Why assess gut health?
A disruption in gut microbiome leads to health disorders. An early evaluation of gut microbial imbalance can help overall health.
Science has explained to us enough that the presence of good bacteria is a must for the human gut to function properly. A lesser-known truth is that good gut health is often an indicator of good overall health. Accuracy in assessing gut health is key to understanding gut abnormalities. This assessment involves analyzing microbes such as bacteria in the gastrointestinal (GI) tract. Many diseases that can affect not just adjacent organs like the liver, but also distant organs like the lungs, and the brain, are linked to dysbiosis, or imbalances caused by microbiota or microbiome in the gut. The microbiome is nothing but colonies of microbes like bacteria and viruses that reside in the gut. Microbes, especially bacteria, can influence metabolism, changes in the immune system and intestinal health, and protect against pathogens.
While the composition of microbiome can vary based on an individual’s geographical lineage, history, age, and diet, the functions contributed by these microbes are similar in nature. Therefore, any difference in microbial functions must be identified specific to an individual and used as a marker to assess dysbiosis.
TCS Research has designed a gut health assessment framework to provide an early, non-invasive detection of dysbiosis in patients that have gut disorders.
Diagnosing gut dysbiosis
Analyzing the metabolic functions of microbes may hold crucial answers.
Increasingly, microbiome-based approaches are being used to understand dysbiosis. But while most focus on finding microbes that help in digestion or build immunity, they don’t zero in as strongly on metabolic functions or imbalances in the microbial cell that aggravate progression of certain diseases. It is necessary to identify metabolic functions that are harmful (pathogenic) or beneficial (commensal) to the gut. For instance, the production of short-chain fatty acids by fermenting complex carbohydrates is a prominent metabolic function of good bacteria.
To understand these functions, it is important to study metabolic pathways, a linked series of chemical reactions occurring within a cell, and their resulting intermediate or by-products.
Metabolic pathways help recognize good and bad bacteria. Pathogens and their corresponding functions, as well as any drop in commensals and their functions within a pathway that indicates imbalance, are used as markers for dysbiosis.
TCS’ computational model to diagnose gut health
Creating an in silico system based on biomarkers.
The setting up of a computational model to assess gut health begins with a knowledge base that is curated through proprietary and publicly available data, literature, and pathway annotations of beneficial and harmful microbial functions. This information is then used to define functions that are marked commensal or pathogenic. An in silico model uses these markers to assess the health of the gut in a clinical set up.
How the gut health assessment framework is executed – from metabolic analysis to recommending probiotic solutions.
The first step in the framework’s operations is microbial DNA from a sample in the gut; a stool sample, for instance. Next generation sequencing (NGS) technology is then used to obtain DNA sequence data. NGS is a technology that helps with genome (DNA information found in a cell) analysis.
This DNA sequence data is processed through the computational model for a functional analysis that uncovers commensal and/or pathogenic gut bacteria. Machine learning and deep learning technology are used for advanced data analytics to match and identify the bacterial function from the sample with that in the knowledge base. Any dysbiosis observed during the analysis can help identify gut disorders.
A report is generated based on the outcome of the analysis and, using a devised methodology, produces a gut health score. The process involves comparing two or more datasets. These are obtained from subsets of an entire population displaying varied compositional traits or that of an individual, at two different points in time. As a part of the process, two sets of microbiome sequence data of a person, before and after the treatment, are assessed. The gut health report gives a comparative status, indicating improvement, deterioration, or no change in health, between the two time points.
An early risk assessment using this approach allows timely investigation and treatment before a disease or disorder becomes more serious.
The analysis helps draw information on the metabolic functions that need to be replenished or curbed to improve gut health once the presence of beneficial and / or harmful bacteria or any imbalance is established. Based on this and data from the proprietary and common knowledge base, one or more personalized probiotic (bacterial) strain(s) is recommended.
Addressing a spectrum of GI disorders
The computational model can be extended to a wider range of treatments and therapeutics.
The in silico assessment model can address a range of gastrointestinal problems from inflammatory bowel disease (IBD) to colorectal cancer.
The microbiome-based approach developed can also be extended to treatment such as fecal microbiota transplantation (FMT)–a microbiome-dependent therapy—used to treat clostridium difficile (C. diff), a gastrointestinal infection. This treatment sees the transfer of healthy microbes (microbiota) from a donor’s feces to a patient’s GI tract. The treatment is also being explored to treat ailments like IBD.
The in silico approach to assess gut health can bring significant value to industry given the growth of global markets for personalized biotherapeutics and research around gut health. Microbiome multi-omics research—which involves the study of genomics, metabolomics, transcriptomics, and epigenomics—is a rapidly emerging field in the pharma industry and the consumer health market. This research approach, along with decreasing costs due to technological advances and better access to next-generation sequencing, are among many reasons that could lead to an increase in the adoption of microbiome-based solutions.
The authors would like to thank Dr Rajgopal Srinivasan, chief scientist, TCS Research; Dr Sharmila Mande, distinguished chief scientist, TCS Research (retired); Dr Anirban Dutta, principal scientist, TCS Research; and Dr Kuntal Kumar Bhusan, senior scientist, TCS Research, for their inputs.