, only the DpnI Type II RM system had been described in detail. Switchable Type I systems had been described previously, but these reports did not provide evidence for differential methylation or for phenotypic impact.

As it turned out, the Type I system embodied a mechanism capable of randomly changing the bacterium’s characteristics into six alternative states. The mechanism’s details were presented September 30 in Nature Communications, in an article entitled, “A random six-phase switch regulates pneumococcal virulence via global epigenetic changes.”

“The underlying mechanism for such phase variation consists of genetic rearrangements in a Type I restriction-modification system (SpnD39III),” wrote the authors. “The rearrangements generate six alternative specificities with distinct methylation patterns, as defined by single-molecule, real-time (SMRT) methylomics.”

Eradication of multidrug-resistant A. baumanniii in burn wounds by antiseptic pulsed electric field.

A Golberg, GF Broelsch, D Vecchio,S Khan, MR Hamblin, WG Austen, Jr, RL Sheridan,  ML Yarmush.

Emerging bacterial resistance to multiple drugs is an increasing problem in burn wound management. New non-pharmacologic interventions are needed for wound disinfection. Here we report on a novel physical method for disinfection: antiseptic pulsed electric field (PEF) applied externally to the infected wounds.  In an animal model, we show that PEF can reduce the load of multidrug resistant Acinetobacter baumannii present in a full thickness burn wound by more than four orders of magnitude, as detected by bioluminescence imaging. Furthermore, using a finite element numerical model, we demonstrate that PEF provides non-thermal, homogeneous, full thickness treatment for the burn wound, thus, overcoming the limitation of treatment depth for many topical antimicrobials. These modeling tools and our in vivo results will be extremely useful for further translation of the PEF technology to the clinical setting. We believe that PEF, in combination with systemic antibiotics, will synergistically eradicate multidrug-resistant burn wound infections, prevent biofilm formation and restore natural skin microbiome. PEF provides a new platform for infection combat in patients, therefore it has a potential to significantly decreasing morbidity and mortality.

Golberg, A. & Yarmush, M. L. Nonthermal irreversible electroporation: fundamentals, applications, and challenges. IEEE Trans Biomed Eng 60, 707-14 (2013).

Mechanisms Of Antibiotic Resistance In Salmonella: Efflux Pumps, Genetics, Quorum Sensing And Biofilm Formation.

Martins M, McCusker M, Amaral L, Fanning S
Perspectives in Drug Discovery and Design 02/2011; 8:114-123.

In Salmonella the main mechanisms of antibiotic resistance are mutations in target genes (such as DNA gyrase and topoisomerase IV) and the over-expression of efflux pumps. However, other mechanisms such as changes in the cell envelope; down regulation of membrane porins; increased lipopolysaccharide (LPS) component of the outer cell membrane; quorum sensing and biofilm formation can also contribute to the resistance seen in this microorganism. To overcome this problem new therapeutic approaches are urgently needed. In the case of efflux-mediated multidrug resistant isolates, one of the treatment options could be the use of efflux pump inhibitors (EPIs) in combination with the antibiotics to which the bacteria is resistant. By blocking the efflux pumps resistance is partly or wholly reversed, allowing antibiotics showing no activity against the MDR strains to be used to treat these infections. Compounds that show potential as an EPI are therefore of interest, as well as new strategies to target the efflux systems. Quorum sensing (QS) and biofilm formation are systems also known to be involved in antibiotic resistance. Consequently, compounds that can disrupt or inhibit these bacterial “communication systems” will be of use in the treatment of these infections.

Role of Phenothiazines and Structurally Similar Compounds of Plant Origin in the Fight against Infections by Drug Resistant Bacteria

SG Dastidar, JE Kristiansen, J Molnar and L Amaral
Antibiotics 2013, 2, 58-71;
http://dx.doi.org:/10.3390/antibiotics2010058

Phenothiazines have their primary effects on the plasma membranes of prokaryotes and eukaryotes. Among the components of the prokaryotic plasma membrane affected are efflux pumps, their energy sources and energy providing enzymes, such as ATPase, and genes that regulate and code for the permeability aspect of a bacterium. The response of multidrug and extensively drug resistant tuberculosis to phenothiazines shows an alternative therapy for its treatment. Many phenothiazines have shown synergistic activity with several antibiotics thereby lowering the doses of antibiotics administered for specific bacterial infections. Trimeprazine is synergistic with trimethoprim. Flupenthixol (Fp) has been found to be synergistic with penicillin and chlorpromazine (CPZ); in addition, some antibiotics are also synergistic. Along with the antibacterial action described in this review, many phenothiazines possess plasmid curing activities, which render the bacterial carrier of the plasmid sensitive to antibiotics. Thus, simultaneous applications of a phenothiazine like TZ would not only act as an additional antibacterial agent but also would help to eliminate drug resistant plasmid from the infectious bacterial cells.

Multidrug Efflux Pumps Described for Staphylococcus aureus

Efflux Pump  Family Regulator(s) Substrate Specificity  References 
Chromosomally-encoded Efflux Systems 
NorA MFS MgrA, NorG(?) Hydrophilic fluoroquinolones (ciprofloxacin, norfloxacin)QACs (tetraphenylphosphonium, benzalkonium chloride)

Dyes (e.g. ethidium bromide, rhodamine)

[16,18,19]
NorB MFS MgrA, NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)TetracyclineQACs (e.g. tetraphenylphosphonium, cetrimide)Dyes (e.g. ethidium bromide)
[31]
NorC MFS MgrA(?), NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin and hydrophobic: moxifloxacin)Dyes (e.g. rhodamine) [35,36]
MepA MATE MepR Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)Glycylcyclines (e.g. tigecycline)QACs (e.g. tetraphenylphosphonium, cetrimide, benzalkonium chloride)Dyes (e.g. ethidium bromide)
[37,38]
MdeA MFS n.i. Hydrophilic fluoroquinolones (e.g. ciprofloxacin, norfloxacin)Virginiamycin, novobiocin, mupirocin, fusidic acid

QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)

Dyes (e.g. ethidium bromide)

[39,40]
SepA n.d. n.i. QACs (e.g. benzalkonium chloride)Biguanidines (e.g. chlorhexidine)

Dyes (e.g. acriflavine)

[41]
SdrM MFS n.i. Hydrophilic fluoroquinolones (e.g. norfloxacin)Dyes (e.g. ethidium bromide, acriflavine) [42]
LmrS MFS n.i. Oxazolidinone (linezolid)Phenicols (e.g. choramphenicol, florfenicol)

Trimethoprim, erythromycin, kanamycin, fusidic acid

QACs (e.g. tetraphenylphosphonium)

Detergents (e.g. sodium docecyl sulphate)

Dyes (e.g. ethidium bromide)

[43]

Plasmid-encoded Efflux Systems

QacA MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)Biguanidines (e.g. chlorhexidine)

Diamidines (e.g. pentamidine)

Dyes (e.g. ethidium bromide, rhodamine, acriflavine)

[45,49]
QacB MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride)Dyes (e.g. ethidium bromide, rhodamine, acriflavine) [53]
Smr SMR n.i. QACs (e.g. benzalkonium chloride, cetrimide)Dyes (e.g. ethidium bromide) [58,61]
QacG SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [67]
QacH SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [68]
QacJ SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [69]

a n.d.: The family of transporters to which SepA belongs is not elucidated to date.
b n.i.: The transporter has no regulator identified to date.
QACs: quaternary ammonium compounds
The importance of efflux pumps in bacterial antibiotic resistance

  1. A. Webber and L. J. V. Piddock
    Journal of Antimicrobial Chemotherapy (2003) 51, 9–11
    http://dx.doi.org:/10.1093/jac/dkg050Efflux pumps are transport proteins involved in the extrusion of toxic substrates (including virtually all classes of clinically relevant antibiotics) from within cells into the external environment. These proteins are found in both Gram-positive and -negative bacteria as well as in eukaryotic organisms. Pumps may be specific for one substrate or may transport a range of structurally dissimilar compounds (including antibiotics of multiple classes); such pumps can be associated with multiple drug resistance (MDR). In the prokaryotic kingdom there are five major families of efflux transporter: MF (major facilitator), MATE (multidrug and toxic efflux), RND (resistance-nodulation-division), SMR (small multidrug resistance) and ABC (ATP binding cassette). All these systems utilize the proton motive force as an energy source. Advances in DNA technology have led to the identification of members of the above families. Transporters that efflux multiple substrates, including antibiotics, have not evolved in response to the stresses of the antibiotic era. All bacterial genomes studied contain efflux pumps that indicate their ancestral origins. It has been estimated that ∼5–10% of all bacterial genes are involved in transport and a large proportion of these encode efflux pumps.
The efflux pump

The efflux pump

Multidrug-resistance efflux pumps — not just for resistance

Laura J. V. Piddock
Nature Reviews | Microbiology | Aug 2006; 4: 629

It is well established that multidrug-resistance efflux pumps encoded by bacteria can confer clinically relevant resistance to antibiotics. It is now understood that these efflux pumps also have a physiological role(s). They can confer resistance to natural substances produced by the host, including bile, hormones and host defense molecules. In addition, some efflux pumps of the resistance nodulation division (RND) family have been shown to have a role in the colonization and the persistence of bacteria in the host. Here, I present the accumulating evidence that multidrug-resistance efflux pumps have roles in bacterial pathogenicity and propose that these pumps therefore have greater clinical relevance than is usually attributed to them.

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Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer


Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer

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

This summary is the last of a series on the impact of transcriptomics, proteomics, and metabolomics on disease investigation, and the sorting and integration of genomic signatures and metabolic signatures to explain phenotypic relationships in variability and individuality of response to disease expression and how this leads to  pharmaceutical discovery and personalized medicine.  We have unquestionably better tools at our disposal than has ever existed in the history of mankind, and an enormous knowledge-base that has to be accessed.  I shall conclude here these discussions with the powerful contribution to and current knowledge pertaining to biochemistry, metabolism, protein-interactions, signaling, and the application of the -OMICS to diseases and drug discovery at this time.

The Ever-Transcendent Cell

Deriving physiologic first principles By John S. Torday | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41282/title/The-Ever-Transcendent-Cell/

Both the developmental and phylogenetic histories of an organism describe the evolution of physiology—the complex of metabolic pathways that govern the function of an organism as a whole. The necessity of establishing and maintaining homeostatic mechanisms began at the cellular level, with the very first cells, and homeostasis provides the underlying selection pressure fueling evolution.

While the events leading to the formation of the first functioning cell are debatable, a critical one was certainly the formation of simple lipid-enclosed vesicles, which provided a protected space for the evolution of metabolic pathways. Protocells evolved from a common ancestor that experienced environmental stresses early in the history of cellular development, such as acidic ocean conditions and low atmospheric oxygen levels, which shaped the evolution of metabolism.

The reduction of evolution to cell biology may answer the perennially unresolved question of why organisms return to their unicellular origins during the life cycle.

As primitive protocells evolved to form prokaryotes and, much later, eukaryotes, changes to the cell membrane occurred that were critical to the maintenance of chemiosmosis, the generation of bioenergy through the partitioning of ions. The incorporation of cholesterol into the plasma membrane surrounding primitive eukaryotic cells marked the beginning of their differentiation from prokaryotes. Cholesterol imparted more fluidity to eukaryotic cell membranes, enhancing functionality by increasing motility and endocytosis. Membrane deformability also allowed for increased gas exchange.

Acidification of the oceans by atmospheric carbon dioxide generated high intracellular calcium ion concentrations in primitive aquatic eukaryotes, which had to be lowered to prevent toxic effects, namely the aggregation of nucleotides, proteins, and lipids. The early cells achieved this by the evolution of calcium channels composed of cholesterol embedded within the cell’s plasma membrane, and of internal membranes, such as that of the endoplasmic reticulum, peroxisomes, and other cytoplasmic organelles, which hosted intracellular chemiosmosis and helped regulate calcium.

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.  ….

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.

Given that the unicellular toolkit is complete with all the traits necessary for forming multicellular organisms (Science, 301:361-63, 2003), it is distinctly possible that metazoans are merely permutations of the unicellular body plan. That scenario would clarify a lot of puzzling biology: molecular commonalities between the skin, lung, gut, and brain that affect physiology and pathophysiology exist because the cell membranes of unicellular organisms perform the equivalents of these tissue functions, and the existence of pleiotropy—one gene affecting many phenotypes—may be a consequence of the common unicellular source for all complex biologic traits.  …

The cell-molecular homeostatic model for evolution and stability addresses how the external environment generates homeostasis developmentally at the cellular level. It also determines homeostatic set points in adaptation to the environment through specific effectors, such as growth factors and their receptors, second messengers, inflammatory mediators, crossover mutations, and gene duplications. This is a highly mechanistic, heritable, plastic process that lends itself to understanding evolution at the cellular, tissue, organ, system, and population levels, mediated by physiologically linked mechanisms throughout, without having to invoke random, chance mechanisms to bridge different scales of evolutionary change. In other words, it is an integrated mechanism that can often be traced all the way back to its unicellular origins.

The switch from swim bladder to lung as vertebrates moved from water to land is proof of principle that stress-induced evolution in metazoans can be understood from changes at the cellular level.

http://www.the-scientist.com/Nov2014/TE_21.jpg

A MECHANISTIC BASIS FOR LUNG DEVELOPMENT: Stress from periodic atmospheric hypoxia (1) during vertebrate adaptation to land enhances positive selection of the stretch-regulated parathyroid hormone-related protein (PTHrP) in the pituitary and adrenal glands. In the pituitary (2), PTHrP signaling upregulates the release of adrenocorticotropic hormone (ACTH) (3), which stimulates the release of glucocorticoids (GC) by the adrenal gland (4). In the adrenal gland, PTHrP signaling also stimulates glucocorticoid production of adrenaline (5), which in turn affects the secretion of lung surfactant, the distension of alveoli, and the perfusion of alveolar capillaries (6). PTHrP signaling integrates the inflation and deflation of the alveoli with surfactant production and capillary perfusion.  THE SCIENTIST STAFF

From a cell-cell signaling perspective, two critical duplications in genes coding for cell-surface receptors occurred during this period of water-to-land transition—in the stretch-regulated parathyroid hormone-related protein (PTHrP) receptor gene and the β adrenergic (βA) receptor gene. These gene duplications can be disassembled by following their effects on vertebrate physiology backwards over phylogeny. PTHrP signaling is necessary for traits specifically relevant to land adaptation: calcification of bone, skin barrier formation, and the inflation and distention of lung alveoli. Microvascular shear stress in PTHrP-expressing organs such as bone, skin, kidney, and lung would have favored duplication of the PTHrP receptor, since sheer stress generates radical oxygen species (ROS) known to have this effect and PTHrP is a potent vasodilator, acting as an epistatic balancing selection for this constraint.

Positive selection for PTHrP signaling also evolved in the pituitary and adrenal cortex (see figure on this page), stimulating the secretion of ACTH and corticoids, respectively, in response to the stress of land adaptation. This cascade amplified adrenaline production by the adrenal medulla, since corticoids passing through it enzymatically stimulate adrenaline synthesis. Positive selection for this functional trait may have resulted from hypoxic stress that arose during global episodes of atmospheric hypoxia over geologic time. Since hypoxia is the most potent physiologic stressor, such transient oxygen deficiencies would have been acutely alleviated by increasing adrenaline levels, which would have stimulated alveolar surfactant production, increasing gas exchange by facilitating the distension of the alveoli. Over time, increased alveolar distension would have generated more alveoli by stimulating PTHrP secretion, impelling evolution of the alveolar bed of the lung.

This scenario similarly explains βA receptor gene duplication, since increased density of the βA receptor within the alveolar walls was necessary for relieving another constraint during the evolution of the lung in adaptation to land: the bottleneck created by the existence of a common mechanism for blood pressure control in both the lung alveoli and the systemic blood pressure. The pulmonary vasculature was constrained by its ability to withstand the swings in pressure caused by the systemic perfusion necessary to sustain all the other vital organs. PTHrP is a potent vasodilator, subserving the blood pressure constraint, but eventually the βA receptors evolved to coordinate blood pressure in both the lung and the periphery.

Gut Microbiome Heritability

Analyzing data from a large twin study, researchers have homed in on how host genetics can shape the gut microbiome.
By Tracy Vence | The Scientist Nov 6, 2014

Previous research suggested host genetic variation can influence microbial phenotype, but an analysis of data from a large twin study published in Cell today (November 6) solidifies the connection between human genotype and the composition of the gut microbiome. Studying more than 1,000 fecal samples from 416 monozygotic and dizygotic twin pairs, Cornell University’s Ruth Ley and her colleagues have homed in on one bacterial taxon, the family Christensenellaceae, as the most highly heritable group of microbes in the human gut. The researchers also found that Christensenellaceae—which was first described just two years ago—is central to a network of co-occurring heritable microbes that is associated with lean body mass index (BMI).  …

Of particular interest was the family Christensenellaceae, which was the most heritable taxon among those identified in the team’s analysis of fecal samples obtained from the TwinsUK study population.

While microbiologists had previously detected 16S rRNA sequences belonging to Christensenellaceae in the human microbiome, the family wasn’t named until 2012. “People hadn’t looked into it, partly because it didn’t have a name . . . it sort of flew under the radar,” said Ley.

Ley and her colleagues discovered that Christensenellaceae appears to be the hub in a network of co-occurring heritable taxa, which—among TwinsUK participants—was associated with low BMI. The researchers also found that Christensenellaceae had been found at greater abundance in low-BMI twins in older studies.

To interrogate the effects of Christensenellaceae on host metabolic phenotype, the Ley’s team introduced lean and obese human fecal samples into germ-free mice. They found animals that received lean fecal samples containing more Christensenellaceae showed reduced weight gain compared with their counterparts. And treatment of mice that had obesity-associated microbiomes with one member of the Christensenellaceae family, Christensenella minuta, led to reduced weight gain.   …

Ley and her colleagues are now focusing on the host alleles underlying the heritability of the gut microbiome. “We’re running a genome-wide association analysis to try to find genes—particular variants of genes—that might associate with higher levels of these highly heritable microbiota.  . . . Hopefully that will point us to possible reasons they’re heritable,” she said. “The genes will guide us toward understanding how these relationships are maintained between host genotype and microbiome composition.”

J.K. Goodrich et al., “Human genetics shape the gut microbiome,” Cell,  http://dx.doi.org:/10.1016/j.cell.2014.09.053, 2014.

Light-Operated Drugs

Scientists create a photosensitive pharmaceutical to target a glutamate receptor.
By Ruth Williams | The Scentist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41279/title/Light-Operated-Drugs/

light operated drugs MO1

light operated drugs MO1

http://www.the-scientist.com/Nov2014/MO1.jpg

The desire for temporal and spatial control of medications to minimize side effects and maximize benefits has inspired the development of light-controllable drugs, or optopharmacology. Early versions of such drugs have manipulated ion channels or protein-protein interactions, “but never, to my knowledge, G protein–coupled receptors [GPCRs], which are one of the most important pharmacological targets,” says Pau Gorostiza of the Institute for Bioengineering of Catalonia, in Barcelona.

Gorostiza has taken the first step toward filling that gap, creating a photosensitive inhibitor of the metabotropic glutamate 5 (mGlu5) receptor—a GPCR expressed in neurons and implicated in a number of neurological and psychiatric disorders. The new mGlu5 inhibitor—called alloswitch-1—is based on a known mGlu receptor inhibitor, but the simple addition of a light-responsive appendage, as had been done for other photosensitive drugs, wasn’t an option. The binding site on mGlu5 is “extremely tight,” explains Gorostiza, and would not accommodate a differently shaped molecule. Instead, alloswitch-1 has an intrinsic light-responsive element.

In a human cell line, the drug was active under dim light conditions, switched off by exposure to violet light, and switched back on by green light. When Gorostiza’s team administered alloswitch-1 to tadpoles, switching between violet and green light made the animals stop and start swimming, respectively.

The fact that alloswitch-1 is constitutively active and switched off by light is not ideal, says Gorostiza. “If you are thinking of therapy, then in principle you would prefer the opposite,” an “on” switch. Indeed, tweaks are required before alloswitch-1 could be a useful drug or research tool, says Stefan Herlitze, who studies ion channels at Ruhr-Universität Bochum in Germany. But, he adds, “as a proof of principle it is great.” (Nat Chem Biol, http://dx.doi.org:/10.1038/nchembio.1612, 2014)

Enhanced Enhancers

The recent discovery of super-enhancers may offer new drug targets for a range of diseases.
By Eric Olson | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41281/title/Enhanced-Enhancers/

To understand disease processes, scientists often focus on unraveling how gene expression in disease-associated cells is altered. Increases or decreases in transcription—as dictated by a regulatory stretch of DNA called an enhancer, which serves as a binding site for transcription factors and associated proteins—can produce an aberrant composition of proteins, metabolites, and signaling molecules that drives pathologic states. Identifying the root causes of these changes may lead to new therapeutic approaches for many different diseases.

Although few therapies for human diseases aim to alter gene expression, the outstanding examples—including antiestrogens for hormone-positive breast cancer, antiandrogens for prostate cancer, and PPAR-γ agonists for type 2 diabetes—demonstrate the benefits that can be achieved through targeting gene-control mechanisms.  Now, thanks to recent papers from laboratories at MIT, Harvard, and the National Institutes of Health, researchers have a new, much bigger transcriptional target: large DNA regions known as super-enhancers or stretch-enhancers. Already, work on super-enhancers is providing insights into how gene-expression programs are established and maintained, and how they may go awry in disease.  Such research promises to open new avenues for discovering medicines for diseases where novel approaches are sorely needed.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions (Cell, 153:307-19, 2013). They also appear to bind a large percentage of the transcriptional machinery compared to typical enhancers, allowing them to better establish and enforce cell-type specific transcriptional programs (Cell, 153:320-34, 2013).

Super-enhancers are closely associated with genes that dictate cell identity, including those for cell-type–specific master regulatory transcription factors. This observation led to the intriguing hypothesis that cells with a pathologic identity, such as cancer cells, have an altered gene expression program driven by the loss, gain, or altered function of super-enhancers.

Sure enough, by mapping the genome-wide location of super-enhancers in several cancer cell lines and from patients’ tumor cells, we and others have demonstrated that genes located near super-enhancers are involved in processes that underlie tumorigenesis, such as cell proliferation, signaling, and apoptosis.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions.

Genome-wide association studies (GWAS) have found that disease- and trait-associated genetic variants often occur in greater numbers in super-enhancers (compared to typical enhancers) in cell types involved in the disease or trait of interest (Cell, 155:934-47, 2013). For example, an enrichment of fasting glucose–associated single nucleotide polymorphisms (SNPs) was found in the stretch-enhancers of pancreatic islet cells (PNAS, 110:17921-26, 2013). Given that some 90 percent of reported disease-associated SNPs are located in noncoding regions, super-enhancer maps may be extremely valuable in assigning functional significance to GWAS variants and identifying target pathways.

Because only 1 to 2 percent of active genes are physically linked to a super-enhancer, mapping the locations of super-enhancers can be used to pinpoint the small number of genes that may drive the biology of that cell. Differential super-enhancer maps that compare normal cells to diseased cells can be used to unravel the gene-control circuitry and identify new molecular targets, in much the same way that somatic mutations in tumor cells can point to oncogenic drivers in cancer. This approach is especially attractive in diseases for which an incomplete understanding of the pathogenic mechanisms has been a barrier to discovering effective new therapies.

Another therapeutic approach could be to disrupt the formation or function of super-enhancers by interfering with their associated protein components. This strategy could make it possible to downregulate multiple disease-associated genes through a single molecular intervention. A group of Boston-area researchers recently published support for this concept when they described inhibited expression of cancer-specific genes, leading to a decrease in cancer cell growth, by using a small molecule inhibitor to knock down a super-enhancer component called BRD4 (Cancer Cell, 24:777-90, 2013).  More recently, another group showed that expression of the RUNX1 transcription factor, involved in a form of T-cell leukemia, can be diminished by treating cells with an inhibitor of a transcriptional kinase that is present at the RUNX1 super-enhancer (Nature, 511:616-20, 2014).

Fungal effector Ecp6 outcompetes host immune receptor for chitin binding through intrachain LysM dimerization 
Andrea Sánchez-Vallet, et al.   eLife 2013;2:e00790 http://elifesciences.org/content/2/e00790#sthash.LnqVMJ9p.dpuf

LysM effector

LysM effector

http://img.scoop.it/ZniCRKQSvJOG18fHbb4p0Tl72eJkfbmt4t8yenImKBVvK0kTmF0xjctABnaLJIm9

While host immune receptors

  • detect pathogen-associated molecular patterns to activate immunity,
  • pathogens attempt to deregulate host immunity through secreted effectors.

Fungi employ LysM effectors to prevent

  • recognition of cell wall-derived chitin by host immune receptors

Structural analysis of the LysM effector Ecp6 of

  • the fungal tomato pathogen Cladosporium fulvum reveals
  • a novel mechanism for chitin binding,
  • mediated by intrachain LysM dimerization,

leading to a chitin-binding groove that is deeply buried in the effector protein.

This composite binding site involves

  • two of the three LysMs of Ecp6 and
  • mediates chitin binding with ultra-high (pM) affinity.

The remaining singular LysM domain of Ecp6 binds chitin with

  • low micromolar affinity but can nevertheless still perturb chitin-triggered immunity.

Conceivably, the perturbation by this LysM domain is not established through chitin sequestration but possibly through interference with the host immune receptor complex.

Mutated Genes in Schizophrenia Map to Brain Networks
From www.nih.gov –  Sep 3, 2013

Previous studies have shown that many people with schizophrenia have de novo, or new, genetic mutations. These misspellings in a gene’s DNA sequence

  • occur spontaneously and so aren’t shared by their close relatives.

Dr. Mary-Claire King of the University of Washington in Seattle and colleagues set out to

  • identify spontaneous genetic mutations in people with schizophrenia and
  • to assess where and when in the brain these misspelled genes are turned on, or expressed.

The study was funded in part by NIH’s National Institute of Mental Health (NIMH). The results were published in the August 1, 2013, issue of Cell.

The researchers sequenced the exomes (protein-coding DNA regions) of 399 people—105 with schizophrenia plus their unaffected parents and siblings. Gene variations
that were found in a person with schizophrenia but not in either parent were considered spontaneous.

The likelihood of having a spontaneous mutation was associated with

  • the age of the father in both affected and unaffected siblings.

Significantly more mutations were found in people

  • whose fathers were 33-45 years at the time of conception compared to 19-28 years.

Among people with schizophrenia, the scientists identified

  • 54 genes with spontaneous mutations
  • predicted to cause damage to the function of the protein they encode.

The researchers used newly available database resources that show

  • where in the brain and when during development genes are expressed.

The genes form an interconnected expression network with many more connections than

  • that of the genes with spontaneous damaging mutations in unaffected siblings.

The spontaneously mutated genes in people with schizophrenia

  • were expressed in the prefrontal cortex, a region in the front of the brain.

The genes are known to be involved in important pathways in brain development. Fifty of these genes were active

  • mainly during the period of fetal development.

“Processes critical for the brain’s development can be revealed by the mutations that disrupt them,” King says. “Mutations can lead to loss of integrity of a whole pathway,
not just of a single gene.”

These findings support the concept that schizophrenia may result, in part, from

  • disruptions in development in the prefrontal cortex during fetal development.

James E. Darnell’s “Reflections”

A brief history of the discovery of RNA and its role in transcription — peppered with career advice
By Joseph P. Tiano

James Darnell begins his Journal of Biological Chemistry “Reflections” article by saying, “graduate students these days

  • have to swim in a sea virtually turgid with the daily avalanche of new information and
  • may be momentarily too overwhelmed to listen to the aging.

I firmly believe how we learned what we know can provide useful guidance for how and what a newcomer will learn.” Considering his remarkable discoveries in

  • RNA processing and eukaryotic transcriptional regulation

spanning 60 years of research, Darnell’s advice should be cherished. In his second year at medical school at Washington University School of Medicine in St. Louis, while
studying streptococcal disease in Robert J. Glaser’s laboratory, Darnell realized he “loved doing the experiments” and had his first “career advancement event.”
He and technician Barbara Pesch discovered that in vivo penicillin treatment killed streptococci only in the exponential growth phase and not in the stationary phase. These
results were published in the Journal of Clinical Investigation and earned Darnell an interview with Harry Eagle at the National Institutes of Health.

Darnell arrived at the NIH in 1956, shortly after Eagle  shifted his research interest to developing his minimal essential cell culture medium, still used. Eagle, then studying cell metabolism, suggested that Darnell take up a side project on poliovirus replication in mammalian cells in collaboration with Robert I. DeMars. DeMars’ Ph.D.
adviser was also James  Watson’s mentor, so Darnell met Watson, who invited him to give a talk at Harvard University, which led to an assistant professor position
at the MIT under Salvador Luria. A take-home message is to embrace side projects, because you never know where they may lead: this project helped to shape
his career.

Darnell arrived in Boston in 1961. Following the discovery of DNA’s structure in 1953, the world of molecular biology was turning to RNA in an effort to understand how
proteins are made. Darnell’s background in virology (it was discovered in 1960 that viruses used RNA to replicate) was ideal for the aim of his first independent lab:
exploring mRNA in animal cells grown in culture. While at MIT, he developed a new technique for purifying RNA along with making other observations

  • suggesting that nonribosomal cytoplasmic RNA may be involved in protein synthesis.

When Darnell moved to Albert Einstein College of Medicine for full professorship in 1964,  it was hypothesized that heterogenous nuclear RNA was a precursor to mRNA.
At Einstein, Darnell discovered RNA processing of pre-tRNAs and demonstrated for the first time

  • that a specific nuclear RNA could represent a possible specific mRNA precursor.

In 1967 Darnell took a position at Columbia University, and it was there that he discovered (simultaneously with two other labs) that

  • mRNA contained a polyadenosine tail.

The three groups all published their results together in the Proceedings of the National Academy of Sciences in 1971. Shortly afterward, Darnell made his final career move
four short miles down the street to Rockefeller University in 1974.

Over the next 35-plus years at Rockefeller, Darnell never strayed from his original research question: How do mammalian cells make and control the making of different
mRNAs? His work was instrumental in the collaborative discovery of

  • splicing in the late 1970s and
  • in identifying and cloning many transcriptional activators.

Perhaps his greatest contribution during this time, with the help of Ernest Knight, was

  • the discovery and cloning of the signal transducers and activators of transcription (STAT) proteins.

And with George Stark, Andy Wilks and John Krowlewski, he described

  • cytokine signaling via the JAK-STAT pathway.

Darnell closes his “Reflections” with perhaps his best advice: Do not get too wrapped up in your own work, because “we are all needed and we are all in this together.”

Darnell Reflections - James_Darnell

Darnell Reflections – James_Darnell

http://www.asbmb.org/assets/0/366/418/428/85528/85529/85530/8758cb87-84ff-42d6-8aea-96fda4031a1b.jpg

Recent findings on presenilins and signal peptide peptidase

By Dinu-Valantin Bălănescu

γ-secretase and SPP

γ-secretase and SPP

Fig. 1 from the minireview shows a schematic depiction of γ-secretase and SPP

http://www.asbmb.org/assets/0/366/418/428/85528/85529/85530/c2de032a-daad-41e5-ba19-87a17bd26362.png

GxGD proteases are a family of intramembranous enzymes capable of hydrolyzing

  • the transmembrane domain of some integral membrane proteins.

The GxGD family is one of the three families of

  • intramembrane-cleaving proteases discovered so far (along with the rhomboid and site-2 protease) and
  • includes the γ-secretase and the signal peptide peptidase.

Although only recently discovered, a number of functions in human pathology and in numerous other biological processes

  • have been attributed to γ-secretase and SPP.

Taisuke Tomita and Takeshi Iwatsubo of the University of Tokyo highlighted the latest findings on the structure and function of γ-secretase and SPP
in a recent minireview in The Journal of Biological Chemistry.

  • γ-secretase is involved in cleaving the amyloid-β precursor protein, thus producing amyloid-β peptide,

the main component of senile plaques in Alzheimer’s disease patients’ brains. The complete structure of mammalian γ-secretase is not yet known; however,
Tomita and Iwatsubo note that biochemical analyses have revealed it to be a multisubunit protein complex.

  • Its catalytic subunit is presenilin, an aspartyl protease.

In vitro and in vivo functional and chemical biology analyses have revealed that

  • presenilin is a modulator and mandatory component of the γ-secretase–mediated cleavage of APP.

Genetic studies have identified three other components required for γ-secretase activity:

  1. nicastrin,
  2. anterior pharynx defective 1 and
  3. presenilin enhancer 2.

By coexpression of presenilin with the other three components, the authors managed to

  • reconstitute γ-secretase activity.

Tomita and Iwatsubo determined using the substituted cysteine accessibility method and by topological analyses, that

  • the catalytic aspartates are located at the center of the nine transmembrane domains of presenilin,
  • by revealing the exact location of the enzyme’s catalytic site.

The minireview also describes in detail the formerly enigmatic mechanism of γ-secretase mediated cleavage.

SPP, an enzyme that cleaves remnant signal peptides in the membrane

  • during the biogenesis of membrane proteins and
  • signal peptides from major histocompatibility complex type I,
  • also is involved in the maturation of proteins of the hepatitis C virus and GB virus B.

Bioinformatics methods have revealed in fruit flies and mammals four SPP-like proteins,

  • two of which are involved in immunological processes.

By using γ-secretase inhibitors and modulators, it has been confirmed

  • that SPP shares a similar GxGD active site and proteolytic activity with γ-secretase.

Upon purification of the human SPP protein with the baculovirus/Sf9 cell system,

  • single-particle analysis revealed further structural and functional details.

HLA targeting efficiency correlates with human T-cell response magnitude and with mortality from influenza A infection

From www.pnas.org –  Sep 3, 2013 4:24 PM

Experimental and computational evidence suggests that

  • HLAs preferentially bind conserved regions of viral proteins, a concept we term “targeting efficiency,” and that
  • this preference may provide improved clearance of infection in several viral systems.

To test this hypothesis, T-cell responses to A/H1N1 (2009) were measured from peripheral blood mononuclear cells obtained from a household cohort study
performed during the 2009–2010 influenza season. We found that HLA targeting efficiency scores significantly correlated with

  • IFN-γ enzyme-linked immunosorbent spot responses (P = 0.042, multiple regression).

A further population-based analysis found that the carriage frequencies of the alleles with the lowest targeting efficiencies, A*24,

  • were associated with pH1N1 mortality (r = 0.37, P = 0.031) and
  • are common in certain indigenous populations in which increased pH1N1 morbidity has been reported.

HLA efficiency scores and HLA use are associated with CD8 T-cell magnitude in humans after influenza infection.
The computational tools used in this study may be useful predictors of potential morbidity and

  • identify immunologic differences of new variant influenza strains
  • more accurately than evolutionary sequence comparisons.

Population-based studies of the relative frequency of these alleles in severe vs. mild influenza cases

  • might advance clinical practices for severe H1N1 infections among genetically susceptible populations.

Metabolomics in drug target discovery

J D Rabinowitz et al.

Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ.
Cold Spring Harbor Symposia on Quantitative Biology 11/2011; 76:235-46.
http://dx.doi.org:/10.1101/sqb.2011.76.010694 

Most diseases result in metabolic changes. In many cases, these changes play a causative role in disease progression. By identifying pathological metabolic changes,

  • metabolomics can point to potential new sites for therapeutic intervention.

Particularly promising enzymatic targets are those that

  • carry increased flux in the disease state.

Definitive assessment of flux requires the use of isotope tracers. Here we present techniques for

  • finding new drug targets using metabolomics and isotope tracers.

The utility of these methods is exemplified in the study of three different viral pathogens. For influenza A and herpes simplex virus,

  • metabolomic analysis of infected versus mock-infected cells revealed
  • dramatic concentration changes around the current antiviral target enzymes.

Similar analysis of human-cytomegalovirus-infected cells, however, found the greatest changes

  • in a region of metabolism unrelated to the current antiviral target.

Instead, it pointed to the tricarboxylic acid (TCA) cycle and

  • its efflux to feed fatty acid biosynthesis as a potential preferred target.

Isotope tracer studies revealed that cytomegalovirus greatly increases flux through

  • the key fatty acid metabolic enzyme acetyl-coenzyme A carboxylase.
  • Inhibition of this enzyme blocks human cytomegalovirus replication.

Examples where metabolomics has contributed to identification of anticancer drug targets are also discussed. Eventual proof of the value of

  • metabolomics as a drug target discovery strategy will be
  • successful clinical development of therapeutics hitting these new targets.

 Related References

Use of metabolic pathway flux information in targeted cancer drug design. Drug Discovery Today: Therapeutic Strategies 1:435-443, 2004.

Detection of resistance to imatinib by metabolic profiling: clinical and drug development implications. Am J Pharmacogenomics. 2005;5(5):293-302. Review. PMID: 16196499

Medicinal chemistry, metabolic profiling and drug target discovery: a role for metabolic profiling in reverse pharmacology and chemical genetics.
Mini Rev Med Chem.  2005 Jan;5(1):13-20. Review. PMID: 15638788 [PubMed – indexed for MEDLINE] Related citations

Development of Tracer-Based Metabolomics and its Implications for the Pharmaceutical Industry. Int J Pharm Med 2007; 21 (3): 217-224.

Use of metabolic pathway flux information in anticancer drug design. Ernst Schering Found Symp Proc. 2007;(4):189-203. Review. PMID: 18811058

Pharmacological targeting of glucagon and glucagon-like peptide 1 receptors has different effects on energy state and glucose homeostasis in diet-induced obese mice. J Pharmacol Exp Ther. 2011 Jul;338(1):70-81. http://dx.doi.org:/10.1124/jpet.111.179986. PMID: 21471191

Single valproic acid treatment inhibits glycogen and RNA ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the
[U-C(6)]-d-glucose tracer in mice. Metabolomics. 2009 Sep;5(3):336-345. PMID: 19718458

Metabolic Pathways as Targets for Drug Screening, Metabolomics, Dr Ute Roessner (Ed.), ISBN: 978-953-51-0046-1, InTech, Available from: http://www.intechopen.com/books/metabolomics/metabolic-pathways-as-targets-for-drug-screening

Iron regulates glucose homeostasis in liver and muscle via AMP-activated protein kinase in mice. FASEB J. 2013 Jul;27(7):2845-54.
http://dx.doi.org:/10.1096/fj.12-216929. PMID: 23515442

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Drug Discov. Today 19 (2014), 171–182     http://dx.doi.org:/10.1016/j.drudis.2013.07.014

Highlights

  • We now have metabolic network models; the metabolome is represented by their nodes.
  • Metabolite levels are sensitive to changes in enzyme activities.
  • Drugs hitchhike on metabolite transporters to get into and out of cells.
  • The consensus network Recon2 represents the present state of the art, and has predictive power.
  • Constraint-based modelling relates network structure to metabolic fluxes.

Metabolism represents the ‘sharp end’ of systems biology, because changes in metabolite concentrations are

  • necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs.

To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of

  • the human metabolic network that include the important transporters.

Small molecule ‘drug’ transporters are in fact metabolite transporters, because

  • drugs bear structural similarities to metabolites known from the network reconstructions and
  • from measurements of the metabolome.

Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia:

(i) the effects of inborn errors of metabolism;

(ii) which metabolites are exometabolites, and

(iii) how metabolism varies between tissues and cellular compartments.

However, even these qualitative network models are not yet complete. As our understanding improves

  • so do we recognise more clearly the need for a systems (poly)pharmacology.

Introduction – a systems biology approach to drug discovery

It is clearly not news that the productivity of the pharmaceutical industry has declined significantly during recent years

  • following an ‘inverse Moore’s Law’, Eroom’s Law, or
  • that many commentators, consider that the main cause of this is
  • because of an excessive focus on individual molecular target discovery rather than a more sensible strategy
  • based on a systems-level approach (Fig. 1).
drug discovery science

drug discovery science

Figure 1.

The change in drug discovery strategy from ‘classical’ function-first approaches (in which the assay of drug function was at the tissue or organism level),
with mechanistic studies potentially coming later, to more-recent target-based approaches where initial assays usually involve assessing the interactions
of drugs with specified (and often cloned, recombinant) proteins in vitro. In the latter cases, effects in vivo are assessed later, with concomitantly high levels of attrition.

Arguably the two chief hallmarks of the systems biology approach are:

(i) that we seek to make mathematical models of our systems iteratively or in parallel with well-designed ‘wet’ experiments, and
(ii) that we do not necessarily start with a hypothesis but measure as many things as possible (the ’omes) and

  • let the data tell us the hypothesis that best fits and describes them.

Although metabolism was once seen as something of a Cinderella subject,

  • there are fundamental reasons to do with the organisation of biochemical networks as
  • to why the metabol(om)ic level – now in fact seen as the ‘apogee’ of the ’omics trilogy –
  •  is indeed likely to be far more discriminating than are
  • changes in the transcriptome or proteome.

The next two subsections deal with these points and Fig. 2 summarises the paper in the form of a Mind Map.

metabolomics and systems pharmacology

metabolomics and systems pharmacology

http://ars.els-cdn.com/content/image/1-s2.0-S1359644613002481-gr2.jpg

Metabolic Disease Drug Discovery— “Hitting the Target” Is Easier Said Than Done

David E. Moller, et al.   http://dx.doi.org:/10.1016/j.cmet.2011.10.012

Despite the advent of new drug classes, the global epidemic of cardiometabolic disease has not abated. Continuing

  • unmet medical needs remain a major driver for new research.

Drug discovery approaches in this field have mirrored industry trends, leading to a recent

  • increase in the number of molecules entering development.

However, worrisome trends and newer hurdles are also apparent. The history of two newer drug classes—

  1. glucagon-like peptide-1 receptor agonists and
  2. dipeptidyl peptidase-4 inhibitors—

illustrates both progress and challenges. Future success requires that researchers learn from these experiences and

  • continue to explore and apply new technology platforms and research paradigms.

The global epidemic of obesity and diabetes continues to progress relentlessly. The International Diabetes Federation predicts an even greater diabetes burden (>430 million people afflicted) by 2030, which will disproportionately affect developing nations (International Diabetes Federation, 2011). Yet

  • existing drug classes for diabetes, obesity, and comorbid cardiovascular (CV) conditions have substantial limitations.

Currently available prescription drugs for treatment of hyperglycemia in patients with type 2 diabetes (Table 1) have notable shortcomings. In general,

Therefore, clinicians must often use combination therapy, adding additional agents over time. Ultimately many patients will need to use insulin—a therapeutic class first introduced in 1922. Most existing agents also have

  • issues around safety and tolerability as well as dosing convenience (which can impact patient compliance).

Pharmacometabolomics, also known as pharmacometabonomics, is a field which stems from metabolomics,

  • the quantification and analysis of metabolites produced by the body.

It refers to the direct measurement of metabolites in an individual’s bodily fluids, in order to

  • predict or evaluate the metabolism of pharmaceutical compounds, and
  • to better understand the pharmacokinetic profile of a drug.

Alternatively, pharmacometabolomics can be applied to measure metabolite levels

  • following the administration of a pharmaceutical compound, in order to
  • monitor the effects of the compound on certain metabolic pathways(pharmacodynamics).

This provides detailed mapping of drug effects on metabolism and

  • the pathways that are implicated in mechanism of variation of response to treatment.

In addition, the metabolic profile of an individual at baseline (metabotype) provides information about

  • how individuals respond to treatment and highlights heterogeneity within a disease state.

All three approaches require the quantification of metabolites found

relationship between -OMICS

relationship between -OMICS

http://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/OMICS.png/350px-OMICS.png

Pharmacometabolomics is thought to provide information that

Looking at the characteristics of an individual down through these different levels of detail, there is an

  • increasingly more accurate prediction of a person’s ability to respond to a pharmaceutical compound.
  1. the genome, made up of 25 000 genes, can indicate possible errors in drug metabolism;
  2. the transcriptome, made up of 85,000 transcripts, can provide information about which genes important in metabolism are being actively transcribed;
  3. and the proteome, >10,000,000 members, depicts which proteins are active in the body to carry out these functions.

Pharmacometabolomics complements the omics with

  • direct measurement of the products of all of these reactions, but with perhaps a relatively
  • smaller number of members: that was initially projected to be approximately 2200 metabolites,

but could be a larger number when gut derived metabolites and xenobiotics are added to the list. Overall, the goal of pharmacometabolomics is

  • to more closely predict or assess the response of an individual to a pharmaceutical compound,
  • permitting continued treatment with the right drug or dosage
  • depending on the variations in their metabolism and ability to respond to treatment.

Pharmacometabolomic analyses, through the use of a metabolomics approach,

  • can provide a comprehensive and detailed metabolic profile or “metabolic fingerprint” for an individual patient.

Such metabolic profiles can provide a complete overview of individual metabolite or pathway alterations,

This approach can then be applied to the prediction of response to a pharmaceutical compound

  • by patients with a particular metabolic profile.

Pharmacometabolomic analyses of drug response are

Pharmacogenetics focuses on the identification of genetic variations (e.g. single-nucleotide polymorphisms)

  • within patients that may contribute to altered drug responses and overall outcome of a certain treatment.

The results of pharmacometabolomics analyses can act to “inform” or “direct”

  • pharmacogenetic analyses by correlating aberrant metabolite concentrations or metabolic pathways to potential alterations at the genetic level.

This concept has been established with two seminal publications from studies of antidepressants serotonin reuptake inhibitors

  • where metabolic signatures were able to define a pathway implicated in response to the antidepressant and
  • that lead to identification of genetic variants within a key gene
  • within the highlighted pathway as being implicated in variation in response.

These genetic variants were not identified through genetic analysis alone and hence

  • illustrated how metabolomics can guide and inform genetic data.

en.wikipedia.org/wiki/Pharmacometabolomics

Benznidazole Biotransformation and Multiple Targets in Trypanosoma cruzi Revealed by Metabolomics

Andrea Trochine, Darren J. Creek, Paula Faral-Tello, Michael P. Barrett, Carlos Robello
Published: May 22, 2014   http://dx.doi.org:/10.1371/journal.pntd.0002844

The first line treatment for Chagas disease, a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi,

  • involves administration of benznidazole (Bzn).

Bzn is a 2-nitroimidazole pro-drug which requires nitroreduction to become active. We used a

  • non-targeted MS-based metabolomics approach to study the metabolic response of T. cruzi to Bzn.

Parasites treated with Bzn were minimally altered compared to untreated trypanosomes, although the redox active thiols

  1. trypanothione,
  2. homotrypanothione and
  3. cysteine

were significantly diminished in abundance post-treatment. In addition, multiple Bzn-derived metabolites were detected after treatment.

These metabolites included reduction products, fragments and covalent adducts of reduced Bzn

  • linked to each of the major low molecular weight thiols:
  1. trypanothione,
  2. glutathione,
  3. g-glutamylcysteine,
  4. glutathionylspermidine,
  5. cysteine and
  6. ovothiol A.

Bzn products known to be generated in vitro by the unusual trypanosomal nitroreductase, TcNTRI,

  • were found within the parasites,
  • but low molecular weight adducts of glyoxal, a proposed toxic end-product of NTRI Bzn metabolism, were not detected.

Our data is indicative of a major role of the

  • thiol binding capacity of Bzn reduction products
  • in the mechanism of Bzn toxicity against T. cruzi.

 

 

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Introduction to Proteomics

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

 

We have had a considerable extended discussion of preoteins and peptides, protein sinthesis, amino acid incorporation into protein, and metabolism of carbohydrates and lipids.  It is also clear that the historic practice of medicine, and the classification of biological systems has been highly dependent on the observations related to the observed phenotypical traits and disturbances of normal function that could be measured by traditional metabolic pathways for over a century.

What did we gain from the genomic revolution?

  1. Traceability of protein expression to a basic coded message
  2. The possibility of tracing disturbed cellular function to mutation related loss-of-function
  3. The ability to trace generational traits over long periods of time
  4. The promise of regenerating the enterprise of pharmacology and pharmaceutical intervention based on the silencing of or readjustment of regulated metabolic pathways to bring an adaptive rebalancing favoring extended life

What can we expect as we progress further as a result of the last two decades?

  1. There is a huge amount of information, as well as missing information that is necessary for adequately tackling the mastery of the life processes.
  2. There is a complex web of knowledge that goes beyond the genome and the one-gene one-enzyme, and the DNA-RNA-protein hypotheses that can only be realized by more full disclosure of the many metabolic control circuits involved in cellular homeostasis and adaptive control.
  3. The ability to come to disclosure and understanding of this cellular balancing will require the comprehensive exploration of the proteome and the active role of proteins and peptides in the functioning of all cells, and the organism.
  4. Proteomics will open up the discovery of new approaches to diagnostics and pharmaceutical discovery.

What about proteins?  What can proteins do? What can’t they do!

  • Enzymes are proteins that make sure that chemical reactions in your body take place up to a million times faster than they would without enzymes.
  • Antibodies are proteins that help your immune system to fight disease.
  • When you get an injury, the bleeding stops because of blood clots, thanks to the proteins fibrinogen and thrombin.
  • Transport! Some proteins carry vitamins ot hormones from one place to another, or form tunnels (pores) in cell membranes that will let only specific molecules (or ions) through. Hemoglobin, a protein in your blood, carries oxygen from your lungs to your cells.
  • Strength and support! Other proteins like collagen and keratin are strong and tough and make up your skin, hair, and fingernails. Collagen also supports your cells and organs so they don’t slosh around.
  • Motion! The proteins myosin and actin make up much of your muscle tissue. They work together so your muscles can move you around. Some bacteria have cilia and flagella made out of proteins. The bacteria can whip these around to move from place to place.

http://www.pslc.ws/macrog/kidsmac/protein.htm

Proteins (/ˈprˌtnz/ or /ˈprti.ɨnz/) are large biological molecules, or macromolecules,

Proteins perform a vast array of functions within living organisms, including

  1. catalyzing metabolic reactions,
  2. replicating DNA,
  3. responding to stimuli, and
  4. transporting molecules from one location to another.

Proteins differ from one another primarily in

  1. their sequence of amino acids,
  2. which is dictated by the nucleotide sequence of their genes, and
  3. which usually results in folding of the protein into

A linear chain of amino acid residues is called a polypeptide. A protein contains at least one long polypeptide. Short polypeptides, containing less than about 20-30 residues, are rarely considered to be proteins and are commonly called peptides, or sometimes oligopeptides. The individual amino acid residues are bonded together by peptide bonds and adjacent amino acid residues. The sequence of amino acid residues in a protein is defined by

In general, the genetic code specifies 20 standard amino acids; however, in certain organisms the genetic code can include selenocysteine and—in certain archaeapyrrolysine. Shortly after or even during synthesis,

  • the residues in a protein are often chemically modified by posttranslational modification,
  • which alters the physical and chemical properties, folding, stability, activity, and ultimately, the function of the proteins.

http://en.wikipedia.org/wiki/Protein

Posttranslational modification (PTM) is a step in protein biosynthesis. Proteins created by ribosomes translating mRNA into polypeptide chains may undergo PTM (such as folding, cutting and other processes) before becoming the mature protein product.  After translation, the posttranslational modification of amino acids extends the range of functions of the protein by attaching it to other biochemical functional groups (such as acetate, phosphate, various lipids and carbohydrates), changing the chemical nature of an amino acid (e.g. citrullination), or making structural changes (e.g. formation of disulfide bridges).

Also, enzymes may remove amino acids from the amino end of the protein, or cut the peptide chain in the middle. For instance, the peptide hormone insulin is cut twice after disulfide bonds are formed, and a propeptide is removed from the middle of the chain; the resulting protein consists of two polypeptide chains connected by disulfide bonds. Also, most nascent polypeptides start with the amino acid methionine because the “start” n mRNA also codes for this amino acid. This amino acid is usually taken off during post-translational modification. Other modifications, like phosphorylation, are part of common mechanisms for controlling the behavior of a protein, for instance activating or inactivating an enzyme.

posttranslational modification of insulin

posttranslational modification of insulin

Posttranslational modification of insulin. At the top, the ribosome translates a mRNA sequence into a protein, insulin, and passes the protein through the endoplasmic reticulum, where it is cut, folded and held in shape by disulfide (-S-S-) bonds. Then the protein passes through the golgi apparatus, where it is packaged into a vesicle. In the vesicle, more parts are cut off, and it turns into mature insulin.

Genetic Code mapped

Genetic Code mapped

The genetic code diagram showing the amino acid residues as target of modification.

PTMs involving addition of cofactors for enhanced enzymatic activity

http://en.wikipedia.org/wiki/Posttranslational_modification

Sometimes proteins have non-peptide groups attached, which can be called prosthetic groups or cofactors.  Examples of cofactors include metal ions like iron and zinc. Proteins can also work together to achieve a particular function, and they often associate to form stable protein complexes.

cofactor-examples

cofactor-examples

Coenzymes are molecules that work at the active site of an enzyme and aid in recognizing, attracting, or repulsing a substrate or product. Many are derived from vitamins. The substrate is the molecule upon which an enzyme catalyzes a reaction transforming A to B by removal or addition of a hydrogen, or a hydroxyl group, or a methyl group, and so forth. This is  how an alcohol or an aldehyde is produced. Such a reaction is critical is carbohydrate metabolism for producing two 3-carbon sugars from a 6-carbon sugar. Coenzymes shuttle chemical groups from one enzyme to another enzyme. They may bind loosely to enzymes, while another group of cofactors do not.

Prosthetic groups are cofactors that bind tightly to proteins or enzymes. As if holding on for dear life, they are not easily removed. They can be organic or metal ions and are often attached to proteins by a covalent bond. The same cofactors can bind multiple different types of enzymes and may bind some enzymes loosely, as a coenzyme, and others tightly, as a prosthetic group. Some cofactors may always tightly bind their enzymes. It’s important to note, though, that these prosthetic groups can also bind to proteins other than enzymes.  A holoenzyme is an enzyme with any metal ions or coenzymes attached to it that is now ready to catalyze a reaction.

prosthetic-groups

prosthetic-groups

http://education-portal.com/academy/lesson/coenzymes-cofactors-prosthetic-groups-function-and-interactions.html#lesson

Around the world, millions of people don’t get enough protein. Protein malnutrition leads to the condition known as kwashiorkor. Lack of protein can cause growth failure, loss of muscle mass, decreased immunity, weakening of the heart and respiratory system, and death.

All Protein Isn’t Alike

Protein is built from building blocks called amino acids. Our bodies make amino acids in two different ways: Either from scratch, or by modifying others. A few amino acids (known as the essential amino acids) must come from food.

  • Animal sources of protein tend to deliver all the amino acids we need.
  • Other protein sources, such as fruits, vegetables, grains, nuts and seeds, lack one or more essential amino acids.

Vegetarians need to be aware of this. People who don’t eat meat, fish, poultry, eggs, or dairy products need to eat a variety of protein-containing foods each day in order to get all the amino acids needed to make new protein.

http://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/protein/
Molecular Biologists Guide to Proteomics

PR. Graves and TA.J. Haystead*
Microbiol Mol Biol Rev. Mar 2002; 66(1): 39–63  PMC120780
http://dx.doi.org:/10.1128/MMBR.66.1.39-63.2002

The emergence of proteomics, the large-scale analysis of proteins, has been inspired by the realization that

  • the final product of a gene is inherently more complex and
  • closer to function than the gene itself.

Shortfalls in the ability of bioinformatics to predict

  • both the existence and function of genes have also illustrated
  • the need for protein analysis.

Moreover, only through the study of proteins can posttranslational modifications be determined,

  • which can profoundly affect protein function.

Proteomics has been enabled by

  • the accumulation of both DNA and protein sequence databases,
  • improvements in mass spectrometry, and
  • the development of computer algorithms for database searching.

In this review, we describe why proteomics is important,

  • how it is conducted, and
  • how it can be applied to complement other existing technologies.

We conclude that currently, the most practical application of proteomics is

  • the analysis of target proteins as opposed to entire proteomes.

This type of proteomics, referred to as functional proteomics, is always

  • driven by a specific biological question.

In this way, protein identification and characterization has a meaningful outcome. We discuss some of the advantages

  • of a functional proteomics approach and

provide examples of how different methodologies can be utilized to address a wide variety of biological problems.

Entry of our laboratory into proteomics 5 years ago was driven by a need to define a complex mixture of proteins (∼36 proteins) we had affinity isolated that bound specifically to the catalytic subunit of protein phosphatase 1 (PP-1, a serine/threonine protein phosphatase that regulates multiple dephosphorylation events in cells). We were faced with the task of trying to understand the significance of these proteins, and the only obvious way to begin to do this was to identify them by sequencing. Since the majority of intact eukaryotic proteins are not immediately accessible to Edman sequencing

  • due to posttranslational N-terminal modifications,
  • we invented mixed-peptide sequencing.

This method enables internal peptide sequence information to be derived from proteins

  • electroblotted onto hydrophobic membranes.

Using the mixed-peptide sequencing strategy, we identified all 36 proteins in about a week. The mixture contained at least two known PP-1 regulatory subunits, but most were novel proteins of unknown function. Herein lies the lesson of proteomics. Identifying long lists of potentially interesting proteins often generates more questions than it seeks to answer.

Despite learning this obvious lesson, our early sequencing experiences were an epiphany that has subsequently altered our whole scientific strategy for probing protein function in cells. The sequencing of the 36 proteins has opened new avenues to further explore the functions of PP-1 in intact cells. Because of increased sensitivity, our approaches now routinely use state-of-the-art mass spectrometry (MS) techniques. However, rather than using proteomics to simply characterize large numbers of proteins in complex mixtures, we see the real application of this technology as a tool to enhance the power of existing approaches currently used by the modern molecular biologist such as classical yeast and mouse genetics, tissue culture, protein expression systems, and site-directed mutagenesis.

Importantly, the one message we would want the reader to take away from reading this review is that one should always let the biological question in mind drive the application of proteomics rather than simply engaging in an orgy of protein sequencing. From our experiences, we believe that if the appropriate controls are performed, proteomics is an extremely powerful approach for addressing important physiological questions. One should always design experiments to define a selected number of relevant proteins in the mixture of interest. Examples of such experiments that we routinely perform include defining early phosphorylation events in complex protein mixtures after hormone treatment of intact cells or comparing patterns of protein derived from a stimulated versus nonstimulated cell in an affinity pull-down experiment. Only the proteins that were specifically phosphorylated or bound in response to the stimulus are sequenced in the complex mixtures. Sequencing proteins that are regulated then has a meaningful outcome and directs all subsequent biological investigation.

The term “proteomics” was first coined in 1995 and was defined as the large-scale characterization of the entire protein complement of a cell line, tissue, or organism. Today, two definitions of proteomics are encountered. The first is the more classical definition, restricting the large-scale analysis of gene products to studies involving only proteins. The second and more inclusive definition combines protein studies with analyses that have a genetic readout such as mRNA analysis, genomics, and the yeast two-hybrid analysis. However, the goal of proteomics remains the same, i.e., to obtain a more global and integrated view of biology by studying all the proteins of a cell rather than each one individually.

Using the more inclusive definition of proteomics, many different areas of study are now grouped under the rubric of proteomics (Fig. (Fig.1).1). These include protein-protein interaction studies, protein modifications, protein function, and protein localization studies to name a few. The aim of proteomics is not only to identify all the proteins in a cell but also to create a complete three-dimensional (3-D) map of the cell indicating where proteins are located. These ambitious goals will certainly require the involvement of a large number of different disciplines such as molecular biology, biochemistry, and bioinformatics. It is likely that in bioinformatics alone, more powerful computers will have to be devised to organize the immense amount of information generated from these endeavors.

Types of proteomics and their applications to biology

Types of proteomics and their applications to biology

In the quest to characterize the proteome of a given cell or organism, it should be remembered that the proteome is dynamic. The proteome of a cell will reflect the immediate environment in which it is studied. In response to internal or external cues, proteins can be modified by posttranslational modifications, undergo translocations within the cell, or be synthesized or degraded. Thus, examination of the proteome of a cell is like taking a “snapshot” of the protein environment at any given time. Considering all the possibilities, it is likely that any given genome can potentially give rise to an infinite number of proteomes.

The first major technology to emerge for the identification of proteins was the sequencing of proteins by Edman degradation. A major breakthrough was the development of microsequencing techniques for electroblotted proteins. This technique was used for the identification of proteins from 2-D gels to create the first 2-D databases.  One of the most important developments in protein identification has been the development of MS technology. In the last decade, the sensitivity of analysis and accuracy of results for protein identification by MS have increased by several orders of magnitude. It is now estimated that proteins in the femtomolar range can be identified in gels. Because MS is more sensitive, can tolerate protein mixtures, and is amenable to high-throughput operations, it has essentially replaced Edman sequencing as the protein identification tool of choice.

The growth of proteomics is a direct result of advances made in large-scale nucleotide sequencing of expressed sequence tags and genomic DNA. Without this information, proteins could not be identified even with the improvements made in MS. Protein identification (by MS or Edman sequencing) relies on the presence of some form of database for the given organism. The majority of DNA and protein sequence information has accumulated within the last 5 to 10 years. In 1995, the first complete genome of an organism was sequenced, that of Haemophilus influenzae. At the time of this writing, the sequencing of the genomes of 45 microorganisms has been completed and that of 170 more is under way (http://www.tiger.org/tdb/mdb/mdbcomplete.html). To date, five eukaryotic genomes have been completed: Arabidopsis thaliana, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans, and Drosophila melanogaster. In addition, the rice, mouse, and human genomes are near completion.

One of the first applications of proteomics will be to identify the total number of genes in a given genome. This “functional annotation” of a genome is necessary because

  • it is still difficult to predict genes accurately from genomic data. One problem is that
  • the exon-intron structure of most genes cannot be accurately predicted by bioinformatics.

To achieve this goal, genomic information will have to be integrated with

  • data obtained from protein studies to confirm the existence of a particular gene.

The analysis of mRNA is

  • not a direct reflection of the protein content in the cell.

Many studies have shown a poor correlation

  • between mRNA and protein expression levels.

The formation of mRNA is only the first step in a long sequence of events resulting in the synthesis of a protein (Fig. (Fig.2).2).

  1. mRNA is subject to posttranscriptional control in the form of alternative splicing, polyadenylation, and mRNA editing. Many different protein isoforms can be generated from a single gene at this step.
  2. mRNA then can be subject to regulation at the level of protein translation. Proteins, having been formed, are subject to posttranslational modification. It is estimated that up to 200 different types of posttranslational protein modification exist. Proteins can also be regulated by proteolysis and compartmentalization. It is clear that the tenet of “one gene, one protein” is an oversimplification.
Mechanisms by which a single gene can give rise to multiple gene products

Mechanisms by which a single gene can give rise to multiple gene products

Mechanisms by which a single gene can give rise to multiple gene products. Multiple protein isoforms can be generated by RNA processing when RNA is alternatively spliced or edited to form mature mRNA. mRNA, in turn, can be regulated by stability and efficiency
One of the most important applications of proteomics will be the characterization of posttranslational protein modifications. Proteins are known to be modified posttranslationally in response to a variety of intracellular and extracellular signals. For example, protein phosphorylation is an important signaling mechanism and disregulation of protein kinases or phosphatases can result in oncogenesis. By using a proteomics approach, changes in the modifications of many proteins expressed by a cell can be analyzed simultaneously.
Of fundamental importance in biology is the understanding of protein-protein interactions. The process of cell growth, programmed cell death, and the decision to proceed through the cell cycle are all regulated by signal transduction through protein complexes. Proteomics aims to develop a complete 3-D map of all protein interactions in the cell. One step toward this goal was recently completed for the microorganism Helicobacter pylori. Using the yeast two-hybrid method to detect protein interactions, 1,200 connections were identified between H. pylori proteins covering 46.6% of the genome. A comprehensive two-hybrid analysis has also been performed on all the proteins from the yeast S. cerevisiae.
mixed peptide sequencing with MS

mixed peptide sequencing with MS

The process of mixed-peptide sequencing involves separation of a complex protein mixture by polyacrylamide gel electrophoresis (1-D or 2-D) and then transfer of the proteins to an inert membrane by electroblotting (Fig. (Fig.4).4). The proteins of interest are visualized on the membrane surface, excised, and fragmented chemically at methionine (by CNBr) or tryptophan (by skatole) into several large peptide fragments.
FASTF and FASTS search programs

FASTF and FASTS search programs

The mixed-sequence data are fed into the FASTF or TFASTF algorithms, which sort and match the data against protein (FASTF) and DNA (TFASTF) databases to unambiguously identify the protein. The FASTF and TFASTF programs were written in collaboration with William Pearson (Department of Biochemistry, University of Virginia). Because minimal sample handling is involved, mixed-peptide sequencing can be a sensitive approach for identifying proteins in polyacrylamide gels at the 0.1- to 1-pmol level.  A recent variation of T/FASTF has been devised for MS (101) (Fig. (Fig.5B).5B). The T/FASTF/S programs are available at http://fasta.bioch.virginia.edu/ (Table (Table11).

triple quadrupole MS

triple quadrupole MS

Triple-quadrupole mass spectrometers are most commonly used to obtain amino acid sequences. In the first stage of analysis, the machine is operated in MS scan mode and all ions above a certain m/z ratio are transmitted to the third quadrupole for mass analysis (Fig. (Fig.6)6) (82, 173). In the second stage, the mass spectrometer is operated in MS/MS mode and a particular peptide ion is selectively passed into the collision chamber. Inside the collision chamber, peptide ions are fragmented by interactions with an inert gas by a process known as collision-induced dissociation or collisionally activated dissociation. The peptide ion fragments are then resolved on the basis of their m/z ratio by the third quadrupole (Fig. (Fig.6).6). Since two different mass spectra are obtained in this analysis, it is referred to as tandem mass spectrometry (MS/MS). MS/MS is used to obtain the amino acid sequence of peptides by generating a series of peptides that differ in mass by a single amino acid.

The largest application of proteomics continues to be protein expression profiling. Through the use of two-dimensional gels or novel techniques such as ICAT, the expression levels of proteins or changes in their level of modification between two different samples can be compared and the proteins can be identified. This approach can facilitate the dissection of signaling mechanisms or identify disease-specific proteins.

Cancer cells are good candidates for proteomics studies because they can be compared to their non-transformed counterparts. Analysis of differentially expressed proteins in normal versus cancer cells can

(i) identify novel tumor cell biomarkers that can be used for diagnosis,

(ii) provide clues to mechanisms of cancer development, and

(iii) identify novel targets for therapeutic intervention. Protein expression profiling has been used in the study of breast, esophageal, bladder and prostate cancer. From these studies, tumor-specific proteins were identified and 2-D protein expression databases were generated. Many of these 2-D protein databases are now available on the World Wide Web.

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