In biochemistry, a metabolite is an intermediate or end product of metabolism. The term is usually used for small molecules. Metabolites have various functions, including fuel, structure, signaling, stimulatory and inhibitory effects on enzymes, catalytic activity of their own, defense, and interactions with other organisms.
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. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. 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, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are indeed quantifiable correlations between the metabolome and the other cellular ensembles, which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. 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.
The metabolome refers to the complete set of small-molecule chemicals found within a biological sample. The biological sample can be a cell, a cellular organelle, an organ, a tissue, a tissue extract, a biofluid or an entire organism. The small molecule chemicals found in a given metabolome may include both endogenous metabolites that are naturally produced by an organism as well as exogenous chemicals that are not naturally produced by an organism.
Liquid chromatography–mass spectrometry (LC–MS) is an analytical chemistry technique that combines the physical separation capabilities of liquid chromatography with the mass analysis capabilities of mass spectrometry (MS). Coupled chromatography - MS systems are popular in chemical analysis because the individual capabilities of each technique are enhanced synergistically. While liquid chromatography separates mixtures with multiple components, mass spectrometry provides spectral information that may help to identify each separated component. MS is not only sensitive, but provides selective detection, relieving the need for complete chromatographic separation. LC-MS is also appropriate for metabolomics because of its good coverage of a wide range of chemicals. This tandem technique can be used to analyze biochemical, organic, and inorganic compounds commonly found in complex samples of environmental and biological origin. Therefore, LC-MS may be applied in a wide range of sectors including biotechnology, environment monitoring, food processing, and pharmaceutical, agrochemical, and cosmetic industries. Since the early 2000s, LC-MS has also begun to be used in clinical applications.
In biochemistry, the metallome is the distribution of metal ions in a cellular compartment. The term was coined in analogy with proteome as metallomics is the study of metallome: the "comprehensive analysis of the entirety of metal and metalloid species within a cell or tissue type". Therefore, metallomics can be considered a branch of metabolomics, even though the metals are not typically considered as metabolites.
The Joint Genome Institute (JGI) is a scientific user facility for integrative genomic science at Lawrence Berkeley National Laboratory. The mission of the JGI is to advance genomics research in support of the United States Department of Energy's (DOE) missions of energy and the environment. It is one of three national scientific user facilities supported by the Office of Biological and Environmental Research (BER) within the Department of Energy's Office of Research. These BER facilities are part of a more extensive network of 28 national scientific user facilities that operate at the DOE national laboratories.
Analytical Abstracts is a current awareness and information retrieval service for analytical chemistry, published by the Royal Society of Chemistry in Cambridge, United Kingdom. It was first published in the mid-1950s by the Society for Analytical Chemistry which merged with other societies in 1980 to form the Royal Society of Chemistry.
Calcitroic acid (1α-hydroxy-23-carboxy-24,25,26,27-tetranorvitamin D3) is a major metabolite of 1α,25-dihydroxyvitamin D3 (calcitriol). Often synthesized in the liver and kidneys, calcitroic acid is generated in the body after vitamin D is first converted into calcitriol, an intermediate in the fortification of bone through the formation and regulation of calcium in the body. During this deactivation process, oxidation reactions at C24 and C23 occur which ultimately lead to side-chain cleavage which helps in the formation of calcitroic acid. These pathways managed by calcitriol are thought to be inactivated through its hydroxylation by the enzyme CYP24A1, also called calcitriol 24-hydroxylase. Specifically, it is thought to be the major route to inactivate vitamin D metabolites.
OpenMS is an open-source project for data analysis and processing in mass spectrometry and is released under the 3-clause BSD licence. It supports most common operating systems including Microsoft Windows, MacOS and Linux.
MetaboAnalyst is a set of online tools for metabolomic data analysis and interpretation, created by members of the Wishart Research Group at the University of Alberta. It was first released in May 2009 and version 2.0 was released in January 2012. MetaboAnalyst provides a variety of analysis methods that have been tailored for metabolomic data. These methods include metabolomic data processing, normalization, multivariate statistical analysis, and data annotation. The current version is focused on biomarker discovery and classification.
Pharmacometabolomics, also known as pharmacometabonomics, is a field which stems from metabolomics, the quantification and analysis of metabolites produced by the body. It refers to the direct measurement of metabolites in an individual's bodily fluids, in order to predict or evaluate the metabolism of pharmaceutical compounds, and to better understand the pharmacokinetic profile of a drug. Alternatively, pharmacometabolomics can be applied to measure metabolite levels following the administration of a pharmaceutical compound, in order to monitor the effects of the compound on certain metabolic pathways(pharmacodynamics). This provides detailed mapping of drug effects on metabolism and the pathways that are implicated in mechanism of variation of response to treatment. In addition, the metabolic profile of an individual at baseline (metabotype) provides information about how individuals respond to treatment and highlights heterogeneity within a disease state. All three approaches require the quantification of metabolites found in bodily fluids and tissue, such as blood or urine, and can be used in the assessment of pharmaceutical treatment options for numerous disease states.
In the field of cellular biology, single-cell analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.
Metabolite Set Enrichment Analysis (MSEA) is a method designed to help metabolomics researchers identify and interpret patterns of metabolite concentration changes in a biologically meaningful way. It is conceptually similar to another widely used tool developed for transcriptomics called Gene Set Enrichment Analysis or GSEA. GSEA uses a collection of predefined gene sets to rank the lists of genes obtained from gene chip studies. By using this “prior knowledge” about gene sets researchers are able to readily identify significant and coordinated changes in gene expression data while at the same time gaining some biological context. MSEA does the same thing by using a collection of predefined metabolite pathways and disease states obtained from the Human Metabolome Database. MSEA is offered as a service both through a stand-alone web server and as part of a larger metabolomics analysis suite called MetaboAnalyst.
Metabolomic Pathway Analysis, shortened to MetPA, is a freely available, user-friendly web server to assist with the identification analysis and visualization of metabolic pathways using metabolomic data. MetPA makes use of advances originally developed for pathway analysis in microarray experiments and applies those principles and concepts to the analysis of metabolic pathways. For input, MetPA expects either a list of compound names or a metabolite concentration table with phenotypic labels. The list of compounds can include common names, HMDB IDs or KEGG IDs with one compound per row. Compound concentration tables must have samples in rows and compounds in columns. MetPA’s output is a series of tables indicating which pathways are significantly enriched as well as a variety of graphs or pathway maps illustrating where and how certain pathways were enriched. MetPA’s graphical output uses a colorful Google-Maps visualization system that allows simple, intuitive data exploration that lets users employ a computer mouse or track pad to select, drag and place images and to seamlessly zoom in and out. Users can explore MetPA’s output using three different views or levels: 1) a metabolome view; 2) a pathway view; 3) a compound view.
Receiver Operating Characteristic Curve Explorer and Tester (ROCCET) is an open-access web server for performing biomarker analysis using ROC curve analyses on metabolomic data sets. ROCCET is designed specifically for performing and assessing a standard binary classification test. ROCCET accepts metabolite data tables, with or without clinical/observational variables, as input and performs extensive biomarker analysis and biomarker identification using these input data. It operates through a menu-based navigation system that allows users to identify or assess those clinical variables and/or metabolites that contain the maximal diagnostic or class-predictive information. ROCCET supports both manual and semi-automated feature selection and is able to automatically generate a variety of mathematical models that maximize the sensitivity and specificity of the biomarker(s) while minimizing the number of biomarkers used in the biomarker model. ROCCET also supports the rigorous assessment of the quality and robustness of newly discovered biomarkers using permutation testing, hold-out testing and cross-validation.
Gary Siuzdak is an American chemist best known for his work in the field of metabolomics, activity metabolomics, and mass spectrometry. His lab discovered indole-3-propionic acid as a gut bacteria derived metabolite in 2009. He is currently the Professor and Director of The Center for Metabolomics and Mass Spectrometry at Scripps Research in La Jolla, California. Siuzdak has also made contributions to virus analysis, viral structural dynamics, as well as developing mass spectrometry imaging technology using nanostructured surfaces. The Siuzdak lab is also responsible for creating the research tools XCMS, METLIN, METLIN Neutral Loss and Q-MRM. As of January 2021, the XCMS/METLIN platform has over 50,000 registered users.
Pablo Sinues is an associate professor at the Department of Biomedical Engineering at the University of Basel and lecturer at the Department of Chemistry and Applied Biosciences at ETH Zürich. He received his Ph.D. in Mechanical Engineering from the Charles III University of Madrid (Spain) and Habilitation in Analytical Chemistry at ETH Zürich. Sinues heads the Translational Breath Research group located at the University Children’s Hospital Basel
Royston "Roy" Goodacre is Chair in Biological Chemistry at the University of Liverpool. With training in both Microbiology and Pyrolysis-Mass Spectrometry, Goodacre runs a multidisciplinary Metabolomics and Raman spectroscopy research group in the Institute of Systems, Molecular and Integrative Biology (ISMIB), and leads ISMIB’s Centre for Metabolomics Research and the Laboratory for Bioanalytical Spectroscopy.
David S. Wishart is a Canadian researcher and a Distinguished University Professor in the Department of Biological Sciences and the Department of Computing Science at the University of Alberta. Wishart also holds cross appointments in the Faculty of Pharmacy and Pharmaceutical Sciences and the Department of Laboratory Medicine and Pathology in the Faculty of Medicine and Dentistry. Additionally, Wishart holds a joint appointment in metabolomics at the Pacific Northwest National Laboratory in Richland, Washington. Wishart is well known for his pioneering contributions to the fields of protein NMR spectroscopy, bioinformatics, cheminformatics and metabolomics. In 2011, Wishart founded the Metabolomics Innovation Centre (TMIC), which is Canada's national metabolomics laboratory.
María del Coral Barbas Arribas is a professor at the Universidad CEU San Pablo in Madrid, Spain who is known for her research on metabolomics and integration of chemical data.
