Chapter 6 — Metabolomics

What is Metabolomics?

Metabolomics is the study and comprehensive analysis of metabolites within a biological sample. Metabolites are small molecule intermediates or products of metabolism, which are essential to the functioning of cells, tissues, and organisms. Metabolites each have critical biological roles and comprise a wide range of molecules, such as sugars, lipids, amino acids, and nucleotides. For example, glucose and lactate levels in the blood can provide immediate information about metabolic and energy states; while fluctuations in amino acid or neurotransmitter levels may indicate changes in nutritional status or neurological function, respectively¹.

By studying the entire complement of these molecules—termed the “metabolome” —researchers can gain detailed insight into the biochemical processes that underpin life, making metabolomics a crucial part of systems biology research. This holistic approach to capturing and quantifying metabolites necessitates tools and technologies that can accurately detect the diversity and range of metabolites found in living organisms.

Metabolomics Technologies

There are several technologies that make metabolomics research possible, and they have rapidly evolved over the past few decades to enable metabolite detection and analysis from a diverse range of sample types. These technological advances not only enhance our understanding of biological pathways, but they also push the boundaries of what types of metabolites can be detected, quantified, and interpreted in various biological contexts. Below we discuss some of the key technologies used in metabolomics studies.

Mass spectrometry (MS)²

Mass spectrometry is the cornerstone of metabolomics research due to its high sensitivity, specificity, and broad applicability. It works by ionizing chemical compounds to generate charged molecules or molecular fragments. Different ionization approaches can be used depending on the sample type. For example, electrospray ionization (EI) is used for liquid samples and is highly compatible with liquid chromatography, making it a standard choice for liquid chromatography-mass spectrometry (LC–MS). Matrix-assisted laser desorption/ionization (MALDI) is used for analyzing large biomolecules and is commonly used in combination with time-of-flight (TOF) mass spectrometry (MALDI–TOF), which is a method for detecting gas-phase ions.

Chromatography²

Chromatography techniques are used to separate the components of a mixture and are often coupled with MS to improve detection. The primary types include:

Liquid chromatography (LC)

This technique is often used for nonvolatile, thermally unstable, and polar metabolites. High-performance liquid chromatography (HPLC) and ultra-performance liquid chromatography (UPLC) are particularly prevalent and offer high resolution and efficiency. However, UPLC offers many advantages over HPLC, such as faster analysis, improved sensitivity, and lower solvent consumption.

Gas chromatography (GC)

This technique is often used for volatile and semi-volatile compounds. GC requires sample derivatization to increase their volatility but provides excellent separation. It is typically used to increase the sensitivity of MS.

Imaging mass spectrometry (IMS)⁴

IMS maps the distribution of molecules or metabolites across a sample’s surface, which provides a link between metabolomic and histological information. This technique is particularly valuable in medical research while analyzing tissue sections to study disease pathology or the effects of drug treatments at the molecular level.

Nuclear magnetic resonance (NMR) spectroscopy⁵

NMR spectroscopy offers a non-destructive method for detecting metabolites in a sample based on the magnetic properties of atomic nuclei. While less sensitive than MS, NMR spectroscopy can provide detailed structural information that MS cannot. It requires minimal sample preparation and can quantitatively analyze mixtures without prior separation. Advances in NMR technology, such as higher field strengths and cryogenically cooled probes, have improved its sensitivity and resolution.

Capillary electrophoresis (CE)⁶

CE is a separation technique that uses an electric field and is particularly effective at separating charged molecules. It is efficient and requires small sample volumes, making it suitable for limited biological samples.

Applications of Metabolomics

Metabolomics offers a profound and detailed window into the molecular underpinnings of life and reveals much about the health, physiological processes, and environmental interactions of organisms. In addition to healthcare applications, metabolomics can reveal insights into plant and crop health. This area of study has become indispensable in both research and applied sciences and has generated novel discoveries and applications across healthcare and biotechnology.

Human healthcare applications

Metabolomics is a particularly powerful approach for detecting biomarkers⁷, biological molecules whose presence indicates a particular disease, physiological state, or therapeutic response. Due to this ability, metabolomics analyses have important research implications for a variety of diseases, such as cancer⁸, cardiovascular disease⁹, and more. Including metabolomics analyses in academic and preclinical research can contribute to improved diagnostic techniques¹⁰ and the development of targeted, safe, and effective therapies as well as the opportunity for personalized medicine^11,12.

Agriculture applications

Metabolomics is used in agriculture to improve crop yields and address the challenges posed by climate change and environmental stressors. By adopting untargeted monitoring and ecosurveillance using metabolomics, subtle biochemical changes induced by chronic man-made toxicant exposure can be detected, providing additional lines of evidence for environmental monitoring programs¹³.

In agriculture, metabolomics is used to measure the impact of environmental conditions on fruit quality and plant performance by measuring changes in fruit metabolism. These tools aid in developing crops with enhanced adaptability to climate change¹⁴. Furthermore, metabolomics plays a vital role in modern crop breeding by predicting metabolic markers for plant performance under stress, enabling the creation of climate-smart crops¹⁵. This integrated approach not only accelerates metabolomics-assisted plant breeding but also supports the rapid commercialization of genome-edited crops, ultimately contributing to global food security amidst a changing climate.

Modern Uses of Metabolomics in Multiomics: Case Studies

Metabolomics plays a pivotal role in multiomics research by providing detailed insights into the metabolic states that link genetic potential to observable traits. It enhances the understanding of metabolic pathways and furthers systems biology. Below we discuss some key case studies showcasing the use of metabolomics along with other omics methods that advanced our understanding of disease.

Diet-omics in the Study of Urban and Rural Crohn Disease Evolution (SOURCE) Cohort

Within the past decades, Crohn’s disease (CD) has become more prevalent and correlates with increased globalization and urbanization. By combining transcriptomics, metabolomics, and microbiome datasets with host dietary information, one study unraveled key genetic–environment interactions that contribute to CD pathogenesis¹⁶.

This study analyzed 380 Chinese and Israeli healthy and newly diagnosed CD individuals who lived in either a rural or urban environment. By collecting and analyzing samples from individuals undergoing a transition between rural and urban environments, the study identified important genetic–environmental signatures associated with urban exposure and CD. Figure 1, Graph K, shows that the gut metabolite levels present in rural individuals that transitioned to urban environments mimics those seen in CD.

Furthermore, transcriptomics and metabolomics data revealed that certain dietary elements, including manganese, vitamin D, and coffee positively correlate with a healthy microbiome composition and negatively correlate with CD-associated transcriptomic signatures. Conversely, sugar and saturated fat intake positively correlate with CD-associated transcriptomic signatures. These results highlight the impact of diet on disease development and progression.

The study identified 32 metabolites that correlate with CD-associated transcripts (Figure 1, Graph J). Metabolites such as tryptophan, acetyl-tryptophan, and docosatetraenoic acid positively correlate with CD-associated transcripts, whereas adipate, azelate, and indole 3 methyl acetate positively correlate with healthy control transcripts. Overall, the multiomics approach used here elucidated the complex interactions between diet, microbiome, and environment. This study offers valuable insights that may lead to targeted dietary and therapeutic interventions to manage and potentially prevent CD.

Molecular signatures of post-traumatic stress disorder in war-zone-exposed veteran and active-duty soldiers

Post-traumatic stress disorder (PTSD) impacts about twice as many combat-exposed soldiers as members of the general population in the U.S. By combining genetic, epigenetic, proteomic, and metabolomics data, a study identified several features associated with PTSD¹⁷.

Using blood samples from 340 veterans and 180 active-duty soldiers, the study identified several PTSD-associated signatures that correlated with disease severity and symptom chronicity. Among these transcriptional signatures, activated inflammation, oxidative stress, metabolic dysregulation, and impaired angiogenesis positively correlated with PTSD. Figure 2 shows the correlations between cellular inflammation and injury and PTSD symptoms, such as cardiovascular disease, type 2 diabetes mellitus, and neuropsychiatric disorders.

These findings suggest that PTSD may be a systemic condition that impacts various biological processes, including wound healing. This study combined proteomic, epigenomic, and metabolomic data to reveal a molecular profile of PTSD that may lead to novel prevention, diagnosis, and treatment strategies.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

Disease severity and the corresponding host immune response to SARS-CoV-2 can vary widely depending on the health and genetic background of the patient. To characterize the host response in relation to disease heterogeneity, a study analyzed longitudinal blood and nasal samples from 540 COVID-19 patients. The study revealed distinct biological states associated with different clinical outcomes in COVID-19, identifying potential new areas for diagnostic and therapeutic interventions.

Using a combination of metabolomics, transcriptomics, proteomics, and immunophenotyping, the study identified cellular and molecular signatures present within 72 hours of hospital admission that could distinguish between moderate, severe, and fatal COVID-19 disease. In addition, patients with severe disease that stabilized within 28 days displayed unique cellular and molecular states compared to those that eventually succumbed to infection.

Key metabolite signatures associated with severe disease trajectories include elevated levels of branched-chain amino acids and urea cycle metabolites. This suggests that these pathways may play are role in exacerbating disease severity. In addition, low phospholipid levels also correlated with severe disease, which highlights their role in immune function. Figure 3 shows the levels of branched amino acids and urea metabolites as well as phospholipid metabolites over time in severe patients.

By integrating data from various omics assays, this study identified potential biomarkers for disease severity, including branched amino acids as well as urea and phospholipid metabolites. These findings could inform clinical prognosis and therapeutic interventions. In addition, this study underscores the value of metabolomics in uncovering metabolic dysregulation associated with severe COVID-19, offering a robust framework for precision medicine in disease management.

Multiomics analysis reveals the impact of microbiota on host metabolism in hepatic steatosis

Metabolic dysfunction-associated fatty liver disease (MAFLD) can develop from hepatic steatosis (HS), or fat buildup in the liver, and features metabolic dysfunction that results from a complex interplay between genetic background, obesity, and environmental factors, including the microbiome. To better understand the pathogenesis of HS, one study combined microbiome composition, plasma metabolomics, and inflammatory proteomics data from a cohort of 56 patients with MAFLD and examined biomarkers associated with HS¹⁹. This work revealed key features of HS, which can be used to predict, diagnose, and treat HS and distinguish it from other related diseases.

This study identified increased plasma levels of various metabolites, such as amino acids, lipids, and bile acids in HS patients. The levels of metabolites involved in oxidative stress and inflammation were differentially expressed across various stages of HS. Specifically, lipid metabolites, gamma-glutamyl amino acids, branched-chain amino acid metabolites, fatty acid metabolites, and glutathione-related metabolites are associated with HS (Figure 4). Microbiome composition data revealed how dysbiosis in the oral and gut microbiome influences host metabolism. In patients with steatosis, abundances of Dorea longicatena, Slackia isoflavoniconvertens, Roseburia hominis, and Ruminococcus bromii were reduced. These biomarkers were validated in a follow-up cohort of 22 patients predicted HS incidence.

Conclusions

Metabolomics is a powerful tool that can analyze the metabolic responses of living organisms in response to various conditions, such as disease and environmental changes. Applications of metabolomics span diverse fields, including biomarker discovery for disease diagnosis and prognosis and improving agricultural practices through crop and soil health assessments. By combining metabolomics with other omics technologies, such as genomics and proteomics, we can learn more about complex biological systems, which paves the way for personalized medicine and precision agriculture.

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