bioRxiv2023
The microbiome is a complex micro-ecosystem that provides the host with pathogen defense, food metabolism, and other vital processes. Alterations of the microbiome (dysbiosis) have been linked with a number of diseases such as cancers, multiple sclerosis (MS), Alzheimers disease, etc. Generally, differential abundance testing between the healthy and patient groups is performed to identify important bacteria (enriched or depleted in one group). However, simply providing a singular species of bacteria to an individual lacking that species for health improvement has not been as successful as fecal matter transplant (FMT) therapy. Interestingly, FMT therapy transfers the entire gut microbiome of a healthy (or mixture of) individual to an individual with a disease. FMTs do, however, have limited success, possibly due to concerns that not all bacteria in the community may be responsible for the healthy phenotype. Therefore, it is important to identify the community of microorganisms linked to the health as well as the disease state of the host. Here we applied topic modeling, a natural language processing tool, to assess latent interactions occurring among microbes; thus, providing a representation of the community of bacteria relevant to healthy vs. disease state. Specifically, we utilized our previously published data that studied the gut microbiome of patients with relapsing-remitting MS (RRMS), a neurodegenerative autoimmune disease that has been linked to a variety of factors, including a dysbiotic gut microbiome. With topic modeling we identified communities of bacteria associated with RRMS, including genera previously discovered, but also other taxa that would have been overlooked simply with differential abundance testing. Our work shows that topic modeling can be a useful tool for analyzing the microbiome in dysbiosis and that it could be considered along with the commonly utilized differential abundance tests to better understand the role of the gut microbiome in health and disease. Author SummaryTrillion of bacteria (microbiome) living in and on the human body play an important role in keeping us healthy and an alteration in their composition has been linked to multiple diseases such as cancers, multiple sclerosis (MS), and Alzheimers. Identifying specific bacteria for targeted therapies is crucial, however studying individual bacteria fails to capture their interactions within the microbial community. The relative success of fecal matter transplants (FMTs) from healthy individual(s) to patients and the failure of individual bacterial therapy suggests the importance of the microbiome community in health. Therefore, there is a need to develop tools to identify the communities of microbes making up the healthy and disease state microbiome. Here we applied topic modeling, a natural language processing tool, to identify microbial communities associated with relapsing-remitting MS (RRMS). Specifically, we show the advantage of topic modeling in identifying the bacterial community structure of RRMS patients, which includes previously reported bacteria linked to RRMS but also otherwise overlooked bacteria. These results reveal that integrating topic modeling with traditional approaches improves the understanding of the microbiome in RRMS and it could be employed with other diseases that are known to have an altered microbiome.