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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 musculosletal 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  

 https://pharmaceuticalintelligence.com/2014/08/25/extracellular-evaluation-of-intracellular-flux-in-yeast-cells/

 7.2    Metabolomic analysis of two leukemia cell lines. I.  

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

  7.3   Metabolomic analysis of two leukemia cell lines. II.

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

  7.4   Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeostatic
regulation
  

           https://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/

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

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

    7.6    Isoenzymes in cell metabolic pathways

 https://pharmaceuticalintelligence.com/2014/10/06/isoenzymes-in-cell-metabolic-pathways/

7.7   A Brief Curation of Proteomics, Metabolomics, and Metabolism

https://pharmaceuticalintelligence.com/2014/10/03/a-brief-curation-of-proteomics-metabolomics-and-metabolism/

   7.8   Metabolomics is about Metabolic Systems Integration

     https://pharmaceuticalintelligence.com/2014/10/13/metabolomics-is-about-metabolic-systems-integration/

 7.9  Mechanisms of Drug Resistance

   https://pharmaceuticalintelligence.com/2014/10/09/mechanisms-of-drug-resistance/

7.10  Development Of Super-Resolved Fluorescence Microscopy

    https://pharmaceuticalintelligence.com/2014/10/12/development-of-super-resolved-fluorescence-microscopy  

7.11  Metabolic Reactions Need Just Enough

 https://pharmaceuticalintelligence.com/2014/10/14/metabolic-reactions-need-just-enough/

7.12  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
hypermetabolism; 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
  • disregulated 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
  1. metabolic reactions,
  2. the involved metabolites, and
  3. catalyzing enzymes.

In addition, gene–disease associations were identified by using the Online Mendelian Inheritance in Man (OMIM) database (http://ncbi.nlm.nih.gov/sites/
entrez?dbomim&itooltoolbar). In a last step,

  • a metabolic disease network (MDN) was constructed by connecting
  • two diseases if their associated genes are linked in the metabolic network
  • by a common metabolite or metabolites used in a common reaction.

Metabolites are not only linked by common reactions, but

  • on a larger scale by coupled fluxes within a metabolic network,
  • which may also influence disease phenotypes.

An increase in the concentration of one metabolite may increase several fluxes

  • across reaction pathways that use this compound, which
  • may lead to diverse phenotypes and distinct diseases.

The fluxes within the metabolic network are calculated by using

  • the Flux Coupling Finder method described by Nikolaev et al. (4) and Burgard et al. (5),
  • which is based on the assumption that pools of metabolites are conserved.

To functionally validate the network, coexpression correlations are measured for genes

  • linked by adjacent reactions and those linked by fluxes.

Interestingly, the average coexpression correlation for flux-coupled genes (0.31)

  • is higher than that for genes simply catalyzing adjacent reactions (0.24)
    (compared with 0.10 for all gene pairs in the network).

If the links between diseases identified in the MDN are functionally and causally relevant

  • it should be expected that linked diseases occur more frequently in the same individual.

To test this hypothesis, Lee et al. (3) measured the co-occurrence of diseases in patients by using detailed Medicare information

  • of 13 million patients and 32 million hospital visits within a 3-year period.

A comorbidity index was computed to measure the degree to which one disease

  • will increase the likelihood of a second disease in the same patient.

The average comorbidity for all genes is 0.0008 (Pearson correlation coefficient),

  • which increases 3-fold to 0.0027 when disease pairs that are metabolically linked are analyzed,
  • which is highly statistically significant (P 108).

When diseases are analyzed that are directionally coupled by a flux (see ref. 3 for details),

  • the correlation increases to 0.0062.

Thus, whereas 17% of all diseases in the network show significant comorbidity, this fraction

  • nearly doubles to 31% for metabolically linked diseases.

Further analysis reveals that comorbidity effects can be detected up to three links (metabolites, reactions)

  • apart from each other with statistical significance, but not farther away.

In the MDN, several highly connected hubs, e.g., hypertension and hemolytic anemia, are

  • linked to many different co-occurring diseases not unexpected for such complex diseases
  • that can result from many different genetic alterations or variants.

Importantly, though, most of the connections to the different linked diseases

  • are mediated by diverse connections in the metabolic network.

Thus, in the future such insights may be helpful for finer classification of the complex hub disease.

Furthermore, depending on the onset of the complex (hub) disease in relation to the associated diseases,

  • such relationships may potentially be used to systematically
  • stratify patients and develop targeted treatments acting on
  • the underlying metabolic links.

Returning to the starting point of their study, Lee et al. (3) next investigated

  • whether metabolic diseases are better linked through the metabolic network
  • than they are in the previously described gene–disease network.

When purely metabolic diseases are considered, the comorbidity is, in fact,

  • best predicted by metabolic links.

Interestingly, when all diseases linked to metabolic enzymes are considered,

  • which involves many diseases that are merely related to metabolic diseases through multifunctional enzymes,
  • the gene and metabolic networks are nearly equally predictive of comorbidity,
  • indicating that as a general approach information from
  • many different biological dimensions should be integrated to identify the most relevant connections.

Together, all these findings support the initial hypothesis that metabolic diseases are linked by metabolic networks.

Practically, alteration of one metabolite or one reaction can have numerous repercussions in the network,

  • each of which can manifest as different diseases that frequently occur together in affected patients.

Radoslav Bozov

  1. Glycine, as the only amino acid having no isomer driven central carbon allowing for hing occupancy of ‘free’
    motifs, where quark (proton) ‘fluxes’ play at, is a one – step away observable (1) from synthesis of pyrimidines
    to glyoxylate mitochondrial ‘shunt’ entangling at least two differential compartments longly objected by
    Japanese metabolomics study groups.
  2. One carbon systems emerge out of a glycoprotein ‘complex’, pyrimidine synthase pathway, that possesses
    significant similarity to  BRCA2 and most other transcription factors suggesting that protein allocation is
    coorchestrated by modifications and spatially transforming construes as an outcome of energy processing.
    Directly deduced by TCS, life cannot exist without mutations, as mutations and chromatin states appear to
    be a sort of energy hold and release ‘gates’.
  3. Phosphorylations and small molecules as such as cGMP, cAMP play a role of decompression machinery
    for amplifying bio signal processing C-S, C-N, C-O, interference open systems.  By decompression
    of one relative discrete space, another one becomes compressed, which gets uncertainty of absolute energy
    processing within space scalar wise into vector objected space represented by chromatin remodeling processes,
    possibly seen as network identities information.
  4. Unifying network and quantum theory possess implications to relativity concepts and energy relevant computational methodology.
 translational medicine

translational medicine

Shifts in steady-state profiles caused by kinetic perturbations

Shifts in steady-state profiles caused by kinetic perturbations

mapping metabolomic data using three different approaches

mapping metabolomic data using three different approaches

network genetics metabotypes -  integrated metabolome and interactome mapping (iMIM)

network genetics metabotypes – integrated metabolome and interactome mapping (iMIM)

metabol leukem cell lines

metabol leukem cell lines

Metabolome Informatics Research

Metabolome Informatics Research

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Introduction to 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?

http://masspec.scripps.edu/metabo_science/recommended_readings.php
 Recommended Readings and Historical Perspectives

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 analyse 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 the University 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 biofluids 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 xenometabolites. The metabolome forms a large network of metabolic reactions, where outputs from one enzymatic chemical reaction are inputs to other chemical reactions.

Metabonomics is defined as “the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification”. The word origin is from the Greek μεταβολή meaning change and nomos meaning a rule set or set of laws. This approach was pioneered by Jeremy Nicholson at Imperial College London and has been used in toxicology, disease diagnosis and a number of other fields. Historically, the metabonomics approach was one of the first methods to apply the scope of systems biology to studies of metabolism.

There is a growing consensus that ‘metabolomics’ places a greater emphasis on metabolic profiling at a cellular or organ level and is primarily concerned with normal endogenous metabolism. ‘Metabonomics’ extends metabolic profiling to include information about perturbations of metabolism caused by environmental factors (including diet and toxins), disease processes, and the involvement of extragenomic influences, such as gut microflora. This is not a trivial difference; metabolomic studies should, by definition, exclude metabolic contributions from extragenomic sources, because these are external to the system being studied.

Toxicity assessment/toxicology. Metabolic profiling (especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical (or mixture of chemicals).

Functional genomics. Metabolomics can be an excellent tool for determining the phenotype caused by a genetic manipulation, such as gene deletion or insertion. Sometimes this can be a sufficient goal in itself—for instance, to detect any phenotypic changes in a genetically-modified plant intended for human or animal consumption. More exciting is the prospect of predicting the function of unknown genes by comparison with the metabolic perturbations caused by deletion/insertion of known genes.

Nutrigenomics is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. In general a metabolome in a given body fluid is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. The large bowel microflora are also a very significant potential confounder of metabolic profiles and could be classified as either an endogenous or exogenous factor. The main exogenous factors are diet and drugs. Diet can then be broken down to nutrients and non- nutrients.

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

Jose Eduardo des Salles Roselino

The problem with genomics was it was set as explanation for everything. In fact, when something is genetic in nature the genomic reasoning works fine. However, this means whenever an inborn error is found and only in this case the genomic knowledge afterwards may indicate what is wrong and not the completely way to put biology upside down by reading everything in the DNA genetic as well as non-genetic problems.

Coordination of the transcriptome and metabolome by the circadian clock PNAS 2012

Coordination of the transcriptome and metabolome by the circadian clock PNAS 2012

analysis of metabolomic data and differential metabolic regulation for fetal lungs, and maternal blood plasma

conformational changes leading to substrate efflux.img

conformational changes leading to substrate efflux.img

The cellular response is defined by a network of chemogenomic response signatures.

The cellular response is defined by a network of chemogenomic response signatures.

Dynamic Construct of the –Omics

Dynamic Construct of the –Omics

 genome cartoon

genome cartoon

central dogma phenotype

central dogma phenotype

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