Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Metabolites include various structure classes such as sugars, lipids, steroids, amino acids, etc. In other words, it determines the relative relationship between metabolites and physiological and pathological changes via qualitative and quantitative analysis of all small molecule metabolites in organisms.
In the late 1940s, Roger Williams suggested that a “metabolic profile” was reflected in each individual’s biological fluids. He demonstrated his theory with paper chromatography to establish patterns in the metabolic components of bodily fluids, such as urine and saliva, of patients with schizophrenia.
Several decades later, advancements in technology enabled quantitative measurements of metabolites. In 1971, Horning and his team first coined the term “metabolic profile” when they showed that compounds present in urine or extracts from tissues could be measured with gas chromatography-mass spectrometry (GC-MS).
At the same time, nuclear magnetic resonance (NMR) technology began to be used to detect metabolites in raw biological samples. NMR began to be utilized in the study of metabolomics in the 1970s. With time, this technology's sensitivity increased due to the use of stronger magnetic fields and magic angle spinning. In 1984, Professor Jeremy Nicholson demonstrated the possible use of NMR spectroscopy in diagnosing diabetes mellitus.
Fig1. Omics. Source: Creative Proteomics
So far, metabolomics, genomics, transcriptomics and proteomics are identified as critical parts of systems biology. The four omics* also build a bridge for us to fully understand a living organism from the micro level to the macro level, and explain the whole process of life from the static part of microscopic DNA molecules to the active part of the secretion of small molecule metabolites.
Target isotone-based analysis:
Untargeted: An intended comprehensive analysis of all the measurable analytes in a sample, including chemical unknowns. Due to its comprehensive nature, untargeted metabolomics is coupled to advanced chemometric techniques, such as multivariate analysis, to reduce the extensive datasets generated into a smaller set of manageable signals. The untargeted approach offers the opportunity for novel target discovery, as coverage of the metabolome is only restricted by the methodologies of sample preparation and the inherent sensitivity and specificity of the analytical technique employed. However, the principal challenges of this approach lie in the protocols and time required to process the extensive amounts of raw data generated, the difficulties in identifying and characterizing unknown small molecules, the reliance on the intrinsic analytical coverage of the platform employed, and the bias towards detection of high-abundance molecules.
Fig2.Comparison of untargeted and targeted metabolomics. Source: Researchgate, by Joseph Evaristo
Targeted: In the targeted approach, the chemical properties of the investigated compounds are known. It is more likely to focus on identifying and quantifying selected metabolites or metabolite classes, such as substrates of an enzyme, direct products of a protein, a particular type of compound, or members of a specific pathway. When utilizing targeted metabolomics, sample preparation is optimized to reduce the dominance of high-abundance molecules in the analyses; in addition, since all analyzed species are clearly defined, analytical artifacts are not carried through to downstream analysis. As a result, novel associations between metabolites are more likely to be illuminated in the context of specific physiological states. The major disadvantage of targeted approaches is limited metabolome coverage, which increases the risk of overlooking the metabolomic response of interest.
Table1. The comparison between untargeted metabolomics and targeted metabolomics
Detection technology—Mass spectrometry (MS) Representation of a Mass Spectrometer divided in four parts depicted as squares. First, the sample, which consists of a solution of different compounds, is ionized using different techniques when enters the ion source of the spectrometer, following a black arrow that is pointing to it. The ions are separated based on their mass over charge ratio before entering the mass analyser, which is displayed as a square to the right of the ion source. The sample goes from the mass analyser to another square called detector following a black arrow. The mass separated ions are converted into measurable signal and are ready to be detected by the detector, the last part of the spectrometer.
Fig3. Mass spectrometry working principles. Source: LABSTER THEORY
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 fields and applied to pure samples and complex mixtures.In a typical MS procedure, a sample, which may be solid, liquid, or gaseous, is ionized, for example, by bombarding it with a beam of electrons. This may cause some of the sample's molecules to break up into positively charged fragments or become positively charged without fragmenting.
Fig4. Gas Chromatography Source: (Evers, 2015) https://doi.org/10.13140/2.1.2125.1367
Fig5. Overview of HPLC Source: SHIMADZU
LC-MS: Liquid chromatography–mass spectrometry. The LC-MS technology uses an HPLC. The individual components in a mixture are first separated, followed by ionization and separation of the ions based on their mass/charge ratio. The separated ions are then directed to a photo or electron multiplier tube detector, which identifies and quantifies each ion. The ion source is an essential component in any MS analysis, as this aids in the efficient generation of ions for analysis.
NMR: Nuclear magnetic resonance. The principle behind NMR is that many nuclei have a spin and all nuclei are electrically charged. If an external magnetic field is applied, an energy transfer is possible between the base energy to a higher energy level (generally a single energy gap). The energy transfer occurs at a wavelength corresponding to radio frequencies, and when the spin returns to its base level, energy is emitted at the same frequency. The signal that matches this transfer is measured in many ways and processed to yield an NMR spectrum for the nucleus concerned.
Table2. The comparison between GC-MS, LC-MS, NMR
Developing new pesticides is critical to meet the growing demands on farming. Metabolomics enables us to improve genetically modified plants. In addition, it helps us to estimate associated risks by allowing us to get a glimpse of their complex biochemistry via informative snapshots acquired at different time points during plant development.
Particular metabolites in excellent breeds can be found by detecting metabolites of livestock and poultry, which provides an essential basis for further research and modification of animals and poultry at the molecular level. At the same time, some metabolites that cause disease in livestock and poultry can also be found, creating conditions for preventing subsequent diseases in advance.
Biomarker discovery is another area where metabolomics informs decision-making. For example, a metabolite reliably present in disease samples but not in healthy individuals would be classed as a biomarker. Urine, saliva, bile, or seminal fluid samples contain highly informative metabolites and can be readily analyzed through metabolomics fingerprinting or profiling for biomarker discovery.
Fig6. Mind map of the application of metabolomics
The differences between metabolites of patients and healthy people can be understood by detecting different metabolome research platforms, and appropriate metabolites can be selected as disease-related biomarkers to provide evidence for potential disease diagnosis. In addition, metabolites can be associated with metabolic pathways to explore the causes and mechanisms of diseases further.
Personalized medicine, the ultimate customization of healthcare, requires metabolomics for quick medical diagnosis to identify disease. In healthcare, we currently use classical biochemical tests to measure individual metabolite concentrations to determine disease states, for example, the blood glucose level in the case of diabetes. However, Metabolomics offers the potential for rapidly identifying hundreds of metabolites, enabling us to identify these disease states much earlier.
Metabolomics technology can give full play to the advantages of quantitative characterization of small molecules, help determine whether there are banned substances in food, and select potential biomarkers to identify food quality.
By measuring and comparing the proteome and metabolome data of four groups of patients, the specific proteins and metabolites of severe COVID-19 patients were found, as well as their respective metabolic pathways. Using the machine learning method, 29 biomarkers of severe COVID-19 patients were predicted, and a prediction model was established. The classification accuracy of severe patients reached 93.5%, which provided a basis for researchers to deeply understand the characteristics of COVID-19 and explore biomarkers of COVID-19. In addition, the data also provided some pathophysiological evidence. Based on these data and phenomena, the authors suggested that close observation of platelet changes and intervention measures should be taken to inhibit coronavirus replication by inhibiting the synthesis of cholesterol and lipids. This also provided a reference for the development of subsequent vaccines.
Fig7. Metabolomics study in COVID research
93 specific proteins and 204 small molecule metabolites with characteristic changes were found in samples from severe COVID-19 patients. Among them, 50 proteins were related to macrophages, complement system and platelet degranulation in patients.
More than 100 amino acids and 100 lipids are significantly reduced in the bodies of severe COVID-19 patients, possibly due to the consumption caused by the rapid expansion of the virus. Therefore, the correlation of these metabolites to related metabolic pathways can provide a certain reference for clinical monitoring of the disease and formulation of therapeutic adjustment.
The research team further screened 22 characteristic proteins and characteristic metabolites through machine learning, all of which provided essential references for a comprehensive understanding of the characteristics of COVID-19 and a basis for future vaccine development and application.
The most challenging part of a metabolomic study is the confirmation of biomarker identity due to the limitation of technology and the process's high cost. However, this is an essential step toward understanding the biological changes occurring within the system and remains a significant bottleneck in metabolomics investigations.
The metabolome is sensitive to various genetic and environmental stimuli. Therefore, the execution of a metabolomic study requires the consideration of several factors so that confounders can be limited and information recovery optimized. Greatly influenced by genetic, environmental, and bbb, subtle variations between individuals can result in large perturbations to metabolite concentrations and flux. Environmental factors include diet, stress, xenobiotic use, lifestyle, and disease, while genetic variation can result from differences in gender, epigenetics, and gene polymorphisms.
Fig8. Metabolomics. Source: Metabolomic Technologies Inc.
Metabolomics has the potential to be an effective tool for early diagnosis of disease through identification of one, or a signature of predictive biomarkers. It could also serve as a predictor of treatment response and survival. The metabolome is quick to respond to environmental stimuli, including therapeutic or surgical intervention, and thus could be used to monitor the individual's metabolic status and indicate any possible toxic effects; it could also be used to detect any remaining disease or recurrence after therapy.
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