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- NEW GENRE Volume One: Metabolic Genomics & Pharmaceutics
- Original Volume One: Metabolic Genomics & Pharmaceutics
VOLUME 1: Metabolic Genomics and Pharmaceutics.
On Amazon.com since 7/21/2015
http://www.amazon.com/dp/B012BB0ZF0
NEW GENRE Volume One: Metabolic Genomics & Pharmaceutics – Series D, Volume 1
This volume has the following three parts:
PART A: The eTOCs in Spanish in Audio format
PART B: The eTOCs in Bi-lingual format: Spanish and English in Text format
PART C: The Editorials of the original e-Books in English in Audio format
C.1: Editorials English in Audio format
C.2: Editorials Supplemental English in Text format
PART A:
The eTOCs in Spanish in Audio format
Serie D: Libros electrónicos de Biomedicina. Metabolómica, inmunología, enfermedades infecciosas, genómica reproductiva y endocrinología
Volumen I
Genómica metabólica y farmacéutica
Traducción a español
2015
Metabolic Genomics and Pharmaceutics
Disponible en Amazon.com desde el 21/07/2015
http://www.amazon.com/dp/B012BB0ZF0
Autor, redactor y editor
Larry H Bernstein, MD, FCAP
Director científico
Leaders in Pharmaceutical Business Intelligence
Redactora jefe de la serie de libros electrónicos BioMed
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
Lista de colaboradores y sus biografías
Autor, redactor y editor
Prefacio
Introducción del volumen y capítulo y resumen de cada capítulo
Capítulo 1: 1.1 a 1.6
Capítulo 2: 2.1 a 2.7
Capítulo 3: 3.1 a 3.6
Capítulo 4: 4.1 a 4.11
Capítulo 5: 5.1 a 5.9
Capítulo 6: 6.1 a 6.11
Capítulo 7: 7.1 a 7.11
Capítulo 8: 8.1 a 8.3, 8.5 a 8.10
Capítulo 9: 9.6
Resumen del volumen y epílogo
Autores, redactores y reporteros invitados:
4.9, 9.3
5.3, 5.4, 5.5
5.9, 8.4
9.2
4.3, 4.10, 5.6, 5.7, 5.8, 5.9, 6.10, 7.5, 9.1, 9.4, 9.5, 9.7, 9.8, 9.9
Prefacio del capítulo «Metabolómica como disciplina en medicina»
Autor: Larry H Bernstein, MD, FCAP
Introducción a la metabolómica
Autor: Larry H Bernstein, MD, FCAP
Indice de contenidos electrónico (IDCe)
Los enlaces indicados llevan al contenido original en inglés
|
MD |
Licenciado/a en medicina y cirugía (Estados Unidos) |
|
PhD |
Doctorado/a |
|
FCAP |
Miembro distinguido (Fellow) del Colegio de Anatomopatólogos de los Estados Unidos |
|
RN |
Enfermero/a titulado/a (National Board of Nursing Registration) |
Capítulo 1: Vías metabólicas
Introducción a las vías metabólicas
Autor: Larry H Bernstein, MD, FCAP
1.1 Metabolismo de los carbohidratos
https://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/
1.2 Estudios sobre la respiración dan lugar al descubrimiento de la acetil-CoA
https://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/
1.3 Resumen sobre la ruta de la pentosa, la transferencia de electrones, la galactosa y más lípidos
1.4 La transferencia en varias etapas del enlace fosfato y la energía del intercambio de hidrógeno
1.5 Diabetes mellitus
https://pharmaceuticalintelligence.com/2014/10/24/diabetes-mellitus/
1.6 Glicosaminoglicanos, mucopolisacáridos, L-iduronidasa y tratamiento enzimático
Resumen de las vías metabólicas
Autor y redactor: Larry H Bernstein, MD, FCAP
Capítulo 2: Metabolismo de los lípidos
Introducción al metabolismo de los lípidos
Autor: Larry H Bernstein, MD, FCAP
2.1 Sistema de clasificación de los lípidos
https://pharmaceuticalintelligence.com/2014/10/26/lipid-classification/
2.2 Ácidos grasos esenciales
https://pharmaceuticalintelligence.com/2014/10/26/essential-fatty-acids/
2.3 Oxidación de lípidos y síntesis de ácidos grasos
https://pharmaceuticalintelligence.com/2014/10/25/oxidation-and-synthesis-of-fatty-acids/
2.4 El colesterol y la regulación de las vías sintéticas del hígado
2.5 Hormonas sexuales, cortisol suprarrenal y prostaglandinas
https://pharmaceuticalintelligence.com/2014/10/27/sex-hormones-adrenal-cortisol-prostaglandins/
2.6 Fisiología del citoesqueleto y de la membrana celular
https://pharmaceuticalintelligence.com/2014/10/28/cytoskeleton-and-cell-membrane-physiology/
2.7 Acción farmacológica de las hormonas esteroideas
https://pharmaceuticalintelligence.com/2014/10/27/pharmacological-action-of-steroid-hormones/
Resumen del metabolismo de los lípidos
Autor y redactor: Larry H Bernstein, MD, FCAP
Capítulo 3: Señalización celular
Introducción a la señalización celular
Autor: Larry H Bernstein, MD, FCAP
3.1 La señalización y las vías de señalización
https://pharmaceuticalintelligence.com/2014/08/12/signaling-and-signaling-pathways/
3.2 Tutorial sobre la transducción de señales
https://pharmaceuticalintelligence.com/2014/08/12/signaling-and-signaling-pathways/
3.3 Referencias seleccionadas a las vías de señalización y metabólicas en la revista Leaders in Pharmaceutical Intelligence
3.4 Integrinas, cadherinas, señalización y el citoesqueleto
3.5 Modelos complejos de señalización: implicaciones terapéuticas
3.6 Correlaciones funcionales de las vías de señalización
https://pharmaceuticalintelligence.com/2014/11/04/functional-correlates-of-signaling-pathways/
Resumen de la señalización y las vías de señalización
Autor y redactor: Larry H Bernstein, MD, FCAP
Capítulo 4: Síntesis y degradación de proteínas
Introducción a la síntesis y la degradación de proteínas
Autor: Larry H Bernstein, MD, FCAP
4.1 El papel y la importancia de los factores de transcripción
https://pharmaceuticalintelligence.com/2014/08/06/the-role-and-importance-of-transcription-factors/
4.2 El ARN y la transcripción del código genético
https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
4.3 13/06/2014, de 9:30 a 10:00, David Bartel “MicroARN, colas de poli(A) y regulación génica postranscripcional”
4.4 Silenciamiento transcripcional y la proteína de longevidad Sir2
4.5 Señalización del Ca2+: control transcripcional
https://pharmaceuticalintelligence.com/2013/03/06/ca2-signaling-transcriptional-control/
4.6 La red de los ARN largos no codificantes regula la transcripción de PTEN
4.7 Nucleasas de dedos de zinc (ZFN) y nucleasas efectoras similares al activador de la transcripción (TALEN)
https://pharmaceuticalintelligence.com/2013/03/04/talens-and-zfns/
4.8 Señalización cardíaca del Ca2+: control transcripcional
https://pharmaceuticalintelligence.com/2013/03/02/cardiac-ca2-signaling-transcriptional-control/
4.9 El factor de transcripción Lyl-1 es crítico en la producción de progenitores tempranos de los linfocitos T
4.10 El lóbulo frontal del cerebro humano: redes transcripcionales específicas
4.11 ADN somático, de células germinales y de secuencia completa en los linajes celulares y la enfermedad
Resumen de la transcripción, la traducción y los factores de transcripción
Autor y redactor: Larry H Bernstein, MD, FCAP
Capítulo 5: Estructura subcelular
Introducción a la estructura subcelular
Autor: Larry H Bernstein, MD, FCAP
5.1 Mitocondrias: origen en el ambiente sin oxígeno; función en la glucólisis aeróbica y la adaptación metabólica
5.2 Metabolismo mitocondrial y función cardíaca
https://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
5.3 Mitocondrias: no solo el «motor de la célula»
5.4 Fisión y fusión mitocondrial: ¿son posibles dianas terapéuticas?
5.5 El análisis de las mutaciones mitocondriales podría estar a un paso de distancia
5.6 Proteínas moduladoras de la autofagia y moléculas pequeñas que son posibles dianas para el tratamiento del cáncer: comentario acerca de las estrategias bioinformáticas
Larry H Bernstein, MD, FCAP y Aviva Lev-Ari, PhD, RN
5.7 Cromatofagia, un nuevo tratamiento contra el cáncer: matar de hambre a la célula enferma hasta que se coma su propio ADN
5.8 Censo seleccionado de las proteínas moduladoras de la autofagia y las moléculas pequeñas que son posibles dianas para el tratamiento del cáncer
5.9 El papel del calcio, el esqueleto de actina y las estructuras lipídicas en la señalización y la motilidad celular
Larry H Bernstein, MD, FCAP y Stephan J Williams, PhD y
Resumen de la estructura celular y correlaciones anatómicas de la función metabólica
Autor y redactor: Larry H Bernstein, MD, FCAP
Capítulo 6: Proteómica
Introducción a la proteómica
Autor: Larry H Bernstein, MD, FCAP
6.1 Proteómica, metabolómica, vías de señalización y regulación celular: una recopilación de artículos de la revista
6.2 Una breve selección de estudios sobre proteómica, metabolómica y el metabolismo
6.3 Uso de la RNA-seq y las nucleasas dirigidas para identificar los mecanismos de resistencia a los fármacos en la leucemia mieloide aguda.
SK Rathe en Nature, 2014
www.nature.com/srep/2014/140813/srep06048/full/srep06048.html
y
6.4 Proteómica: el camino hacia la comprensión y la toma de decisiones en medicina
6.5 Avances en la tecnología de separación para las «ómicas» y clarificación de dianas terapéuticas
6.6 Ampliación del alfabeto genético y vinculación del genoma con el metaboloma
6.7 Genómica, proteómica y sus estándares
https://pharmaceuticalintelligence.com/2014/07/06/genomics-proteomics-and-standards/
6.8 Las proteínas y la adaptación celular al estrés
https://pharmaceuticalintelligence.com/2014/07/08/proteins-and-cellular-adaptation-to-stress/
6.9 Genes, proteomas y su interacción
https://pharmaceuticalintelligence.com/2014/07/28/genes-proteomes-and-their-interaction/
6.10 Regulación de la función de las células madre somáticas
https://pharmaceuticalintelligence.com/2014/07/29/regulation-of-somatic-stem-cell-function/
Larry H Bernstein, MD, FCAP y Aviva Lev-Ari, PhD, RN
6.11 Los científicos descubren que el factor de pluripotencia NANOG también es activo en el organismo adulto
Resumen de la proteómica
Autor y redactor: Larry H Bernstein, MD, FCAP
Capítulo 7: Metabolómica
Introducción a la metabolómica
Autor: Larry H Bernstein, MD, FCAP
7.1 Evaluación extracelular del flujo intracelular en células de levadura
7.2 Análisis metabolómico de dos líneas celulares de leucemia, parte I
7.3 Análisis metabolómico de dos líneas celulares de leucemia, parte II
7.4 La amortiguación de los módulos genéticos implicados en el metabolismo del ciclo de los ácidos tricarboxílicos proporciona regulación homeostática
7.5 Metabolómica Metabolómica y nutrición funcional: el siguiente paso en el metabolismo nutricional y la bioterapéutica
Larry H Bernstein, MD, FCAP y Aviva Lev-Ari, PhD, RN
7.6 Isoenzimas en las vías metabólicas celulares
https://pharmaceuticalintelligence.com/2014/10/06/isoenzymes-in-cell-metabolic-pathways/
7.7 Una breve selección de artículos sobre la proteómica, la metabolómica y el metabolismo
7.8 La metabolómica es el estudio de la integración de los sistemas metabólicos
7.9 Mecanismos de resistencia a los medicamentos
https://pharmaceuticalintelligence.com/2014/10/09/mechanisms-of-drug-resistance/
7.10 Desarrollo de la microscopía de superresolución basada en la fluorescencia
7.11 Las reacciones metabólicas necesitan lo justo
https://pharmaceuticalintelligence.com/2014/10/14/metabolic-reactions-need-just-enough/
Resumen de la metabolómica
Autor y redactor: Larry H Bernstein, MD, FCAP
Capítulo 8: Disfunciones en los estados patológicos: Trastornos endocrinos; hipermetabolismo del estrés y cáncer
Introducción a las disfunciones en los estados patológicos: Trastornos endocrinos; hipermetabolismo del estrés y cáncer
Autor: Larry H Bernstein, MD, FCAP
8.1 Ácidos grasos omega-3. El agotamiento de la fuente y la insuficiencia proteica en la enfermedad renal
8.2 Estrés del retículo endoplásmico del hígado y esteatosis hepática
8.3 Cómo causa hiperhomocisteinemia el desequilibrio de la metionina con insuficiencia de azufre
https://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/
8.4 La AMPK es un regulador negativo del efecto Warburg y suprime el crecimiento tumoral in vivo
8.5 Una segunda mirada al enigma inflamatorio de la nutrición y la transtiretina
8.6 Daño y reparación mitocondrial bajo estrés oxidativo
8.7 Metformina, eje hipofisario-tiroideo, diabetes de tipo 2 y metabolismo
8.8 El efecto Warburg ¿causa o efecto del cáncer? ¿Una visión del siglo XXI?
8.9 Rasgos de comportamiento social integrados en la expresión génica
8.10 El futuro de la metabolómica plasmática en la evaluación de las enfermedades cardiovasculares
Capítulo 9: Expresión genómica en la salud y la enfermedad
Introducción
Autor: Larry H Bernstein, MD, FCAP
9.1 Genética de las enfermedades de la conducción: enfermedad (bloqueo) de la conducción auriculoventricular (AV). Mutaciones genéticas: transcripción, excitabilidad y homeostasis energética
9.2 El BRCA1, supresor tumoral del cáncer de mama y de ovario: funciones en la transcripción, ubiquitinación y reparación del ADN
9.3 Factores metabólicos en los tumores cerebrales agresivos
https://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/
9.4 Una levadura modificada produce por primera vez una serie de opiáceos
9.5 La cuscuta, una planta parásita, inyecta al huésped más de 9.000 transcritos de ARN
9.6 Nutrición basada en plantas, nutracéuticos y medicina alternativa: recopilación de artículos de la revista
9.7 Genes de referencia en el microbioma intestinal humano: el catálogo de BGI
9.8 Dos mutaciones del gen PCSK9: eliminan una proteína implicada en el control del colesterol unido a LDL
9.9 C-HDL: objetivo del tratamiento – Steven E. Nissen, MD, MACC, Cleveland Clinic frente a Peter Libby, MD, BWH
Resumen del capítulo 9
Autor y redactor: Larry H Bernstein, MD, FCAP
Resumen del volumen y epílogo: resumen y perspectiva de la metabolómica
Este Resumen y Epílogo consta de OCHO partes, como sigue:
Parte 1: La metabolómica continúa con su prometedor ascenso
Parte 2: Los biólogos encuentran el “eslabón perdido” en la producción de las fábricas de proteínas en las células
Parte 3: Neurociencia
Parte 4: Investigación sobre el cáncer
Parte 5: Síndrome metabólico
Parte 6: Biomarcadores
Parte 7: Epigenética y metabolismo de los medicamentos
Parte 8: Mapas ilustrativos
Volumen I
Genómica metabólica y farmacéutica
2015
Metabolic Genomics and Pharmaceutics
Disponible en Amazon.com desde el 21/07/2015
http://www.amazon.com/dp/B012BB0ZF0
PART C.1:
C.1: Editorials in English in Audio format
[15 pages]
C.2: Editorials in English in Text format
[85 pages] is following
PART B: eTOCs Bilingual
Prefacing the e-Book Epilogue: Metabolic Genomics and Pharmaceutics
Author and Curator: Larry H. Bernstein, MD, FCAP
This work has been a coming to terms with my scientific and medical end of career balancing in a difficult time after retiring, but it has been rewarding. In the clinical laboratories, radiology, anesthesiology, and in pharmacy, there has been some significant progress in support of surgical, gynecological, developmental, medical practices, and even neuroscience directed disciplines, as well as epidemiology over a period of half a century. Even then, cancer and neurological diseases have been most difficult because the scientific basic research has either not yet uncovered a framework, or because that framework has proved to be multidimensional. In the clinical laboratory sciences, there has been enormous progress in instrumental analysis, with the recent opening of molecular methods not yet prepared for routine clinical use, which will be a very great challenge to the profession, which has seen the development of large sample volume, multi-analyte, high-throughput, low-cost support emerging for decades. The capabilities now underway will also enrich the capabilities of the anatomic pathology suite and the capabilities of 3-dimensional radiological examination. In both pathology and radiology, we have seen the division of the fields into major subspecialties. The development of the electronic health record had to take lessons from the first developments in the separate developments of laboratory, radiology, and pharmacy health record systems, to which were added, full cardiology monitoring systems. These have been unintegrated. This made it difficult to bring forth a suitable patient health record because the information needed to support decision-making by practitioners was in separate “silos”. The mathematical methods that are being applied to the -OMICS sciences, can be brought to bear on the simplification and amplification of the clinicians’ ability to make decisions with near “errorless” discrimination, still allowing for an element of “art” in carrying out the history, physical examination, and knowledge unique to every patient.
We are at this time opening a very large, complex, study of biology in relationship to the human condition. This will require sufficient resources to be invested in the development of these for a better society, which I suspect, will go on beyond the life of my grandchildren. Hopefully, the long-term dangers of climate change will be controlled in that time. As a society, or as a group of interdependent societies, we have no long-term interest in continuing self-destructive behaviors that have predominated in the history of mankind. I now top off these discussions with some further elucidation of what lies before us.
Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery
Douglas B. Kell and Royston Goodacre
School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
Drug Discovery Today Feb 2014;19(2) http://dx.doi.org/10.1016/j.drudis.2013.07.014
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 behavior, 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:
- the effects of inborn errors of metabolism;
- which metabolites are exo-metabolites, and
- how metabolism varies between tissues and cellular compartments.
Even these qualitative network models are not yet complete. As our
understanding improves so do we recognize more clearly the need for a systems (poly)pharmacology.
Modeling biochemical networks – why we do so
There are at least four types of reasons as to why one would wish to model a biochemical network:
- Assessing whether the model is accurate, in the sense that it
reflects – or can be made to reflect – known experimental facts. - Establishing what changes in the model would improve the
consistency of its behavior with experimental observations
and improved predictability, such as with respect to metabolite
concentrations or fluxes. - Analyzing the model, typically by some form of sensitivity
analysis, to understand which parts of the system contribute
most to some desired functional properties of interest. - Hypothesis generation and testing, enabling one to analyze
rapidly the effects of manipulating experimental conditions in
the model without having to perform complex and costly
experiments (or to restrict the number that are performed).
In particular, it is normally considerably cheaper to perform studies of metabolic networks in silicon before trying a smaller number of possibilities experimentally; indeed for combinatorial reasons it is often the only approach possible. Although our focus here is on drug discovery, similar principles apply to the modification of biochemical networks for purposes of ‘industrial’ or ‘white’ biotechnology.
Why we choose to model metabolic networks more than
- transcriptomic or
- proteomic networks
comes from the recognition – made particularly clear by workers in the field of metabolic control analysis that, although changes in the activities of individual enzymes tend to have rather small effects on
- metabolic fluxes, they can and do have very large effects on
- metabolite concentrations (i.e. the metabolome).
Modeling biochemical networks – how we do so
Although one could seek to understand the
- time-dependent spatial distribution of signaling and metabolic substances within individual cellular compartments and
- while spatially discriminating analytical methods such as Raman spectroscopy and
mass spectrometry do exist for the analysis of drugs in situ,
- the commonest type of modeling, as in the spread of substances in
ecosystems, - assumes ‘fully mixed’ compartments and thus ‘pools’ of metabolites.
Although an approximation, this ‘bulk’ modeling will be necessary for complex ecosystems such as humans where, in addition to the need for tissue- and cell-specific models, microbial communities inhabit this super-organism and the gut serves as a source for nutrients courtesy of these symbionts.
Topology and stoichiometry of metabolic networks as major constraints on fluxes
Given their topology, which admits a wide range of parameters for delivering the same output effects and thereby reflects biological robustness, metabolic networks have two especially important constraints that assist their accurate modeling:
(i) the conservation of mass and charge, and
(ii) stoichiometric and thermodynamic constraints.
These are tighter constraints than apply to signaling networks.
New developments in modeling the human metabolic network
Since 2007, several groups have been developing improved but nonidentical models of the human metabolic network at a generalized level and in tissue-specific forms. Following a similar community-driven strategy in Saccharomyces cerevisiae, surprisingly similar to humans, and in Salmonella typhimurium. We focus in particular on a recent consensus paper that provides a highly curated and semantically annotated model of the human metabolic network, termed
- Recon2 (http://humanmetabolism.org/).
In this work, a substantial number of the major groups active in this area came together to provide a carefully and manually constructed/curated network, consisting of some 1789 enzyme-encoding genes, 7440 reactions and 2626 unique metabolites distributed over eight cellular compartments. A variety of dead-end metabolites and blocked reactions remain (essentially orphans and widows). But Recon2 was able to
- account for some 235 inborn errors of metabolism,
- a variety of metabolic ‘tasks’ (defined as a non-zero flux through a reaction or through a pathway leading to the production of a metabolite Q from a metabolite P).
- filtering based on expression profiling allowed the construction of 65 cell-type-specific models.
- Excreted or exo-metabolites are an interesting set of metabolites,
- and Recon2 could predict successfully a substantial fraction of those
Role of transporters in metabolic fluxes
The uptake and excretion of metabolites between cells and their macro-compartments
- requires specific transporters and in the order of one third of ‘metabolic’ enzymes,
- and indeed, of membrane proteins, are in fact transporters or equivalent.
What is of particular interest (to drug discovery), based on their structural similarities, is the increasing recognition (Fig. 3) that pharmaceutical drugs also
- get into and out of cells by ‘hitchhiking’ on such transporters, and not – to any significant extent –
- by passing through phospholipid bilayer portions
of cellular membranes.
This makes drug discovery even more a problem of systems biology than of biophysics.
Two views of the role of solute carriers and other transporters in cellular drug uptake.
(a) A more traditional view in which all so-called ‘passive’ drug uptake occurs through any unperturbed bilayer portion of membrane that might be present.
(b) A view in which the overwhelming fraction of drug is taken up via solute transporters or other carriers that are normally used for the uptake of intermediary metabolites.
Noting that the protein:lipid ratio of bio-membranes is typically 3:1 to 1:1 and that of proteins vary in mass and density (a typical density is 1.37 g/ml) as does their extension, for example, normal to the ca. 4.5 nm lipid bilayer region, the figure attempts to portray a section of a membrane with realistic or typical sizes and amounts of proteins and lipids. Typical protein areas when viewed normal to the membrane are 30%, membranes are rather more ‘mosaic’ than ‘fluid’ and there is some evidence that there might be no genuinely ‘free’ bulk lipids (typical phospholipid masses are 750 Da) in bio-membranes that are uninfluenced by proteins. Also shown is a typical drug: atorvastatin (LipitorW) – with a molecular mass of 558.64 Da – for size comparison purposes. If proteins are modelled as cylinders, a cylinder with a diameter of 3.6 nm and a length of 6 nm has a molecular mass of ca. 50 kDa. Note of course that in a ‘static’ picture we cannot show the dynamics of either phospholipid chains or lipid or protein diffusion.
‘Newly discovered’ metabolites and/or their roles
To illustrate the ‘unfinished’ nature even of Recon2, which concentrates on the metabolites created via enzymes encoded in the human genome, and leaving aside the more exotic metabolites of drugs and foodstuffs and the ‘secondary’ metabolites of microorganisms, there are several examples of interesting ‘new’ (i.e. more or less recently recognized) human metabolites or roles thereof that are worth highlighting, often from studies seeking biomarkers of various diseases – for caveats of biomarker discovery, which is not a topic that we are covering here, and the need for appropriate experimental design. In addition, classes of metabolites not well represented in Recon2 are oxidized molecules such as those caused by nonenzymatic reaction of metabolites with free radicals such as the hydroxyl radical generated by unliganded iron. There is also significant interest in using methods of determining small molecules such as those in the metabolome (inter alia) for assessing the ‘exposome’, in other words all the potentially polluting agents to which an individual has been exposed.
Recently discovered effects of metabolites on enzymes
Another combinatorial problem reflects the fact that in molecular enzymology it is not normally realistic to assess every possible metabolite to determine whether it is an effector (i.e.activator or inhibitor) of the enzyme under study. Typical proteins are highly promiscuous and there is increasing evidence for the comparative promiscuity of metabolites and pharmaceutical drugs. Certainly the contribution of individual small effects of multiple parameter changes can have substantial effects on the potential flux through an overall pathway, which makes ‘bottom up’ modeling an inexact science. Even merely mimicking the vivo (in Escherichia coli) concentrations of K+, Na+, Mg2+, phosphate, glutamate, sulphate and Cl significantly modulated the activities of several enzymes tested relative to the ‘usual’ assay conditions. Consequently, we need to be alive to the possibility of many (potentially major) interactions of which we are as yet ignorant. One class of example relates to the effects of the very widespread post-translational modification on metabolic enzyme activities.
A recent and important discovery (Fig. 4) is that a single transcriptome experiment, serving as a surrogate for fluxes through individual steps, provides a huge constraint on possible models, and predicts in a numerically tractable way and with much improved accuracy the fluxes to exo-metabolites without the need for such a variable ‘biomass’ term. Other recent and related strategies that exploit modern advances in ‘omics and network biology to limit the search space in constraint-based metabolic modeling.
Fig 4. Workflow for expression-profile-constrained metabolic flux estimation
- Genome-scale metabolic model with gene-protein-reaction relationships
- Map absolute gene expression levels to reactions
- Maximize correlation between absolute gene expression and metabolic flux
- Predict fluxes to exo-metabolites
- Compare predicted with experimental fluxes to exo-metabolites
Drug Discovery Today
The steps in a workflow that uses constraints based on (i) metabolic network stoichiometry and chemical reaction properties (both encoded in the model) plus, and (ii) absolute (RNA-Seq) transcript expression profiles to enable the accurate modeling of pathway and exo-metabolite fluxes.
Concluding remarks – the role of metabolomics in systems pharmacology
What is becoming increasingly clear, as we recognize that to understand living organisms in health and disease we must treat them as systems, is that we must bring together our knowledge of the topologies and kinetics of metabolic networks with our knowledge of the metabolite concentrations (i.e. metabolomes) and fluxes. Because of the huge constraints imposed on metabolism by reaction stoichiometries, mass conservation and thermodynamics, comparatively few well-chosen ‘omics measurements might be needed to do this reliably (Fig. 4). Indeed, a similar approach exploiting constraints has come to the fore in de-novo protein folding and interaction studies.
What this leads us to in drug discovery is the need to develop and exploit a ‘systems pharmacology’ where multiple binding targets are chosen purposely and simultaneously. Along with other measures such as phenotypic screening, and the integrating of the full suite of e-science approaches, one can anticipate considerable improvements in the rate of discovery of safe and effective drugs.
Metabolomics: the apogee of the “omics” trilogy
Gary J. Patti, Oscar Yanes and Gary Siuzdak
Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.
Metabolites are small molecules that are chemically transformed during metabolism and, as such, they provide a functional readout of cellular state. Unlike genes and proteins, the functions of which are subject to epigenetic regulation and post-translational modifications, respectively, metabolites serve as direct signatures of biochemical activity and are therefore easier to correlate with phenotype. In this context, metabolite profiling, or metabolomics, has become a powerful approach that has been widely adopted for clinical diagnostics.
The field of metabolomics has made remarkable progress within the past decade and has implemented new tools that have offered mechanistic insights by allowing for the correlation of biochemical changes with phenotype.
In this Innovation article, we first define and differentiate between the targeted and untargeted approaches to metabolomics. We then highlight the value of untargeted metabolomics in particular and outline a guide to performing such studies. Finally, we describe selected applications of un targeted metabolomics and discuss their potential in cell biology.
Metabolites serve as direct signatures of biochemical activity
- In some instances, it may be of interest to examine a defined set of metabolites by using a targeted approach.
- In other cases, an untargeted or global approach may be taken in which as many metabolites as possible are measured and compared between samples without bias.
- Ultimately, the number and chemical composition of metabolites to be studied is a defining attribute of any metabolomic experiment and shapes experimental design with respect to sample preparation and choice of instrumentation.
The targeted and untargeted workflow for LC/MS-based metabolomics.
- In the triple quadrupole (QqQ)-based targeted metabolomic workflow, standard compounds for the metabolites of interest are first used to set up selected reaction monitoring methods. Here, optimal instrument voltages are determined and response curves are generated for absolute quantification. After the targeted methods have been established on the basis of standard metabolites, metabolites are extracted from tissues, bio-fluids or cell cultures and analyzed. The data output provides quantification only of those metabolites for which standard methods have been built.
- In the untargeted metabolomic workflow, metabolites are first isolated from biological samples and subsequently analyzed by liquid chromatography followed by mass spectrometry (LC/MS). After data acquisition, the results are processed by using bioinformatic software such as XCMS to perform nonlinear retention time alignment and identify peaks that are changing between the groups of samples measured. The m/z value s for the peaks of interest are searched in metabolite databases to obtain putative identifications. Putative identifications are then confirmed by comparing tandem mass spectrometry (MS/MS) data and retention time data to that of standard compounds. The untargeted workflow is global in scope and outputs data related to comprehensive cellular metabolism.
Metabolic Biomarker and Kinase Drug Target Discovery in Cancer Using Stable Isotope-Based Dynamic Metabolic Profiling (SIDMAP)
László G. Boros1*, Daniel J. Brackett2 and George G. Harrigan3
1UCLA School of Medicine, Harbor-UCLA Research and Education Institute, Torrance, CA. 2Department of Surgery, University of Oklahoma Health Sciences Center & VA Medical Center, Oklahoma City, OK, Global High Throughput Screening (HTS), Pharmacia Corporation, Chesterfield, MO. Current Cancer Drug Targets, 2003, 3, 447-455.
Tumor cells respond to growth signals by the activation of protein kinases, altered gene expression and significant modifications in substrate flow and redistribution among biosynthetic pathways. This results in a proliferating phenotype with altered cellular function. These transformed cells exhibit unique anabolic characteristics, which includes increased and preferential utilization of glucose through the non-oxidative steps of the pentose cycle for nucleic acid synthesis but limited denovo fatty acid synthesis and TCA cycle glucose oxidation. This primarily nonoxidative anabolic profile reflects an undifferentiated highly proliferative aneuploid cell phenotype and serves as a reliable metabolic biomarker to determine cell proliferation rate and the level of cell transformation/differentiation in response to drug treatment. Novel drugs effective in particular cancers exert their anti-proliferative effects by inducing significant reversions of a few specific non-oxidative anabolic pathways. Here we present evidence that cell transformation of various mechanisms is sustained by a unique disproportional substrate distribution between the two branches of the pentose cycle for nucleic acid synthesis, glycolysis and the TCA cycle for fatty acid synthesis and glucose oxidation. This can be demonstrated by the broad labeling and unique specificity of [1,2-13C2] glucose to trace a large number of metabolites in the metabolome. Stable isotope-based dynamic metabolic profiles (SIDMAP) serve the drug discovery process by providing a powerful new tool that integrates the metabolome into a functional genomics approach to developing new drugs. It can be used in screening kinases and their metabolic targets, which can therefore be more efficiently characterized, speeding up and improving drug testing, approval and labeling processes by saving trial and error type study costs in drug testing.
Navigating the Human Metabolome for Biomarker Identification and Design of Pharmaceutical Molecules
Irene Kouskoumvekaki and Gianni Panagiotou
Department of Systems Biology, Center for Biological Sequence Analysis, Building 208, Technical University of Denmark, Lyngby, Denmark
Hindawi Publishing Corporation Journal of Biomedicine and Biotechnology 2011, Article ID 525497, 19 pages
http://dx.doi.org:/10.1155/2011/525497
Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics.
Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants.
Metabolites are the byproducts of metabolism, which is itself the process of converting food energy to mechanical energy or heat. Experts believe there are at least 3,000 metabolites that are essential for normal growth and development (primary metabolites) and thousands more unidentified (around 20,000, compared to an estimated 30,000 genes and 100,000 proteins) that are not essential for growth and development (secondary metabolites) but could represent prognostic, diagnostic, and surrogate markers for a disease state and a deeper understanding of mechanisms of disease.
Metabolomics, the study of metabolism at the global level, has the potential to contribute significantly to biomedical research, and ultimately to clinical medical practice. It is a close counterpart to the genome, the transcriptome and the proteome. Metabolomics, genomics, proteomics, and other “-omics” grew out of the Human Genome Project, a massive research effort that began in the mid-1990s and culminated in 2003 with a complete mapping of all the genes in the human body. When discussing the clinical advantages of metabolomics, scientists point to the “real-world” assessment of patient physiology that the metabolome provides since it can be regarded as the end-point of the “-omics” cascade. Other functional genomics technologies do not necessarily predict drug effects, toxicological response, or disease states at the phenotype but merely indicate the potential cause for phenotypical response. Metabolomics can bridge this information gap. The identification and measurement of metabolite profile dynamics of host changes provides the closest link to the various phenotypic responses. Thus it is clear that the global mapping of metabolic signatures pre- and post- drug treatment is a promising approach to identify possible functional relationships between medication and medical phenotype.
Human Metabolome Database (HMDB). Focusing on quantitative, analytic, or molecular scale information about metabolites, the enzymes and transporters associated with them, as well as disease related properties the HMDB represents the most complete bioinformatics and chemoinformatics medical information database. It contains records for thousands of endogenous metabolites identified by literature surveys (PubMed, OMIM, OMMBID, text books), data mining (KEGG, Metlin, BioCyc) or experimental analyses performed on urine, blood, and cerebrospinal fluid samples.
The annotation effort is aided by chemical parameter calculators and protein annotation tools originally developed for DrugBank.
A key feature that distinguishes the HMDB from other metabolic resources is its extensive support for higher level database searching and selecting functions. More than 175 hand-drawn-zoomable, fully hyperlinked human metabolic pathway maps can be found in HMDB and all these maps are quite specific to human metabolism and explicitly show the sub-cellular compartments where specific reactions are known to take place. As an equivalent to BLAST the HMDB contains a structure similarity search tool for chemical structures and users may sketch or paste a SMILES string of a query compound into the Chem-Query window. Submitting the query launches a structure similarity search tool that looks for common substructures from the query compound that match the HMDB’s metabolite database. The wealth of information and especially the extensive linkage to metabolic diseases to normal and abnormal metabolite concentration ranges, to mutation/SNP data and to the genes, enzymes, reactions and pathways associated with many diseases of interest makes the HMDB one the most valuable tool in the hands of clinical chemists, nutritionists, physicians and medical geneticists.
Metabolomics in Drug Discovery and Poly-pharmacology Studies
Drug molecules generally act on specific targets at the cellular level, and upon binding to the receptors, they exert a desirable alteration of the cellular activities, regarded as the pharmaceutical effect. Current drug discovery depends largely on ransom screening, either high-throughput screening (HTS) in vitro, or virtual screening (VS) in silicon. Because the number of available compounds is huge, several druglikeness filters are proposed to reduce the number of compounds that need to be evaluated. The ability to effectively predict if a chemical compound is “drug-like” or “nondruglike” is, thus, a valuable tool in the design, optimization, and selection of drug candidates for development. Druglikeness is a general descriptor of the potential of a small molecule to become a drug. It is not a unified descriptor but a global property of a compound processing many specific characteristics such as good solubility, membrane permeability, half-life, and having a pharmacophore pattern to interact specifically with a target protein. These characteristics can be reflected as molecular descriptors such as molecular weight, log P, the number of hydrogen bond donors, the number of hydrogen-bond acceptors, the number of rotatable bonds, the number of rigid bonds, the number of rings in a molecule, and so forth.
Metabolomics for the Study of Polypharmacology of Natural Compounds
Internationally, there is a growing and sustained interest from both pharmaceutical companies and public in medicine from natural sources. For the public, natural medicine represent a holistic approach to disease treatment, with potentially less side effects than conventional medicine. For the pharmaceutical companies, bioactive natural products constitute attractive drug leads, as they have been optimized in a long-term natural selection process for optimal interaction with biomolecules. To promote the ecological survival of plants, structures of secondary products have evolved to interact with molecular targets affecting the cells, tissues and physiological functions in competing microorganisms, plants, and animals. In this, respect, some plant secondary products may exert their action by resembling endogenous metabolites, ligands, hormones, signal transduction molecules, or neurotransmitters and thus have beneficial effects on humans.
Future Perspectives
Metabolomics, the study of metabolism at the global level, is moving to exciting directions.With the development of more sensitive and advanced instrumentation and computational tools for data interpretation in the physiological context, metabolomics have the potential to impact our understanding of molecular mechanisms of diseases. A state-of-theart metabolomics study requires knowledge in many areas and especially at the interface of chemistry, biology, and
computer science. High-quality samples, improvements in automated metabolite identification, complete coverage of the human metabolome, establishment of spectral databases of metabolites and associated biochemical identities, innovative experimental designs to best address a hypothesis, as well as novel computational tools to handle metabolomics data are critical hurdles that must be overcome to drive the inclusion of metabolomics in all steps of drug discovery and drug development. The examples presented above demonstrated that metabolite profiles reflect both environmental and genetic influences in patients and reveal new links between metabolites and diseases providing needed prognostic, diagnostic, and surrogate biomarkers. The integration of these signatures with other “omics” technologies is of utmost importance to characterize the entire spectrum of malignant phenotype.
Volume Summary & Epilogue
Metabolomics Summary and Perspective
Author and Curator: Larry H Bernstein, MD, FCAP
This Summary & Epilogue has EIGHT parts, as follows:
Part 1
Metabolomics Continues Auspicious Climb
Part 2
Biologists Find ‘Missing Link’ in the Production of Protein Factories in Cells
Part 3
Neuroscience
Part 4
Cancer Research
Part 5
Metabolic Syndrome
Part 6
Biomarkers
Part 7
Epigenetics and Drug Metabolism
Part 8
Pictorial
This is the final article in a robust series on metabolism, metabolomics, and the “OMICS“ – biological synthesis that is creating a more holistic and interoperable view of natural sciences, including the biological disciplines, climate science, physics, chemistry, toxicology, pharmacology, and pathophysiology with as yet unforeseen consequences.
There have been impressive advances already in the research into developmental biology, plant sciences, microbiology, mycology, and human diseases, most notably, cancer, metabolic, and infectious, as well as neurodegenerative diseases.
PART B:
The eTOCs in Bi-lingual format: Spanish and English in Text format
Serie D: Libros electrónicos de Biomedicina. Metabolómica, inmunología, enfermedades infecciosas, genómica reproductiva y endocrinología
Volumen I
Genómica metabólica y farmacéutica
Traducción a español
2015
Disponible en Amazon.com desde el 21/07/2015
http://www.amazon.com/dp/B012BB0ZF0
Autor, redactor y editor
Larry H Bernstein, MD, FCAP
Director científico
Leaders in Pharmaceutical Business Intelligence
Redactora jefe de la serie de libros electrónicos BioMed
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology
Volume I
Metabolic Genomics & Pharmaceutics
2015
Available on Amazon.com since 7/21/2015
http://www.amazon.com/dp/B012BB0ZF0
Author, Curator and Editor
Larry H Bernstein, MD, FCAP
Chief Scientific Officer
Leaders in Pharmaceutical Business Intelligence
Editor-in-Chief BioMed e-Series of e-Books
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
Lista de colaboradores y sus biografías
List of Contributors & Contributors’ Biographies
Prefacio
Preface
Introducción del volumen y capítulo y resumen de cada capítulo
Volume Introduction & Chapter and Summary for each chapter
Capítulo 1: 1.1 a 1.6
Chapter 1: 1.1 to 1.6
Capítulo 2: 2.1 a 2.7
Chapter 2: 2.1 to 2.7
Capítulo 3: 3.1 a 3.6
Chapter 3: 3.1 to 3.6
Capítulo 4: 4.1 a 4.11
Chapter 4: 4.1 to 4.11
Capítulo 5: 5.1 a 5.9
Chapter 5: 5.1 to 5.9
Capítulo 6: 6.1 a 6.11
Chapter 6: 6.1 to 6.11
Capítulo 7: 7.1 a 7.11
Chapter 7: 7.1 to 7.11
Capítulo 8: 8.1 a 8.3, 8.5 a 8.10
Chapter 8: 8.1 to 8.3, 8.5 to 8.10
Capítulo 9: 9.6
Chapter 9: 9.6
Resumen del volumen y epílogo
Volume Summary & Epilogue
Autores, redactores y reporteros invitados:
Guest Authors, Curators and Reporters:
4.9, 9.3
5.3, 5.4, 5.5
5.9, 8.4
9.2
4.3, 4.10, 5.6, 5.7, 5.8, 5.9, 6.10, 7.5, 9.1, 9.4, 9.5, 9.7, 9.8, 9.9
Prefacio del capítulo «Metabolómica como disciplina en medicina»
Preface to Metabolomics as a Discipline in Medicine
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
Introducción a la metabolómica
Introduction to Metabolomics
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
Indice de contenidos electrónico (IDCe)
electronic Table of Contents
Los enlaces indicados llevan al contenido original en inglés
|
MD |
Licenciado/a en medicina y cirugía (Estados Unidos) |
|
PhD |
Doctorado/a |
|
FCAP |
Miembro distinguido (Fellow) del Colegio de Anatomopatólogos de los Estados Unidos |
|
RN |
Enfermero/a titulado/a (National Board of Nursing Registration) |
Capítulo 1: Vías metabólicas
Chapter 1: Metabolic Pathways
Introducción a las vías metabólicas
Introduction to Metabolic Pathways
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
1.1 Metabolismo de los carbohidratos
1.1 Carbohydrate Metabolism
https://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/
1.2 Estudios sobre la respiración dan lugar al descubrimiento de la acetil-CoA
1.2 Studies of Respiration Lead to Acetyl CoA
https://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/
1.3 Resumen sobre la ruta de la pentosa, la transferencia de electrones, la galactosa y más lípidos
1.3 Pentose Shunt. Electron Transfer. Galactose, more Lipids in brief
1.4 La transferencia en varias etapas del enlace fosfato y la energía del intercambio de hidrógeno
1.4 The Multi-step Transfer of Phosphate Bond and Hydrogen Exchange Energy
1.5 Diabetes mellitus
1.5 Diabetes Mellitus
https://pharmaceuticalintelligence.com/2014/10/24/diabetes-mellitus/
1.6 Glicosaminoglicanos, mucopolisacáridos, L-iduronidasa y tratamiento enzimático
1.6 Glycosaminoglycans. Mucopolysaccharides. L-iduronidase. Enzyme Therapy
Resumen de las vías metabólicas
Summary of Metabolic Pathways
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Capítulo 2: Metabolismo de los lípidos
Chapter 2: Lipid Metabolism
Introducción al metabolismo de los lípidos
Introduction to Lipid Metabolism
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
2.1 Sistema de clasificación de los lípidos
2.1 Lipid Classification System
https://pharmaceuticalintelligence.com/2014/10/26/lipid-classification/
2.2 Ácidos grasos esenciales
2.2 Essential Fatty Acids
https://pharmaceuticalintelligence.com/2014/10/26/essential-fatty-acids/
2.3 Oxidación de lípidos y síntesis de ácidos grasos
2.3 Lipid Oxidation and Synthesis of Fatty Acids
https://pharmaceuticalintelligence.com/2014/10/25/oxidation-and-synthesis-of-fatty-acids/
2.4 El colesterol y la regulación de las vías sintéticas del hígado
2.4 Cholesterol and Regulation of Liver Synthetic Pathways
2.5 Hormonas sexuales, cortisol suprarrenal y prostaglandinas
2.5 Sex hormones. Adrenal cortisol. Prostaglandins
https://pharmaceuticalintelligence.com/2014/10/27/sex-hormones-adrenal-cortisol-prostaglandins/
2.6 Fisiología del citoesqueleto y de la membrana celular
2.6 Cytoskeleton and Cell Membrane Physiology
https://pharmaceuticalintelligence.com/2014/10/28/cytoskeleton-and-cell-membrane-physiology/
2.7 Acción farmacológica de las hormonas esteroideas
2.7 Pharmacological Action of Steroid hormone
https://pharmaceuticalintelligence.com/2014/10/27/pharmacological-action-of-steroid-hormones/
Resumen del metabolismo de los lípidos
Summary for Lipid Metabolism
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Capítulo 3: Señalización celular
Chapter 3: Cell Signaling
Introducción a la señalización celular
Introduction to Cell Signaling
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
3.1 La señalización y las vías de señalización
3.1 Signaling and Signaling Pathways
https://pharmaceuticalintelligence.com/2014/08/12/signaling-and-signaling-pathways/
3.2 Tutorial sobre la transducción de señales
3.2 Signaling Transduction Tutorial
https://pharmaceuticalintelligence.com/2014/08/12/signaling-and-signaling-pathways/
3.3 Referencias seleccionadas a las vías de señalización y metabólicas en la revista Leaders in Pharmaceutical Intelligence
3.3 Selected References to Signaling and Metabolic Pathways in Leaders in Pharmaceutical Intelligence Journal
3.4 Integrinas, cadherinas, señalización y el citoesqueleto
3.4 Integrins, Cadherins, Signaling and the Cytoskeleton
3.5 Modelos complejos de señalización: implicaciones terapéuticas
3.5 Complex Models of Signaling: Therapeutic Implications
3.6 Correlaciones funcionales de las vías de señalización
3.6 Functional Correlates of Signaling Pathways
https://pharmaceuticalintelligence.com/2014/11/04/functional-correlates-of-signaling-pathways/
Resumen de la señalización y las vías de señalización
Summary of Signaling and Signaling Pathways
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Capítulo 4: Síntesis y degradación de proteínas
Chapter 4: Protein Synthesis and Degradation
Introducción a la síntesis y la degradación de proteínas
Introduction to Protein Synthesis and Degradation
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
4.1 El papel y la importancia de los factores de transcripción
4.1 The Role and Importance of Transcription Factors
https://pharmaceuticalintelligence.com/2014/08/06/the-role-and-importance-of-transcription-factors/
4.2 El ARN y la transcripción del código genético
4.2 RNA and the Transcription of the Genetic Code
https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
4.3 13/06/2014, de 9:30 a 10:00, David Bartel “MicroARN, colas de poli(A) y regulación génica postranscripcional”
4.3 9:30 – 10:00, 6/13/2014, David Bartel “MicroRNAs. Poly(A) tails and Post-transcriptional Gene Regulation
4.4 Silenciamiento transcripcional y la proteína de longevidad Sir2
4.4 Transcriptional Silencing and Longevity Protein Sir2
4.5 Señalización del Ca2+: control transcripcional
4.5 Ca2+ Signaling: Transcriptional Control
https://pharmaceuticalintelligence.com/2013/03/06/ca2-signaling-transcriptional-control/
4.6 La red de los ARN largos no codificantes regula la transcripción de PTEN
4.6 Long Noncoding RNA Network regulates PTEN Transcription
4.7 Nucleasas de dedos de zinc (ZFN) y nucleasas efectoras similares al activador de la transcripción (TALEN)
4.7 Zinc-Finger Nucleases (ZFNs) and Transcription Activator-Like Effector Nucleases (TALENs)
https://pharmaceuticalintelligence.com/2013/03/04/talens-and-zfns/
4.8 Señalización cardíaca del Ca2+: control transcripcional
4.8 Cardiac Ca2+ Signaling: Transcriptional Control
https://pharmaceuticalintelligence.com/2013/03/02/cardiac-ca2-signaling-transcriptional-control/
4.9 El factor de transcripción Lyl-1 es crítico en la producción de progenitores tempranos de los linfocitos T
4.9 Transcription Factor Lyl-1 Critical in Producing Early T-Cell Progenitors
4.10 El lóbulo frontal del cerebro humano: redes transcripcionales específicas
4.10 Human Frontal Lobe Brain: Specific Transcriptional Networks
4.11 ADN somático, de células germinales y de secuencia completa en los linajes celulares y la enfermedad
4.11 Somatic, Germ-cell, and Whole Sequence DNA in Cell Lineage and Disease
Resumen de la transcripción, la traducción y los factores de transcripción
Summary of Transcription, Translation and Transcription Factors
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Capítulo 5: Estructura subcelular
Chapter 5: Sub-cellular Structure
Introducción a la estructura subcelular
Introduction to Subcellular Structure
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
5.1 Mitocondrias: origen en el ambiente sin oxígeno; función en la glucólisis aeróbica y la adaptación metabólica
5.1 Mitochondria: Origin from Oxygen free environment. Role in Aerobic Glycolysis and Metabolic Adaptation
5.2 Metabolismo mitocondrial y función cardíaca
5.2 Mitochondrial Metabolism and Cardiac Function
https://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
5.3 Mitocondrias: no solo el «motor de la célula»
5.3 Mitochondria: More than just the “Powerhouse of the Cell”
5.4 Fisión y fusión mitocondrial: ¿son posibles dianas terapéuticas?
5.4 Mitochondrial Fission and Fusion: Potential Therapeutic Targets?
5.5 El análisis de las mutaciones mitocondriales podría estar a un paso de distancia
5.5 Mitochondrial Mutation Analysis might be “1-step” Away
5.6 Proteínas moduladoras de la autofagia y moléculas pequeñas que son posibles dianas para el tratamiento del cáncer: comentario acerca de las estrategias bioinformáticas
5.6 Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches
Larry H Bernstein, MD, FCAP y Aviva Lev-Ari, PhD, RN
5.7 Cromatofagia, un nuevo tratamiento contra el cáncer: matar de hambre a la célula enferma hasta que se coma su propio ADN
5.7 Chromatophagy, A New Cancer Therapy: Starve The Diseased Cell Until It Eats Its Own DNA
5.8 Censo seleccionado de las proteínas moduladoras de la autofagia y las moléculas pequeñas que son posibles dianas para el tratamiento del cáncer
5.8 A Curated Census of Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy
5.9 El papel del calcio, el esqueleto de actina y las estructuras lipídicas en la señalización y la motilidad celular
5.9 Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
Larry H Bernstein, MD, FCAP y Stephan J Williams, PhD y
Resumen de la estructura celular y correlaciones anatómicas de la función metabólica
Summary of Cell Structure, Anatomic Correlates of Metabolic Function
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Capítulo 6: Proteómica
Chapter 6: Proteomics
Introducción a la proteómica
Introduction to Proteomics
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
6.1 Proteómica, metabolómica, vías de señalización y regulación celular: una recopilación de artículos de la revista
6.1 Proteomics, Metabolomics, Signaling Pathways, and Cell Regulation: a Compilation of Articles in the Journal
6.2 Una breve selección de estudios sobre proteómica, metabolómica y el metabolismo
6.2 A Brief Curation of Proteomics. Metabolomics, and Metabolism
6.3 Uso de la RNA-seq y las nucleasas dirigidas para identificar los mecanismos de resistencia a los fármacos en la leucemia mieloide aguda.
SK Rathe en Nature, 2014
6.3 Using RNA-seq and Targeted Nucleases to Identify Mechanisms of Drug Resistance in Acute Myeloid Leukemia.
SK Rathe in Nature, 2014
www.nature.com/srep/2014/140813/srep06048/full/srep06048.html
y
6.4 Proteómica: el camino hacia la comprensión y la toma de decisiones en medicina
6.4 Proteomics – The Pathway to Understanding and Decision-making in Medicine
6.5 Avances en la tecnología de separación para las «ómicas» y clarificación de dianas terapéuticas
6.5 Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
6.6 Ampliación del alfabeto genético y vinculación del genoma con el metaboloma
6.6 Expanding the Genetic Alphabet and Linking the Genome to the Metabolome
6.7 Genómica, proteómica y sus estándares
6.7 Genomics. Proteomics and Standards
https://pharmaceuticalintelligence.com/2014/07/06/genomics-proteomics-and-standards/
6.8 Las proteínas y la adaptación celular al estrés
6.8 Proteins and Cellular Adaptation to Stress
https://pharmaceuticalintelligence.com/2014/07/08/proteins-and-cellular-adaptation-to-stress/
6.9 Genes, proteomas y su interacción
6.9 Genes, Proteomes, and their Interaction
https://pharmaceuticalintelligence.com/2014/07/28/genes-proteomes-and-their-interaction/
6.10 Regulación de la función de las células madre somáticas
6.10 Regulation of Somatic Stem Cell Function
https://pharmaceuticalintelligence.com/2014/07/29/regulation-of-somatic-stem-cell-function/
Larry H Bernstein, MD, FCAP y Aviva Lev-Ari, PhD, RN
6.11 Los científicos descubren que el factor de pluripotencia NANOG también es activo en el organismo adulto
6.11 Scientists discover that Pluripotency factor NANOG is also active in Adult Organism
Resumen de la proteómica
Summary of Proteomics
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Capítulo 7: Metabolómica
Chapter 7: Metabolomics
Introducción a la metabolómica
Introduction to Metabolomics
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
7.1 Evaluación extracelular del flujo intracelular en células de levadura
7.1 Extracellular Evaluation of Intracellular Flux in Yeast Cells
7.2 Análisis metabolómico de dos líneas celulares de leucemia, parte I
7.2 Metabolomic Analysis of Two Leukemia Cell Lines Part I
7.3 Análisis metabolómico de dos líneas celulares de leucemia, parte II
7.3 Metabolomic Analysis of Two Leukemia Cell Lines Part II
7.4 La amortiguación de los módulos genéticos implicados en el metabolismo del ciclo de los ácidos tricarboxílicos proporciona regulación homeostática
7.4 Buffering of Genetic Modules involved in Tricarboxylic Acid Cycle Metabolism provides Homeostatic Regulation
7.5 Metabolómica Metabolómica y nutrición funcional: el siguiente paso en el metabolismo nutricional y la bioterapéutica
7.5 Metabolomics. Metabonomics and Functional Nutrition: The Next Step in Nutritional Metabolism and Biotherapeutics
Larry H Bernstein, MD, FCAP y Aviva Lev-Ari, PhD, RN
7.6 Isoenzimas en las vías metabólicas celulares
7.6 Isoenzymes in Cell Metabolic Pathways
https://pharmaceuticalintelligence.com/2014/10/06/isoenzymes-in-cell-metabolic-pathways/
7.7 Una breve selección de artículos sobre la proteómica, la metabolómica y el metabolismo
7.7 A Brief Curation of Proteomics, Metabolomics. and Metabolism
7.8 La metabolómica es el estudio de la integración de los sistemas metabólicos
7.8 Metabolomics is about Metabolic Systems Integration
7.9 Mecanismos de resistencia a los medicamentos
7.9 Mechanisms of Drug Resistance
https://pharmaceuticalintelligence.com/2014/10/09/mechanisms-of-drug-resistance/
7.10 Desarrollo de la microscopía de superresolución basada en la fluorescencia
7.10 Development Of Super-Resolved Fluorescence Microscopy
7.11 Las reacciones metabólicas necesitan lo justo
7.11 Metabolic Reactions Need Just Enough
https://pharmaceuticalintelligence.com/2014/10/14/metabolic-reactions-need-just-enough/
Resumen de la metabolómica
Summary of Metabolomics
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Capítulo 8: Disfunciones en los estados patológicos: Trastornos endocrinos; hipermetabolismo del estrés y cáncer
Chapter 8: Impairments in Pathological States: Endocrine Disorders; Stress Hypermetabolism and Cancer
Introducción a las disfunciones en los estados patológicos: Trastornos endocrinos; hipermetabolismo del estrés y cáncer
Introduction to Impairments in Pathological States: Endocrine Disorders; Stress Hypermetabolism and Cancer
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
8.1 Ácidos grasos omega-3. El agotamiento de la fuente y la insuficiencia proteica en la enfermedad renal
8.1 Omega3 Fatty Acids. Depleting the Source, and Protein Insufficiency in Renal Disease
8.2 Estrés del retículo endoplásmico del hígado y esteatosis hepática
8.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis
8.3 Cómo causa hiperhomocisteinemia el desequilibrio de la metionina con insuficiencia de azufre
8.3 How Methionine Imbalance with Sulfur Insufficiency Leads to Hyperhomocysteinemia
https://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/
8.4 La AMPK es un regulador negativo del efecto Warburg y suprime el crecimiento tumoral in vivo
8.4 AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth InVivo
8.5 Una segunda mirada al enigma inflamatorio de la nutrición y la transtiretina
8.5 A Second Look at the Transthyretin Nutrition Inflammatory Conundrum
8.6 Daño y reparación mitocondrial bajo estrés oxidativo
8.6 Mitochondrial Damage and Repair under Oxidative Stress
8.7 Metformina, eje hipofisario-tiroideo, diabetes de tipo 2 y metabolismo
8.7 Metformin, Thyroid Pituitary Axis, Diabetes Mellitus, and Metabolism
8.8 El efecto Warburg ¿causa o efecto del cáncer? ¿Una visión del siglo XXI?
8.8 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?
8.9 Rasgos de comportamiento social integrados en la expresión génica
8.9 Social Behavior Traits Embedded in Gene Expression
8.10 El futuro de la metabolómica plasmática en la evaluación de las enfermedades cardiovasculares
8.10 A Future for Plasma Metabolomics in Cardiovascular Disease Assessment
Capítulo 9: Expresión genómica en la salud y la enfermedad
Chapter 9: Genomic Expression in Health and Disease
Introducción
Introduction
Autor: Larry H Bernstein, MD, FCAP
Author: Larry H Bernstein, MD, FCAP
9.1 Genética de las enfermedades de la conducción: enfermedad (bloqueo) de la conducción auriculoventricular (AV). Mutaciones genéticas: transcripción, excitabilidad y homeostasis energética
9.1 Genetics of Conduction Disease: Atrioventricular (AV) Conduction Disease (block): Gene Mutations – Transcription. Excitability, and Energy Homeostasis
9.2 El BRCA1, supresor tumoral del cáncer de mama y de ovario: funciones en la transcripción, ubiquitinación y reparación del ADN
9.2 BRCA1 a Tumour Suppressor in Breast and Ovarian Cancer – Functions in Transcription, Ubiquitination and DNA Repair
9.3 Factores metabólicos en los tumores cerebrales agresivos
9.3 Metabolic Drivers in Aggressive Brain Tumors
https://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/
9.4 Una levadura modificada produce por primera vez una serie de opiáceos
9.4 Modified Yeast Produces a Range of Opiates for the First time
9.5 La cuscuta, una planta parásita, inyecta al huésped más de 9.000 transcritos de ARN
9.5 Parasitic Plant Strangleweed Injects Host With Over 9,000 RNA Transcripts
9.6 Nutrición basada en plantas, nutracéuticos y medicina alternativa: recopilación de artículos de la revista
9.6 Plant-based Nutrition, Neutraceuticals and Alternative Medicine: Article Compilation the Journal
9.7 Genes de referencia en el microbioma intestinal humano: el catálogo de BGI
9.7 Reference Genes in the Human Gut Microbiome: The BGI Catalogue
9.8 Dos mutaciones del gen PCSK9: eliminan una proteína implicada en el control del colesterol unido a LDL
9.8 Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol
9.9 C-HDL: objetivo del tratamiento – Steven E. Nissen, MD, MACC, Cleveland Clinic frente a Peter Libby, MD, BWH
9.9 HDL-C: Target of Therapy – Steven E. Nissen, MD, MACC, Cleveland Clinic vs Peter Libby, MD, BWH
Resumen del capítulo 9
Summary of Chapter 9
Autor y redactor: Larry H Bernstein, MD, FCAP
Author and Curator: Larry H Bernstein, MD, FCAP
Resumen del volumen y epílogo: resumen y perspectiva de la metabolómica
Volume Summary & Epilogue: Metabolomics Summary and Perspective
Este Resumen y Epílogo consta de OCHO partes, como sigue:
Parte 1: La metabolómica continúa con su prometedor ascenso
Parte 2: Los biólogos encuentran el “eslabón perdido” en la producción de las fábricas de proteínas en las células
Parte 3: Neurociencia
Parte 4: Investigación sobre el cáncer
Parte 5: Síndrome metabólico
Parte 6: Biomarcadores
Parte 7: Epigenética y metabolismo de los medicamentos
Parte 8: Mapas ilustrativos
This Summary & Epilogue has EIGHT parts, as follows:
Part 1: Metabolomics Continues Auspicious Climb
Part 2: Biologists Find ‘Missing Link’ in the Production of Protein Factories in Cells
Part 3: Neuroscience
Part 4: Cancer Research
Part 5: Metabolic Syndrome
Part 6: Biomarkers
Part 7: Epigenetics and Drug Metabolism
Part 8: Pictorial
Serie D: Libros electrónicos de Biomedicina. Metabolómica, inmunología, enfermedades infecciosas, genómica reproductiva y endocrinología
Volumen I
Genómica metabólica y farmacéutica
2015
Disponible en Amazon.com desde el 21/07/2015
http://www.amazon.com/dp/B012BB0ZF0
PART C:
The Editorials of the original e-Books in
C.1: English in Audio format
[15 pages]
and
C.2: English Text format
[85 pages]
NEW GENRE Volume One: Metabolic Genomics & Pharmaceutics –
Series D, Volume 1
Metabolic Genomics and Pharmaceutics
Disponible en Amazon.com desde el 21/07/2015
http://www.amazon.com/dp/B012BB0ZF0
C.1: Editorials in English in Audio format
[15 pages]
Prefacing the e-Book Epilogue: Metabolic Genomics and Pharmaceutics
Author and Curator: Larry H. Bernstein, MD, FCAP
This work has been a coming to terms with my scientific and medical end of career balancing in a difficult time after retiring, but it has been rewarding. In the clinical laboratories, radiology, anesthesiology, and in pharmacy, there has been some significant progress in support of surgical, gynecological, developmental, medical practices, and even neuroscience directed disciplines, as well as epidemiology over a period of half a century. Even then, cancer and neurological diseases have been most difficult because the scientific basic research has either not yet uncovered a framework, or because that framework has proved to be multidimensional. In the clinical laboratory sciences, there has been enormous progress in instrumental analysis, with the recent opening of molecular methods not yet prepared for routine clinical use, which will be a very great challenge to the profession, which has seen the development of large sample volume, multi-analyte, high-throughput, low-cost support emerging for decades. The capabilities now underway will also enrich the capabilities of the anatomic pathology suite and the capabilities of 3-dimensional radiological examination. In both pathology and radiology, we have seen the division of the fields into major subspecialties. The development of the electronic health record had to take lessons from the first developments in the separate developments of laboratory, radiology, and pharmacy health record systems, to which were added, full cardiology monitoring systems. These have been unintegrated. This made it difficult to bring forth a suitable patient health record because the information needed to support decision-making by practitioners was in separate “silos”. The mathematical methods that are being applied to the -OMICS sciences, can be brought to bear on the simplification and amplification of the clinicians’ ability to make decisions with near “errorless” discrimination, still allowing for an element of “art” in carrying out the history, physical examination, and knowledge unique to every patient.
We are at this time opening a very large, complex, study of biology in relationship to the human condition. This will require sufficient resources to be invested in the development of these for a better society, which I suspect, will go on beyond the life of my grandchildren. Hopefully, the long-term dangers of climate change will be controlled in that time. As a society, or as a group of interdependent societies, we have no long-term interest in continuing self-destructive behaviors that have predominated in the history of mankind. I now top off these discussions with some further elucidation of what lies before us.
Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery
Douglas B. Kell and Royston Goodacre
School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
Drug Discovery Today Feb 2014;19(2) http://dx.doi.org/10.1016/j.drudis.2013.07.014
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 behavior, 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:
- the effects of inborn errors of metabolism;
- which metabolites are exo-metabolites, and
- how metabolism varies between tissues and cellular compartments.
Even these qualitative network models are not yet complete. As our
understanding improves so do we recognize more clearly the need for a systems (poly)pharmacology.
Modeling biochemical networks – why we do so
There are at least four types of reasons as to why one would wish to model a biochemical network:
- Assessing whether the model is accurate, in the sense that it
reflects – or can be made to reflect – known experimental facts. - Establishing what changes in the model would improve the
consistency of its behavior with experimental observations
and improved predictability, such as with respect to metabolite
concentrations or fluxes. - Analyzing the model, typically by some form of sensitivity
analysis, to understand which parts of the system contribute
most to some desired functional properties of interest. - Hypothesis generation and testing, enabling one to analyze
rapidly the effects of manipulating experimental conditions in
the model without having to perform complex and costly
experiments (or to restrict the number that are performed).
In particular, it is normally considerably cheaper to perform studies of metabolic networks in silicon before trying a smaller number of possibilities experimentally; indeed for combinatorial reasons it is often the only approach possible. Although our focus here is on drug discovery, similar principles apply to the modification of biochemical networks for purposes of ‘industrial’ or ‘white’ biotechnology.
Why we choose to model metabolic networks more than
- transcriptomic or
- proteomic networks
comes from the recognition – made particularly clear by workers in the field of metabolic control analysis that, although changes in the activities of individual enzymes tend to have rather small effects on
- metabolic fluxes, they can and do have very large effects on
- metabolite concentrations (i.e. the metabolome).
Modeling biochemical networks – how we do so
Although one could seek to understand the
- time-dependent spatial distribution of signaling and metabolic substances within individual cellular compartments and
- while spatially discriminating analytical methods such as Raman spectroscopy and
mass spectrometry do exist for the analysis of drugs in situ,
- the commonest type of modeling, as in the spread of substances in
ecosystems, - assumes ‘fully mixed’ compartments and thus ‘pools’ of metabolites.
Although an approximation, this ‘bulk’ modeling will be necessary for complex ecosystems such as humans where, in addition to the need for tissue- and cell-specific models, microbial communities inhabit this super-organism and the gut serves as a source for nutrients courtesy of these symbionts.
Topology and stoichiometry of metabolic networks as major constraints on fluxes
Given their topology, which admits a wide range of parameters for delivering the same output effects and thereby reflects biological robustness, metabolic networks have two especially important constraints that assist their accurate modeling:
(i) the conservation of mass and charge, and
(ii) stoichiometric and thermodynamic constraints.
These are tighter constraints than apply to signaling networks.
New developments in modeling the human metabolic network
Since 2007, several groups have been developing improved but nonidentical models of the human metabolic network at a generalized level and in tissue-specific forms. Following a similar community-driven strategy in Saccharomyces cerevisiae, surprisingly similar to humans, and in Salmonella typhimurium. We focus in particular on a recent consensus paper that provides a highly curated and semantically annotated model of the human metabolic network, termed
- Recon2 (http://humanmetabolism.org/).
In this work, a substantial number of the major groups active in this area came together to provide a carefully and manually constructed/curated network, consisting of some 1789 enzyme-encoding genes, 7440 reactions and 2626 unique metabolites distributed over eight cellular compartments. A variety of dead-end metabolites and blocked reactions remain (essentially orphans and widows). But Recon2 was able to
- account for some 235 inborn errors of metabolism,
- a variety of metabolic ‘tasks’ (defined as a non-zero flux through a reaction or through a pathway leading to the production of a metabolite Q from a metabolite P).
- filtering based on expression profiling allowed the construction of 65 cell-type-specific models.
- Excreted or exo-metabolites are an interesting set of metabolites,
- and Recon2 could predict successfully a substantial fraction of those
Role of transporters in metabolic fluxes
The uptake and excretion of metabolites between cells and their macro-compartments
- requires specific transporters and in the order of one third of ‘metabolic’ enzymes,
- and indeed, of membrane proteins, are in fact transporters or equivalent.
What is of particular interest (to drug discovery), based on their structural similarities, is the increasing recognition (Fig. 3) that pharmaceutical drugs also
- get into and out of cells by ‘hitchhiking’ on such transporters, and not – to any significant extent –
- by passing through phospholipid bilayer portions
of cellular membranes.
This makes drug discovery even more a problem of systems biology than of biophysics.
Two views of the role of solute carriers and other transporters in cellular drug uptake.
(a) A more traditional view in which all so-called ‘passive’ drug uptake occurs through any unperturbed bilayer portion of membrane that might be present.
(b) A view in which the overwhelming fraction of drug is taken up via solute transporters or other carriers that are normally used for the uptake of intermediary metabolites.
Noting that the protein:lipid ratio of bio-membranes is typically 3:1 to 1:1 and that of proteins vary in mass and density (a typical density is 1.37 g/ml) as does their extension, for example, normal to the ca. 4.5 nm lipid bilayer region, the figure attempts to portray a section of a membrane with realistic or typical sizes and amounts of proteins and lipids. Typical protein areas when viewed normal to the membrane are 30%, membranes are rather more ‘mosaic’ than ‘fluid’ and there is some evidence that there might be no genuinely ‘free’ bulk lipids (typical phospholipid masses are 750 Da) in bio-membranes that are uninfluenced by proteins. Also shown is a typical drug: atorvastatin (LipitorW) – with a molecular mass of 558.64 Da – for size comparison purposes. If proteins are modelled as cylinders, a cylinder with a diameter of 3.6 nm and a length of 6 nm has a molecular mass of ca. 50 kDa. Note of course that in a ‘static’ picture we cannot show the dynamics of either phospholipid chains or lipid or protein diffusion.
‘Newly discovered’ metabolites and/or their roles
To illustrate the ‘unfinished’ nature even of Recon2, which concentrates on the metabolites created via enzymes encoded in the human genome, and leaving aside the more exotic metabolites of drugs and foodstuffs and the ‘secondary’ metabolites of microorganisms, there are several examples of interesting ‘new’ (i.e. more or less recently recognized) human metabolites or roles thereof that are worth highlighting, often from studies seeking biomarkers of various diseases – for caveats of biomarker discovery, which is not a topic that we are covering here, and the need for appropriate experimental design. In addition, classes of metabolites not well represented in Recon2 are oxidized molecules such as those caused by nonenzymatic reaction of metabolites with free radicals such as the hydroxyl radical generated by unliganded iron. There is also significant interest in using methods of determining small molecules such as those in the metabolome (inter alia) for assessing the ‘exposome’, in other words all the potentially polluting agents to which an individual has been exposed.
Recently discovered effects of metabolites on enzymes
Another combinatorial problem reflects the fact that in molecular enzymology it is not normally realistic to assess every possible metabolite to determine whether it is an effector (i.e.activator or inhibitor) of the enzyme under study. Typical proteins are highly promiscuous and there is increasing evidence for the comparative promiscuity of metabolites and pharmaceutical drugs. Certainly the contribution of individual small effects of multiple parameter changes can have substantial effects on the potential flux through an overall pathway, which makes ‘bottom up’ modeling an inexact science. Even merely mimicking the vivo (in Escherichia coli) concentrations of K+, Na+, Mg2+, phosphate, glutamate, sulphate and Cl significantly modulated the activities of several enzymes tested relative to the ‘usual’ assay conditions. Consequently, we need to be alive to the possibility of many (potentially major) interactions of which we are as yet ignorant. One class of example relates to the effects of the very widespread post-translational modification on metabolic enzyme activities.
A recent and important discovery (Fig. 4) is that a single transcriptome experiment, serving as a surrogate for fluxes through individual steps, provides a huge constraint on possible models, and predicts in a numerically tractable way and with much improved accuracy the fluxes to exo-metabolites without the need for such a variable ‘biomass’ term. Other recent and related strategies that exploit modern advances in ‘omics and network biology to limit the search space in constraint-based metabolic modeling.
Fig 4. Workflow for expression-profile-constrained metabolic flux estimation
- Genome-scale metabolic model with gene-protein-reaction relationships
- Map absolute gene expression levels to reactions
- Maximize correlation between absolute gene expression and metabolic flux
- Predict fluxes to exo-metabolites
- Compare predicted with experimental fluxes to exo-metabolites
Drug Discovery Today
The steps in a workflow that uses constraints based on (i) metabolic network stoichiometry and chemical reaction properties (both encoded in the model) plus, and (ii) absolute (RNA-Seq) transcript expression profiles to enable the accurate modeling of pathway and exo-metabolite fluxes.
Concluding remarks – the role of metabolomics in systems pharmacology
What is becoming increasingly clear, as we recognize that to understand living organisms in health and disease we must treat them as systems, is that we must bring together our knowledge of the topologies and kinetics of metabolic networks with our knowledge of the metabolite concentrations (i.e. metabolomes) and fluxes. Because of the huge constraints imposed on metabolism by reaction stoichiometries, mass conservation and thermodynamics, comparatively few well-chosen ‘omics measurements might be needed to do this reliably (Fig. 4). Indeed, a similar approach exploiting constraints has come to the fore in de-novo protein folding and interaction studies.
What this leads us to in drug discovery is the need to develop and exploit a ‘systems pharmacology’ where multiple binding targets are chosen purposely and simultaneously. Along with other measures such as phenotypic screening, and the integrating of the full suite of e-science approaches, one can anticipate considerable improvements in the rate of discovery of safe and effective drugs.
Metabolomics: the apogee of the “omics” trilogy
Gary J. Patti, Oscar Yanes and Gary Siuzdak
Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.
Metabolites are small molecules that are chemically transformed during metabolism and, as such, they provide a functional readout of cellular state. Unlike genes and proteins, the functions of which are subject to epigenetic regulation and post-translational modifications, respectively, metabolites serve as direct signatures of biochemical activity and are therefore easier to correlate with phenotype. In this context, metabolite profiling, or metabolomics, has become a powerful approach that has been widely adopted for clinical diagnostics.
The field of metabolomics has made remarkable progress within the past decade and has implemented new tools that have offered mechanistic insights by allowing for the correlation of biochemical changes with phenotype.
In this Innovation article, we first define and differentiate between the targeted and untargeted approaches to metabolomics. We then highlight the value of untargeted metabolomics in particular and outline a guide to performing such studies. Finally, we describe selected applications of un targeted metabolomics and discuss their potential in cell biology.
Metabolites serve as direct signatures of biochemical activity
- In some instances, it may be of interest to examine a defined set of metabolites by using a targeted approach.
- In other cases, an untargeted or global approach may be taken in which as many metabolites as possible are measured and compared between samples without bias.
- Ultimately, the number and chemical composition of metabolites to be studied is a defining attribute of any metabolomic experiment and shapes experimental design with respect to sample preparation and choice of instrumentation.
The targeted and untargeted workflow for LC/MS-based metabolomics.
- In the triple quadrupole (QqQ)-based targeted metabolomic workflow, standard compounds for the metabolites of interest are first used to set up selected reaction monitoring methods. Here, optimal instrument voltages are determined and response curves are generated for absolute quantification. After the targeted methods have been established on the basis of standard metabolites, metabolites are extracted from tissues, bio-fluids or cell cultures and analyzed. The data output provides quantification only of those metabolites for which standard methods have been built.
- In the untargeted metabolomic workflow, metabolites are first isolated from biological samples and subsequently analyzed by liquid chromatography followed by mass spectrometry (LC/MS). After data acquisition, the results are processed by using bioinformatic software such as XCMS to perform nonlinear retention time alignment and identify peaks that are changing between the groups of samples measured. The m/z value s for the peaks of interest are searched in metabolite databases to obtain putative identifications. Putative identifications are then confirmed by comparing tandem mass spectrometry (MS/MS) data and retention time data to that of standard compounds. The untargeted workflow is global in scope and outputs data related to comprehensive cellular metabolism.
Metabolic Biomarker and Kinase Drug Target Discovery in Cancer Using Stable Isotope-Based Dynamic Metabolic Profiling (SIDMAP)
László G. Boros1*, Daniel J. Brackett2 and George G. Harrigan3
1UCLA School of Medicine, Harbor-UCLA Research and Education Institute, Torrance, CA. 2Department of Surgery, University of Oklahoma Health Sciences Center & VA Medical Center, Oklahoma City, OK, Global High Throughput Screening (HTS), Pharmacia Corporation, Chesterfield, MO. Current Cancer Drug Targets, 2003, 3, 447-455.
Tumor cells respond to growth signals by the activation of protein kinases, altered gene expression and significant modifications in substrate flow and redistribution among biosynthetic pathways. This results in a proliferating phenotype with altered cellular function. These transformed cells exhibit unique anabolic characteristics, which includes increased and preferential utilization of glucose through the non-oxidative steps of the pentose cycle for nucleic acid synthesis but limited denovo fatty acid synthesis and TCA cycle glucose oxidation. This primarily nonoxidative anabolic profile reflects an undifferentiated highly proliferative aneuploid cell phenotype and serves as a reliable metabolic biomarker to determine cell proliferation rate and the level of cell transformation/differentiation in response to drug treatment. Novel drugs effective in particular cancers exert their anti-proliferative effects by inducing significant reversions of a few specific non-oxidative anabolic pathways. Here we present evidence that cell transformation of various mechanisms is sustained by a unique disproportional substrate distribution between the two branches of the pentose cycle for nucleic acid synthesis, glycolysis and the TCA cycle for fatty acid synthesis and glucose oxidation. This can be demonstrated by the broad labeling and unique specificity of [1,2-13C2] glucose to trace a large number of metabolites in the metabolome. Stable isotope-based dynamic metabolic profiles (SIDMAP) serve the drug discovery process by providing a powerful new tool that integrates the metabolome into a functional genomics approach to developing new drugs. It can be used in screening kinases and their metabolic targets, which can therefore be more efficiently characterized, speeding up and improving drug testing, approval and labeling processes by saving trial and error type study costs in drug testing.
Navigating the Human Metabolome for Biomarker Identification and Design of Pharmaceutical Molecules
Irene Kouskoumvekaki and Gianni Panagiotou
Department of Systems Biology, Center for Biological Sequence Analysis, Building 208, Technical University of Denmark, Lyngby, Denmark
Hindawi Publishing Corporation Journal of Biomedicine and Biotechnology 2011, Article ID 525497, 19 pages
http://dx.doi.org:/10.1155/2011/525497
Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics.
Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants.
Metabolites are the byproducts of metabolism, which is itself the process of converting food energy to mechanical energy or heat. Experts believe there are at least 3,000 metabolites that are essential for normal growth and development (primary metabolites) and thousands more unidentified (around 20,000, compared to an estimated 30,000 genes and 100,000 proteins) that are not essential for growth and development (secondary metabolites) but could represent prognostic, diagnostic, and surrogate markers for a disease state and a deeper understanding of mechanisms of disease.
Metabolomics, the study of metabolism at the global level, has the potential to contribute significantly to biomedical research, and ultimately to clinical medical practice. It is a close counterpart to the genome, the transcriptome and the proteome. Metabolomics, genomics, proteomics, and other “-omics” grew out of the Human Genome Project, a massive research effort that began in the mid-1990s and culminated in 2003 with a complete mapping of all the genes in the human body. When discussing the clinical advantages of metabolomics, scientists point to the “real-world” assessment of patient physiology that the metabolome provides since it can be regarded as the end-point of the “-omics” cascade. Other functional genomics technologies do not necessarily predict drug effects, toxicological response, or disease states at the phenotype but merely indicate the potential cause for phenotypical response. Metabolomics can bridge this information gap. The identification and measurement of metabolite profile dynamics of host changes provides the closest link to the various phenotypic responses. Thus it is clear that the global mapping of metabolic signatures pre- and post- drug treatment is a promising approach to identify possible functional relationships between medication and medical phenotype.
Human Metabolome Database (HMDB). Focusing on quantitative, analytic, or molecular scale information about metabolites, the enzymes and transporters associated with them, as well as disease related properties the HMDB represents the most complete bioinformatics and chemoinformatics medical information database. It contains records for thousands of endogenous metabolites identified by literature surveys (PubMed, OMIM, OMMBID, text books), data mining (KEGG, Metlin, BioCyc) or experimental analyses performed on urine, blood, and cerebrospinal fluid samples.
The annotation effort is aided by chemical parameter calculators and protein annotation tools originally developed for DrugBank.
A key feature that distinguishes the HMDB from other metabolic resources is its extensive support for higher level database searching and selecting functions. More than 175 hand-drawn-zoomable, fully hyperlinked human metabolic pathway maps can be found in HMDB and all these maps are quite specific to human metabolism and explicitly show the sub-cellular compartments where specific reactions are known to take place. As an equivalent to BLAST the HMDB contains a structure similarity search tool for chemical structures and users may sketch or paste a SMILES string of a query compound into the Chem-Query window. Submitting the query launches a structure similarity search tool that looks for common substructures from the query compound that match the HMDB’s metabolite database. The wealth of information and especially the extensive linkage to metabolic diseases to normal and abnormal metabolite concentration ranges, to mutation/SNP data and to the genes, enzymes, reactions and pathways associated with many diseases of interest makes the HMDB one the most valuable tool in the hands of clinical chemists, nutritionists, physicians and medical geneticists.
Metabolomics in Drug Discovery and Poly-pharmacology Studies
Drug molecules generally act on specific targets at the cellular level, and upon binding to the receptors, they exert a desirable alteration of the cellular activities, regarded as the pharmaceutical effect. Current drug discovery depends largely on ransom screening, either high-throughput screening (HTS) in vitro, or virtual screening (VS) in silicon. Because the number of available compounds is huge, several druglikeness filters are proposed to reduce the number of compounds that need to be evaluated. The ability to effectively predict if a chemical compound is “drug-like” or “nondruglike” is, thus, a valuable tool in the design, optimization, and selection of drug candidates for development. Druglikeness is a general descriptor of the potential of a small molecule to become a drug. It is not a unified descriptor but a global property of a compound processing many specific characteristics such as good solubility, membrane permeability, half-life, and having a pharmacophore pattern to interact specifically with a target protein. These characteristics can be reflected as molecular descriptors such as molecular weight, log P, the number of hydrogen bond donors, the number of hydrogen-bond acceptors, the number of rotatable bonds, the number of rigid bonds, the number of rings in a molecule, and so forth.
Metabolomics for the Study of Polypharmacology of Natural Compounds
Internationally, there is a growing and sustained interest from both pharmaceutical companies and public in medicine from natural sources. For the public, natural medicine represent a holistic approach to disease treatment, with potentially less side effects than conventional medicine. For the pharmaceutical companies, bioactive natural products constitute attractive drug leads, as they have been optimized in a long-term natural selection process for optimal interaction with biomolecules. To promote the ecological survival of plants, structures of secondary products have evolved to interact with molecular targets affecting the cells, tissues and physiological functions in competing microorganisms, plants, and animals. In this, respect, some plant secondary products may exert their action by resembling endogenous metabolites, ligands, hormones, signal transduction molecules, or neurotransmitters and thus have beneficial effects on humans.
Future Perspectives
Metabolomics, the study of metabolism at the global level, is moving to exciting directions.With the development of more sensitive and advanced instrumentation and computational tools for data interpretation in the physiological context, metabolomics have the potential to impact our understanding of molecular mechanisms of diseases. A state-of-theart metabolomics study requires knowledge in many areas and especially at the interface of chemistry, biology, and
computer science. High-quality samples, improvements in automated metabolite identification, complete coverage of the human metabolome, establishment of spectral databases of metabolites and associated biochemical identities, innovative experimental designs to best address a hypothesis, as well as novel computational tools to handle metabolomics data are critical hurdles that must be overcome to drive the inclusion of metabolomics in all steps of drug discovery and drug development. The examples presented above demonstrated that metabolite profiles reflect both environmental and genetic influences in patients and reveal new links between metabolites and diseases providing needed prognostic, diagnostic, and surrogate biomarkers. The integration of these signatures with other “omics” technologies is of utmost importance to characterize the entire spectrum of malignant phenotype.
Volume Summary & Epilogue
Metabolomics Summary and Perspective
Author and Curator: Larry H Bernstein, MD, FCAP
This Summary & Epilogue has EIGHT parts, as follows:
Part 1
Metabolomics Continues Auspicious Climb
Part 2
Biologists Find ‘Missing Link’ in the Production of Protein Factories in Cells
Part 3
Neuroscience
Part 4
Cancer Research
Part 5
Metabolic Syndrome
Part 6
Biomarkers
Part 7
Epigenetics and Drug Metabolism
Part 8
Pictorial
This is the final article in a robust series on metabolism, metabolomics, and the “OMICS“ – biological synthesis that is creating a more holistic and interoperable view of natural sciences, including the biological disciplines, climate science, physics, chemistry, toxicology, pharmacology, and pathophysiology with as yet unforeseen consequences.
There have been impressive advances already in the research into developmental biology, plant sciences, microbiology, mycology, and human diseases, most notably, cancer, metabolic, and infectious, as well as neurodegenerative diseases.
C.2: Editorials in English Text format
[85 pages]
A supplement for the English Audio format
The family of “omics” fields has rapidly outpaced its siblings over the decade since the completion of the Human Genome Project. It has derived much benefit from the development of Proteomics, which has recently completed a first draft of the human proteome. Since genomics, transcriptomics, and proteomics, have matured, considerably, it has become apparent that the search for a driver or drivers of cellular signaling and metabolic pathways could not depend on a full clarity of the genome. There have been unresolved issues, that are not solely comprehended from assumptions about mutations.
The most common diseases affecting mankind are derangements in metabolic pathways, develop at specific ages periods, and often in adulthood or in the geriatric period, and are at the intersection of signaling pathways. Moreover, the organs involved and systemic features are heavily influenced by physical activity, and by the air we breathe and the water we drink.
The emergence of the new science is also driven by a large body of work on protein structure, mechanisms of enzyme action, the modulation of gene expression, the pH dependent effects on protein binding and conformation.
Beyond what has just been said, a significant portion of DNA has been designated as “dark matter”. It turns out to have enormous importance in gene regulation, even though it is not transcriptional, effected in a modulatory way by “noncoding RNAs. Metabolomics is the comprehensive analysis of small molecule metabolites. These might be substrates of sequenced enzyme reactions, or they might be “inhibiting” RNAs just mentioned. In either case, they occur in the substructures of the cell called organelles, the cytoplasm, and in the cytoskeleton.
The reactions are orchestrated, and they can be modified with respect to the flow of metabolites basedon pH, temperature, membrane structural modifications, and modulators. Since most metabolites are generated byenzymatic proteins that result from gene expression, and metabolites give organisms their biochemical characteristics, the metabolome links genotype with phenotype.
Metabolomics is still developing, and the continued development has relied on two major events. The first is chromatographic separation and mass spectroscopy (MS), MS/MS, as well as advances in fluorescence ultrasensitive optical photonic methods, and the second, as crucial, is the developments in computational biology.
The continuation of this trend brings expectations of an impact on pharmaceutical and on neutraceutical developments, which will have an impact on medical practice. What has lagged behind, and may continue to contribute to the lag is the failure to develop a suitable electronic medical record to assist the physician in decisions confronted with so much as yet, hidden data, the ready availability of which could guide more effective diagnosis and management of the patient. Put all of
this together, and we can meet series challenges as the research community interprets and integrates the complex data they are acquiring.
Introduction to Metabolomics
Author: Larry H. Bernstein, MD, FCAP
This is the first volume of the Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases. It is written for comprehension at the third year medical student level, or as a reference for licensing board exams, but it is also written for the education of a first time bachalaureate degree reader in the biological sciences. Hopefully, it can be read with great interest by the undergraduate student who is undecided in the choice of a career.
In the Preface, I failed to disclose that the term Metabolomics applies to plants, animals, bacteria, and both prokaryotes and eukaryotes. The metabolome for each organism is unique, but from an evolutionary perspective has metabolic pathways in common, and expressed in concert with the environment that these living creatures exist. The metabolome of each has adaptive accommodation with suppression and activation of pathways that are functional and necessary in balance, for its existence. Was it William Faulkner who said in his Nobel Prize acceptance that mankind shall not merely exist, but survive? That seems to be the overlying theme for all of life. If life cannot persist, a surviving “remnant” might continue. The history of life may well be etched into the genetic code, some of which is not expressed.
This work is apportioned into chapters in a sequence that is first directed at the major sources for the energy and the structure of life, in the carbohydrates, lipids, and fats, which are sourced from both plants and animals, and depending on their balance, results in an equilibrium, and a disequilibrium we refer to as disease. There is also a need to consider the nonorganic essentials which are derived from the soil, from water, and from the energy of the sun and the air we breathe, or in the case of water-bound metabolomes, dissolved gases.
In addition to the basic essential nutrients and their metabolic utilization, they are under cellular metabolic regulation that is tied to signaling pathways. In addition, the genetic expression of the organism is under regulatory control by the interaction of RNAs that interact with the chromatin genetic framework, with exosomes, and with protein modulators. This is referred to as epigenetics, but there are also drivers of metabolism that are shaped by the interactions between enzymes and substrates, and are related to the tertiary structure of a protein. The framework for diseases in a separate chapter. Pharmaceutical interventions that are designed to modulate specific metabolic targets are addressed as the pathways are unfolded. Nutraceuticals and plant-based nutrition are covered in Chapter 8.
Chapter 1: Metabolic Pathways
Chapter 2. Lipid Metabolism
Chapter 3. Cell Signaling
Chapter 4. Protein Synthesis and Degradation
Chapter 5: Sub-cellular Structure
Chapter 6: Proteomics
Chapter 7: Metabolomics
Chapter 8. Impairments in Pathological States: Endocrine Disorders; Stress Hypermetabolism and Cancer
Chapter 1: Metabolic Pathways
Introduction to Metabolic Pathways
Author: Larry H. Bernstein, MD, FCAP
Humans, mammals, plants and animals, and eukaryotes and prokaryotes all share a common denominator in their manner of existence. It makes no difference whether they inhabit the land, or the sea, or another living host. They exist by virtue of their metabolic adaptation by way of taking in nutrients as fuel, and converting the nutrients to waste in the expenditure of carrying out the functions of motility, breakdown and utilization of fuel, and replication of their functional mass.
There are essentially two major sources of fuel, mainly, carbohydrate and fat. A third source, amino acids which requires protein breakdown, is utilized to a limited extent as needed from conversion of gluconeogenic amino acids for entry into the carbohydrate pathway. Amino acids follow specific metabolic pathways related to protein synthesis and cell renewal tied to genomic expression.
Carbohydrates are a major fuel utilized by way of either of two pathways. They are a source of readily available fuel that is accessible either from breakdown of disaccharides or from hepatic glycogenolysis by way of the Cori cycle. Fat derived energy is a high energy source that is metabolized by one carbon transfers using the oxidation of fatty acids in mitochondria. In the case of fats, the advantage of high energy is conferred by chain length.
Carbohydrate metabolism has either of two routes of utilization. This introduces an innovation by way of the mitochondrion or its equivalent, for the process of respiration, or aerobic metabolism through the tricarboxylic acid, or Krebs cycle. In the presence of low oxygen supply, carbohydrate is metabolized anaerobically, the six-carbon glucose being split into two three carbon intermediates, which are finally converted from pyruvate to lactate. In the presence of oxygen, the lactate is channeled back into respiration, or mitochondrial oxidation, referred to as oxidative phosphorylation. The actual mechanism of this process was of considerable debate for some years until it was resolved that the mechanism involve hydrogen transfers along the “electron transport chain” on the inner membrane of the mitochondrion, and it was tied to the formation of ATP from ADP linked to the so called “active acetate” in Acetyl-Coenzyme A, discovered by Fritz Lipmann (and Nathan O. Kaplan) at Massachusetts General Hospital. Kaplan then joined with Sidney Colowick at the McCollum Pratt Institute at Johns Hopkins, where they shared tn the seminal discovery of the “pyridine nucleotide transhydrogenases” with Elizabeth Neufeld, who later established her reputation in the mucopolysaccharidoses (MPS) with L-iduronidase and lysosomal storage disease.
This chapter covers primarily the metabolic pathways for glucose, anaerobic and by mitochondrial oxidation, the electron transport chain, fatty acid oxidation, galactose assimilation, and the hexose monophosphate shunt, essential for the generation of NADPH. The is to be more elaboration on lipids and coverage of transcription, involving amino acids and RNA in other chapters.
The subchapters are as follows:
1.1 Carbohydrate Metabolism
1.2 Studies of Respiration Lead to Acetyl CoA
1.3 Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief
1.4 The Multi-step Transfer of Phosphate Bond and Hydrogen Exchange Energy
Summary of Metabolic Pathways
Author and Curator: Larry H. Bernstein, MD, FCAP
This portion of a series of chapters on metabolism, proteomics and metabolomics dealt mainly with carbohydrate metabolism. Amino acids and lipids are presented more fully in the chapters that follow. There are features on the
- functioning of enzymes and proteins,
- on sequential changes in a chain reaction, and
- on conformational changes that we shall also cover.
These are critical to developing a more complete understanding of life processes.
I needed to lay out the scope of metabolic reactions and pathways, and their complementary changes. These may not appear to be adaptive, if the circumstances and the duration is not clear. The metabolic pathways map in total is in interaction with environmental conditions – light, heat, external nutrients and minerals, and toxins – all of which give direction and strength to these reactions. A developing goal is to discover how views introduced by molecular biology and genomics don’t clarify functional cellular dynamics that are not related to the classical view. The work is vast.
Carbohydrate metabolism denotes the various biochemical processes responsible for the formation, breakdown and interconversion of carbohydrates in living organisms. The most important carbohydrate is glucose, a simple sugar (monosaccharide) that is metabolized by nearly all known organisms. Glucose and other carbohydrates are part of a wide variety of metabolic pathways across species: plants synthesize carbohydrates from carbon dioxide and water by photosynthesis storing the absorbed energy internally, often in the form of starch or lipids. Plant components are consumed by animals and fungi, and used as fuel for cellular respiration. Oxidation of one gram of carbohydrate yields approximately 4 kcal of energy and from lipids about 9 kcal. Energy obtained from metabolism (e.g. oxidation of glucose) is usually stored temporarily within cells in the form of ATP. Organisms capable of aerobic respiration metabolize glucose and oxygen to release energy with carbon dioxide and water as byproducts.
Carbohydrates are used for short-term fuel, and even though they are simpler to metabolize than fats, they don’t produce as equivalent energy yield measured by ATP. In animals, the concentration of glucose in the blood is linked to the pancreatic endocrine hormone, insulin. . In most organisms, excess carbohydrates are regularly catabolized to form acetyl-CoA, which is a feed stock for the fatty acid synthesis pathway; fatty acids, triglycerides, and other lipids are commonly used for long-term energy storage. The hydrophobic character of lipids makes them a much more compact form of energy storage than hydrophilic carbohydrates.
Glucose is metabolized obtaining ATP and pyruvate by way of first splitting a six-carbon into two three carbon chains, which are converted to lactic acid from pyruvate in the lactic dehydrogenase reaction. The reverse conversion is by a separate unidirectional reaction back to pyruvate after moving through pyruvate dehydrogenase complex.
Pyruvate dehydrogenase complex (PDC) is a complex of three enzymes that convert pyruvate into acetyl-CoA by a process called pyruvate decarboxylation. Acetyl-CoA may then be used in the citric acid cycle to carry out cellular respiration, and this complex links the glycolysis metabolic pathway to the citric acid cycle. This multi-enzyme complex is related structurally and functionally to the oxoglutarate dehydrogenase and branched-chain oxo-acid dehydrogenase multi-enzyme complexes. In eukaryotic cells the reaction occurs inside the mitochondria, after transport of the substrate, pyruvate, from the cytosol. The transport of pyruvate into the mitochondria is via a transport protein and is active, consuming energy. On entry to the mitochondria pyruvate decarboxylation occurs, producing acetyl CoA. This irreversible reaction traps the acetyl CoA within the mitochondria. Pyruvate dehydrogenase deficiency from mutations in any of the enzymes or cofactors results in lactic acidosis.
Typically, a breakdown of one molecule of glucose by aerobic respiration (i.e. involving both glycolysis and Kreb’s cycle) is about 33-35 ATP. This is categorized as:
- Glycogenolysis – the breakdown of glycogen into glucose, which provides a glucose supply for glucose-dependent tissues.
- Glycogenolysis in liver provides circulating glucose short term.
- Glycogenolysis in muscle is obligatory for muscle contraction.
- Pyruvate from glycolysis enters the Krebs cycle, also known as the citric acid cycle, in aerobic organisms.
- Anaerobic breakdown by glycolysis – yielding 8-10 ATP
- Aerobic respiration by Kreb’s cycle – yielding 25 ATP
The pentose phosphate pathway (shunt) converts hexoses into pentoses and regenerates NADPH. NADPH is an essential antioxidant in cells which prevents oxidative damage and acts as precursor for production of many biomolecules.
Glycogenesis – the conversion of excess glucose into glycogen as a cellular storage mechanism; achieving low osmotic pressure.
Gluconeogenesis – de novo synthesis of glucose molecules from simple organic compounds. An example in humans is the conversion of a few amino acids in cellular protein to glucose.
Metabolic use of glucose is highly important as an energy source for muscle cells and in the brain, and red blood cells.
The hormone insulin is the primary glucose regulatory signal in animals. It mainly promotes glucose uptake by the cells, and it causes the liver to store excess glucose as glycogen. Its absence
- turns off glucose uptake,
- reverses electrolyte adjustments,
- begins glycogen breakdown and glucose release into the circulation by some cells,
- begins lipid release from lipid storage cells, etc.
The level of circulatory glucose (known informally as “blood sugar”) is the most important signal to the insulin-producing cells.
- insulin is made by beta cells in the pancreas,
- fat is stored n adipose tissue cells, and
- glycogen is both stored and released as needed by liver cells.
- no glucose is released to the blood from internal glycogen stores from muscle cells.
The hormone glucagon, on the other hand, opposes that of insulin, forcing the conversion of glycogen in liver cells to glucose, and then release into the blood. Growth hormone, cortisol, and certain catecholamines (such as epinephrine) have glucoregulatory actions similar to glucagon. These hormones are referred to as stress hormones because they are released under the influence of catabolic pro-inflammatory (stress) cytokines – interleukin-1 (IL1) and tumor necrosis factor α (TNFα).
|
Net Yield of GlycolysisThe preparatory phase consumes 2 ATPThe pay-off phase produces 4 ATP. The gross yield of glycolysis is therefore4 ATP – 2 ATP = 2 ATPThe pay-off phase also produces 2 molecules of NADH + H+ which can be further converted to a total of 5 molecules of ATP* by the electron transport chain (ETC) during oxidative phosphorylation.Thus the net yield during glycolysis is 7 molecules of ATP* phosphorylation. |
Cellular respiration involves 3 stages for the breakdown of glucose – glycolysis, Kreb’s cycle and the electron transport system. Kreb’s cycle produces about 60-70% of ATP for release of energy in the body. It directly or indirectly connects with all the other individual pathways in the body.
|
The Kreb’s Cycle occurs in two stages: |
- Conversion of Pyruvate to Acetyl CoA
- Acetyl CoA Enters the Kreb’s Cycle
Each pyruvate in the presence of pyruvate dehydrogenase (PDH) complex in the mitochondria gets converted to acetyl CoA which in turn enters the Kreb’s cycle. This reaction is called as oxidative decarboxylation as the carboxyl group is removed from the pyruvate molecule in the form of CO2 thus yielding 2-carbon acetyl group which along with the coenzyme A forms acetyl CoA.
The PDH requires the sequential action of five co-factors or co-enzymes for the combined action of dehydrogenation and decarboxylation to take place. These five are TPP (thiamine phosphate), FAD (flavin adenine dinucleotide), NAD (nicotinamide adenine dinucleotide), coenzyme A (denoted as CoA-SH at times to depict role of -SH group) and lipoamide.
Acetyl CoA condenses with oxaloacetate (4C) to form a citrate (6C) by transferring its acetyl group in the presence of enzyme citrate synthase. The CoA liberated in this reaction is ready to participate in the oxidative decarboxylation of another molecule of pyruvate by PDH complex.
Isocitrate undergoes oxidative decarboxylation by the enzyme isocitrate dehydrogenase to form oxalosuccinate (intermediate- not shown) which in turn forms α-ketoglutarate (also known as oxoglutarate) which is a five-carbon compound. CO2 and NADH are released in this step. α-ketoglutarate (5C) undergoes oxidative decarboxylation once again to form succinyl CoA (4C) catalysed by the enzyme α-ketoglutarate dehydrogenase complex.
Succinyl CoA is then converted to succinate-by-succinate thiokinase or succinyl coA synthetase in a reversible manner. This reaction involves an intermediate step in which the enzyme gets phosphorylated and then the phosphoryl group which has a high group transfer potential is transferred to GDP to form GTP.
Succinate then gets oxidised reversibly to fumarate by succinate dehydrogenase. The enzyme contains iron-sulfur clusters and covalently bound FAD which when undergoes electron exchange in the mitochondria causes the production of FADH2.
Fumarate is then by the enzyme fumarase converted to malate by hydration (addition of H2O) in a reversible manner.
Malate is then reversibly converted to oxaloacetate by malate dehydrogenase which is NAD linked and thus produces NADH.
The oxaloacetate produced is now ready to be utilized in the next cycle by the citrate synthase reaction and thus the equilibrium of the cycle shifts to the right.
The NADH formed in the cytosol can yield variable amounts of ATP depending on the shuttle system utilized to transport them into the mitochondrial matrix. This NADH, formed in the cytosol, is impermeable to the mitochondrial inner-membrane where oxidative phosphorylation takes place. Thus to carry this NADH to the mitochondrial matrix there are special shuttle systems in the body. The most active shuttle is the malate-aspartate shuttle via which 2.5 molecules of ATP are generated for 1 NADH molecule. This shuttle is mainly used by the heart, liver and kidneys. The brain and skeletal muscles use the other shuttle known as glycerol 3-phosphate shuttle which synthesizes 1.5 molecules of ATP for 1 NADH.
Glucose-6-phosphate Dehydrogenase is the committed step of the Pentose Phosphate Pathway. This enzyme is regulated by availability of the substrate NADP+. As NADPH is utilized in reductive synthetic pathways, the increasing concentration of NADP+ stimulates the Pentose Phosphate Pathway, to replenish NADPH. The importance of this pathway can easily be underestimated. The main source for energy in respiration was considered to be tied to the high energy phosphate bond in phosphorylation and utilizes NADPH, converting it to NADP+. The pentose phosphate shunt is essential for the generation of nucleic acids, in regeneration of red cells and lens – requiring NADPH.
NAD+ serves as electron acceptor in catabolic pathways in which metabolites are oxidized. The resultant NADH is reoxidized by the respiratory chain, producing ATP.
The pyridine nucleotide transhydrogenase reaction concerns the energy-dependent reduction of TPN by DPNH. In 1959, Klingenberg and Slenczka made the important observation that incubation of isolated liver mitochondria with DPN-specific substrates or succinate in the absence of phosphate acceptor resulted in a rapid and almost complete reduction of the intramitochondrial TPN. These and related findings led Klingenberg and co-workers (1-3) to postulate the occurrence of an ATP-controlled transhydrogenase reaction catalyzing the reduction of TPN by DPNH. (The role of transhydrogenase in the energy-linked reduction of TPN. Fritz Hommes, Ronald W. Estabrook, The Wenner-Gren Institute, University of Stockholm, Stockholm, Sweden. Biochemical and Biophysical Research Communications 11, (1), 2 Apr 1963, Pp 1–6.
http://dx.doi.org:/10.1016/0006-291X(63)90017-2/).
Further studies observed the coupling of TPN-specific dehydrogenases with the transhydrogenase and observing the reduction of large amounts of diphosphopyridine nucleotide (DPN) in the presence of catalytic amounts of triphosphopyridine nucleotide (TPN). The studies showed the direct interaction between TPNHz and DPN, in the presence of transhydrogenase to yield products having the properties of TPN and DPNHZ. The reaction involves a transfer of electrons (or hydrogen) rather than a phosphate. (Pyridine Nucleotide Transhydrogenase II. Direct Evidence for and Mechanism of the Transhydrogenase Reaction* by Nathan 0. Kaplan, Sidney P. Colowick, And Elizabeth F. Neufeld. (From The Mccollum-Pratt Institute, The Johns Hopkins University, Baltimore, Maryland) J. Biol. Chem. 1952, 195:107-119.)
http://www.JBC.org/Content/195/1/107.Citation
Notation: TPN, NADP; DPN, NAD+; reduced pyridine nucleotides: TPNH (NADPH2), DPNH (NADH).
Note: In this discussion there is a detailed presentation of the activity of lactic acid conversion in the mitochondria by way of PDH. In a later section there is mention of the bidirectional reaction of lactate dehydrogenase. However, the forward reaction is dominant (pyruvate to lactate) and is described. This is not related to the kinetics of the LD reaction with respect to the defining characteristic – Km.
Biochemical Education Jan 1977; 5(1):15. Kinetics of Lactate Dehydrogenase: A Textbook Problem.
K.L. MANCHESTER. Department of Biochemistry, University of Witwatersrand, Johannesburg South Africa.
One presupposes that determined Km values are meaningful under intracellular conditions. In relation to teaching it is a simple experiment for students to determine for themselves the Km towards pyruvate of LDH in a post-mitochondrial supernatant of rat heart and thigh muscle. The difference in Km may be a factor of 3 or 4-fold. It is pertinent then to ask what is the range of substrate concentrations over which a difference in Km may be expected to lead to significant differences in activity and how these concentrations compare with pyruvate concentrations in the cell. The evidence of Vesell and co-workers that inhibition by pyruvate is more readily seen at low than at high enzyme concentration is important in emphasizing that under intracellular conditions enzyme concentrations may be relatively large in relation to the substrate available. This will be particularly so in relation to [NADH] which in the cytoplasm is likely to be in the ~M range.
A final point concerns the kinetic parameters for LDH quoted by Bergmeyer for lactate estimations a pH of 9 is recommended and the Km towards lactate at that pH is likely to be appreciably different from the quoted values at pH 7 — Though still at pH 9 showing a substantially lower value for lactate with the heart preparation. http://onlinelibrary.wiley.com/doi/10.1016/0307-4412%2877%2990013-9/pdf
Several investigators have established that epidermis converts most of the glucose it uses to lactic acid even in the presence of oxygen. This is in contrast to most tissues where lactic acid production is used for energy production only when oxygen is not available. This large amount of lactic acid being continually produced within the epidermal cell must be excreted by the cell and then carried away by the blood stream to other tissues where the lactate can be utilized. The LDH reaction with pyruvate and NADH is reversible although at physiological pH the equilibrium position for the reaction lies very far to the right, i.e., in favor of lactate production. The speed of this reaction depends not only on the amount of enzyme present but also on the concentrations of the substances involved on both sides of the equation. The net direction in which the reaction will proceed depends solely on the relative concentrations of the substances on each side of the equation.
In vivo there is net conversion of pyruvate (formed from glucose) to lactate. Measurements of the speed of lactate production by sheets of epidermis floating on a medium containing glucose indicate a rate of lactate production of approximately 0.7 rn/sm/mm/mg of fresh epidermis. Slice incubation
experiments are presumably much closer to the actual in vivo conditions than the homogenate experiments. The discrepancy between the two indicates that in vivo conditions are far from optimal for the conversion of pyruvate to lactate. Only 1/100th of the maximal activity of the enzyme present is being achieved. The concentrations of the various substances involved are not
optimal in vivo since pyruvate and NADH concentrations are lower than lactate and NAD concentrations and this might explain the in vivo inhibition of LDH activity. (Lactate Production And Lactate Dehydrogenase In The Human Epidermis*. KM. Halprin, A Ohkawara. J Invest Dermat 1966; 47(3): 222-6.)
http://www.nature.com/jid/journal/v47/n3/pdf/jid1966133a.pdf
Chapter 2. Lipid Metabolism
Introduction to Lipid Metabolism
Author: Larry H. Bernstein, MD, FCAP
This series of articles is concerned with lipid metabolism. These discussions lay the groundwork to proceed to discussions that will take on a somewhat different approach, but they are critical to developing a more complete point of view of life processes. I have indicated that there are
- protein-protein interactions or
- protein membrane interactions
and associated regulatory features, but the focus of the discussion or the points made were different, and will be returned to. The role of lipids in circulating plasma proteins as biomarkers for coronary vascular disease can be traced to the early work of Frederickson and the classification of lipid disorders. The very critical role of lipids in membrane structure in health and disease has had much less attention, despite the enormous importance, especially in the nervous system.
This portion of the discussions of metabolism will have several topics on lipid metabolism.
The first is concerned with the basic types of lipids which are defined structurally and have different carbon chain length, and have two basic types of indispensible fatty acid derivations – along pro-inflammatory and anti-inflammatory pathways:
- Alpha-linolenic acid (ALA) and linoleic acid(LA), n-3 polyunsaturated fatty acids LCPUFAs (EPA, DHA, and AA), eicosanoids,
delta-3-desaturase, prostaglandins, and leukotrienes. - the role of the mitochondrial electron transport chain in hydrogen transfers
and oxidative phosphorylation with respect to the oxidation of fatty acids
and fatty acid synthesis. - The membrane structures of the cell, including
- the cytoskeleton, essential organelles, and the intercellular matrix, which
is a critical consideration for - cell motility, membrane conductivity, flexibility, and signaling.
- The membrane structure involves aggregation of lipids with proteins, and is associated with
- The pathophysiology of systemic circulating lipid disorders.
- The fifth is the pathophysiology of cell structures under oxidative
- Lipid disposal and storage diseases.
Summary for Lipid Metabolism
Author: Larry H. Bernstein, MD, FCAP
Lipid Classification System
The LIPID MAPS Lipid Classification System is comprised of eight lipid categories, each with its own sublassification hierarchy.
http://www.lipidmaps.org/resources/tutorials/lipid_cns.html
Each LMSD record contains an image of the
- molecular structure,
- common and systematic names,
- links to external databases,
- Wikipedia pages (where available),
- other annotations and links to structure viewing tools.
All lipids in the LIPID MAPS Structure Database (LMSD) have been classified using this system and have been assigned LIPID MAPS ID’s (LM_ID) which reflects their position in the classification hierarchy.
The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. As of May 3, 2013, LMSD contains over 37,500 unique lipid structures, making it the largest public lipid-only database in the world. Structures of lipids in the database come from several sources:
- LIPID MAPS Consortium’s core laboratories and partners;
- lipids identified by LIPID MAPS experiments;
- biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources;
- novel lipids submitted to peer-reviewed journals;
- computationally generated structures for appropriate classes.
All the lipid structures in LMSD adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet).
Number of lipids per category (as of 10/8/14)
Fatty acyls 5869
Glycerolipids 7541
Glycerophospholipids 8002
Sphingolipids 4338
Sterol lipids 2715
Prenol lipids 1259
Sacccharolipids 1293
Polyketides 6742
TOTAL 37,759 structures
References
Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Subramaniam S. LMSD: LIPID MAPS structure database Nucleic Acids Research 35: p. D527-32. PMID:17098933 [doi:10.1093/nar/gkl838] PMID: 17098933
Fahy E, Sud M, Cotter D & Subramaniam S. LIPID MAPS online tools for lipid research Nucleic Acids Research (2007) 35: p. W606-12.PMID:17584797 [doi:10.1093/nar/gkm324] PMID: 17584797
The Recognition of Essential Fatty Acids
Dietary fat has long been recognized as an important source of energy for mammals, but in the late 1920s, researchers demonstrated the dietary requirement for particular fatty acids, which came to be called essential fatty acids. It was not until the advent of intravenous feeding, however, that the importance of essential fatty acids was widely accepted: Clinical signs of essential fatty acid deficiency are generally observed only in patients on total parenteral nutrition who received mixtures devoid of essential fatty acids or in those with malabsorption syndromes.
These signs include dermatitis and changes in visual and neural function. Over the past 40 years, an increasing number of physiological functions, such as immunomodulation, have been attributed to the essential fatty acids and their metabolites, and this area of research remains quite active.1, 2
Fatty Acid Nomenclature
The fat found in foods consists largely of a heterogeneous mixture of triacylglycerols (triglycerides)–glycerol molecules that are each combined with three fatty acids. The fatty acids can be divided into two categories, based on chemical properties: saturated fatty acids, which are usually solid at room temperature, and unsaturated fatty acids, which are liquid at room temperature. The term “saturation” refers to a chemical structure in which each carbon atom in the fatty acyl chain is bound to (saturated with) four other atoms, these carbons are linked by single bonds, and no other atoms or molecules can attach; unsaturated fatty acids contain at least one pair of carbon atoms linked by a double bond, which allows the attachment of additional atoms to those carbons (resulting in saturation). Despite their differences in structure, all fats contain approximately the same amount of energy (37 kilojoules/gram, or 9 kilocalories/gram).
The class of unsaturated fatty acids can be further divided into monounsaturated and polyunsaturated fatty acids. Monounsaturated fatty acids (the primary constituents of olive and canola oils) contain only one double bond. Polyunsaturated fatty acids (PUFAs) (the primary constituents of corn, sunflower, flax seed and many other vegetable oils) contain more than one double bond. Fatty acids are often referred to using the number of carbon atoms in the acyl chain, followed by a colon, followed by the number of double bonds in the chain (e.g., 18:1 refers to the 18-carbon monounsaturated fatty acid, oleic acid; 18:3 refers to any 18-carbon PUFA with three double bonds).
PUFAs are further categorized on the basis of the location of their double bonds. An omega or n notation indicates the number of carbon atoms from the methyl end of the acyl chain to the first double bond. Thus, for example, in the omega-3 (n-3) family of PUFAs, the first double bond is 3 carbons from the methyl end of the molecule. Finally, PUFAs can be categorized according to their chain length. The 18-carbon n-3 and n-6 short-chain PUFAs are precursors to the longer 20- and 22-carbon PUFAs, called long-chain PUFAs (LCPUFAs).
Fatty Acid Metabolism
Mammalian cells can introduce double bonds into all positions on the fatty acid chain except the n-3 and n-6 position. Thus, the short-chain alpha- linolenic acid (ALA, chemical abbreviation: 18:3n-3) and linoleic acid (LA, chemical abbreviation: 18:2n-6) are essential fatty acids.
No other fatty acids found in food are considered ‘essential’ for humans, because they can all be synthesized from the short chain fatty acids.
Following ingestion, ALA and LA can be converted in the liver to the long chain, more unsaturated n-3 and n-6 LCPUFAs by a complex set of synthetic pathways that share several enzymes (Figure 1). LC PUFAs retain the original sites of desaturation (including n-3 or n-6). The omega-6 fatty acid LA is converted to gamma-linolenic acid (GLA, 18:3n-6), an omega- 6 fatty acid that is a positional isomer of ALA. GLA, in turn, can be converted to the longerchain omega-6 fatty acid, arachidonic acid (AA, 20:4n-6). AA is the precursor for certain classes of an important family of hormone- like substances called the eicosanoids (see below).
The omega-3 fatty acid ALA (18:3n-3) can be converted to the long-chain omega-3 fatty acid, eicosapentaenoic acid (EPA; 20:5n-3). EPA can be elongated to docosapentaenoic acid (DPA 22:5n-3), which is further desaturated to docosahexaenoic acid (DHA; 22:6n-3). EPA and DHA are also precursors of several classes of eicosanoids and are known to play several other critical roles, some of which are discussed further below.
The conversion from parent fatty acids into the LC PUFAs – EPA, DHA, and AA – appears to occur slowly in humans. In addition, the regulation of conversion is not well understood, although it is known that ALA and LA compete for entry into the metabolic pathways.
Physiological Functions of EPA and AA
As stated earlier, fatty acids play a variety of physiological roles. The specific biological functions of a fatty acid are determined by the number and position of double bonds and the length of the acyl chain.
Both EPA (20:5n-3) and AA (20:4n-6) are precursors for the formation of a family of hormone- like agents called eicosanoids. Eicosanoids are rudimentary hormones or regulating – molecules that appear to occur in most forms of life. However, unlike endocrine hormones, which travel in the blood stream to exert their effects at distant sites, the eicosanoids are autocrine or paracrine factors, which exert their effects locally – in the cells that synthesize them or adjacent cells. Processes affected include the movement of calcium and other substances into and out of cells, relaxation and contraction of muscles, inhibition and promotion of clotting, regulation of secretions including digestive juices and hormones, and control of fertility, cell division, and growth.3
The eicosanoid family includes subgroups of substances known as prostaglandins, leukotrienes, and thromboxanes, among others. As shown in Figure 1.1, the long-chain omega-6 fatty acid, AA (20:4n-6), is the precursor of a group of eicosanoids that include series-2 prostaglandins and series-4 leukotrienes. The omega-3 fatty acid, EPA (20:5n-3), is the precursor to a group of eicosanoids that includes series-3 prostaglandins and series-5 leukotrienes. The AA-derived series-2 prostaglandins and series-4 leukotrienes are often synthesized in response to some emergency such as injury or stress, whereas the EPA-derived series-3 prostaglandins and series-5 leukotrienes appear to modulate the effects of the series-2 prostaglandins and series-4 leukotrienes (usually on the same target cells). More specifically, the series-3 prostaglandins are formed at a slower rate and work to attenuate the effects of excessive levels of series-2 prostaglandins. Thus, adequate production of the series-3 prostaglandins seems to protect against heart attack and stroke as well as certain inflammatory diseases like arthritis, lupus, and asthma.3.
EPA (22:6 n-3) also affects lipoprotein metabolism and decreases the production of substances – including cytokines, interleukin 1ß (IL-1ß), and tumor necrosis factor a (TNF-a) – that have pro-inflammatory effects (such as stimulation of collagenase synthesis and the expression of adhesion molecules necessary for leukocyte extravasation [movement from the circulatory system into tissues]).2 The mechanism responsible for the suppression of cytokine production by omega-3 LC PUFAs remains unknown, although suppression of omega-6-derived eicosanoid production by omega-3 fatty acids may be involved, because the omega-3 and omega-6 fatty acids compete for a common enzyme in the eicosanoid synthetic pathway, delta-6 desaturase.
DPA (22:5n-3) (the elongation product of EPA) and its metabolite DHA (22:6n-3) are frequently referred to as very long chain n-3 fatty acids (VLCFA). Along with AA, DHA is the major PUFA found in the brain and is thought to be important for brain development and function. Recent research has focused on this role and the effect of supplementing infant formula with DHA (since DHA is naturally present in breast milk but not in formula).
Overview of Lipid Catabolism:
http://www.elmhurst.edu/~chm/vchembook/622overview.html
The major aspects of lipid metabolism are involved with
- Fatty Acid Oxidation to produce energy or
- the synthesis of lipids which is called Lipogenesis.
The metabolism of lipids and carbohydrates are related by the conversion of lipids from carbohydrates. This can be seen in the diagram. Notice the link through actyl-CoA, the seminal discovery of Fritz Lipmann. The metabolism of both is upset by diabetes mellitus, which results in the release of ketones (2/3 betahydroxybutyric acid) into the circulation.
The first step in lipid metabolism is the hydrolysis of the lipid in the cytoplasm to produce glycerol and fatty acids.
Since glycerol is a three-carbon alcohol, it is metabolized quite readily into an intermediate in glycolysis, dihydroxyacetone phosphate. The last reaction is readily reversible if glycerol is needed for the synthesis of a lipid.
The hydroxyacetone, obtained from glycerol is metabolized into one of two possible compounds. Dihydroxyacetone may be converted into pyruvic acid, a 3-C intermediate at the last step of glycolysis to make energy. In addition, the dihydroxyacetone may also be used in gluconeogenesis (usually dependent on conversion of gluconeogenic amino acids) to make glucose-6-phosphate for glucose to the blood or glycogen depending upon what is required at that time.
Fatty acids are oxidized to acetyl CoA in the mitochondria using the fatty acid spiral. The acetyl CoA is then ultimately converted into ATP, CO2, and H2O using the citric acid cycle and the electron transport chain.
- There are two major types of fatty acids – ω-3 and ω-6.
- There are also saturated and unsaturated with respect to the existence of double bonds, and
- monounsaturated and polyunsatured.
Polyunsaturated fatty acids (PUFAs) are important in long term health, and it will be seen that high cardiovascular risk is most associated with a low ratio of ω-3/ω-6, the denominator being from animal fat. Ω-3 fatty acids are readily available from fish, seaweed, and flax seed. More can be said of this later.
Fatty acids are synthesized from carbohydrates and occasionally from proteins. Actually, the carbohydrates and proteins have first been catabolized into acetyl CoA. Depending upon the energy requirements, the acetyl CoA enters the citric acid cycle or is used to synthesize fatty acids in a process known as LIPOGENESIS.
Energy Production Fatty Acid Oxidation:
“Visible” ATP:
In the fatty acid spiral, there is only one reaction which directly uses ATP and that is in the initiating step. So this is a loss of ATP and must be subtracted later.
A large amount of energy is released and restored as ATP during the oxidation of fatty acids. The ATP is formed from both the fatty acid spiral and the citric acid cycle.
Connections to Electron Transport and ATP:
One turn of the fatty acid spiral produces ATP from the interaction of the coenzymes FAD (step 1) and NAD+ (step 3) with the electron transport chain. Total ATP per turn of the fatty acid spiral is:
Electron Transport Diagram – (e.t.c.)
Step 1 – FAD into e.t.c. = 2 ATP
Step 3 – NAD+ into e.t.c. = 3 ATP
Total ATP per turn of spiral = 5 ATP
In order to calculate total ATP from the fatty acid spiral, you must calculate the number of turns that the spiral makes. Remember that the number of turns is found by subtracting one from the number of acetyl CoA produced. See the graphic on the left bottom.
Example with Palmitic Acid = 16 carbons = 8 acetyl groups
Number of turns of fatty acid spiral = 8-1 = 7 turns
ATP from fatty acid spiral = 7 turns and 5 per turn = 35 ATP.
This would be a good time to remember that single ATP that was needed to get the fatty acid spiral started.
Therefore, subtract it now.
NET ATP from Fatty Acid Spiral = 35 – 1 = 34 ATP
SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver
Jay D. Horton1,2, Joseph L. Goldstein1 and Michael S. Brown1
1Department of Molecular Genetics, and
2Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
J Clin Invest. 2002;109(9):1125–1131.
http://dx.doi.org:/10.1172/JCI15593
Lipid homeostasis in vertebrate cells is regulated by a family of membrane-bound transcription factors designated sterol regulatory element–binding proteins (SREBPs). SREBPs directly activate the expression of more than 30 genes dedicated to the synthesis and uptake of cholesterol, fatty acids, triglycerides, and phospholipids, as well as the NADPH cofactor required to synthesize these molecules (1–4). In the liver, three SREBPs regulate the production of lipids for export into the plasma as lipoproteins and into the bile as micelles. The complex, interdigitated roles of these three SREBPs have been dissected through the study of ten different lines of gene-manipulated mice. These studies form the subject of this review.
SREBPs: activation through proteolytic processing
SREBPs belong to the basic helix-loop-helix–leucine zipper (bHLH-Zip) family of transcription factors, but they differ from other bHLH-Zip proteins in that they are synthesized as inactive precursors bound to the endoplasmic reticulum (ER) (1, 5). Each SREBP precursor of about 1150 amino acids is organized into three domains: (a) an NH2-terminal domain of about 480 amino acids that contains the bHLH-Zip region for binding DNA; (b) two hydrophobic transmembrane–spanning segments interrupted by a short loop of about 30 amino acids that projects into the lumen of the ER; and (c) a COOH-terminal domain of about 590 amino acids that performs the essential regulatory function described below.
In order to reach the nucleus and act as a transcription factor, the NH2-terminal domain of each SREBP must be released from the membrane proteolytically (Figure1). Three proteins required for SREBP processing have been delineated in cultured cells, using the tools of somatic cell genetics (see ref. 5for review). One is an escort protein designated SREBP cleavage–activating protein (SCAP). The other two are proteases, designated Site-1 protease (S1P) and Site-2 protease (S2P). Newly synthesized SREBP is inserted into the membranes of the ER, where its COOH-terminal regulatory domain binds to the COOH-terminal domain of SCAP (Figure 1).
Adrenocorticoid Hormones
The adrenocorticoid hormones are products of the adrenal glands.
The most important mineralcorticoid is aldosterone, which regulates the reabsorption of sodium
and chloride ions in the kidney tubules and increases the loss of potassium ions.
- Aldosterone is secreted when blood sodium ion levels are too low to cause the kidney to retain
sodium ions. If sodium levels are elevated, aldosterone is not secreted, so that some sodium will be lost in the urine. Aldosterone also controls swelling in the tissues.
- Cortisol, the most important glucocortinoid, has the function of increasing
glucose and glycogen concentrations in the body. These reactions are
completed in the liver by taking fatty acids from lipid storage cells and
amino acids from body proteins to make glucose and glycogen.
In addition, cortisol is elevated in the circulation with cytokine mediated
(IL1, IL1, TNFα) inflammatory reaction, called the systemic inflammatory
response syndrome. Its ketone derivative,
- cortisone, has the ability to relieve inflammatory effects.
- Cortisone or similar synthetic derivatives such as prednisolone are used to treat
- inflammatory diseases: rheumatoid arthritis (RA) and bronchial asthma.
There are many side effects with the use of cortisone drugs, such as bone resorption, so there use
must be monitored carefully.
Hormone Receptors
Steroid hormone receptors are found on the plasma membrane, in the cytosol and also in the nucleus of target cells. They are generally intracellular receptors (typically cytoplasmic) and initiate signal transduction for steroid hormones which lead to changes in gene expression over a time period of hours to days. The best studied steroid hormone receptors are members of the nuclear receptor subfamily 3 (NR3) that include receptors for estrogen (group NR3A)[1] and 3-ketosteroids (group NR3C).[2] In addition to nuclear receptors, several G protein-coupled receptors and ion channels act as cell surface receptors for certain steroid hormones.
Steroid Hormone Receptors and their Response Elements
Steroid hormone receptors are proteins that have a binding site for a particular steroid molecule. Their response elements are DNA sequences that are bound by the complex of the steroid bound to its Steroid receptor.
The response element is part of the promoter of a gene. Binding by the receptor activates or represses, as the case may be, the gene controlled by that promoter.
It is through this mechanism that steroid hormones turn genes on (or off).
http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/S/Sigler.jpg
The glucocorticoid receptor, like all steroid hormone receptors, is a zinc-finger transcription factor; the zinc atoms are the four yellow spheres. Each is attached to four cysteines.
For a steroid hormone to regulate (turn on or off) gene transcription, its receptor must:
- bind to the hormone (cortisol in the case of the glucocorticoid receptor)
- bind to a second copy of itself to form a homodimer
- be in the nucleus, moving from the cytosol if necessary
- bind to its response element
- bind to other protein cofactors
Each of these functions depend upon a particular region of the protein (e.g., the zinc fingers for binding DNA).
Each of these functions depend upon a particular region of the protein (e.g., the zinc fingers for binding DNA). Mutations in any one region may upset the function of that region without necessarily interfering with other functions of the receptor.
Positive and Negative Response Elements
Some of the hundreds of glucocorticoid response elements in the human genome activate gene transcription when bound by the hormone/receptor complex. Others inhibit gene transcription when bound by the hormone/receptor complex.
Example: When the stress hormone cortisol — bound to its receptor — enters the nucleus of a liver cell, the complex binds to the positive response elements of the many genes needed for gluconeogenesis — the conversion of protein and fat into glucose resulting in a rise in the level of blood sugar.
the negative response element of the insulin receptor gene thus diminishing the ability of the cells to remove glucose from the blood. (This hyperglycemic effect is enhanced by the binding of the cortisol/receptor complex to a negative response element in the beta cells of the pancreas thus reducing the production of insulin.)
Note that every type of cell in the body contains the same response elements in its genome. What determines if a given cell responds to the arrival of a hormone depends on the presence of the hormone’s receptor in the cell.
The zinc-finger proteins that serve as receptors for glucocorticoids and progesterone are members of a large family of similar proteins that serve as receptors for a variety of small, hydrophobic molecules. These include:
- other steroid hormones like
- the mineralocorticoid aldosterone
- estrogens
- the thyroid hormone, T3
- calcitriol, the active form of vitamin D
- retinoids: vitamin A (retinol) and its relatives
- retinal
- retinoic acid (tretinoin — also available as the drug Retin-A®); and its isomer
- isotretinoin (sold as Accutane® for the treatment of acne).
- bile acids
- fatty acids.
These bind members of the superfamily called peroxisome-proliferator-activated receptors (PPARs). They got their name from their initial discovery as the receptors for
- drugs that increase the number and size of peroxisomes in cells.
In every case, the receptors consist of at least
- three functional modules or domains.
From N-terminal to C-terminal, these are:
- a domain needed
- the zinc-finger domain needed for DNA binding (to the response element)
- the domain responsible for binding the particular hormone as well as the second unit of the dimer.
- for the receptor to activate the promoters of the genes being controlled
Schematic diagram of type II zinc finger proteins characteristic of the DNA-binding domain structure of members of the steroid hormone receptor superfamily. Zinc fingers are common features of many transcription factors, allowing proteins to bind to DNA. Each circle represents one amino acid. The CI zinc finger interacts specifically with five base pairs of DNA and determines the DNA sequence recognized by the particular steroid receptor. The three shaded amino acids indicated by the arrows in the knuckle of the CI zinc finger are in the “P box” that allows HRE sequence discrimination between the GR and ERα. The vertically striped aa within the knuckle of the CII zinc finger constitutes the “D box” that is important for dimerization and contacts with the DNA phosphate backbone.
Cytoskeleton and Cell Membrane Physiology
http://pharmaceuticalinnovation.com/10/28/2014/larryhbern/Cytoskeleton_
and_Cell_Membrane_Physiology
Definition and Function
The cytoskeleton is a series of intercellular proteins that help a cell with
- shape,
- support,
Cytoskeleton has three main structural components:
- microfilaments,
- intermediate filaments, and
- movement
The cytoskeleton mediates movement by
- helping the cell move in its environment and
- mediating the movement of the cell’s components.
Thereby it provides an important structural framework for the cell –
- the framework for the movement of organelles, contiguous with the cell membrane, around the cytoplasm. By the activity of
- the network of protein microfilaments, intermediate filaments, and microtubules.
The structural framework supports cell function as follows:
Cell shape. For cells without cell walls, the cytoskeleton determines the shape of the cell. This is one of the functions of the intermediate filaments.
Cell movement. The dynamic collection of microfilaments and microtubles can be continually in the process of assembly and disassembly, resulting in forces that move the cell. There can also be sliding motions of these structures. Audesirk and Audesirk give examples of white blood cells “crawling” and the migration and shape changes of cells during the development of multicellular organisms.
Organelle movement. Microtubules and microfilaments can help move organelles from place to place in the cell. In endocytosis a vesicle formed engulfs a particle abutting the cell. Microfilaments then attach to the vesicle and pull it into the cell. Much of the complex synthesis and distribution function of the endoplasmic reticulum and the Golgi complex makes use of transport vescicules, associated with the cytoskeleton.
Cell division. During cell division, microtubules accomplish the movement of the chromosomes to the daughter nucleus. Also, a ring of microfilaments helps divide two developing cells by constricting the central region between the cells (fission).
References:
Hickman, et al. Ch 4 Hickman, Cleveland P., Roberts, Larry S., and Larson, Allan, Integrated Principles of Zoology, 9th. Ed., Wm C. Brown, 1995.
Audesirk & Audesirk Ch 6 Audesirk, Teresa and Audesirk, Gerald, Biology, Life on Earth, 5th Ed., Prentice-Hall, 1999.
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/bioref.html#c1
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/cytoskel.html
Chapter 3. Cell Signaling
Introduction to Signaling
Larry H. Bernstein, MD, FCAP
We have laid down a basic structure and foundation for the remaining presentations. It was essential to begin with the genome, which changed the course of teaching of biology and medicine in the 20th century, and introduced a central dogma of translation by transcription. Nevertheless, there were significant inconsistencies and unanswered questions entering the twenty first century, accompanied by vast improvements in technical advances to clarify these issues. We have covered carbohydrate, protein, and lipid metabolism, which function in concert with the development of cellular structure, organ system development, and physiology. To be sure, the progress in the study of the microscopic and particulate can’t be divorced from the observation of the whole. We were left in the not so distant past with the impression of the Sufi story of the elephant and the three blind men, who one at a time held the tail, the trunk, and the ear, each proclaiming that it was the elephant.
I introduce here a story from the Brazilian biochemist, Jose Eduardo des Salles Rosalino, on a formative experience he had with the Nobelist, Luis Leloir.
Just at the beginning, when phosphorylation of proteins is presented, I assume you must mention that some proteins are activated by phosphorylation. This is fundamental in order to present self –organization reflex upon fast regulatory mechanisms. Even from an historical point of view. The first observation arrived from a sample due to be studied on the following day of glycogen synthesase. It was unintended left overnight out of the refrigerator. The result was it has changed from active form of the previous day to a non-active form. The story could have being finished here, if the researcher did not decide to spent this day increasing substrate levels (it could be a simple case of denaturation of proteins that changes its conformation despite the same order of amino acids). He kept on trying and found restoration of maximal activity. This assay was repeated with glycogen phosphorylase and the result was the opposite – it increases its activity. This led to the discovery
- of cAMP activated protein kinase and
- the assembly of a very complex system in the glycogen granule
- that is not a simple carbohydrate polymer.
Instead, it has several proteins assembled and
- preserves the capacity to receive from a single event (rise in cAMP)
- two opposing signals with maximal efficiency,
- stops glycogen synthesis,
- as long as levels of glucose 6 phosphate are low
- and increases glycogen phosphorylation as long as AMP levels are high).
I did everything I was able to do by the end of 1970 in order to repeat the assays with PK I, PKII and PKIII of M. Rouxii and using the Sutherland route to cAMP failed in this case. I then asked Leloir to suggest to my chief (SP) the idea of AA, AB, BB subunits as was observed in lactic dehydrogenase (tetramer) indicating this as his idea. The reason was my “chief” (SP) more than once, had said to me: “Leave these great ideas for the Houssay, Leloir etc…We must do our career with small things.” However, as she also had a faulty ability for recollection she also used to arrive sometime later, with the very same idea but in that case, as her idea.
Leloir, said to me: I will not offer your interpretation to her as mine. I think it is not phosphorylation, however I think it is glycosylation that explains the changes in the isoenzymes with the same molecular weight preserved. This dialogue explains why during the reading and discussing “What is life” with him he asked me if as a biochemist in exile, talking to another biochemist, I expressed myself fully. I had considered that Schrödinger would not have confronted Darlington & Haldane because he was in U.K. in exile. This might explain why Leloir could have answered a bad telephone call from P. Boyer, Editor of The Enzymes, in a way that suggested that the pattern could be of covalent changes over a protein. Our FEBS and Eur J. Biochemistry papers on pyruvate kinase of M. Rouxii is wrongly quoted in this way on his review about pyruvate kinase of that year (1971).
Another aspect I think you must call attention to the following. Show in detail with different colors what carbons belongs to CoA, a huge molecule in comparison with the single two carbons of acetate that will produce the enormous jump in energy yield
- in comparison with anaerobic glycolysis.
The idea is
- how much must have been spent in DNA sequences to build that molecule in order to use only two atoms of carbon.
Very limited aspects of biology could be explained in this way. In case we follow an alternative way of thinking, it becomes clearer that proteins were made more stable by interaction with other molecules (great and small). Afterwards, it’s rather easy to understand how the stability of protein-RNA complexes where transmitted to RNA (vibrational + solvational reactivity stability pair of conformational energy).
Millions of years later, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.
Summary of Signaling and Signaling Pathways
Larry H Bernstein, MD, FCAP
In the introduction to this series of discussions I pointed out JEDS Rosalino’s observation about the construction of a complex molecule of acetyl coenzyme A, and the amount of genetic coding that had to go into it. Furthermore, he observes – Millions of years later, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.
In the tutorial that follows we find support for the view that mechanisms and examples from the current literature, which give insight into the developments in cell metabolism, are achieving a separation from inconsistent views introduced by the classical model of molecular biology and genomics, toward a more functional cellular dynamics that is not dependent on the classic view. The classical view fits a rigid framework that is to genomics and metabolomics as Mendelian genetics if to multidimensional, multifactorial genetics. The inherent difficulty lies in two places:
- Interactions between differently weighted determinants
- A large part of the genome is concerned with regulatory function, not expression of the code
The goal of the tutorial was to achieve an understanding of how cell signaling occurs in a cell. Completion of the tutorial would provide
- a basic understanding signal transduction and
- the role of phosphorylation in signal transduction.
We are constantly receiving and interpreting signals from our environment, which can come
- in the form of light, heat, odors, touch or sound.
The cells of our bodies are also
- constantly receiving signals from other cells.
These signals are important to
- keep cells alive and functioning as well as
- to stimulate important events such as
- cell division and differentiation.
Signals are most often chemicals that can be found
- in the extracellular fluid around cells.
These chemicals can come
- from distant locations in the body (endocrine signaling by hormones), from
- nearby cells (paracrine signaling) or can even
- be secreted by the same cell (autocrine signaling).
Signaling molecules may trigger any number of cellular responses, including
- changing the metabolism of the cell receiving the signal or
- result in a change in gene expression (transcription) within the nucleus of the cell or both.
- result in either an inhibitory or a stimulatory effect
Cell signaling can be divided into 3 stages:
Reception: A cell detects a signaling molecule from the outside of the cell.
Transduction: When the signaling molecule binds the receptor it changes the receptor protein in some way. This change initiates the process of transduction. Signal transduction is usually a pathway of several steps. Each relay molecule in the signal transduction pathway changes the next molecule in the pathway. The initiation is depicted as follows:
Signal Transduction: ligand binds to surface receptor
Membrane receptors function by binding the signal molecule (ligand) and causing the production of a second signal (also known as a second messenger) that then causes a cellular response. These types of receptors transmit information from the extracellular environment to the inside of the cell.
- by changing shape or
- by joining with another protein
- once a specific ligand binds to it.
Examples of membrane receptors include
- G Protein-Coupled Receptors and
Intracellular receptors are found inside the cell, either in the cytoplasm or in the nucleus of the target cell (the cell receiving the signal).
Note that though change in gene expression is stated, the change in gene expression does not here imply a change in the genetic information – such as – mutation. That does not have to be the case in the normal homeostatic case.
This point is the differentiating case between what JEDS Roselino has referred as
- a fast, adaptive reaction, that is the feature of protein molecules, and distinguishes this interaction from
- a one-to-one transcription of the genetic code.
The rate of transcription can be controlled, or it can be blocked. This is in large part in response to the metabolites in the immediate interstitium.
This might only be
- a change in the rate of a transcription or a suppression of expression through RNA.
- Or through a conformational change in an enzyme
Since signaling systems need to be
- responsive to small concentrations of chemical signals and act quickly,
- cells often use a multi-step pathway that transmits the signal quickly,
- while amplifying the signal to numerous molecules at each step.
Signal transduction occurs when an
- extracellular signaling molecule activates a specific receptor located on the cell surface or inside the cell.
- In turn, this receptor triggers a biochemical chain of events inside the cell, creating a response.
- Depending on the cell, the response alters the cell’s metabolism, shape, gene expression, or ability to divide.
- The signal can be amplified at any step. Thus, one signaling molecule can cause many responses.
In 1970, Martin Rodbell examined the effects of glucagon on a rat’s liver cell membrane receptor. He noted that guanosine triphosphate disassociated glucagon from this receptor and stimulated the G-protein, which strongly influenced the cell’s metabolism. Thus, he deduced that the G-protein is a transducer that accepts glucagon molecules and affects the cell. For this, he shared the 1994 Nobel Prize in Physiology or Medicine with Alfred G. Gilman.
In 2007, a total of 48,377 scientific papers—including 11,211 e-review papers—were published on the subject. The term first appeared in a paper’s title in 1979. Widespread use of the term has been traced to a 1980 review article by Rodbell: Research papers focusing on signal transduction first appeared in large numbers in the late 1980s and early 1990s.
Signal transduction involves the binding of extracellular signaling molecules and ligands to cell-surface receptors that trigger events inside the cell. The combination of messenger with receptor causes a change in the conformation of the receptor, known as receptor activation.
This activation is always the initial step (the cause) leading to the cell’s ultimate responses (effect) to the messenger. Despite the myriad of these ultimate responses, they are all directly due to changes in particular cell proteins. Intracellular signaling cascades can be started through cell-substratum interactions; examples are the integrin that binds ligands in the extracellular matrix and steroids.
Most steroid hormones have receptors within the cytoplasm and act by stimulating the binding of their receptors to the promoter region of steroid-responsive genes. Various environmental stimuli exist that initiate signal transmission processes in multicellular organisms; examples include photons hitting cells in the retina of the eye, and odorants binding to odorant receptors in the nasal epithelium. Certain microbial molecules, such as viral nucleotides and protein antigens, can elicit an immune system response against invading pathogens mediated by signal transduction processes. This may occur independent of signal transduction stimulation by other molecules, as is the case for the toll-like receptor. It may occur with help from stimulatory molecules located at the cell surface of other cells, as with T-cell receptor signaling. Receptors can be roughly divided into two major classes: intracellular receptors and extracellular receptors.
Signal transduction by a GPCR begins with an inactive G protein coupled to the receptor; it exists as a heterotrimer consisting of Gα, Gβ, and Gγ. Once the GPCR recognizes a ligand, the conformation of the receptor changes to activate the G protein, causing Gα to bind a molecule of GTP and dissociate from the other two G-protein subunits.
The dissociation exposes sites on the subunits that can interact with other molecules. The activated G protein subunits detach from the receptor and initiate signaling from many downstream effector proteins such as phospholipases and ion channels, the latter permitting the release of second messenger molecules.
Receptor tyrosine kinases (RTKs) are transmembrane proteins with an intracellular kinase domain and an extracellular domain that binds ligands; examples include growth factor receptors such as the insulin receptor.
Integrin-mediated signal transduction
An overview of integrin-mediated signal transduction, adapted from Hehlgans et al. (2007).
Integrins are produced by a wide variety of cells; they play a role in
- cell attachment to other cells and the extracellular matrix and
- in the transduction of signals from extracellular matrix components such as fibronectin and collagen.
Ligand binding to the extracellular domain of integrins
- changes the protein’s conformation,
- clustering it at the cell membrane to
- initiate signal transduction.
Integrins lack kinase activity; hence, integrin-mediated signal transduction is achieved through a variety of intracellular protein kinases and adaptor molecules, the main coordinator being integrin-linked kinase.
As shown in the picture, cooperative integrin-RTK signaling determines the
- timing of cellular survival,
- apoptosis,
- proliferation, and
ion channel
A ligand-gated ion channel, upon binding with a ligand, changes conformation
- to open a channel in the cell membrane
- through which ions relaying signals can pass.
An example of this mechanism is found in the receiving cell of a neural synapse. The influx of ions that occurs in response to the opening of these channels
- induces action potentials, such as those that travel along nerves,
- by depolarizing the membrane of post-synaptic cells,
- resulting in the opening of voltage-gated ion channels.
An example of an ion allowed into the cell during a ligand-gated ion channel opening is Ca2+;
- it acts as a second messenger
- initiating signal transduction cascades and
- altering the physiology of the responding cell.
This results in amplification of the synapse response between synaptic cells
- by remodeling the dendritic spines involved in the synapse.
In eukaryotic cells, most intracellular proteins activated by a ligand/receptor interaction possess an enzymatic activity; examples include tyrosine kinase and phosphatases. Some of them create second messengers such as cyclic AMP and IP3,
- the latter controlling the release of intracellular calcium stores into the cytoplasm.
Many adaptor proteins and enzymes activated as part of signal transduction possess specialized protein domains that bind to specific secondary messenger molecules. For example,
- calcium ions bind to the EF hand domains of calmodulin,
- allowing it to bind and activate calmodulin-dependent kinase.
PIP3 and other PhosphoInositides (PI) do the same thing to the Pleckstrin homology domains of proteins such as the kinase protein AKT.
Signals can be generated within organelles, such as chloroplasts and mitochondria, modulating the nuclear gene expression in a process called retrograde signaling.
Recently, integrative genomics approaches, in which correlation analysis has been applied on transcript and metabolite profiling data of Arabidopsis thaliana, revealed the identification of metabolites which are putatively acting as mediators of nuclear gene expression.
Related articles
- Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes (plosone.org)
- Gene Expression and Thiopurine Metabolite Profiling in Inflammatory Bowel Disease – Novel Clues to Drug Targets and Disease Mechanisms? (plosone.org)
- Activation of the Jasmonic Acid Plant Defence Pathway Alters the Composition of Rhizosphere Nutrients 2014, 6, 3245-3258; http://dx.doi.org:/10.3390/nu6083245
Omega-3 (ω-3) fatty acids are one of the two main families of long chain polyunsaturated fatty acids (PUFA). The main omega-3 fatty acids in the mammalian body are
- α-linolenic acid (ALA),
- docosahexaenoic acid (DHA) and
- eicosapentaenoic acid (EPA).
Central nervous tissues of vertebrates are characterized by a high concentration of omega-3 fatty acids. Moreover, in the human brain,
- DHA is considered as the main structural omega-3 fatty acid, which comprises about 40% of the PUFAs in total.
DHA deficiency may be the cause of many disorders such as depression, inability to concentrate, excessive mood swings, anxiety, cardiovascular disease, type 2 diabetes, dry skin and so on.
On the other hand,
- zinc is the most abundant trace metal in the human brain.
There are many scientific studies linking zinc, especially
- excess amountsof free zinc, to cellular death.
Neurodegenerative diseases, such as Alzheimer’s disease, are characterized by altered zinc metabolism. Both animal model studies and human cell culture studies have shown a possible link between
- omega-3 fatty acids, zinc transporter levels and
- free zinc availability at cellular levels.
Many other studies have also suggested a possible
- omega-3 and zinc effect on neurodegeneration and cellular death.
Therefore, in this review, we will examine
- the effect of omega-3 fatty acids on zinc transporters and
- the importance of free zinc for human neuronal cells.
Moreover, we will evaluate the collective understanding of
- mechanism(s) for the interaction of these elements in neuronal research and their
- significance for the diagnosis and treatment of neuro-degeneration.
Epidemiological studies have linked high intake of fish and shellfish as part of the daily diet to
- reduction of the incidence and/or severity of Alzheimer’s disease (AD) and senile mental decline in
Omega-3 fatty acids are one of the two main families of a broader group of fatty acids referred to as polyunsaturated fatty acids (PUFAs). The other main family of PUFAs encompasses the omega-6 fatty acids. In general, PUFAs are essential in many biochemical events, especially in early post-natal development processes such as
- cellular differentiation,
- photoreceptor membrane biogenesis and
- active synapto-genesis.
Despite the significance of these two families, mammals cannot synthesize PUFA de novo, so they must be ingested from dietary sources. Though belonging to the same family, both
- omega-3 and omega-6 fatty acids are metabolically and functionally distinct and have
- opposing physiological effects. In the human body,
- high concentrations of omega-6 fatty acids are known to increase the formation of prostaglandins and
- thereby increase inflammatory processes [10].
the reverse process can be seen with increased omega-3 fatty acids in the body.
Many other factors, such as
- thromboxane A2 (TXA2),
- leukotriene
- B4 (LTB4),
- IL-1,
- IL-6,
- tumor necrosis factor (TNF) and
- C-reactive protein,
which are implicated in various health conditions, have been shown to be increased with high omega-6 fatty acids but decreased with omega-3 fatty acids in the human body.
Dietary fatty acids have been identified as protective factors in coronary heart disease, and PUFA levels are known to play a critical role in
- immune responses,
- gene expression and
- intercellular communications.
omega-3 fatty acids are known to be vital in
- the prevention of fatal ventricular arrhythmias, and
- are also known to reduce thrombus formation propensity by decreasing platelet aggregation, blood viscosity and fibrinogen levels
Since omega-3 fatty acids are prevalent in the nervous system, it seems logical that a deficiency may result in neuronal problems, and this is indeed what has been identified and reported.
In another study conducted with individuals of 65 years of age or older (n = 6158), it was found that
- only high fish consumption, but
- not dietary omega-3 acid intake,
- had a protective effect on cognitive decline
In 2005, based on a meta-analysis of the available epidemiology and preclinical studies, clinical trials were conducted to assess the effects of omega-3 fatty acids on cognitive protection. Four of the trials completed have shown
a protective effect of omega-3 fatty acids only among those with mild cognitive impairment conditions.
A trial of subjects with mild memory complaints demonstrated
- an improvement with 900 mg of DHA.
We review key findings on
- the effect of the omega-3 fatty acid DHA on zinc transporters and the
- importance of free zinc to human neuronal cells.
DHA is the most abundant fatty acid in neural membranes, imparting appropriate
- fluidity and other properties,
and is thus considered as the most important fatty acid in neuronal studies. DHA is well conserved throughout the mammalian species despite their dietary differences. It is mainly concentrated
- in membrane phospholipids at synapses and
- in retinal photoreceptors and
- also, in the testis and sperm.
In adult rats’ brain, DHA comprises approximately
- 17% of the total fatty acid weight, and
- in the retina it is as high as 33%.
DHA is believed to have played a major role in the evolution of the modern human –
- in particular the well-developed brain.
Premature babies fed on DHA-rich formula show improvements in vocabulary and motor performance.
Analysis of human cadaver brains have shown that
- people with AD have less DHA in their frontal lobe
- and hippocampus compared with unaffected individuals
Furthermore, studies in mice have increased support for the
- protective role of omega-3 fatty acids.
Mice administrated with a dietary intake of DHA showed
- an increase in DHA levels in the hippocampus.
Errors in memory were decreased in these mice and they demonstrated
- reduced peroxide and free radical levels,
- suggesting a role in antioxidant defense.
Another study conducted with a Tg2576 mouse model of AD demonstrated that dietary
- DHA supplementation had a protective effect against reduction in
- drebrin (actin associated protein), elevated oxidation, and to some extent, apoptosis via
- decreased caspase activity.
Zinc
Zinc is a trace element, which is indispensable for life, and it is the second most abundant trace element in the body. It is known to be related to
- growth,
- development,
- differentiation,
- immune response,
- receptor activity,
- DNA synthesis,
- gene expression,
- neuro-transmission,
- enzymatic catalysis,
- hormonal storage and release,
- tissue repair,
- memory,
- the visual process
and many other cellular functions. Moreover, the indispensability of zinc to the body can be discussed in many other aspects, as
- a component of over 300 different enzymes
- an integral component of a metallo-thioneins
- a gene regulatory protein.
Approximately 3% of all proteins contain
- zinc binding motifs.
The broad biological functionality of zinc is thought to be due to its stable chemical and physical properties. Zinc is considered to have three different functions in enzymes;
- catalytic,
- co-active and
Indeed, ZINC is the only metal found in all six different subclasses of enzymes. The essential nature of zinc to the human body can be clearly displayed by studying the wide range of pathological effects of zinc deficiency. Anorexia, embryonic and post-natal growth retardation, alopecia, skin lesions, difficulties in wound healing, increased hemorrhage tendency and severe reproductive abnormalities, emotional instability, irritability and depression are just some of the detrimental effects of zinc deficiency.
Proper development and function of the central nervous system (CNS) is highly dependent on zinc levels. In the mammalian organs, zinc is mainly concentrated in the brain at around 150 μm. However, free zinc in the mammalian brain is calculated to be around 10 to 20 nm and the rest exists in either protein-, enzyme- or nucleotide- bound form. The brain and zinc relationship is thought to be mediated
- through glutamate receptors, and
- it inhibits excitatory and inhibitory receptors.
Vesicular localization of zinc in pre-synaptic terminals is a characteristic feature of brain-localized zinc, and
- its release is dependent on neural activity.
Retardation of the growth and development of CNS tissues have been linked to low zinc levels. Peripheral neuropathy, spina bifida, hydrocephalus, anencephalus, epilepsy and Pick’s disease have been linked to zinc deficiency. However, the body cannot tolerate excessive amounts of zinc.
The relationship between zinc and neurodegeneration, specifically AD, has been interpreted in several ways. One study has proposed that β-amyloid has a greater propensity to
- form insoluble amyloid in the presence of
- high physiological levels of zinc.
Insoluble amyloid is thought to
- aggregate to form plaques,
which is a main pathological feature of AD. Further studies have shown that
- chelation of zinc ions can deform and disaggregate plaques.
In AD, the most prominent injuries are found in
- hippocampal pyramidal neurons, acetylcholine-containing neurons in the basal forebrain, and in
- somatostatin-containing neurons in the forebrain.
All of these neurons are known to favor
- rapid and direct entry of zinc in high concentration
- leaving neurons frequently exposed to high dosages of zinc.
This is thought to promote neuronal cell damage through oxidative stress and mitochondrial dysfunction. Excessive levels of zinc are also capable of
- inhibiting Ca2+ and Na+ voltage gated channels
- and up-regulating the cellular levels of reactive oxygen species (ROS).
High levels of zinc are found in Alzheimer’s brains indicating a possible zinc related neuro-degeneration. A study conducted with mouse neuronal cells has shown that even a 24-h exposure to high levels of zinc (40 μm) is sufficient to degenerate cells.
If the human diet is deficient in zinc, the body
- efficiently conserves zinc at the tissue level by compensating other cellular mechanisms
to delay the dietary deficiency effects of zinc. These include reduction of cellular growth rate and zinc excretion levels, and
- redistribution of available zinc to more zinc dependent cells or organs.
A novel method of measuring metallo-thionein (MT) levels was introduced as a biomarker for the
- assessment of the zinc status of individuals and populations.
In humans, erythrocyte metallo-thionein (E-MT) levels may be considered as an indicator of zinc depletion and repletion, as E-MT levels are sensitive to dietary zinc intake. It should be noted here that MT plays an important role in zinc homeostasis by acting
- as a target for zinc ion binding and thus
- assisting in the trafficking of zinc ions through the cell,
- which may be similar to that of zinc transporters
Zinc Transporters
Deficient or excess amounts of zinc in the body can be catastrophic to the integrity of cellular biochemical and biological systems. The gastrointestinal system controls the absorption, excretion and the distribution of zinc, although the hydrophilic and high-charge molecular characteristics of zinc are not favorable for passive diffusion across the cell membranes. Zinc movement is known to occur
- via inter-membrane proteins and zinc transporter (ZnT) proteins
These transporters are mainly categorized under two metal transporter families; Zip (ZRT, IRT like proteins) and CDF/ZnT (Cation Diffusion Facilitator), also known as SLC (Solute Linked Carrier) gene families: Zip (SLC-39) and ZnT (SLC-30). More than 20 zinc transporters have been identified and characterized over the last two decades (14 Zips and 8 ZnTs).
Members of the SLC39 family have been identified as the putative facilitators of zinc influx into the cytosol, either from the extracellular environment or from intracellular compartments (Figure 1).
The identification of this transporter family was a result of gene sequencing of known Zip1 protein transporters in plants, yeast and human cells. In contrast to the SLC39 family, the SLC30 family facilitates the opposite process, namely zinc efflux from the cytosol to the extracellular environment or into luminal compartments such as secretory granules, endosomes and synaptic vesicles; thus decreasing intracellular zinc availability (Figure 1). ZnT3 is the most important in the brain where
- it is responsible for the transport of zinc into the synaptic vesicles of
- glutamatergic neurons in the hippocampus and neocortex,
Figure 1: Subcellular localization and direction of transport of the zinc transporter families, ZnT and ZIP. Arrows show the direction of zinc mobilization for the ZnT (green) and ZIP (red) proteins. A net gain in cytosolic zinc is achieved by the transportation of zinc from the extracellular region and organelles such as the endoplasmic reticulum (ER) and Golgi apparatus by the ZIP transporters. Cytosolic zinc is mobilized into early secretory compartments such as the ER and Golgi apparatus by the ZnT transporters. Figures were produced using Servier Medical Art, http://www.servier.com/. http://www.hindawi.com/journals/jnme/2012/173712.fig.001.jpg
Figure 2: Early zinc signaling (EZS) and late zinc signaling (LZS). EZS involves transcription-independent mechanisms where an extracellular stimulus directly induces an increase in zinc levels within several minutes by releasing zinc from intracellular stores (e.g., endoplasmic reticulum). LSZ is induced several hours after an external stimulus and is dependent on transcriptional changes in zinc transporter expression. Components of this figure were produced using Servier Medical Art
http://www.servier.com/ and adapted from Fukada et al. [30].
Omega-3 fatty acids in the mammalian body are
- α-linolenic acid (ALA),
- docosahexenoic acid (DHA) and
- eicosapentaenoic acid (EPA).
In general, seafood is rich in omega-3 fatty acids, more specifically DHA and EPA (Table 1). Thus far, there are nine separate epidemiological studies that suggest a possible link between
- increased fish consumption and reduced risk of AD
- and eight out of ten studies have reported a link between higher blood omega-3 levels
DHA and Zinc Homeostasis
Many studies have identified possible associations between DHA levels, zinc homeostasis, neuroprotection and neurodegeneration. Dietary DHA deficiency resulted in
- increased zinc levels in the hippocampus and
- elevated expression of the putative zinc transporter, ZnT3, in the rat brain.
Altered zinc metabolism in neuronal cells has been linked to neurodegenerative conditions such as AD. A study conducted with transgenic mice has shown a significant link between ZnT3 transporter levels and cerebral amyloid plaque pathology. When the ZnT3 transporter was silenced in transgenic mice expressing cerebral amyloid plaque pathology,
- a significant reduction in plaque load
- and the presence of insoluble amyloid were observed.
In addition to the decrease in plaque load, ZnT3 silenced mice also exhibited a significant
- reduction in free zinc availability in the hippocampus
- and cerebral cortex.
Collectively, the findings from this study are very interesting and indicate a clear connection between
- zinc availability and amyloid plaque formation,
thus, indicating a possible link to AD.
DHA supplementation has also been reported to limit the following:
- amyloid presence,
- synaptic marker loss,
- hyper-phosphorylation of Tau,
- oxidative damage and
- cognitive deficits in transgenic mouse model of AD.
In addition, studies by Stoltenberg, Flinn and colleagues report on the modulation of zinc and the effect in transgenic mouse models of AD. Given that all of these are classic pathological features of AD, and considering the limiting nature of DHA in these processes, it can be argued that DHA is a key candidate in preventing or even curing this debilitating disease.
In order to better understand the possible links and pathways of zinc and DHA with neurodegeneration, we designed a study that incorporates all three of these aspects, to study their effects at the cellular level. In this study, we were able to demonstrate a possible link between omega-3 fatty acid (DHA) concentration, zinc availability and zinc transporter expression levels in cultured human neuronal cells.
When treated with DHA over 48 h, ZnT3 levels were markedly reduced in the human neuroblastoma M17 cell line. Moreover, in the same study, we were able to propose a possible
- neuroprotective mechanism of DHA,
which we believe is exerted through
- a reduction in cellular zinc levels (through altering zinc transporter expression levels)
- that in turn inhibits apoptosis.
DHA supplemented M17 cells also showed a marked depletion of zinc uptake (up to 30%), and
- free zinc levels in the cytosol were significantly low compared to the control
This reduction in free zinc availability was specific to DHA; cells treated with EPA had no significant change in free zinc levels (unpublished data). Moreover, DHA-repleted cells had
- low levels of active caspase-3 and
- high Bcl-2 levels compared to the control treatment.
These findings are consistent with previous published data and further strengthen the possible
- correlation between zinc, DHA and neurodegeneration.
On the other hand, recent studies using ZnT3 knockout (ZnT3KO) mice have shown the importance of
- ZnT3 in memory and AD pathology.
For example, Sindreu and colleagues have used ZnT3KO mice to establish the important role of
- ZnT3 in zinc homeostasis that modulates presynaptic MAPK signaling
- required for hippocampus-dependent memory
Results from these studies indicate a possible zinc-transporter-expression-level-dependent mechanism for DHA neuroprotection.
Chapter 4. Protein Synthesis and Degradation
Introduction to Protein Synthesis and Degradation
Curator: Larry H. Bernstein, MD, FCAP
This chapter I made to follow signaling, rather than to precede it. I had already written much of the content before reorganizing the contents. The previous chapters on carbohydrate and on lipid metabolism have already provided much material on proteins and protein function, which was persuasive of the need to introduce signaling, which entails a substantial introduction to conformational changes in proteins that direct the trafficking of metabolic pathways, but more subtly uncovers an important role for microRNAs, not divorced from transcription, but involved in a non-transcriptional role. This is where the classic model of molecular biology lacked any integration with emerging metabolic concepts concerning regulation. Consequently, the science was bereft of understanding the ties between the multiple convergence of transcripts, the selective inhibition of transcriptions, and the relative balance of aerobic and anaerobic metabolism, the weight of the pentose phosphate shunt, and the utilization of available energy source for synthetic and catabolic adaptive responses.
The first subchapter serves to introduce the importance of transcription in translational science. The several subtitles that follow are intended to lay out the scope of the transcriptional activity, and also to direct attention toward the huge role of proteomics in the cell construct. As we have already seen, proteins engage with carbohydrates and with lipids in important structural and signaling processes. They are integral to the composition of the cytoskeleton, and also to the extracellular matrix. Many proteins are actually enzymes, carrying out the transformation of some substrate, a derivative of the food we ingest. They have a catalytic site, and they function with a cofactor – either a multivalent metal or a nucleotide.
The amino acids that go into protein synthesis include “indispensable” nutrients that are not made for use, but must be derived from animal protein, although the need is partially satisfied by plant sources. The essential amino acids are classified into well-established groups. There are 20 amino acids commonly found in proteins. They are classified into the following groups based on the chemical and/or structural properties of their side chains:
- Aliphatic Amino Acids
- Cyclic Amino Acid
- AAs with Hydroxyl or Sulfur-containing side chains
- Aromatic Amino Acids
- Basic Amino Acids
- Acidic Amino Acids and their Amides
Examples of Amino Acids include:
Alanine aliphatic hydrophobic neutral
Arginine polar hydrophilic charged (+)
Cysteine polar hydrophobic neutral
Glutamine polar hydrophilic neutral
Histidine aromatic polar hydrophilic charged (+)
Lysine polar hydrophilic charged (+)
Methionine hydrophobic neutral
Serine polar hydrophilic neutral
Tyrosine aromatic polar hydrophobic
Transcribe and Translate a Gene
- For each RNA base there is a corresponding DNA base
- Cells use the two-step process of transcription and translation to read each gene and produce the string of amino acids that makes up a protein.
- mRNA is produced in the nucleus, and is transferred to the ribosome
- mRNA uses uracil instead of thymine
- the ribosome reads the RNA sequence and makes protein
- There is a sequence combination to fit each amino acid to a three letter RNA code
- The ribosome starts at AUG (start), and it reads each codon three letters at a time
- Stop codons are UAA, UAG and UGA
What about the purine inosine?
Inosine triphosphate pyrophosphatase – Pyrophosphatase that hydrolyzes the non-canonical purine nucleotides inosine triphosphate (ITP), deoxyinosine triphosphate (dITP) as well as 2′-deoxy-N-6-hydroxylaminopurine triposphate (dHAPTP) and xanthosine 5′-triphosphate (XTP) to their respective monophosphate derivatives. The enzyme does not distinguish between the deoxy- and ribose forms. Probably excludes non-canonical purines from RNA and DNA precursor pools, thus preventing their incorporation into RNA and DNA and avoiding chromosomal lesions.
Gastroenterology. 2011 Apr;140(4):1314-21. http://dx.doi.org:/10.1053/j.gastro.2010.12.038. Epub 2011 Jan 1.
Inosine triphosphate protects against ribavirin-induced adenosine triphosphate loss by adenylosuccinate synthase function.
Hitomi Y1, Cirulli ET, Fellay J, McHutchison JG, Thompson AJ, Gumbs CE, Shianna KV, Urban TJ, Goldstein DB.
Genetic variation of inosine triphosphatase (ITPA) causing an accumulation of inosine triphosphate (ITP) has been shown to protect patients against ribavirin (RBV)-induced anemia during treatment for chronic hepatitis C infection by genome-wide association study (GWAS). However, the biologic mechanism by which this occurs is unknown.
Although ITP is not used directly by human erythrocyte ATPase, it can be used for ATP biosynthesis via ADSS in place of guanosine triphosphate (GTP). With RBV challenge, erythrocyte ATP reduction was more severe in the wild-type ITPA genotype than in the hemolysis protective ITPA genotype. This difference also remains after inhibiting adenosine uptake using nitrobenzyl mercaptopurine riboside (NBMPR).
ITP confers protection against RBV-induced ATP reduction by substituting for erythrocyte GTP, which is depleted by RBV, in the biosynthesis of ATP. Because patients with excess ITP appear largely protected against anemia, these results confirm that RBV-induced anemia is due primarily to the effect of the drug on GTP and consequently ATP levels in erythrocytes.
Ther Drug Monit. 2012 Aug;34(4):477-80. http://dx.doi.org:/10.1097/FTD.0b013e31825c2703.
Determination of inosine triphosphate pyrophosphatase phenotype in human red blood cells using HPLC.
Citterio-Quentin A1, Salvi JP, Boulieu R.
Thiopurine drugs, widely used in cancer chemotherapy, inflammatory bowel disease, and autoimmune hepatitis, are responsible for common adverse events. Only some of these may be explained by genetic polymorphism of thiopurine S-methyltransferase. Recent articles have reported that inosine triphosphate pyrophosphatase (ITPase) deficiency was associated with adverse drug reactions toward thiopurine drug therapy. Here, we report a weak anion exchange high-performance liquid chromatography method to determine ITPase activity in red blood cells and to investigate the relationship with the occurrence of adverse events during azathioprine therapy.
The chromatographic method reported allows the analysis of IMP, inosine diphosphate, and ITP in a single run in <12.5 minutes. The method was linear in the range 5-1500 μmole/L of IMP. Intraassay and interassay precisions were <5% for red blood cell lysates supplemented with 50, 500, and 1000 μmole/L IMP. Km and Vmax evaluated by Lineweaver-Burk plot were 677.4 μmole/L and 19.6 μmole·L·min, respectively. The frequency distribution of ITPase from 73 patients was investigated.
The method described is useful to determine the ITPase phenotype from patients on thiopurine therapy and to investigate the potential relation between ITPase deficiency and the occurrence of adverse events.
System wide analyses have underestimated protein abundances and the importance of transcription in mammals
Jingyi Jessica Li1, 2, Peter J Bickel1 and Mark D Biggin3
PeerJ 2: e270; http://dx.doi.org:/10.7717/peerj.270
Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R2 D 0.13). Based on this, our second strategy suggests that mRNA levels explain 81% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimat that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the ab-sence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the 40% of genes that are non-expressed.
Related studies that reveal issues that are not part of this chapter:
- Ubiquitylation in relationship to tissue remodeling
- Post-translational modification of proteins
- Glycosylation
- Phosphorylation
- Methylation
- Nitrosylation
- Sulfation – sulfotransferases
cell-matrix communication - Acetylation and histone deacetylation (HDAC)
Connecting Protein Phosphatase to 1α (PP1α)
Acetylation complexes (such as CBP/p300 and PCAF)
Sirtuins
Rel/NF-kB Signal Transduction
Homologous Recombination Pathway of Double-Strand DNA Repair - Glycination
- cyclin dependent kinases (CDKs)
- lyase
- transferase
This year, 2014, the Lasker award for basic medical research went to Kazutoshi Mori (Kyoto University) and Peter Walter (University of California, San Francisco) for their “discoveries concerning the unfolded protein response (UPR) — an intracellular quality control system that
- detects harmful misfolded proteins in the endoplasmic reticulum and signals the nucleus to carry out corrective measures.”
About UPR: Approximately a third of cellular proteins pass through the Endoplasmic Reticulum (ER) which performs stringent quality control of these proteins. All proteins need to assume the proper 3-dimensional shape in order to function properly in the harsh cellular environment. Related to this is the fact that cells are under constant stress and have to make rapid, real time decisions about survival or death.
A major indicator of stress is the accumulation of unfolded proteins within the Endoplasmic Reticulum (ER), which triggers a transcriptional cascade in order to increase the folding capacity of the ER. If the metabolic burden is too great and homeostasis cannot be achieved, the response shifts from
- damage control to the induction of pro-apoptotic pathways that would ultimately cause cell death.
This response to unfolded proteins or the UPR is conserved among all eukaryotes, and dysfunction in this pathway underlies many human diseases, including Alzheimer’s, Parkinson’s, Diabetes and Cancer.
The discovery of a new class of human proteins with previously unidentified activities
In a landmark study conducted by scientists at the Scripps Research Institute, The Hong Kong University of Science and Technology, aTyr Pharma and their collaborators, a new class of human proteins has been discovered. These proteins [nearly 250], called Physiocrines belong to the aminoacyl tRNA synthesase gene family and carry out novel, diverse and distinct biological functions.
The aminoacyl tRNA synthesase gene family codes for a group of 20 ubiquitous enzymes almost all of which are part of the protein synthesis machinery. Using recombinant protein purification, deep sequencing technique, mass spectroscopy and cell-based assays, the team made this discovery. The finding is significant, also because it highlights the alternate use of a gene family whose protein product normally performs catalytic activities for non-catalytic regulation of basic and complex physiological processes spanning metabolism, vascularization, stem cell biology and immunology
Muscle maintenance and regeneration – key player identified
Muscle tissue suffers from atrophy with age and its regenerative capacity also declines over time. Most molecules discovered thus far to boost tissue regeneration are also implicated in cancers. During a quest to find safer alternatives that can regenerate tissue, scientists reported that the hormone Oxytocin is required for proper muscle tissue regeneration and homeostasis and that its levels decline with age.
Oxytocin could be an alternative to hormone replacement therapy as a way to combat aging and other organ related degeneration.
Oxytocin is an age-specific circulating hormone that is necessary for muscle maintenance and regeneration (June 2014)
Proc Natl Acad Sci U S A. 2014 Sep 30;111(39):14289-94. http://dx.doi.org:/10.1073/pnas.1407640111. Epub 2014 Sep 15.
Role of forkhead box protein A3 in age-associated metabolic decline.
Ma X1, Xu L1, Gavrilova O2, Mueller E3.
Aging is associated with increased adiposity and diminished thermogenesis, but the critical transcription factors influencing these metabolic changes late in life are poorly understood. We recently demonstrated that the winged helix factor forkhead box protein A3 (Foxa3) regulates the expansion of visceral adipose tissue in high-fat diet regimens; however, whether Foxa3 also contributes to the increase in adiposity and the decrease in brown fat activity observed during the normal aging process is currently unknown. Here we report that during aging, levels of Foxa3 are significantly and selectively up-regulated in brown and inguinal white fat depots, and that midage Foxa3-null mice have increased white fat browning and thermogenic capacity, decreased adipose tissue expansion, improved insulin sensitivity, and increased longevity. Foxa3 gain-of-function and loss-of-function studies in inguinal adipose depots demonstrated a cell-autonomous function for Foxa3 in white fat tissue browning. Furthermore, our analysis revealed that the mechanisms of Foxa3 modulation of brown fat gene programs involve the suppression of peroxisome proliferator activated receptor γ coactivtor 1 α (PGC1α) levels through interference with cAMP responsive element binding protein 1-mediated transcriptional regulation of the PGC1α promoter.
Asymmetric mRNA localization contributes to fidelity and sensitivity of spatially localized systems
RJ Weatheritt, TJ Gibson & MM Babu
Nature Structural & Molecular Biology 24 Aug, 2014; 21: 833–839http://dx.do.orgi:/10.1038/nsmb.2876
Although many proteins are localized after translation, asymmetric protein distribution is also achieved by translation after mRNA localization. Why are certain mRNA transported to a distal location and translated on-site? Here we undertake a systematic, genome-scale study of asymmetrically distributed protein and mRNA in mammalian cells. Our findings suggest that asymmetric protein distribution by mRNA localization enhances interaction fidelity and signaling sensitivity. Proteins synthesized at distal locations frequently contain intrinsically disordered segments. These regions are generally rich in assembly-promoting modules and are often regulated by post-translational modifications. Such proteins are tightly regulated but display distinct temporal dynamics upon stimulation with growth factors. Thus, proteins synthesized on-site may rapidly alter proteome composition and act as dynamically regulated scaffolds to promote the formation of reversible cellular assemblies. Our observations are consistent across multiple mammalian species, cell types and developmental stages, suggesting that localized translation is a recurring feature of cell signaling and regulation.
An overview of the potential advantages conferred by distal-site protein synthesis, inferred from our analysis.
Tweaking transcriptional programming for high quality recombinant protein production
Since over-expression of recombinant proteins in E. coli often leads to the formation of inclusion bodies, producing properly folded, soluble proteins is undoubtedly the most important end goal in a protein expression campaign. Various approaches have been devised to bypass the insolubility issues during E. coli expression and in a recent report a group of researchers discuss reprogramming the E. coli proteostasis [protein homeostasis] network to achieve high yields of soluble, functional protein. The premise of their studies is that the basal E. coli proteostasis network is insufficient, and often unable, to fold overexpressed proteins, thus clogging the folding machinery.
By overexpressing a mutant, negative-feedback deficient heat shock transcription factor [σ32 I54N] before and during overexpression of the protein of interest, reprogramming can be achieved, resulting in high yields of soluble and functional recombinant target protein. The authors explain that this method is better than simply co-expressing/over-expressing chaperones, co-chaperones, foldases or other components of the proteostasis network because reprogramming readies the folding machinery and up regulates the essential folding components beforehand thus maintaining system capability of the folding machinery.
The Heat-Shock Response Transcriptional Program Enables High-Yield and High-Quality Recombinant Protein Production in Escherichia coli (July 2014)
Unfolded proteins collapse when exposed to heat and crowded environments
Proteins are important molecules in our body and they fullfil a broad range of functions. For instance as enzymes they help to release energy from food and as muscle proteins they assist with motion. As antibodies they are involved in immune defense and as hormone receptors in signal transduction in cells. Until only recently it was assumed that all proteins take on a clearly defined three-dimensional structure – i.e. they fold in order to be able to assume these functions. Surprisingly, it has been shown that many important proteins occur as unfolded coils. Researchers seek to establish how these disordered proteins are capable at all of assuming highly complex functions.
Ben Schuler’s research group from the Institute of Biochemistry of the University of Zurich has now established that an increase in temperature leads to folded proteins collapsing and becoming smaller. Other environmental factors can trigger the same effect.
Measurements using the “molecular ruler”
“The fact that unfolded proteins shrink at higher temperatures is an indication that cell water does indeed play an important role as to the spatial organization eventually adopted by the molecules”, comments Schuler with regard to the impact of temperature on protein structure. For their studies the biophysicists use what is known as single-molecule spectroscopy. Small colour probes in the protein enable the observation of changes with an accuracy of more than one millionth of a millimetre. With this “molecular yardstick” it is possible to measure how molecular forces impact protein structure.
With computer simulations the researchers have mimicked the behavior of disordered proteins.
(Courtesy of Jose EDS Roselino, PhD.)
MLKL compromises plasma membrane integrity
Necroptosis is implicated in many diseases and understanding this process is essential in the search for new therapies. While mixed lineage kinase domain-like (MLKL) protein has been known to be a critical component of necroptosis induction, how MLKL transduces the death signal was not clear. In a recent finding, scientists demonstrated that the full four-helical bundle domain (4HBD) in the N-terminal region of MLKL is required and sufficient to induce its oligomerization and trigger cell death.
They also found a patch of positively charged amino acids on the surface of the 4HBD that bound to phosphatidylinositol phosphates (PIPs) and allowed the recruitment of MLKL to the plasma membrane that resulted in the formation of pores consisting of MLKL proteins, due to which cells absorbed excess water causing them to explode. Detailed knowledge about how MLKL proteins create pores offers possibilities for the development of new therapeutic interventions for tolerating or preventing cell death.
MLKL compromises plasma membrane integrity by binding to phosphatidylinositol phosphates (May 2014)
Mitochondrial and ER proteins implicated in dementia
Mitochondria and the endoplasmic reticulum (ER) form tight structural associations that facilitate a number of cellular functions. However, the molecular mechanisms of these interactions aren’t properly understood.
A group of researchers showed that the ER protein VAPB interacted with mitochondrial protein PTPIP51 to regulate ER-mitochondria associations and that TDP-43, a protein implicated in dementia, disturbs this interaction to regulate cellular Ca2+ homeostasis. These studies point to a new pathogenic mechanism for TDP-43 and may also provide a potential new target for the development of new treatments for devastating neurological conditions like dementia.
ER-mitochondria associations are regulated by the VAPB-PTPIP51 interaction and are disrupted by ALS/FTD-associated TDP-43. Nature (June 2014)
A novel strategy to improve membrane protein expression in Yeast
Membrane proteins play indispensable roles in the physiology of an organism. However, recombinant production of membrane proteins is one of the biggest hurdles facing protein biochemists today. A group of scientists in Belgium showed that,
- by increasing the intracellular membrane production by interfering with a key enzymatic step of lipid synthesis,
- enhanced expression of recombinant membrane proteins in yeast is achieved.
Specifically, they engineered the oleotrophic yeast, Yarrowia lipolytica, by
- deleting the phosphatidic acid phosphatase, PAH1 gene,
- which led to massive proliferation of endoplasmic reticulum (ER) membranes.
For all 8 tested representatives of different integral membrane protein families, they obtained enhanced protein accumulation.
An unconventional method to boost recombinant protein levels
MazF is an mRNA interferase enzyme in E.coli that functions as and degrades cellular mRNA in a targeted fashion, at the “ACA” sequence. This degradation of cellular mRNA causes a precipitous drop in cellular protein synthesis. A group of scientists at the Robert Wood Johnson Medical School in New Jersey, exploited the degeneracy of the genetic code to modify all “ACA” triplets within their gene of interest in a way that the corresponding amino acid (Threonine) remained unchanged. Consequently, induction of MazF toxin caused degradation of E.coli cellular mRNA but the recombinant gene transcription and protein synthesis continued, causing significant accumulation of high quality target protein. This expression system enables unparalleled signal to noise ratios that could dramatically simplify structural and functional studies of difficult-to-purify, biologically important proteins.
Tandem fusions and bacterial strain evolution for enhanced functional membrane protein production
Membrane protein production remains a significant challenge in its characterization and structure determination. Despite the fact that there are a variety of host cell types, E.coli remains the popular choice for producing recombinant membrane proteins. A group of scientists in Netherlands devised a robust strategy to increase the probability of functional membrane protein over-expression in E.coli.
By fusing Green Fluorescent Protein (GFP) and the Erythromycin Resistance protein (ErmC) to the C-terminus of a target membrane protein they were able to track the folding state of their target protein while using Erythromycin to select for increased expression. By increasing erythromycin concentration in the growth media and testing different membrane targets, they were able to identify four evolved E.coli strains, all of which carried a mutation in the hns gene, whose product is implicated in genome organization and transcriptional silencing. Through their experiments the group showed that partial removal of the transcriptional silencing mechanism was related to production of proteins that were essential for functional over-expression of membrane proteins.
The role of an anti-apoptotic factor in recombinant protein production
In a recent study, scientists at the Johns Hopkins University and Frederick National Laboratory for Cancer Research examined an alternative method of utilizing the benefits of anti-apoptotic gene expression to enhance the transient expression of biotherapeutics, specifically, through the co-transfection of Bcl-xL along with the product-coding target gene.
Chinese Hamster Ovary(CHO) cells were co-transfected with the product-coding gene and a vector containing Bcl-xL, using Polyethylenimine (PEI) reagent. They found that the cells co-transfected with Bcl-xL demonstrated reduced apoptosis, increased specific productivity, and an overall increase in product yield.
B-cell lymphoma-extra-large (Bcl-xL) is a mitochondrial transmembrane protein and a member of the Bcl-2 family of proteins which are known to act as either pro- or anti-apoptotic proteins. Bcl-xL itself acts as an anti-apoptotic molecule by preventing the release of mitochondrial contents such as cytochrome c, which would lead to caspase activation. Higher levels of Bcl-xL push a cell toward survival mode by making the membranes pores less permeable and leaky.
Summary of Transcription, Translation and Transcription Factors
Author and Curator: Larry H. Bernstein, MD, FCAP
Proteins are integral to the composition of the cytoskeleton, and also to the extracellular matrix. Many proteins are actually enzymes, carrying out the transformation of some substrate, a derivative of the food we ingest. They have a catalytic site, and they function with a cofactor – either a multivalent metal or a nucleotide. Proteins also are critically involved in the regulation of cell metabolism, and they are involved in translation of the DNA code, as they make up transcription factors (TFs). There are 20 essential amino acids that go into protein synthesis that are derived from animal or plant protein. Protein synthesis is carried out by the transport of mRNA out of the nucleus to the ribosome, where tRNA is paired with a matching amino acid, and the primary sequence of a protein is constructed as a linear string of amino acids.
In the transcription process an RNA sequence is read. This is essential for protein synthesis through the ordering of the amino acids in the primary structure. However, there are microRNAs and noncoding RNAs, and there are transcription factors. The transcription factors bind to chromatin, and the RNAs also have some role in regulating the transcription process. (see picture above)
Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites.
Four different techniques are currently used to measure their kinetics in live cells,
- fluorescence recovery after photobleaching (FRAP),
- fluorescence correlation spectroscopy (FCS),
- single molecule tracking (SMT) and
- competition ChIP (CC).
A comparison of data from each of these techniques raises an important question:
- do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or
- do they reflect non-specific interactions (i.e. playing TFs)?
There are five key unresolved biological questions related to
- the functionality of transient and prolonged binding events at both
- specific promoter response elements as well as non-specific sites.
In support of functionality,
- there are data suggesting that TF residence times are tightly regulated, and
- that this regulation modulates transcriptional output at single genes.
In addition to this site-specific regulatory role, TF residence times
- also determine the fraction of promoter targets occupied within a cell
- thereby impacting the functional status of cellular gene networks.
- TF residence times, then, are key parameters that could influence transcription in multiple ways.
Quantifying transcription factor kinetics: At work or at play?
Mueller F., et al. http://dx.doi.org:/10.3109/10409238.2013.833891
Dr. Virginie Mattot works in the team “Angiogenesis, endothelium activation and Cancer” directed by Dr. Fabrice Soncin at the Institut de Biologie de Lille in France where she studies the roles played by microRNAs in endothelial cells during physiological and pathological processes such as angiogenesis or endothelium activation. She has been using Target Site Blockers to investigate the role of microRNAs on putative targets.
A few years ago, the team identified
- an endothelial cell-specific gene which
- harbors a microRNA in its intronic sequence.
They have since been working on understanding the functions of
- both this new gene and its intronic microRNA in endothelial cells.
While they were searching for the functions of the intronic microRNA,
- theye identified an unknown gene as a putative target.
The aim of my project was to investigate if this unknown gene was actually a genuine target and
- if regulation of this gene by the microRNA was involved in endothelial cell function.
They had already shown the endothelial cell phenotype is associated with the inhibition of the intronic microRNA.
They then used miRCURY LNA™ Target Site Blockers to demonstrate
- the expression of this unknown gene is actually controlled by this microRNA.
- the microRNA regulates specific endothelial cell properties through regulation of this unknown gene.
MicroRNA function in endothelial cells – Solving the mystery of an unknown target gene using Target Site Blockers to investigate the role of microRNAs on putative targets
We first verified that this TSB was functional by analyzing
- the expression of the miRNA target against which the TSB was directed
- we then showed the TSB induced similar phenotypes as those when we inhibited the microRNA in the same cells.
Target Site Blockers were shown to be efficient tools to demonstrate the specific involvement of
- putative microRNA targets
- in the function played by this microRNA.
Some genes are known to have several different alternatively spliced protein variants, but the Scripps Research Institute’s Paul Schimmel and his colleagues have uncovered almost 250 protein splice variants of an essential, evolutionarily conserved family of human genes. The results were published July 17 in Science.
Focusing on the 20-gene family of aminoacyl tRNA synthetases (AARSs),
- the team captured AARS transcripts from human tissues—some fetal, some adult—and showed that
- many of these messenger RNAs (mRNAs) were translated into proteins.
Previous studies have identified several splice variants of these enzymes that have novel functions, but uncovering so many more variants was unexpected, Schimmel said. Most of these new protein products
- lack the catalytic domain but retain other AARS non-catalytic functional domains.
This study fundamentally effects how we view protein-synthesis, according to Michael Ibba (who was not involved in the work), The Scientist reported. “The unexpected and potentially vast expanded functional networks that emerge from this study have the potential to influence virtually any aspect of cell growth.”
The team—comprehensively captured and sequenced the AARS mRNAs from six human tissue types using high-throughput deep sequencing. They next showed that a proportion of these transcripts, including those missing the catalytic domain, indeed resulted in stable protein products:
- 48 of these splice variants associated with polysomes.
In vitro translation assays and the expression of more than 100 of these variants in cells confirmed that
- many of these variants could be made into stable protein products.
The AARS enzymes—of which there’s one for each of the 20 amino acids—bring together an amino acid with its appropriate transfer RNA (tRNA) molecule. This reaction allows a ribosome to add the amino acid to a growing peptide chain during protein translation. AARS enzymes can be found in all living organisms and are thought to be among the first proteins to have originated on Earth.
One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly
- play an important role in determining gene expression outputs, yet
- the regulatory logic underlying functional transcription factor binding is poorly understood.
An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and
- it is generally accepted that much of the binding does not strongly influence gene expression.
DA Cusanovich et al. PLoS Genet 2014;10(3):e1004226. http://dx.doi.org:/10.1371/journal.pgen.1004226
We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line
- to evaluate the context of functional TF binding.
We then identified genes whose expression was affected by the knockdowns
- by intersecting the gene expression data with transcription factor binding data
(based on ChIP-seq and DNase-seq) - within 10 kb of the transcription start sites of expressed genes.
This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were
- differentially expressed following the knockdown of that factor,
- suggesting that most interactions between TF and chromatin
- do not result in measurable changes in gene expression levels
- of putative target genes.
We found that functional TF binding is enriched
- in regulatory elements that harbor a large number of TF binding sites,
- at sites with predicted higher binding affinity, and
- at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’
We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone.
Combining a TF knockdown approach with TF binding data can help us to
- distinguish functional binding from non-functional binding
This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on
- the regulatory relationship of a single factor with its downstream targets.
The FANTOM consortium knocked down 52 different transcription factors in
- the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
- used a subset of these to validate certain regulatory predictions based on binding motif enrichments.
We and others previously studied the regulatory architecture of gene expression in
- the model system of HapMap lymphoblastoid cell lines (LCLs) using both
- binding map strategies and QTL mapping strategies.
We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL
- to refine our understanding of the gene regulatory circuitry of the human genome.
Therefore, we integrated the results of the knockdown experiments with previous data on TF binding to
- better characterize the regulatory targets of 59 different factors and
- to learn when a disruption in transcription factor binding
- is most likely to be associated with variation in the expression level of a nearby gene.
Gene expression levels following the knockdown were compared to
- expression data collected from six samples that were transfected with negative control siRNA.
Depending on the factor targeted, the knockdowns resulted in
- between 39 and 3,892 differentially expressed genes at an FDR of 5%
(Figure 1B; see Table S3 for a summary of the results).
The knockdown efficiency for the 59 factors ranged
- from 50% to 90% (based on qPCR; Table S1).
The qPCR measurements of the knockdown level were significantly
- correlated with estimates of the TF expression levels
- based on the microarray data (P =0.001; Figure 1C).
Did the factors tended to have a consistent effect (either up- or down-regulation)
- on the expression levels of genes they purportedly regulated?
All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).
While there is compelling evidence for our inferences, the current chromatin functional annotations
- do not fully explain the regulatory effects of the knockdown experiments.
For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),
- much beyond what is expected by chance alone, but far from accounting for all functional binding.
A slight majority of downstream target genes were expressed at higher levels
- following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).
The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,
- the enzymatic component of the Polycomb group complex.
On the other end of the spectrum was JUND, a member of the AP-1 complex, for which
- 7% of differentially expressed targets were down-regulated following the knockdown.
Our results, combined with the previous work from our group and others make for a complicated view
- of the role of transcription factors in gene regulation as
- it seems difficult to reconcile the inference from previous work that
- many transcription factors should primarily act as activators with the results presented here.
One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that
- the ‘‘repressive’’ effects we observe for known activators may be
- at sites in which the activator is acting as a weak enhancer of transcription and
- that reducing the cellular concentration of the factor
- releases the regulatory region to binding by an alternative, stronger activator.
Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data
with the interactive MarVis-Graph software
M Landesfeind, A Kaever, K Feussner, C Thurow, C Gatz, I Feussner and P Meinicke
PeerJ 2:e239; http://dx.doi.org /10.7717/peerj.239
High-throughput technologies notoriously generate large datasets often including data from different omics platforms. Each dataset contains data for several thousand experimental markers, e.g., mass-to-charge ratios in mass spectrometry or spots in DNA microarray analysis. An experimental marker is associated with an intensity profile which may include several measurements according to different experimental conditions (Dettmer, Aronov & Hammock, 2007).
The combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics. We present here the MarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and
- sub-networks, according to connected high-scoring reactions, are identified.
Finally, MarVis-Graph scores the detected sub-networks,
- evaluates them by means of a random permutation test and
- presents them as a ranked list.
Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results.
The key advantage of MarVis-Graph is the analysis of reactions detached from their pathways so that
- it is possible to identify new pathways or
- to connect known pathways by previously unrelated reactions.
The MarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.
Significant differences or clusters may be explained by associated annotations, e.g., in terms of metabolic pathways or biological functions. During recent years, numerous specialized tools have been developed to aid biological researchers in automating all these steps (e.g., Medina et al., 2010; Kaever et al., 2009; Waegele et al., 2012). Comprehensive studies can be performed by combining technologies from different omics fields. The combination of transcriptomic and proteomic data sets revealed a strong
correlation between both kinds of data (Nie et al., 2007) and supported the detection of complex interactions, e.g., in RNA silencing (Haq et al., 2010). Moreover, correlations
were detected between RNA expression levels and metabolite abundances (Gibon et al., 2006). Therefore, tools that integrate, analyze and visualize experimental markers from different platforms are needed. To cope with the complexity of genome-wide studies, pathway models are utilized extensively as a simple abstraction of the underlying complex mechanisms. Set Enrichment Analysis (Subramanian et al., 2005) and Over-Representation Analysis (Huang, Sherman & Lempicki, 2009) have become state-of-the-art tools for analyzing large-scale datasets: both methods evaluate predefined sets of entities, e.g., the accumulation of differentially expressed genes in a pathway.
While manually curated pathways are convenient and easy to interpret, experimental studies have shown that all metabolic and signaling pathways are heavily interconnected (Kunkel & Brooks, 2002; Laule et al., 2003). Data from biomolecular databases support these studies: the metabolic network of Arabidopsis thaliana in the KEGG database (Kanehisa et al., 2012; Kanehisa & Goto, 2000) contains 1606 reactions from which 1464 are connected in a single sub-network (>91%), i.e., they
share a metabolite as product or substrate. In the AraCyc 10.0 database (Mueller, Zhang & Rhee, 2003; Rhee et al., 2006), more than 89% of the reactions are counted in a single sub-network. In both databases, most other reactions are completely disconnected. Additionally, Set Enrichment Analyses can not identify links between the predefined sets easily. This becomes even more important when analyzing smaller pathways as provided by the MetaCyc (Caspi et al., 2008; Caspi et al., 2012) database. Moreover, methods that utilize pathways as predefined sets ignore reactions and related biomolecular entities (e.g., metabolites, genes) which are not associated with a single pathway. For example, this affects 4000 reactions in MetaCyc and 2500 in KEGG, respectively (Altman et al., 2013). Therefore, it is desirable to develop additional methods
- that do not require predefined sets but may detect enriched sub-networks in the full metabolic network.
While several tools support the statistical analysis of experimental markers from one or more omics technologies and then utilize variants of Set Enrichment Analysis (Xia et al., 2012; Chen et al., 2013; Howe et al., 2011),
- no tool is able to explicitly search for connected reactions that include
- most of the metabolites, genes, and enyzmes with experimental evidence.
However, the automatic identification of sub-networks has been proven useful in other contexts, e.g., in the analysis of protein–protein-interaction networks (Alcaraz et al., 2012; Baumbach et al., 2012; Maeyer et al., 2013).
MarVis-Graph imports experimental markers from different high-throughput experiments and
- analyses them in the context of reaction-chains in full metabolic networks.
Then, MarVis-Graph scores the reactions in the metabolic network
- according to the number of associated experimental markers and
- identifies sub-networks consisting of subsequent, high-scoring reactions.
The resulting sub-networks are
- ranked according to a scoring method and visualized interactively.
Hereby, sub-networks consisting of reactions from different pathways may be identified to be important
- whereas the single pathways may not be found to be significantly enriched.
MarVis-Graph may also connect reactions without an assigned pathway
- to reactions within a particular pathway.
The MarVis-Graph tool was applied in a case-study investigating the wound response in Arabidopsis thaliana to analyze combined metabolomic and transcriptomic high-throughput data.
Figure 1 Schema of the metabolic network representation in MarVis-Graph. Metabolite markers are shown in gray, metabolites in red, reactions in blue, enzymes in green, genes in yellow, transcript markers in pink, and pathways in turquoise color. The edges are shown in black with labels that comply with the biological meaning. The orange arrows depict the flow of score for the initial scoring (described in section “Initial Scoring”). (not shown)
In MarVis-Graph, metabolite markers obtained from mass-spectrometry experiments additionally contain the experimental mass. The experimental mass has to be
calculated based on the mass-to-charge ratio (m/z-value) and specific isotope- or adduct-corrections (Draper et al., 2009) by means of specialized tools, e.g.,MarVis-Filter
(Kaever et al., 2012).
For each transcript marker the corresponding annotation has to be given. In DNA microarray experiments, each spot (transcript marker) is specific for a gene and can
therefore be used for annotation. For other technologies an annotation has to be provided by external tools.
In MarVis-Graph, each reaction is scored initially based on the associated experimental data (see “Initial scoring”). This initial scoring is refined (see “Refining the scoring”) and afterwards reactions with a score below a user-defined threshold are removed. The network is
- decomposed into subsequent high-scoring reactions that constitute the sub-networks.
The weight of each experimental marker (see “Experimental markers”) is equally distributed over all metabolites and genes associated with the metabolite marker or
transcript marker, respectively. For all vertices, this is repeated as illustrated in Fig. 1 until the weights are accumulated by the reactions.
The initial reaction scores are used as input scoring for the random walk algorithm. The algorithm is performed as described by Glaab et al. (2012) with a user-defined
restart-probability r (default value 0.8). After convergence of the algorithm, reactions with a score lower than the user-defined threshold t (default value t = 1−r) are removed from the reaction network. During the removal process,
- the network is decomposed into pairwise disconnected sub-networks containing only high-scoring reactions.
In the following, a resulting sub-network is denoted by a prime: G′ = (V′,L′) with V′ = M′ ∪C′ ∪R′ ∪E′ ∪G′ ∪T′ ∪P′.
The scores of the identified sub-networks can be assessed using a random permutation test, evaluating the marker annotations under the null hypothesis of being connected
randomly. Here, the assignments
- from metabolite markers to metabolites and from transcript markers to genes are randomized.
For each association between a metabolite marker and a metabolite,
- this connection is replaced by a connection between a randomly chosen metabolite marker and a randomly chosen metabolite.
The random metabolite marker is chosen from the pool of formerly connected metabolite markers. Each connected transcript marker
- is associated with a randomly chosen gene.
Choosing from the list of already connected experimental markers ensures that
- the sum of weights from the original and the permuted network are equal.
This method differs from the commonly utilized XSwap permutation (Hanhij¨arvi, Garriga & Puolam¨aki, 2009) that is based on swapping endpoints of two random edges. The main difference of our permutation method is that it results in a network with different topological structure, i.e., different degree of the metabolite and gene nodes.
Finally, the sub-networks are detected and scored with the same parameters applied for the original network. Based on the scores of the networks identified in the random
permutations, the family-wise-error-rate (FWER) and false-discovery-rate (FDR) are calculated for each originally identified sub-network.
MarVis-Graph was applied in a case study investigating the A. thaliana wound response. Data from a metabolite fingerprinting (Meinicke et al., 2008) and a DNA microarray
experiment (Yan et al., 2007) were imported into a metabolic network specific for A. thaliana created from the AraCyc 10.0 database (Lamesch et al., 2011). The metabolome
and transcriptome have been measured before wounding as control and at specific time points after wounding in wild-type and in the allene oxide synthase (AOS) knock-out
mutant dde-2-2 (Park et al., 2002) of A. thaliana Columbia (see Table 1). The AOS mutant was chosen, because AOS catalyzes the first specific step in the biosynthesis of the hormone jasmonic acid, which is the key regulator in wound response of plants (Wasternack & Hause, 2013).
Both datasets have been preprocessed with the MarVis-Filter tool (Kaever et al., 2012) utilizing the Kruskal–Wallis p-value calculation on the intensity profiles. Based on the ranking of ascending p-values,
- the first 25% of the metabolite markers and 10% of the transcript markers have been selected for further investigation (Data S2).
The filtered metabolite and transcript markers were imported into the metabolic network. For metabolite markers, metabolites were associated
- if the metabolite marker’s detected mass differs from the metabolites monoisotopic mass by a maximum of 0.005u.
Transcript markers were linked to the genes whose ID equaled the ID given in the CATMA database (Sclep et al., 2007) for that transcript marker.
Table 2 Vertices in the A. thaliana specific metabolic network after import of experimental markers. Number of objects in the metabolic network in absolute counts and relative abundances. For experimental markers, the with annotation column gives the number of metabolite markers and
transcript markers that were annotated with a metabolite or gene, respectively. The direct evidence column contains the number of metabolites and genes, that are associated with a metabolite marker or transcript marker. For enzymes, this is the number of enzymes encoded by a gene with
direct evidence. The number of vertices with an association to a reaction is given in the with reaction column. In the last column, this is given for associations to metabolic pathways. (not shown)
MarVis-Graph detected a total of 133 sub-networks. The sub-networks were ranked according to size Ss, diameter Sd, and sum-of-weights Ssow scores (Table S4). Interestingly, the different rankings show a high correlation with all pairwise correlations higher than 0.75 (Pearson correlation
coefficient) and 0.6 (Spearman rank correlation).
Allene-oxide cyclase sub-network
In all rankings, the sub-network allene-oxide cyclase (named after the reaction with the highest score in this sub-network) appeared as top candidate.
This sub-network is constituted of reactions from different pathways related to fatty acids. Figure 2 shows a visualization of the sub-network.
Jasmonic acid biosynthesis. The main part of the sub-network is formed by reactions from the “jasmonic acid biosynthesis” (PlantMetabolic Network, 2013) resulting in jasmonic acid (jasmonate). The presence of this pathway is very well established because of its central role in mediating the plants wound response (Reymond & Farmer, 1998; Creelman, Tierney & Mullet, 1992).
Additionally, metabolites and transcripts from this pathway were expected to show prominent
expression profiles because AOS, a key enzyme in this pathway, is knocked-out in themutant plant. Jasmonic acid derivatives and hormones. Jasmonic acid derivatives and hormones. Jasmonate is a precursor for a broad variety of plant hormones (Wasternack & Hause, 2013), e.g., the derivative (-) – jasmonic acid methyl ester (also Methyl Jasmonic Acid; MeJA) is a volatile, airborne signal mediating wound response between plants (Farmer&Ryan, 1990).
Reactions from the jasmonoyl-amino acid conjugates biosynthesis I (PMN, 2013a) pathway connect jasmonate to different amino acids, including L-valine, L-leucine, and L-isoleucine. Via these amino acids, this sub-network is connected to the indole-3-acetylamino acid biosynthesis (PMN, 2013b) (IAA biosynthesis).
Again, this pathway produces a well-known plant hormone: Auxine (Woodward & Bartel, 2005). Even though, jasmonate and auxin are both plant hormones, their connection in this subnetwork is of minor relevance because amino acid conjugates are often utilized as active or storage forms of signaling molecules. While jasmonoyl-amino acid conjugates represent the active signaling form of jasmonates, IAA amino acid conjugates are the storage form of this hormone (Staswick et al.,
2005).
Chapter 5: Sub-cellular Structure
Introduction to Subcellular Structure
Author and Curator: Larry H. Bernstein, MD, FCAP
The following chapter of the metabolism/transcriptomics/proteomics/metabolomics series deals with the subcellular structure of the cell. This would have to include the cytoskeleton, which has a key role in substrate and ion efflux and influx, and in cell movement mediated by tubulins. It has been extensively covered already. Much of the contributions here are concerned with the mitochondrion, which is also covered in metabolic pathways. The ribosome is the organelle that we have discussed with respect to the transcription and translation of the genetic code through mRNA and tRNA, and the therapeutic implications of SiRNA as well as the chromatin regulation of lncRNA.
We have also encountered the mitochondrion and the lysosome in the discussion of apoptosis and autophagy, maintaining the balance between cell regeneration and cell death.
List of the organelles:
- Nucleus
- Centrosome
- Nuclear Membrane
- Ribosome
- Endoplasmic Reticulum
- Mitochondria
- Lysosome
- Cytoskeleton
- Golgi apparatus
- Cytoplasm
Summary of Cell Structure, Anatomic Correlates of Metabolic Function
Author and Curator: Larry H. Bernstein, MD, FCAP
This chapter has been concerned with the subcellular ultrastructure of organelles, and importantly, their function. There is no waste in the cell structure. The nucleus has the instructions necessary to carry out the cell’s functions. In the Eukaryotic cell there is significant differentiation so that the cells are regulated for the needs that they uniquely carry out. When there is dis-regulation, it leads to remodeling or to cell death.
Here I shall note some highlights of this chapter.
- In every aspect of cell function, proteins are involved embedded in the structure, for most efficient functioning.
- Metabolic regulation is dependent on pathways that are also linkages of proteins.
- Energy utilization is dependent on enzymatic reactions, often involving essential metal ions of high valence numbers, which facilitates covalent and anion binding, and has an essential role in allostericity.
Chapter 6: Proteomics
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?
- Traceability of protein expression to a basic coded message
- The possibility of tracing disturbed cellular function to mutation related loss-of-function
- The ability to trace generational traits over long periods of time
- 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?
- There is a huge amount of information, as well as missing information that is necessary for adequately tackling the mastery of the life processes.
- 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.
- 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.
- 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 (/ˈproʊˌtiːnz/ or /ˈproʊti.ɨnz/) are large biological molecules, or macromolecules,
- consisting of one or more long chains of amino acidresidues.
Proteins perform a vast array of functions within living organisms, including
- catalyzing metabolic reactions,
- replicating DNA,
- responding to stimuli, and
- transporting molecules from one location to another.
Proteins differ from one another primarily in
- their sequence of amino acids,
- which is dictated by the nucleotide sequenceof their genes, and
- which usually results in folding of the protein into
- a specific three-dimensional structure that determines its activity.
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
- the sequence of a gene, which is encoded in the genetic code.
In general, the genetic code specifies 20 standard amino acids; however, in certain organisms the genetic code can include selenocysteine and—in certain archaea—pyrrolysine. 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 acidmethionine because the “start” an 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.
Summary of Proteomics
Author and Curator: Larry H. Bernstein, MD, FCAP
We have completed a series of discussions on proteomics, a scientific endeavor that is essentially 15 years old. It is quite remarkable what has been accomplished in that time. The interest is abetted by the understanding of the limitations of the genomic venture that has preceded it. The thorough, yet incomplete knowledge of the genome, has led to the clarification of its limits. It is the coding for all that lives, but all that lives has evolved to meet a demanding and changing environment with respect to
- availability of nutrients
- salinity
- temperature
- radiation exposure
- toxicities in the air, water, and food
- stresses – both internal and external
We have seen how both transcription and translation of the code results in a protein, lipoprotein, or other complex than the initial transcript that was modeled from tRNA. What you see in the DNA is not what you get in the functioning cell, organ, or organism. There are compatibilities as well as significant differences between plants, prokaryotes, and eukaryotes. There is extensive variation. The variation goes beyond genomic expression, and includes the functioning cell, organ type, and species.
Here, I return to the introductory discussion. Proteomics is a goal directed, sophisticated science that uses a combination of methods to find the answers to biological questions. Graves PR and Haystead TAJ. Molecular Biologist’s Guide to Proteomics.
Chapter 7: Metabolomics
Introduction to Metabolomics
Author and Curator: Larry H. Bernstein, MD, FCAP
This concludes a long step-by-step journey into rediscovering biological processes from the genome as a framework to the remodeled and reconstituted cell through a number of posttranscription and posttranslation processes that modify the proteome and determine the metabolome. The remodeling process continues over a lifetime. The process requires a balance between nutrient intake, energy utilization for work in the lean body mass, energy reserves, endocrine, paracrine and autocrine mechanisms, and autophagy. It is true when we look at this in its full scope – What a creature is man?
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Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the “systematic study of the unique chemical fingerprints that specific cellular processes leave behind.” The study of their small molecule metabolite profiles.[1]
The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes.[2] mRNA gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to provide a better understanding of cellular biology.
The term “metabolic profile” was introduced by Horning, et al. in 1971 after they demonstrated that gas chromatography-mass spectrometry (GC-MS) could be used to measure compounds present in human urine and tissue extracts. The Horning group, along with that of Linus Pauling and Arthur B. Robinson led the development of GC-MS methods to monitor the metabolites present in urine through the 1970s.
Concurrently, NMR spectroscopy, which was discovered in the 1940s, was also undergoing rapid advances. In 1974, Seeley et al. demonstrated the utility of using NMR to detect metabolites in unmodified biological samples.This first study on muscle highlighted the value of NMR in that it was determined that 90% of cellular ATP is complexed with magnesium. As sensitivity has improved with the evolution of higher magnetic field strengths and magic angle spinning, NMR continues to be a leading analytical tool to investigate metabolism. Efforts to utilize NMR for metabolomics have been influenced by the laboratory of Dr. Jeremy Nicholson at Birkbeck College, University of London and later at Imperial College London. In 1984, Nicholson showed 1H NMR spectroscopy could potentially be used to diagnose diabetes mellitus, and later pioneered the application of pattern recognition methods to NMR spectroscopic data.
In 2005, the first metabolomics web database, METLIN, for characterizing human metabolites was developed in the Siuzdak laboratory at The Scripps Research Institute and contained over 10,000 metabolites and tandem mass spectral data. As of September 2012, METLIN contains over 60,000 metabolites as well as the largest repository of tandem mass spectrometry data in metabolomics.
On 23 January 2007, the Human Metabolome Project, led by Dr. David Wishart of the University of Alberta, Canada, completed the first draft of the human metabolome, consisting of a database of approximately 2500 metabolites, 1200 drugs and 3500 food components. Similar projects have been underway in several plant species, most notably Medicago truncatula and Arabidopsis thaliana for several years.
As late as mid-2010, metabolomics was still considered an “emerging field”. Further, it was noted that further progress in the field depended in large part, through addressing otherwise “irresolvable technical challenges”, by technical evolution of mass spectrometry instrumentation.
Metabolome refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism. The word was coined in analogy with transcriptomics and proteomics; like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyze the entire range of metabolites by a single analytical method. The first metabolite database(called METLIN) for searching m/z values from mass spectrometry data was developed by scientists at The Scripps Research Institute in 2005. In January 2007, scientists at the University of Alberta and theUniversity of Calgary completed the first draft of the human metabolome. They catalogued approximately 2500 metabolites, 1200 drugs and 3500 food components that can be found in the human body, as reported in the literature. This information, available at the Human Metabolome Database (www.hmdb.ca) and based on analysis of information available in the current scientific literature, is far from complete.
Each type of cell and tissue has a unique metabolic ‘fingerprint’ that can elucidate organ or tissue-specific information, while the study of bio-fluids can give more generalized though less specialized information. Commonly used biofluids are urine and plasma, as they can be obtained non-invasively or relatively non-invasively, respectively. The ease of collection facilitates high temporal resolution, and because they are always at dynamic equilibrium with the body, they can describe the host as a whole.
Metabolites are the intermediates and products of metabolism. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1 kDa in size.
A primary metabolite is directly involved in the normal growth, development, and reproduction. A secondary metabolite is not directly involved in those processes. By contrast, in human-based metabolomics, it is more common to describe metabolites as being either endogenous (produced by the host organism) or exogenous. Metabolites of foreign substances such as drugs are termed xeno-metabolites. The metabolome forms a large network of metabolic reactions, where outputs from one enzymatic chemical reaction are inputs to other chemical reactions.
Summary of Metabolomics
Author and Curator: Larry H. Bernstein, MD, FCAP
This concludes the series on metabolomics, a rapidly developing science that is interconnected with a group termed – OMICS: proteomics, transcriptomics, genomics, and metabolomics. This chapter is most representative of the many important studies being done in the field, which ranges most widely because it has opened doors into nutrition and nutritional supplements, plant biochemistry, agricultural crops and breeding, animal breeding, worldwide malnutrition, diabetes, cancer, neurosciences, circulatory, respiratory, and musculoskeletal disorders, infectious diseases and immune system disorders. Obviously, it is not possible to cover the full range of activity, but metabolomics is most comprehensive in exploring the full range of metabolic changes that occur in health during the full age range from development to the geriatric years. It can be integrated well with gene expression, proteomics studies, and epidemiological investigations.
The subchapters are given here:
7.1 Extracellular Evaluation of Intracellular Flux in Yeast Cells
7.2 Metabolomic Analysis of Two Leukemia Cell Lines Part I
7.3 Metabolomic Analysis of Two Leukemia Cell Lines Part II
7.6 Isoenzymes in Cell Metabolic Pathways
7.7 A Brief Curation of Proteomics, Metabolomics, and Metabolism
7.8 Metabolomics is about Metabolic Systems Integration
7.9 Mechanisms of Drug Resistance
7.10 Development Of Super-Resolved Fluorescence Microscopy
7.11 Metabolic Reactions Need Just Enough
Metabolomics Summary and Perspective
This chapter will be followed by an exploration of disease and pharmaceutical directed studies using these methods 8. Impairments in pathological states: endocrine disorders, stress hyper-metabolism and cancer
Networking metabolites and diseases
P Braun, E Rietman, and M Vidal
Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA; and Physical Sciences Inc., Andover, MA 01810
PNAS July 22, 2008; 105(29): 9849–9850. http://pnas.org/cgi/doi/10.1073/pnas.0805644105
Biological systems are increasingly viewed and analyzed as
- highly complex networks of interlinked macromolecules and metabolites.
Network analysis has been applied to
- interactome maps of protein–protein, protein–DNA, and protein–RNA interactions
- as well as transcriptional, metabolic, and genetic data.
Such network views of biological systems should facilitate the detection of
- nonlinear long-range effects of perturbations, for example, by mutations, and
- help identification of unanticipated indirect causal connections.
Diseasome and Drug-Target Network
Recently, Goh et al. (1) constructed a ‘‘diseasome’’ network in which
- two diseases are linked to each other if
- they share at least one gene, in which mutations are associated with both diseases.
In the resulting network, related disease families cluster tightly together, thus
- phenotypically defining functional modules.
Importantly, for the first time this study applied concepts from network biology to human diseases,
- thus, opening the door for discovering causal relationships between
- dis-regulated networks and resulting ailments.
Subsequently Yilderim et al. (2) linked drugs to protein targets in a drug–target network,
- which could then be overlaid with the diseasome network.
One notable finding was the recent trend toward the development of
- new compounds directly targeted at disease gene products, whereas previous drugs,
- often found by trial and error, appear to target proteins only indirectly related to
- the actual disease molecular mechanisms.
An important question that remains in this emerging field of network analysis consists of
- investigating the extent to which directly targeting the product of mutated genes is an efficient approach or
- whether targeting network properties instead, and
- thereby accounting for indirect nonlinear effects of system perturbations by drugs, may prove more fruitful.
However, to answer such questions it is important to have a good understanding of the various influences that can lead to diseases.
Metabolic Connections
One group of diseases that was very poorly connected in the original diseasome network was the family of metabolic diseases.
In this issue of PNAS, Lee et al. (3) hypothesize that metabolic diseases may instead be connected
- via metabolites and
- common reactions.
To investigate this hypothesis Lee et al. first constructed a metabolic network from data available in two manually curated databases detailing well known
- metabolic reactions, the involved
- metabolites, and
- catalyzing enzymes.
Chapter 8. Impairments in Pathological States: Endocrine Disorders; Stress Hypermetabolism and Cancer
Introduction to Impairments in Pathological States: Endocrine Disorders, Stress Hyper–metabolism and Cancer
Author and Curator: Larry H. Bernstein, MD, FCAP
This leads into a series of presentations and the metabolic imbalance central to findings of endocrine, metabolic, inflammatory, immune diseases and cancer. All of this has been a result of discoveries based on the methods of study of genomics, proteomics, transcriptomics, and metabolomics that have preceded this. In some cases there has been the use of knockout methods. The completion of the human genomic and other catalogues have been instrumental in the past few years. In all cases there has been a thorough guidance by a biological concept of mechanism based on gene expression, metabolic disturbance, signaling pathways, and up- or down- regulation of metabolic circuits. It is interesting to recall that a concept of metabolic circuits was not yet formulated at the time of the mid 20th century physiology, except perhaps with respect to the coagulation pathways, and to some extent, glycolysis, gluco-neogenesis, the hexose monophosphate shunt, and mitochondrial respiration, which were linear strings of enzyme substrate reactions that intersected and that had flow restraints not then understood as to the complexity we now appreciate. We did know the importance of cytochrome c, the adenine and pyridine nucleotides, and the energy balance. Electron microscopy had opened the door to understanding the mechanism of contraction of skeletal muscle and myocardium, but it also opened the door to understanding kidney structure and function, explaining the “mesangium”. The first cardiac maker was discovered by Arthur Karmen in the serum alanine and aspartate aminotransferases, with a consequent differentiation between hepatic and myocardial damage. This was followed by lactic dehydrogenase and the H- and M-type isoenzymes in the 1960s, and in the next decade, by the MB-isoenzyme of creatine kinase. Troponins T and then I would not be introduced until the mid 1980s, and they have become a gold standard for the diagnosis of myocardial infarction.
In the 1980s we also saw the development of antiplatelets therapy that rapidly advanced interventional cardiology. But advances in surgical as well as medical intervention also proceeded as the understanding of the lipid metabolism was opened by the work of Brown and Goldstein, and UTSW Medical Campus, and major advances in treatment came at Baylor and UT Medical Center in Houston, and at the Cleveland Clinic. The next important advance came with the discovery of nitric oxide synthase role in endothelium and oxidative stress. The field of endocrinology saw advances as well for a solid period of 30 years in a comparable period for the adrenals, thyroid, and pituitary glands, and for the understanding of the male and female sex hormones, and discoveries in breast, ovarian, and prostate cancer. There were cancer markers, such as, CA125 and CA15-3, and PSA. This had more of an impact on timely surgical intervention, and if not that, post-surgical follow up. Despite a long time into the war on cancer, introduced by President Lynden Johnson, the fundamental knowledge needed was not sufficient. In the meantime, there were advances in the treatment of diabetes, with eventual introduction of the insulin pump for type I diabetes. The problem of Type 2 DM increased in prevalence, reaching into the childhood age group, with ascendent obesity. An epidemiological pattern of disease co-morbidities was emergent. Our population has aged out, and with it we are seeing an increase in dementias, especially Alzheimer’s disease. But the knowledge of the brain has lagged far behind.
What follows is a series of chapters that address what has currently been advanced with repect to the alignment of our knowledge of the last decade and pharmaceutical discovery. Pharmaceuticals were suitable for bacterial infections until the 1990s, when we saw the rise of resistance to penicillins and Vancomycin, and we had issues with gram negative enterobacter, salmonella, and E. coli strains. That has been and is a significant challenge. The elucidation of the gut microbiome in recent years will help to relieve this problem. The problem of the variety and different aggressive types of cancer has been another challenge. The door has been opened to better diagnostic tools with respect to imaging and targeted biomarkers for localization. I am not dealing with imaging, which is not the subject here.
Chapter 9
Genomic Expression in Health and Disease
Intoduction
Larry H Bernstein, MD, FACP
The previous chapter was focused on metabolic disorders with a focus to altered gene expression, which does not have to be related to a prior mutation. This is in the inherent complexity of the organism response to changes in the environment, which may be considered adaptive, or maladaptive. Whether the change is preceded by or precedes a mutation event is somewhat arguable.This is because the cell adapts to immediate and proximate changes quickly, and it can be sustained for a time. This is miraculously governed by signaling pathways and catalysts that interact with and drive metabolic processes that can be measured by intracellular and efflux substrates.
This chapter follows with processes that are established metabolic processes within the organism, external to the organism, and tied to both nutrient intake and to the gut (or other) mucosal barrier.
As a result, it can be considered within the scope of three questions:
- What are examples of disease associated with mutant gene expression?
- What are examples of benefits from genetically determined plant nutrition?
- What is the potential of genome targeting for regulating human diseases?
Summary
Larry H Bernstein, MD, FACP
From what has been presented, even though there is only one article on the human gut proteome, this has been a very active part of current research. There are other related articles in the Journal of Pharmaceutical Intelligence:
Natural Drug Target Discovery and Translational Medicine in Human Microbiome
Demet Sag, PhD
Diarrheas – Bacterial and Nonbacterial
Larry H Bernstein, MD, FACP
Larry H Bernstein, MD, FACP
Malnutrition in India, high newborn death rate and stunting of children age under five years
Larry H Bernstein, MD, FACP
Larry H Bernstein, MD, FACP
The issue of genetically driven diseases is actually of considerable interest since the mapping of the human chromosome. Mutations occur frequently, but the cell has mechanisms of deleting errors, or the errors may be ignored because they are inconsequential. The majority of diseases that afflict mankind are related to metabolic dysregulation in which there may also be a variable combination of multigene oligonucleotide sequence changes, none of which can be definitively causal as a univariate factor.
There are a number of related articles that I can refer to in the J Pharmaceutical Intelligence:
Crohn’s disease driven by inflammation – not genetics
Aviva Lev-Ari, PhD, RN
Larry H Bernstein, MD, FACP
What is the Future for Genomics in Clinical Medicine?
Larry H Bernstein, MD, FACP
Larry H Bernstein, MD, FACP
Larry H Bernstein, MD, FACP
It may take guts to cure diabetes: Human GI cells retrained to produce insulin
Larry H Bernstein, MD, FACP
Aviva Lev-Ari, PhD, RN
Molecule as a Switchpoint discovered @ETH: Catalyst for Adult-onset diabetes (DM2) Decoded
Aviva Lev-Ari, PhD, RN
Larry H Bernstein, MD, FACP
Research on inflammasomes opens therapeutic ways for treatment of rheumatoid arthritis
Larry H Bernstein, MD, FACP
Larry H Bernstein, MD, FACP
This brings us to the implications for pharmacotherapeutics. There is a search for the drivers of the most common diseases, which may be a combination of human and environmental factors. The drivers are not always quite certain, but there is sufficient experimental evidence to determine how the interruption of metabolic pathways may be consequential. This leads to targeting. As noted, there are also other articles to support this notion.
Approach to Controlling Pathogenic Inflammation in Arthritis
Larry H Bernstein, MD, FACP
Anamika Sarkar, Ph.D and Ritu Saxena, Ph.D.
Prabodh Kandala, PhD
Genomics and Metabolomics Advances in Cancer
Larry H Bernstein, MD, FACP
Mitochondrial fission and fusion: potential therapeutic targets?
Ritu Saxena, Ph.D.
Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
Larry H Bernstein, MD, FACP
Therapeutic Targets for Diabetes and Related Metabolic Disorders
Larry H Bernstein, MD, FACP
The Essential Role of Nitric Oxide and Therapeutic NO Donor Targets in Renal Pharmacotherapy
Larry H Bernstein, MD, FACP
Complex Models of Signaling: Therapeutic Implications
Larry H Bernstein, MD, FACP
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 upregulate 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.”
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.
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 behavior, we therefore need (and increasingly have) reliable consensus
(community) models of
- the human metabolic network that includes 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 exo-metabolites, 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 recognize 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 (1).
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