Do specific microbiota determine levels of specific metabolites? How does the metabolite profile develop during establishment of the microbiome in infants? Can we assess an individual’s physiological state based on gut flora and metabolites?
These questions are key to translating scientific findings related to intestinal metabolites in order to bring them to the clinic.
The relatively new scientific field of metabolomics is defined as “the non-biased identification and quantification of all of the metabolites in a biological system”. “The Intestinal Metabolome” was discussed during the American Association of Gastroenterology (AGA) translational symposium “Microbiome, Metabolites and Cancer” at this year’s Digestive Disease Week (DDW) in Washington D.C.
Tiffany L. Weir, an assistant professor of human nutrition from Colorado State University, presented evidence that the intestinal metabolome is comprised of complex interactions between host, microbe, and dietary metabolites. These metabolites can be associated with either beneficial effects or disease development. The emerging field of metabolomics serves as a powerful tool for elucidating mechanisms underlying intestinal microbiota association with diseases like colorectal cancer (CRC). Nuclear magnetic resonance and mass spectrometry techniques for metabolomics could improve strategies for detection, monitoring and treatment of intestinal diseases (CRC and inflammatory bowel diseases, IBD, for example).
Metabolomics can detect small molecules that reflect changes that occur to gene expression and protein activities in a biological system in response to environmental perturbation. These changes can be associated with disease development (cancer, obesity, IBD, diabetes) and are therefore of great diagnostic and therapeutic interest.
Examples of intestinal metabolites whose abundance is associated with disease modulation and ‘dysbiosis’ include short chain fatty acids (SCFAs), dietary fats and secondary bile acids. Less prominent molecules are trimethylamine (TMA), indoles, equol, 8-prenylnaringen and coprostanol.
The development of new diagnostic biomarkers for CRC and IBD are potential practical applications for metabolomics in medicine. SCFA (e.g. butyrate), branched chain fatty acids and amino acids are metabolites that are currently studied as potential diagnostic biomarkers for CRC, while SCFA, dimethylamine and TMA are candidates for IBD. Diagnosis of IBD through these biomarkers has several advantages: These metabolites can be detected by non-invasive collection of stool (or urine), the disease can be identified in an early stage and monitored over time and the two forms of IBD, Crohn’s disease and ulcerative colitis can be distinguished.
Another potential application of metabolomics is the development of biomarkers of dietary intake levels in order to improve nutritional epidemiology, to monitor effectiveness of nutrition education and to determine specific responses to diets (metabotyping). Additionally, metabolomics can define new therapeutic targets (butyrate replacement, targeted probiotics/contrabiotics) and can assist in identifying interactions between drugs and bacteria to determine individual responses and minimize undesirable side effects.
Although initial discoveries in understanding how the intestinal metabolome influences the gastrointestinal environment and overall health point out the enormous potential of metabolomics, “we still have a way to go”, Tiffany Weir concluded her talk at DDW. “While data acquisition technologies are improving, computational challenges in integrating data from various levels need to be addressed”, she added.
In summary, metabolites are one important area of interest when trying to understand the mechanisms behind microbiome functions at the whole systems level. Metabolomic profiling may advance our understanding, diagnosis and treatment of diseases like CRC and IBD. However, metabolomics mainly provides associations between metabolite profiles and certain phenotypes, which does not clarify whether changes in these profiles are the cause or consequence of the phenotype (disease). Therefore, various ‘omics’ platforms have to be integrated together with metabolomics to elucidate disease mechanisms.
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