Metabolomics
Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. 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 complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes.[2] Messenger RNA (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,[3] and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism.[4] There are indeed quantifiable correlations between the metabolome and the other cellular ensembles (genome, transcriptome, proteome, and lipidome), which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances.[5] One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.
History
The concept that individuals might have a "metabolic profile" that could be reflected in the makeup of their biological fluids was introduced by Roger Williams in the late 1940s,
Concurrently,
In 1994 and 1996, liquid chromatography mass spectrometry metabolomics experiments[15][16] were performed by Gary Siuzdak while working with Richard Lerner (then president of the Scripps Research Institute) and Benjamin Cravatt, to analyze the cerebral spinal fluid from sleep deprived animals. One molecule of particular interest, oleamide, was observed and later shown to have sleep inducing properties. This work is one of the earliest such experiments combining liquid chromatography and mass spectrometry in metabolomics.
In 2005, the first metabolomics tandem mass spectrometry database, METLIN,[17][18] for characterizing human metabolites was developed in the Siuzdak laboratory at the Scripps Research Institute. METLIN has since grown and as of December, 2023, METLIN contains MS/MS experimental data on over 930,000 molecular standards and other chemical entities,[19] each compound having experimental tandem mass spectrometry data generated from molecular standards at multiple collision energies and in positive and negative ionization modes. METLIN is the largest repository of tandem mass spectrometry data of its kind. The dedicated academic journal Metabolomics first appeared in 2005, founded by its current editor-in-chief Roy Goodacre.
In 2005, the Siuzdak lab was engaged in identifying metabolites associated with sepsis and in an effort to address the issue of statistically identifying the most relevant dysregulated metabolites across hundreds of LC/MS datasets, the first algorithm was developed to allow for the nonlinear alignment of mass spectrometry metabolomics data. Called XCMS,[20] it has since (2012)[21] been developed as an online tool and as of 2019 (with METLIN) has over 30,000 registered users.
On 23 January 2007, the
As late as mid-2010, metabolomics was still considered an "emerging field".[26] 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.[26]
In 2015, real-time metabolome profiling was demonstrated for the first time.[27]
Metabolome
The
The first metabolite database (called
In January 2007, scientists at the University of Alberta and the University of Calgary completed the first draft of the human metabolome. The Human Metabolome Database (HMDB) is perhaps the most extensive public metabolomic spectral database to date[30] and is a freely available electronic database (www.hmdb.ca) containing detailed information about small molecule metabolites found in the human body. It is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education. The database is designed to contain or link three kinds of data:
- Chemical data,
- Clinical data and
- Molecular biology/biochemistry data.
The database contains 220,945 metabolite entries including both water-soluble and lipid soluble metabolites. Additionally, 8,610 protein sequences (enzymes and transporters) are linked to these metabolite entries. Each MetaboCard entry contains 130 data fields with 2/3 of the information being devoted to chemical/clinical data and the other 1/3 devoted to enzymatic or biochemical data.[31] The version 3.5 of the HMDB contains >16,000 endogenous metabolites, >1,500 drugs and >22,000 food constituents or food metabolites.[32] This information, available at the Human Metabolome Database and based on analysis of information available in the current scientific literature, is far from complete.[33] In contrast, much more is known about the metabolomes of other organisms. For example, over 50,000 metabolites have been characterized from the plant kingdom, and many thousands of metabolites have been identified and/or characterized from single plants.[34][35]
Each type of cell and tissue has a unique metabolic ‘fingerprint’ that can elucidate organ or tissue-specific information. Bio-specimens used for metabolomics analysis include but not limit to plasma, serum, urine, saliva, feces, muscle, sweat, exhaled breath and gastrointestinal fluid.[36] 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.[37] Genome can tell what could happen, transcriptome can tell what appears to be happening, proteome can tell what makes it happen and metabolome can tell what has happened and what is happening.[38]
Metabolites
Part of a series on |
Microbiomes |
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The
Metabonomics
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.[44] This approach was pioneered by Jeremy Nicholson at Murdoch University 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.[45][46][47]
There has been some disagreement over the exact differences between 'metabolomics' and 'metabonomics'. The difference between the two terms is not related to choice of analytical platform: although metabonomics is more associated with
Exometabolomics
Exometabolomics, or "metabolic footprinting", is the study of extracellular metabolites. It uses many techniques from other subfields of metabolomics, and has applications in
Analytical technologies
The typical workflow of metabolomics studies is shown in the figure. First, samples are collected from tissue, plasma, urine, saliva, cells, etc. Next, metabolites extracted often with the addition of internal standards and derivatization.
Separation methods
Initially, analytes in a metabolomic sample comprise a highly complex mixture. This complex mixture can be simplified prior to detection by separating some analytes from others. Separation achieves various goals: analytes which cannot be resolved by the detector may be separated in this step; in MS analysis, ion suppression is reduced; the retention time of the analyte serves as information regarding its identity. This separation step is not mandatory and is often omitted in NMR and "shotgun" based approaches such as shotgun lipidomics.
Capillary electrophoresis (CE) has a higher theoretical separation efficiency than HPLC (although requiring much more time per separation), and is suitable for use with a wider range of metabolite classes than is GC. As for all electrophoretic techniques, it is most appropriate for charged analytes.[53]
Detection methods
For analysis by mass spectrometry, the analytes must be imparted with a charge and transferred to the gas phase. Electron ionization (EI) is the most common ionization technique applied to GC separations as it is amenable to low pressures. EI also produces fragmentation of the analyte, both providing structural information while increasing the complexity of the data and possibly obscuring the molecular ion. Atmospheric-pressure chemical ionization (APCI) is an atmospheric pressure technique that can be applied to all the above separation techniques. APCI is a gas phase ionization method, which provides slightly more aggressive ionization than ESI which is suitable for less polar compounds. Electrospray ionization (ESI) is the most common ionization technique applied in LC/MS. This soft ionization is most successful for polar molecules with ionizable functional groups. Another commonly used soft ionization technique is secondary electrospray ionization (SESI).
In the 2000s, surface-based mass analysis has seen a resurgence, with new MS technologies focused on increasing sensitivity, minimizing background, and reducing sample preparation. The ability to analyze metabolites directly from biofluids and tissues continues to challenge current MS technology, largely because of the limits imposed by the complexity of these samples, which contain thousands to tens of thousands of metabolites. Among the technologies being developed to address this challenge is Nanostructure-Initiator MS (NIMS),
Nuclear magnetic resonance (NMR) spectroscopy is the only detection technique which does not rely on separation of the analytes, and the sample can thus be recovered for further analyses. All kinds of small molecule metabolites can be measured simultaneously - in this sense, NMR is close to being a universal detector. The main advantages of NMR are high analytical reproducibility and simplicity of sample preparation. Practically, however, it is relatively insensitive compared to mass spectrometry-based techniques.[57][58]
Although NMR and MS are the most widely used, modern day techniques other methods of detection that have been used. These include
Technology | Sensitivity (LOD) | Sample volume | Compatible with gases | Compatible with liquids | Compatible with solids | Start-up cost | Can be used in metabolite imaging (MALDI or DESI) | Advantages | Disadvantages |
---|---|---|---|---|---|---|---|---|---|
GC-MS | 0.5 µM | 0.1-0.2 mL | Yes | Yes | No | <$300,000 | No |
|
|
LC-MS | 0.5 nM | 10—100 µL | No | Yes | Yes | >$300,000 | Yes |
|
|
NMR spectroscopy | 5 µM | 10—100 µL | No | Yes | Yes | >US$1 million | Yes |
|
|
Statistical methods
The data generated in metabolomics usually consist of measurements performed on subjects under various conditions. These measurements may be digitized spectra, or a list of metabolite features. In its simplest form, this generates a matrix with rows corresponding to subjects and columns corresponding with metabolite features (or vice versa).[8] Several statistical programs are currently available for analysis of both NMR and mass spectrometry data. A great number of free software are already available for the analysis of metabolomics data shown in the table. Some statistical tools listed in the table were designed for NMR data analyses were also useful for MS data.[61] For mass spectrometry data, software is available that identifies molecules that vary in subject groups on the basis of mass-over-charge value and sometimes retention time depending on the experimental design.[62]
Once metabolite data matrix is determined, unsupervised data reduction techniques (e.g. PCA) can be used to elucidate patterns and connections. In many studies, including those evaluating drug-toxicity and some disease models, the metabolites of interest are not known a priori. This makes unsupervised methods, those with no prior assumptions of class membership, a popular first choice. The most common of these methods includes principal component analysis (PCA) which can efficiently reduce the dimensions of a dataset to a few which explain the greatest variation.[37] When analyzed in the lower-dimensional PCA space, clustering of samples with similar metabolic fingerprints can be detected. PCA algorithms aim to replace all correlated variables with a much smaller number of uncorrelated variables (referred to as principal components (PCs)) and retain most of the information in the original dataset.[63] This clustering can elucidate patterns and assist in the determination of disease biomarkers – metabolites that correlate most with class membership.
Linear models are commonly used for metabolomics data, but are affected by
Machine learning and data mining
Machine learning is a powerful tool that can be used in metabolomics analysis. Recently, scientists have developed retention time prediction software. These tools allow researchers to apply artificial intelligence to the retention time prediction of small molecules in complex mixture, such as human plasma, plant extracts, foods, or microbial cultures. Retention time prediction increases the identification rate in liquid chromatography and can lead to an improved biological interpretation of metabolomics data.[66]
Key applications
Toxicity assessment/toxicology by metabolic profiling (especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical (or mixture of chemicals). In many cases, the observed changes can be related to specific syndromes, e.g. a specific lesion in liver or kidney. This is of particular relevance to pharmaceutical companies wanting to test the toxicity of potential drug candidates: if a compound can be eliminated before it reaches clinical trials on the grounds of adverse toxicity, it saves the enormous expense of the trials.[48]
For
Metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes.[69] This bioinformatics-based pairing method enables natural product discovery at a larger-scale by refining non-targeted metabolomic analyses to identify small molecules with related biosynthesis and to focus on those that may not have previously well known structures.
Fluxomics is a further development of metabolomics. The disadvantage of metabolomics is that it only provides the user with abundances or concentrations of metabolites, while fluxomics determines the reaction rates of metabolic reactions and can trace metabolites in a biological system over time.
Plant metabolomics is designed to study the overall changes in metabolites of plant samples and then conduct deep data mining and chemometric analysis. Specialized metabolites are considered components of plant defense systems biosynthesized in response to biotic and abiotic stresses.[74] Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops.[75]
See also
- Epigenomics
- Fluxomics
- Genomics
- Lipidomics
- Molecular epidemiology
- Molecular medicine
- Molecular pathology
- Precision medicine
- Proteomics
- Transcriptomics
- XCMS Online, a bioinformatics software designed for statistical analysis of mass spectrometry data
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Further reading
- Bundy JG, Davey MP, Viant MR (2009). "Environmental metabolomics: A critical review and future perspectives". Metabolomics. 5: 3–21. S2CID 22179989.
- Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A (July 2007). "Metabolomics: available results, current research projects in breast cancer, and future applications". Journal of Clinical Oncology. 25 (19): 2840–2846. PMID 17502626. Archived from the originalon 2008-01-20.
- Ellis DI, Dunn WB, Griffin JL, Allwood JW, Goodacre R (September 2007). "Metabolic fingerprinting as a diagnostic tool". Pharmacogenomics. 8 (9): 1243–1266. PMID 17924839.
- Ellis DI, Goodacre R (August 2006). "Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy". The Analyst. 131 (8): 875–885. PMID 17028718.
- Fan TW, Lorkiewicz PK, Sellers K, Moseley HN, Higashi RM, Lane AN (March 2012). "Stable isotope-resolved metabolomics and applications for drug development". Pharmacology & Therapeutics. 133 (3): 366–391. PMID 22212615.
- Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, et al. (January 2013). "MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data". Nucleic Acids Research. 41 (Database issue): D781–D786. PMID 23109552.
- Tomita M, Nishioka T (2005). Metabolomics: The Frontier of Systems Biology. Springer. ISBN 4-431-25121-9.
- Weckwerth W (2006). Metabolomics: Methods And Protocols (Methods in Molecular Biology). Humana Press. OCLC 493824826.