Metabolism is the assembly of biochemical reactions in living organisms, the material basis for life maintenance of living organisms, and an important basis for the study of life activities. Metabolomics studies the endogenous metabolic activities of life under the influence of the internal and external environments, including the detection and analysis of metabolite types and quantities and their change patterns, thus studying the nature of the occurrence and development of collective life activities. Compared to other omics technologies, metabolomics offers several advantages:
- Changes in the type and quantity of metabolites are easy to detect.
- The technical means are simpler compared to genomics and proteomics.
- Compared with genomics and of proteomics, the number of metabolites is small and easy to detect, verify and analyze.
- Changes in metabolite levels can reveal the physiopathological state of the organism in real time.
What Can Metabolomics Tell Us?
Metabolomics offers a wealth of information that can be leveraged across multiple disciplines. By analyzing the metabolome, researchers can:
Identify Biomarkers: Metabolites can serve as valuable biomarkers for various diseases. For instance, altered metabolite profiles have been associated with cancer, diabetes, cardiovascular diseases, and neurodegenerative disorders.
Uncover Drug Targets: Metabolomics aids in the identification of potential therapeutic targets by highlighting specific metabolites or metabolic pathways that are dysregulated in diseases. This information enables the development of targeted therapies aimed at restoring normal metabolic balance.
Understand Biological Pathways: Metabolomics allows researchers to map and comprehend intricate metabolic pathways, unveiling the interplay between genes, proteins, and metabolites. This understanding can be crucial in elucidating the mechanisms of diseases and identifying novel therapeutic interventions.
Assess Environmental Impact: Environmental metabolomics focuses on studying the effects of environmental factors, such as pollutants or toxins, on the metabolome. By identifying metabolic alterations induced by environmental stressors, researchers can assess the impact on ecosystems and human health.
Why is Metabolomics Difficult?
Metabolomics presents several challenges that must be addressed to ensure reliable and reproducible results:
Biological samples used in metabolomics, such as blood, urine, tissues, or cells, are inherently complex. They contain a vast array of metabolites with a wide dynamic range of concentrations. The presence of high-abundance compounds, such as amino acids and lipids, can mask the detection of lower-abundance metabolites, leading to challenges in identifying and quantifying specific metabolites of interest. Strategies such as sample fractionation, enrichment techniques, or targeted extraction methods are employed to mitigate the complexity of samples and enhance the detection of low-abundance metabolites.
Metabolite identification is a critical challenge in metabolomics. While advanced analytical techniques like mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy provide valuable information about the mass and structure of metabolites, confidently identifying metabolites from complex spectra remains a complex task. Several factors contribute to this challenge, including the presence of isomers or structurally similar compounds, database limitations, and spectral overlap. To overcome these challenges, researchers rely on techniques such as tandem MS, high-resolution MS, and the use of reference standards and databases for metabolite identification.
Metabolomics generates vast amounts of data, often referred to as "big data." Effective data analysis is crucial for extracting meaningful insights from this data deluge. However, metabolomics data analysis is complex and requires sophisticated computational tools, statistical methods, and bioinformatics expertise. Data processing, feature extraction, statistical analysis, metabolite quantification, and multivariate analysis are essential steps in metabolomic data analysis. Additionally, data normalization and quality control procedures are critical for ensuring the reliability and comparability of metabolomics data across different samples or studies.
Data Integration and Interpretation:
Metabolomics often involves integrating data from multiple analytical platforms, such as different types of mass spectrometers or NMR instruments. Integrating data from different platforms poses challenges due to variations in instrument performance, sensitivity, and data formats. Data integration approaches and normalization techniques are employed to address these challenges and enable meaningful comparisons and analysis across datasets. Furthermore, the interpretation of metabolomic data requires expertise in metabolite pathway analysis, statistical modeling, and systems biology approaches to unravel the biological significance of the observed metabolic changes.
Standardization and Reproducibility:
Standardization and reproducibility are essential aspects of metabolomics research. Variability in sample collection, storage, extraction methods, analytical techniques, and data processing can introduce bias and affect the reproducibility of results. Establishing standardized protocols and quality control measures is crucial to ensure consistency and comparability of metabolomics data across different laboratories and studies. The development of metabolomics-specific reporting standards, such as the Metabolomics Standards Initiative (MSI), promotes data reproducibility and facilitates data sharing and collaboration in the metabolomics community.
Which Methods Are Used in Metabolomics?
Metabolomics can be divided into untargeted and targeted metabolomics depending on the purpose of the study.
Untargeted metabolomics is a comprehensive and systematic analysis of endogenous metabolites in organisms, and is an unbiased metabolomic analysis that can identify new biomarkers. Targeted metabolomics focuses on the study of a specific class of metabolites.
As both metabolomics approaches have their own advantages and disadvantages. Untargeted metabolomics is unbiased and provides a comprehensive and systematic response to the metabolomic profile of living organisms, but is less reproducible and has a limited linear range. Targeted metabolomics, on the other hand, has improved reproducibility and sensitivity, simple metabolite confirmation, and a wide linear range, but requires a prior knowledge background and is a biased metabolomic analysis method. Therefore, in practice, both are often used in combination and work together.
General Workflow of Metabolomics
Metabolomics involves several key steps to ensure accurate and reliable results. The general workflow includes:
Sample Preparation: Sample preparation is a critical step in metabolomics to extract metabolites while minimizing bias and degradation. Various techniques such as solvent extraction, liquid-liquid extraction, and solid-phase extraction are employed to isolate metabolites from the biological matrix.
- Quenching: The immediate arrest of metabolic activity is essential to preserve the metabolite composition at the time of sampling. Quenching methods such as rapid freezing or chemical fixation are employed to halt enzymatic reactions.
- Extraction: Metabolites are extracted from the biological matrix using appropriate solvents or extraction methods. Different extraction techniques are employed depending on the sample type, including liquid-liquid extraction, solid-phase extraction, or protein precipitation.
- Derivatization: Derivatization is performed to improve the detectability and stability of certain metabolites, particularly those with low volatility or poor ionization properties. It involves chemically modifying metabolites to enhance their chromatographic or spectrometric characteristics.
Instrumental Analysis: Following sample preparation, the extracted metabolites are subjected to analytical techniques such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy. These techniques generate data on metabolite identity and abundance, forming the basis for further analysis.
Data Analysis: The vast amount of data generated in metabolomics requires sophisticated computational tools and statistical analyses. Data processing, feature extraction, metabolite identification, and pathway analysis are essential steps to decipher the biological information contained within the metabolomic dataset.
Fig 1. Flowchart for metabolomic studies (Tugizimana et al., 2013).
What Types of Samples Can Metabolomics Test?
Metabolomics focuses on small molecule metabolites (molecular weight <1000) that serve as substrates and products of various metabolic pathways. The samples analyzed in metabolomics are mainly plasma or serum, urine, saliva, cells, and animal and plant tissues. In addition, plant, fungal and microbial extracts, cerebrospinal fluid, lymphatic fluid, insect hemolymph, amniotic fluid, follicular fluid, knee synovial fluid, tears, semen, placenta, fecal and intestinal contents extracts can also be tested.
How Many Substances can be Detected by Metabolomics?
Different mass spectrometry platforms have different sensitivities and biases, and the different platforms are complementary to each other. In general, GC-MS can accurately characterize about 200 substances for serum samples, about 200 substances for urine samples, and several hundred for other samples (e.g. liver, stool, intestinal contents). If comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC–MS) is used, the number of detectable substances can be more than 1,000. LC-MS can detect far more substances than GC-MS, and depending on the detector (TQ, QTOF, etc.), it may be possible to detect tens to 2-3 hundred substances.
What is Metabolomics Used For?
Metabolomics plays a crucial role in understanding the metabolic changes associated with various diseases. By comparing the metabolite profiles of healthy individuals with those affected by diseases, researchers can identify specific metabolic alterations that are indicative of disease development, progression, or response to treatment. These insights can lead to the discovery of biomarkers, which are measurable indicators of disease presence or progression. Biomarkers identified through metabolomics can be used for early disease detection, monitoring disease progression, and evaluating the efficacy of therapeutic interventions.
Metabolomics is a valuable tool in the field of pharmaceutical research and drug development. It helps elucidate the mechanisms of action of drugs and provides insights into their metabolism, distribution, and elimination within the body. Metabolomics can identify metabolic pathways affected by drugs and reveal potential off-target effects or adverse drug reactions. This knowledge aids in the optimization of drug efficacy, the development of personalized medicine approaches, and the identification of new drug targets.
Metabolomics has applications in agriculture and crop science, where it contributes to understanding the metabolism of plants and improving crop yield, quality, and resistance to stress. By analyzing the metabolite profiles of crops, researchers can identify metabolic pathways involved in important agronomic traits, such as nutrient uptake, response to pathogens, and tolerance to environmental stressors. Metabolomics can also be used to assess the nutritional content of food crops and ensure food safety by detecting the presence of harmful contaminants.
Metabolomics plays a role in environmental science by providing insights into the effects of environmental pollutants and toxins on living organisms and ecosystems. Environmental metabolomics, also known as exposomics, focuses on studying the metabolic responses of organisms to environmental exposures. By analyzing the metabolite profiles of organisms exposed to different environmental conditions, researchers can identify metabolic changes associated with exposure to pollutants, toxins, or other stressors. This information helps in assessing environmental health, understanding the impact of pollution on ecosystems, and identifying potential biomarkers of exposure.
Metabolomics contributes to the field of nutrition and food science by studying the metabolic effects of diet and identifying bioactive compounds in food. By analyzing the metabolite profiles of individuals in response to different diets or dietary interventions, researchers can understand how nutrients are metabolized and utilized in the body. Metabolomics can also identify bioactive compounds present in food, such as antioxidants or anti-inflammatory agents, which contribute to the health benefits associated with certain diets or food products. This knowledge can inform the development of personalized nutrition recommendations and the creation of functional foods with specific health-promoting properties.
Systems Biology and Network Analysis
Metabolomics is an integral part of systems biology, which aims to understand biological systems as a whole by integrating data from various omics disciplines. Metabolomics data, combined with genomic, transcriptomic, and proteomic data, can be used to construct comprehensive metabolic networks and models. These models help in understanding the interactions between genes, proteins, and metabolites, and provide insights into the underlying regulatory mechanisms of cellular metabolism. By analyzing these networks, researchers can gain a deeper understanding of how biological systems function and respond to internal and external stimuli.
Basic applications of metabolomics that contribute to multiple aspects of understanding, detecting, and treating multiple sclerosis (Zahooret al., 2021)
- Tugizimana, F., Piater, L., & Dubery, I. (2013). Plant metabolomics: A new frontier in phytochemical analysis. South African Journal of Science, 109(5-6), 01-11.
- Reference: Zahoor, Insha, et al. "An emerging potential of metabolomics in multiple sclerosis: A comprehensive overview." Cellular and Molecular Life Sciences 78 (2021): 3181-3203.
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