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Metabolomics

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The central dogma of biology showing the flow of information from DNA to the phenotype. Associated with each stage is the corresponding systems biology tool, from genomics to metabolomics.
The central dogma of biology showing the flow of information from DNA to the phenotype. Associated with each stage is the corresponding systems biology tool, from genomics to 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.

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Metabolite

Metabolite

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.

Metabolome

Metabolome

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.

Messenger RNA

Messenger RNA

In molecular biology, messenger ribonucleic acid (mRNA) is a single-stranded molecule of RNA that corresponds to the genetic sequence of a gene, and is read by a ribosome in the process of synthesizing a protein.

Gene expression

Gene expression

Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product that enables it to produce end products, protein or non-coding RNA, and ultimately affect a phenotype, as the final effect. These products are often proteins, but in non-protein-coding genes such as transfer RNA (tRNA) and small nuclear RNA (snRNA), the product is a functional non-coding RNA. Gene expression is summarized in the central dogma of molecular biology first formulated by Francis Crick in 1958, further developed in his 1970 article, and expanded by the subsequent discoveries of reverse transcription and RNA replication.

Proteomics

Proteomics

Proteomics is the large-scale study of proteins. Proteins are vital parts of living organisms, with many functions such as the formation of structural fibers of muscle tissue, enzymatic digestion of food, or synthesis and replication of DNA. In addition, other kinds of proteins include antibodies that protect an organism from infection, and hormones that send important signals throughout the body.

Gene product

Gene product

A gene product is the biochemical material, either RNA or protein, resulting from expression of a gene. A measurement of the amount of gene product is sometimes used to infer how active a gene is. Abnormal amounts of gene product can be correlated with disease-causing alleles, such as the overactivity of oncogenes which can cause cancer. A gene is defined as "a hereditary unit of DNA that is required to produce a functional product". Regulatory elements include:Promoter region TATA box Polyadenylation sequences Enhancers

Genome

Genome

In the fields of molecular biology and genetics, a genome is all the genetic information of an organism. It consists of nucleotide sequences of DNA. The nuclear genome includes protein-coding genes and non-coding genes, other functional regions of the genome such as regulatory sequences, and often a substantial fraction of 'junk' DNA with no evident function. Almost all eukaryotes have mitochondria and a small mitochondrial genome. Algae and plants also contain chloroplasts with a chloroplast genome.

Transcriptome

Transcriptome

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

Proteome

Proteome

The proteome is the entire set of proteins that is, or can be, expressed by a genome, cell, tissue, or organism at a certain time. It is the set of expressed proteins in a given type of cell or organism, at a given time, under defined conditions. Proteomics is the study of the proteome.

Lipidome

Lipidome

The lipidome refers to the totality of lipids in cells. Lipids are one of the four major molecular components of biological organisms, along with proteins, sugars and nucleic acids. Lipidome is a term coined in the context of omics in modern biology, within the field of lipidomics. It can be studied using mass spectrometry and bioinformatics as well as traditional lab-based methods. The lipidome of a cell can be subdivided into the membrane-lipidome and mediator-lipidome.

Systems biology

Systems biology

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

Omics

Omics

The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics, such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms.

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,[6] who used paper chromatography to suggest characteristic metabolic patterns in urine and saliva were associated with diseases such as schizophrenia. However, it was only through technological advancements in the 1960s and 1970s that it became feasible to quantitatively (as opposed to qualitatively) measure metabolic profiles.[7] 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.[8][9] 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.[10]

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.[11] 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.[8][12] Recent efforts to utilize NMR for metabolomics have been largely driven by the laboratory of Jeremy K. 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.[13][14]

In 1995 liquid chromatography mass spectrometry metabolomics experiments[15] 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,[16][17] for characterizing human metabolites was developed in the Siuzdak laboratory at The Scripps Research Institute. METLIN has since grown and as of July 1, 2019, METLIN contains over 450,000 metabolites and other chemical entities, 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. 2005 was also the year in which the dedicated academic journal Metabolomics first appeared, 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,[18] where the "X" constitutes any chromatographic technology, it has since (2012)[19] been developed as an online tool and as of 2019 (with METLIN) has over 30,000 registered users.

On 23 January 2007, the Human Metabolome Project, led by David S. Wishart, completed the first draft of the human metabolome, consisting of a database of approximately 2500 metabolites, 1200 drugs and 3500 food components.[20][21] Similar projects have been underway in several plant species, most notably Medicago truncatula[22] and Arabidopsis thaliana[23] for several years.

As late as mid-2010, metabolomics was still considered an "emerging field".[24] 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.[24]

In 2015, real-time metabolome profiling was demonstrated for the first time.[25]

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Paper chromatography

Paper chromatography

Paper chromatography is an analytical method used to separate coloured chemicals or substances. It is now primarily used as a teaching tool, having been replaced in the laboratory by other chromatography methods such as thin-layer chromatography (TLC).

Schizophrenia

Schizophrenia

Schizophrenia is a mental disorder characterized by continuous or relapsing episodes of psychosis. Major symptoms include hallucinations, delusions, and disorganized thinking. Other symptoms include social withdrawal, decreased emotional expression, and apathy. Symptoms typically develop gradually, begin during young adulthood, and in many cases never become resolved. There is no objective diagnostic test; diagnosis is based on observed behavior, a history that includes the person's reported experiences, and reports of others familiar with the person. To be diagnosed with schizophrenia, symptoms and functional impairment need to be present for six months (DSM-5) or one month (ICD-11). Many people with schizophrenia have other mental disorders, especially substance use disorders, depressive disorders, anxiety disorders, and obsessive–compulsive disorder.

Linus Pauling

Linus Pauling

Linus Carl Pauling was an American chemist, biochemist, chemical engineer, peace activist, author, and educator. He published more than 1,200 papers and books, of which about 850 dealt with scientific topics. New Scientist called him one of the 20 greatest scientists of all time, and as of 2000, he was rated the 16th most important scientist in history. For his scientific work, Pauling was awarded the Nobel Prize in Chemistry in 1954. For his peace activism, he was awarded the Nobel Peace Prize in 1962. He is one of five people to have won more than one Nobel Prize. Of these, he is the only person to have been awarded two unshared Nobel Prizes, and one of two people to be awarded Nobel Prizes in different fields, the other being Marie Curie.

Adenosine triphosphate

Adenosine triphosphate

Adenosine triphosphate (ATP) is an organic compound that provides energy to drive many processes in living cells, such as muscle contraction, nerve impulse propagation, condensate dissolution, and chemical synthesis. Found in all known forms of life, ATP is often referred to as the "molecular unit of currency" of intracellular energy transfer. When consumed in metabolic processes, it converts either to adenosine diphosphate (ADP) or to adenosine monophosphate (AMP). Other processes regenerate ATP. The human body recycles its own body weight equivalent in ATP each day. It is also a precursor to DNA and RNA, and is used as a coenzyme.

Magic angle spinning

Magic angle spinning

In solid-state NMR spectroscopy, magic-angle spinning (MAS) is a technique routinely used to produce better resolution NMR spectra. MAS NMR consists in spinning the sample at the magic angle θm with respect to the direction of the magnetic field.

Jeremy K. Nicholson

Jeremy K. Nicholson

Jeremy K. Nicholson is a professor and pro vice chancellor of Health Sciences at Murdoch University in Perth, Western Australia, where he leads the Australian National Phenome Centre. He is also an emeritus professor of Biological Chemistry at Imperial College London and the director and principal investigator of the MRC-NIHR National Phenome Centre that is based in Imperial College.

Imperial College London

Imperial College London

Imperial College London is a public research university in London, United Kingdom. Its history began with Prince Albert, consort of Queen Victoria, who developed his vision for a cultural area that included the Royal Albert Hall, Victoria & Albert Museum, Natural History Museum and royal colleges. In 1907, Imperial College was established by a royal charter, which unified the Royal College of Science, Royal School of Mines, and City and Guilds of London Institute. In 1988, the Imperial College School of Medicine was formed by merging with St Mary's Hospital Medical School. In 2004, Queen Elizabeth II opened the Imperial College Business School.

Gary Siuzdak

Gary Siuzdak

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.

Richard Lerner

Richard Lerner

Richard Alan Lerner was an American research chemist. Best known for his work on catalytic antibodies, Lerner served as President of The Scripps Research Institute (TSRI) until January 1, 2012, and was a member of its Skaggs Institute for Chemical Biology, in La Jolla, California.

Oleamide

Oleamide

Oleamide is an organic compound with the formula CH3(CH2)7CH=CH(CH2)7CONH2(. It is the amide derived from the fatty acid oleic acid. It is a colorless waxy solid and occurs in nature. Sometimes labeled as a fatty acid primary amide (FAPA), it is biosynthesized from N-oleoylglycine.

METLIN

METLIN

The METLIN Metabolite and Chemical Entity Database is the largest repository of experimental tandem mass spectrometry and neutral loss data acquired from standards. The tandem mass spectrometry data on over 870,000 molecular standards is provided to facilitate the identification of chemical entities from tandem mass spectrometry experiments. In addition to the identification of known molecules it is also useful for identifying unknowns using its similarity searching technology. All tandem mass spectrometry data comes from the experimental analysis of standards at multiple collision energies and in both positive and negative ionization modes.

Roy Goodacre

Roy Goodacre

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.

Metabolome

Human metabolome project
Human metabolome project

The metabolome refers to the complete set of small-molecule ([20] 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.[26][27] 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 fragmentation data from tandem mass spectrometry experiments was developed by the Siuzdak lab at The Scripps Research Institute in 2005.[16][17] METLIN contains over 450,000 metabolites and other chemical entities, each compound having experimental tandem mass spectrometry data. In 2006,[18] the Siuzdak lab also developed the first algorithm to allow for the nonlinear alignment of mass spectrometry metabolomics data. Called XCMS, where the "X" constitutes any chromatographic technology, it has since (2012)[19] been developed as an online tool and as of 2019 (with METLIN) has over 30,000 registered users.

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.[28] The HMDB stores more than 110,000 different metabolite entries. They catalogued approximately 1200 drugs and 3500 food components that can be found in the human body, as reported in the literature.[20] 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.[29] 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.[30][31]

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.[32] 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.[33] 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.[34]

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Metabolome

Metabolome

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.

Human Metabolome Database

Human Metabolome Database

The Human Metabolome Database (HMDB) is a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body. Created by the Human Metabolome Project funded by Genome Canada. One of the first dedicated metabolomics databases, the HMDB facilitates human metabolomics research, including the identification and characterization of human metabolites using NMR spectroscopy, GC-MS spectrometry and LC/MS spectrometry. To aid in this discovery process, the HMDB contains three kinds of data: 1) chemical data, 2) clinical data, and 3) molecular biology/biochemistry data. The chemical data includes 41,514 metabolite structures with detailed descriptions along with nearly 10,000 NMR, GC-MS and LC/MS spectra.

Proteomics

Proteomics

Proteomics is the large-scale study of proteins. Proteins are vital parts of living organisms, with many functions such as the formation of structural fibers of muscle tissue, enzymatic digestion of food, or synthesis and replication of DNA. In addition, other kinds of proteins include antibodies that protect an organism from infection, and hormones that send important signals throughout the body.

METLIN

METLIN

The METLIN Metabolite and Chemical Entity Database is the largest repository of experimental tandem mass spectrometry and neutral loss data acquired from standards. The tandem mass spectrometry data on over 870,000 molecular standards is provided to facilitate the identification of chemical entities from tandem mass spectrometry experiments. In addition to the identification of known molecules it is also useful for identifying unknowns using its similarity searching technology. All tandem mass spectrometry data comes from the experimental analysis of standards at multiple collision energies and in both positive and negative ionization modes.

University of Alberta

University of Alberta

The University of Alberta, also known as U of A or UAlberta, is a public research university located in Edmonton, Alberta, Canada. It was founded in 1908 by Alexander Cameron Rutherford, the first premier of Alberta, and Henry Marshall Tory, the university's first president. It was enabled through the Post-secondary Learning Act.

University of Calgary

University of Calgary

The University of Calgary is a public research university located in Calgary, Alberta, Canada. The University of Calgary started in 1944 as the Calgary branch of the University of Alberta, founded in 1908, prior to being instituted into a separate, autonomous university in 1966. It is composed of 14 faculties and over 85 research institutes and centres. The main campus is located in the northwest quadrant of the city near the Bow River and a smaller south campus is located in the city centre. The main campus houses most of the research facilities and works with provincial and federal research and regulatory agencies, several of which are housed next to the campus such as the Geological Survey of Canada. The main campus covers approximately 200 hectares.

Drug

Drug

A drug is any chemical substance that causes a change in an organism's physiology or psychology when consumed. Drugs are typically distinguished from food and substances that provide nutritional support. Consumption of drugs can be via inhalation, injection, smoking, ingestion, absorption via a patch on the skin, suppository, or dissolution under the tongue.

Genome

Genome

In the fields of molecular biology and genetics, a genome is all the genetic information of an organism. It consists of nucleotide sequences of DNA. The nuclear genome includes protein-coding genes and non-coding genes, other functional regions of the genome such as regulatory sequences, and often a substantial fraction of 'junk' DNA with no evident function. Almost all eukaryotes have mitochondria and a small mitochondrial genome. Algae and plants also contain chloroplasts with a chloroplast genome.

Transcriptome

Transcriptome

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

Proteome

Proteome

The proteome is the entire set of proteins that is, or can be, expressed by a genome, cell, tissue, or organism at a certain time. It is the set of expressed proteins in a given type of cell or organism, at a given time, under defined conditions. Proteomics is the study of the proteome.

Metabolites

Metabolites are the substrates, intermediates and products of metabolism. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1.5 kDa in size.[20] However, there are exceptions to this depending on the sample and detection method. For example, macromolecules such as lipoproteins and albumin are reliably detected in NMR-based metabolomics studies of blood plasma.[35] In plant-based metabolomics, it is common to refer to "primary" and "secondary" metabolites.[3] A primary metabolite is directly involved in the normal growth, development, and reproduction. A secondary metabolite is not directly involved in those processes, but usually has important ecological function. Examples include antibiotics and pigments.[36] By contrast, in human-based metabolomics, it is more common to describe metabolites as being either endogenous (produced by the host organism) or exogenous.[37][38] Metabolites of foreign substances such as drugs are termed xenometabolites.[39]

The metabolome forms a large network of metabolic reactions, where outputs from one enzymatic chemical reaction are inputs to other chemical reactions. Such systems have been described as hypercycles.

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Metabolite

Metabolite

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.

Metabolism

Metabolism

Metabolism is the set of life-sustaining chemical reactions in organisms. The three main purposes of metabolism are: the conversion of the energy in food to energy available to run cellular processes; the conversion of food to building blocks for proteins, lipids, nucleic acids, and some carbohydrates; and the elimination of metabolic wastes. These enzyme-catalyzed reactions allow organisms to grow and reproduce, maintain their structures, and respond to their environments. The word metabolism can also refer to the sum of all chemical reactions that occur in living organisms, including digestion and the transportation of substances into and between different cells, in which case the above described set of reactions within the cells is called intermediary metabolism.

Dalton (unit)

Dalton (unit)

The dalton or unified atomic mass unit is a Non-SI unit of mass widely used in physics and chemistry. It is defined as 1⁄12 of the mass of an unbound neutral atom of carbon-12 in its nuclear and electronic ground state and at rest. The atomic mass constant, denoted mu, is defined identically, giving mu = m(12C)/12 = 1 Da.

Lipoprotein

Lipoprotein

A lipoprotein is a biochemical assembly whose primary function is to transport hydrophobic lipid molecules in water, as in blood plasma or other extracellular fluids. They consist of a triglyceride and cholesterol center, surrounded by a phospholipid outer shell, with the hydrophilic portions oriented outward toward the surrounding water and lipophilic portions oriented inward toward the lipid center. A special kind of protein, called apolipoprotein, is embedded in the outer shell, both stabilising the complex and giving it a functional identity that determines its role.

Albumin

Albumin

Albumin is a family of globular proteins, the most common of which are the serum albumins. All the proteins of the albumin family are water-soluble, moderately soluble in concentrated salt solutions, and experience heat denaturation. Albumins are commonly found in blood plasma and differ from other blood proteins in that they are not glycosylated. Substances containing albumins are called albuminoids.

Secondary metabolite

Secondary metabolite

Secondary metabolites, also called specialised metabolites, toxins, secondary products, or natural products, are organic compounds produced by any lifeform, e.g. bacteria, fungi, animals, or plants, which are not directly involved in the normal growth, development, or reproduction of the organism. Instead, they generally mediate ecological interactions, which may produce a selective advantage for the organism by increasing its survivability or fecundity. Specific secondary metabolites are often restricted to a narrow set of species within a phylogenetic group. Secondary metabolites often play an important role in plant defense against herbivory and other interspecies defenses. Humans use secondary metabolites as medicines, flavourings, pigments, and recreational drugs.

Ecology

Ecology

Ecology is the study of the relationships between living organisms, including humans, and their physical environment. Ecology considers organisms at the individual, population, community, ecosystem, and biosphere level. Ecology overlaps with the closely related sciences of biogeography, evolutionary biology, genetics, ethology, and natural history. Ecology is a branch of biology, and it is not synonymous with environmentalism.

Pigment

Pigment

A pigment is a colored material that is completely or nearly insoluble in water. In contrast, dyes are typically soluble, at least at some stage in their use. Generally dyes are often organic compounds whereas pigments are often inorganic compounds. Pigments of prehistoric and historic value include ochre, charcoal, and lapis lazuli.

Metabolome

Metabolome

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.

Chemical reaction

Chemical reaction

A chemical reaction is a process that leads to the chemical transformation of one set of chemical substances to another. Classically, chemical reactions encompass changes that only involve the positions of electrons in the forming and breaking of chemical bonds between atoms, with no change to the nuclei, and can often be described by a chemical equation. Nuclear chemistry is a sub-discipline of chemistry that involves the chemical reactions of unstable and radioactive elements where both electronic and nuclear changes can occur.

Hypercycle (chemistry)

Hypercycle (chemistry)

In chemistry, a hypercycle is an abstract model of organization of self-replicating molecules connected in a cyclic, autocatalytic manner. It was introduced in an ordinary differential equation (ODE) form by the Nobel Prize in Chemistry winner Manfred Eigen in 1971 and subsequently further extended in collaboration with Peter Schuster. It was proposed as a solution to the error threshold problem encountered during modelling of replicative molecules that hypothetically existed on the primordial Earth. As such, it explained how life on Earth could have begun using only relatively short genetic sequences, which in theory were too short to store all essential information. The hypercycle is a special case of the replicator equation. The most important properties of hypercycles are autocatalytic growth competition between cycles, once-for-ever selective behaviour, utilization of small selective advantage, rapid evolvability, increased information capacity, and selection against parasitic branches.

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.[40] 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.[41][42][43]

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 NMR spectroscopy and metabolomics with mass spectrometry-based techniques, this is simply because of usages amongst different groups that have popularized the different terms. While there is still no absolute agreement, 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. However, in practice, within the field of human disease research there is still a large degree of overlap in the way both terms are used, and they are often in effect synonymous.[44]

Exometabolomics

Exometabolomics, or "metabolic footprinting", is the study of extracellular metabolites. It uses many techniques from other subfields of metabolomics, and has applications in biofuel development, bioprocessing, determining drugs' mechanism of action, and studying intercellular interactions.[45]

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Analytical technologies

Key stages of a metabolomics study
Key stages of a metabolomics study

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.[46] During sample analysis, metabolites are quantified (liquid chromatography or gas chromatography coupled with MS and/or NMR spectroscopy). The raw output data can be used for metabolite feature extraction and further processed before statistical analysis (such as PCA). Many bioinformatic tools and software are available to identify associations with disease states and outcomes, determine significant correlations, and characterize metabolic signatures with existing biological knowledge.[47]

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.

Gas chromatography (GC), especially when interfaced with mass spectrometry (GC-MS), is a widely used separation technique for metabolomic analysis. GC offers very high chromatographic resolution, and can be used in conjunction with a flame ionization detector (GC/FID) or a mass spectrometer (GC-MS). The method is especially useful for identification and quantification of small and volatile molecules.[48] However, a practical limitation of GC is the requirement of chemical derivatization for many biomolecules as only volatile chemicals can be analysed without derivatization. In cases where greater resolving power is required, two-dimensional chromatography (GCxGC) can be applied.

High performance liquid chromatography (HPLC) has emerged as the most common separation technique for metabolomic analysis. With the advent of electrospray ionization, HPLC was coupled to MS. In contrast with GC, HPLC has lower chromatographic resolution, but requires no derivatization for polar molecules, and separates molecules in the liquid phase. Additionally HPLC has the advantage that a much wider range of analytes can be measured with a higher sensitivity than GC methods.[49]

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.[50]

Detection methods

Comparison of most common used metabolomics methods
Comparison of most common used metabolomics methods

Mass spectrometry (MS) is used to identify and quantify metabolites after optional separation by GC, HPLC, or CE. GC-MS was the first hyphenated technique to be developed. Identification leverages the distinct patterns in which analytes fragment. These patterns can be thought of as a mass spectral fingerprint. Libraries exist that allow identification of a metabolite according to this fragmentation pattern. MS is both sensitive and can be very specific. There are also a number of techniques which use MS as a stand-alone technology: the sample is infused directly into the mass spectrometer with no prior separation, and the MS provides sufficient selectivity to both separate and to detect metabolites.

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 applies 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).

Surface-based mass analysis has seen a resurgence in the past decade, 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),[51][52] a desorption/ ionization approach that does not require the application of matrix and thereby facilitates small-molecule (i.e., metabolite) identification. MALDI is also used; however, the application of a MALDI matrix can add significant background at

Secondary ion mass spectrometry (SIMS) was one of the first matrix-free desorption/ionization approaches used to analyze metabolites from biological samples. SIMS uses a high-energy primary ion beam to desorb and generate secondary ions from a surface. The primary advantage of SIMS is its high spatial resolution (as small as 50 nm), a powerful characteristic for tissue imaging with MS. However, SIMS has yet to be readily applied to the analysis of biofluids and tissues because of its limited sensitivity at >500 Da and analyte fragmentation generated by the high-energy primary ion beam. Desorption electrospray ionization (DESI) is a matrix-free technique for analyzing biological samples that uses a charged solvent spray to desorb ions from a surface. Advantages of DESI are that no special surface is required and the analysis is performed at ambient pressure with full access to the sample during acquisition. A limitation of DESI is spatial resolution because "focusing" the charged solvent spray is difficult. However, a recent development termed laser ablation ESI (LAESI) is a promising approach to circumvent this limitation. Most recently, ion trap techniques such as orbitrap mass spectrometry are also applied to metabolomics research.[53]

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.[54][55] Comparison of most common used metabolomics methods is shown in the table.

Although NMR and MS are the most widely used, modern day techniques other methods of detection that have been used. These include Fourier-transform ion cyclotron resonance,[56] ion-mobility spectrometry,[57] electrochemical detection (coupled to HPLC), Raman spectroscopy and radiolabel (when combined with thin-layer chromatography).

Discover more about Analytical technologies related topics

Gas chromatography

Gas chromatography

Gas chromatography (GC) is a common type of chromatography used in analytical chemistry for separating and analyzing compounds that can be vaporized without decomposition. Typical uses of GC include testing the purity of a particular substance, or separating the different components of a mixture. In preparative chromatography, GC can be used to prepare pure compounds from a mixture.

Mass spectrometry

Mass spectrometry

Mass spectrometry (MS) is an analytical technique that is used to measure the mass-to-charge ratio of ions. The results are presented as a mass spectrum, a plot of intensity as a function of the mass-to-charge ratio. Mass spectrometry is used in many different fields and is applied to pure samples as well as complex mixtures.

Principal component analysis

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science.

Ion suppression in liquid chromatography–mass spectrometry

Ion suppression in liquid chromatography–mass spectrometry

Ion suppression in LC-MS and LC-MS/MS refers to reduced detector response, or signal:noise as a manifested effect of competition for ionisation efficiency in the ionisation source, between the analyte(s) of interest and other endogenous or exogenous species which have not been removed from the sample matrix during sample preparation. Ion suppression is not strictly a problem unless interfering compounds elute at the same time as the analyte of interest. In cases where ion suppressing species do co-elute with an analyte, the effects on the important analytical parameters including precision, accuracy and limit of detection can be extensive, severely limiting the validity of an assay's results.

Shotgun lipidomics

Shotgun lipidomics

In lipidomics, the process of shotgun lipidomics uses analytical chemistry to investigate the biological function, significance, and sequelae of alterations in lipids and protein constituents mediating lipid metabolism, trafficking, or biological function in cells. Lipidomics has been greatly facilitated by recent advances in, and novel applications of, electrospray ionization mass spectrometry (ESI/MS).

Flame ionization detector

Flame ionization detector

A flame ionization detector (FID) is a scientific instrument that measures analytes in a gas stream. It is frequently used as a detector in gas chromatography. The measurement of ion per unit time make this a mass sensitive instrument. Standalone FIDs can also be used in applications such as landfill gas monitoring, fugitive emissions monitoring and internal combustion engine emissions measurement in stationary or portable instruments.

Electrospray ionization

Electrospray ionization

Electrospray ionization (ESI) is a technique used in mass spectrometry to produce ions using an electrospray in which a high voltage is applied to a liquid to create an aerosol. It is especially useful in producing ions from macromolecules because it overcomes the propensity of these molecules to fragment when ionized. ESI is different from other ionization processes since it may produce multiple-charged ions, effectively extending the mass range of the analyser to accommodate the kDa-MDa orders of magnitude observed in proteins and their associated polypeptide fragments.

Capillary electrophoresis

Capillary electrophoresis

Capillary electrophoresis (CE) is a family of electrokinetic separation methods performed in submillimeter diameter capillaries and in micro- and nanofluidic channels. Very often, CE refers to capillary zone electrophoresis (CZE), but other electrophoretic techniques including capillary gel electrophoresis (CGE), capillary isoelectric focusing (CIEF), capillary isotachophoresis and micellar electrokinetic chromatography (MEKC) belong also to this class of methods. In CE methods, analytes migrate through electrolyte solutions under the influence of an electric field. Analytes can be separated according to ionic mobility and/or partitioning into an alternate phase via non-covalent interactions. Additionally, analytes may be concentrated or "focused" by means of gradients in conductivity and pH.

High-performance liquid chromatography

High-performance liquid chromatography

High-performance liquid chromatography (HPLC), formerly referred to as high-pressure liquid chromatography, is a technique in analytical chemistry used to separate, identify, and quantify each component in a mixture. It relies on pumps to pass a pressurized liquid solvent containing the sample mixture through a column filled with a solid adsorbent material. Each component in the sample interacts slightly differently with the adsorbent material, causing different flow rates for the different components and leading to the separation of the components as they flow out of the column.

Statistical methods

Software List for Metabolomic Analysis
Software List for Metabolomic Analysis

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.[58] 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.[59]

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.[33] 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.[60] 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 multicollinearity. On the other hand, multivariate statistics are thriving methods for high-dimensional correlated metabolomics data, of which the most popular one is Projection to Latent Structures (PLS) regression and its classification version PLS-DA. Other data mining methods, such as random forest, support-vector machines, etc. are received increasing attention for untargeted metabolomics data analysis.[61] In the case of univariate methods, variables are analyzed one by one using classical statistics tools (such as Student's t-test, ANOVA or mixed models) and only these with sufficient small p-values are considered relevant.[32] However, correction strategies should be used to reduce false discoveries when multiple comparisons are conducted since there is no standard method  for measuring the total amount of metabolites directly in untargeted metabolomics.[62] For multivariate analysis, models should always be validated to ensure the results can be generalized.

Discover more about Statistical methods related topics

Mass spectrometry

Mass spectrometry

Mass spectrometry (MS) is an analytical technique that is used to measure the mass-to-charge ratio of ions. The results are presented as a mass spectrum, a plot of intensity as a function of the mass-to-charge ratio. Mass spectrometry is used in many different fields and is applied to pure samples as well as complex mixtures.

Principal component analysis

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science.

Multicollinearity

Multicollinearity

In statistics, multicollinearity is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. In this situation, the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. Multicollinearity does not reduce the predictive power or reliability of the model as a whole, at least within the sample data set; it only affects calculations regarding individual predictors. That is, a multivariate regression model with collinear predictors can indicate how well the entire bundle of predictors predicts the outcome variable, but it may not give valid results about any individual predictor, or about which predictors are redundant with respect to others.

Multivariate statistics

Multivariate statistics

Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied.

Partial least squares regression

Partial least squares regression

Partial least squares regression is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical.

Data mining

Data mining

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

Random forest

Random forest

Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees' habit of overfitting to their training set. Random forests generally outperform decision trees, but their accuracy is lower than gradient boosted trees. However, data characteristics can affect their performance.

Support vector machine

Support vector machine

In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.

Student's t-test

Student's t-test

A t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribution under the null hypothesis. It is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is estimated based on the data, the test statistic—under certain conditions—follows a Student's t distribution. The t-test's most common application is to test whether the means of two populations are different.

Analysis of variance

Analysis of variance

Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.

Multiple comparisons problem

Multiple comparisons problem

In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.

Machine learning and data mining

Machine learning is also 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.[63]

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.[44]

For 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. Such advances are most likely to come from model organisms such as Saccharomyces cerevisiae and Arabidopsis thaliana. The Cravatt laboratory at The Scripps Research Institute has recently applied this technology to mammalian systems, identifying the N-acyltaurines as previously uncharacterized endogenous substrates for the enzyme fatty acid amide hydrolase (FAAH) and the monoalkylglycerol ethers (MAGEs) as endogenous substrates for the uncharacterized hydrolase KIAA1363.[64][65]

Metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes.[66] 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 steady-state level information, while fluxomics determines the reaction rates of metabolic reactions and can trace metabolites in a biological system over time.

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. Metabolomics is one means to determine a biological endpoint, or metabolic fingerprint, which reflects the balance of all these forces on an individual's metabolism.[67][68]

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.[69]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.[70]

Discover more about Key applications related topics

Medication

Medication

A medication is a drug used to diagnose, cure, treat, or prevent disease. Drug therapy (pharmacotherapy) is an important part of the medical field and relies on the science of pharmacology for continual advancement and on pharmacy for appropriate management.

Clinical trial

Clinical trial

Clinical trials are prospective biomedical or behavioral research studies on human participants designed to answer specific questions about biomedical or behavioral interventions, including new treatments and known interventions that warrant further study and comparison. Clinical trials generate data on dosage, safety and efficacy. They are conducted only after they have received health authority/ethics committee approval in the country where approval of the therapy is sought. These authorities are responsible for vetting the risk/benefit ratio of the trial—their approval does not mean the therapy is 'safe' or effective, only that the trial may be conducted.

Functional genomics

Functional genomics

Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "gene-by-gene" approach.

Phenotype

Phenotype

In genetics, the phenotype is the set of observable characteristics or traits of an organism. The term covers the organism's morphology or physical form and structure, its developmental processes, its biochemical and physiological properties, its behavior, and the products of behavior. An organism's phenotype results from two basic factors: the expression of an organism's genetic code, or its genotype, and the influence of environmental factors. Both factors may interact, further affecting phenotype. When two or more clearly different phenotypes exist in the same population of a species, the species is called polymorphic. A well-documented example of polymorphism is Labrador Retriever coloring; while the coat color depends on many genes, it is clearly seen in the environment as yellow, black, and brown. Richard Dawkins in 1978 and then again in his 1982 book The Extended Phenotype suggested that one can regard bird nests and other built structures such as caddis-fly larva cases and beaver dams as "extended phenotypes".

Gene

Gene

In biology, the word gene can have several different meanings. The Mendelian gene is a basic unit of heredity and the molecular gene is a sequence of nucleotides in DNA that is transcribed to produce a functional RNA. There are two types of molecular genes: protein-coding genes and noncoding genes.

Model organism

Model organism

A model organism is a non-human species that is extensively studied to understand particular biological phenomena, with the expectation that discoveries made in the model organism will provide insight into the workings of other organisms. Model organisms are widely used to research human disease when human experimentation would be unfeasible or unethical. This strategy is made possible by the common descent of all living organisms, and the conservation of metabolic and developmental pathways and genetic material over the course of evolution.

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana, the thale cress, mouse-ear cress or arabidopsis, is a small flowering plant native to Eurasia and Africa. A. thaliana is considered a weed; it is found along the shoulders of roads and in disturbed land.

Benjamin Cravatt III

Benjamin Cravatt III

Benjamin Franklin Cravatt III is a professor in the Department of Chemistry at The Scripps Research Institute in La Jolla, California. Considered a co-inventor of activity based proteomics and a substantial contributor to research on the endocannabinoid system, he is a prominent figure in the nascent field of chemical biology. Cravatt was elected to the National Academy of Sciences in 2014, and the American Academy of Arts and Sciences in 2016. He is Gilula Chair of Chemical Biology, a Cope Scholar, and a Searle Scholar.

Mammal

Mammal

Mammals are a group of vertebrate animals constituting the class Mammalia, characterized by the presence of mammary glands which in females produce milk for feeding (nursing) their young, a neocortex, fur or hair, and three middle ear bones. These characteristics distinguish them from reptiles from which they diverged in the Carboniferous, over 300 million years ago. Around 6,400 extant species of mammals have been described divided into 29 orders. The largest orders, in terms of number of species, are the rodents, bats, and Eulipotyphla. The next three are the Primates, the Artiodactyla, and the Carnivora.

Fatty acid amide hydrolase

Fatty acid amide hydrolase

Fatty acid amide hydrolase or FAAH is a member of the serine hydrolase family of enzymes. It was first shown to break down anandamide in 1993. In humans, it is encoded by the gene FAAH.

Hydrolase

Hydrolase

Hydrolase is a class of enzyme that commonly perform as biochemical catalysts that use water to break a chemical bond, which typically results in dividing a larger molecule into smaller molecules. Some common examples of hydrolase enzymes are esterases including lipases, phosphatases, glycosidases, peptidases, and nucleosidases.

Fluxomics

Fluxomics

Fluxomics describes the various approaches that seek to determine the rates of metabolic reactions within a biological entity. While metabolomics can provide instantaneous information on the metabolites in a biological sample, metabolism is a dynamic process. The significance of fluxomics is that metabolic fluxes determine the cellular phenotype. It has the added advantage of being based on the metabolome which has fewer components than the genome or proteome.

Source: "Metabolomics", Wikipedia, Wikimedia Foundation, (2022, November 25th), https://en.wikipedia.org/wiki/Metabolomics.

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