Our previous article discussed metabolomics as an approach for detecting and quantifying metabolites from biological samples. This article covers the potential applications of this technique in clinical research.
One of the most powerful uses for omics technologies has been in the identification of biomarkers that predict disease. Genetic mutations in the BRCA1 gene have been identified as biomarkers for increased susceptibility to breast cancer as well as responsiveness to different chemotherapy regimens. Identification of biomarkers also has potential commercial applications, as a blood test detecting biomarkers released after brain injury has recently been approved by the United States Food and Drug Administration (FDA). Metabolomics can be applied to a range of disorders to identify metabolites that serve as biomarkers that predict disease, follow disease severity or progression, or can be targeted as a therapeutic.
Human metabolites as markers of disease progression
Detection of disorders:
One clinical application for metabolomics is in the detection of a variety of human disorders. Perturbations in metabolite profiles can be used to diagnose individuals with colorectal cancer as well as predict the response of individuals to different chemotherapy agents. As early detection and diagnosis of disease are essential for successful treatment, the development of new predictors is a vital goal.
Tracking progression of disorders:
Investigation of clinical samples for differences in metabolic profiles can also identify and track progression of a wide range of disorders. This has been observed in several chronic diseases, such as determining the presence and severity of coronary artery disease from human serum metabolites.
Potential targets for therapy:
Because metabolites directly cause biological changes, they can serve not only as markers for disease but also as potential targets for therapy. For example, infection of pregnant women with malaria was associated with decreased levels of the amino acid L-arginine, and supplementation with L-arginine improved birth outcomes.
Detection and prediction of adverse drug effects:
Metabolomics can also serve as a powerful tool in the detection of adverse drug effects. Early detection of toxicity issues from drug treatment is critical to increased patient safety, effective screening at the drug development stage, and prevention of sunk cost on a drug that cannot reach the market. Testing of serum or urine samples can investigate differences in metabolomic profiles between untreated and drug-treated groups to quickly identify adverse side effects. For example, experimental models of toxicity identified unique urine metabolite profiles for different drugs, allowing for non-invasive identification of drug toxicity. Metabolomics has even further been used to identify individuals who are predisposed to adverse reactions to a drug before toxicity occurs, as differences in metabolic profiles predicted individuals with increased susceptibility to acetaminophen-induced liver injury.
Microbial Co-Metabolites Shed Light on Human Health
Another advantage of metabolomics is that it can investigate interactions of gut-resident microorganisms, collectively called the microbiome, with the human host. The role of the microbiome as a major contributor to immune development and human health has been more greatly appreciated in recent years.
Indicator of microbiome disruption:
Differences in metabolic profiles can be an indicator of microbiome disruption, which is associated with a number of disease states. In a study of Malawian twins, metabolite profiles were altered in malnourished compared to well-nourished individuals. Further investigation determined that the well-nourished metabolic profile could be restored in malnourished individuals after transfer of microbiome from well-nourished individuals in an experimental mouse model, indicating that contributions from the microbiome are critical for proper nutrition.
Protection against disease:
Metabolic products of the microbiome can also be detected and contribute to protection against disease. The human microbiome can contribute to and modify human metabolites, leading to downstream consequences on biological processes. Metabolomics studies can identify both metabolites that are produced by human cells as well as co-metabolites that are modified by the microbiome, allowing for the detection of microbiome contributions that might be missed using other omics approaches. The pathogenic bacteria Clostridium difficile is classified as an urgent threat by the United States Centers for Disease Control and Prevention (CDC), and infection is often associated with disruption of the microbiome due to antibiotic use. In a mouse model of C. difficile infection, resistance to infection was associated with a specific bacterium in the microbiome that is capable of modifying a host bile salt into a C. difficile-inhibiting metabolite.
Conclusions
Investigation of metabolic profiles from clinical samples has the potential to identify biomarkers of disease susceptibility or track progression of disease. While metabolomics and other omics approaches represent powerful tools, there are still limitations to translating discoveries into clinical therapies. As with other omics approaches, metabolomics generates massive amounts of data that require sophisticated multivariate analyses to properly analyze. In addition, variations in the quality or preparation of specimens or in the protocol used for assays can result in findings that do not translate into clinical applications.
Approaches combining metabolomics with other omics approaches may provide greater certainty that results obtained are not artifacts of experimental conditions and instead represent true differences that can predict or influence clinical outcomes. Integration of different omics approaches also allows for the identification of a mechanism for metabolic changes, which may identify potential genetic targets for therapy. Overall, use of metabolomics in conjunction with other omics approaches allows for a more thorough understanding of biological processes that can influence clinical diagnosis and treatment.
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