Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

Abstract

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Research hotspot and trend analysis in the diagnosis of inflammatory bowel disease: A machine learning bibliometric analysis from 2012 to 2021

“…PM aims to use the biological characteristics of individual patients to formulate the right treatment plan for the right patient at the right time ( 68 , 69 ). This requires understanding the functions of individual biological components and their multi-factor interactions on the overall impact of patient stratification ( 70 ). It is expected that the multi-omics method may be more robust in guiding the precise treatment of IBD and other complex diseases ( 71 , 72 ).…”

Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data

“…The diversity of classifiers can be increased by applying base classifiers on different sets of variables or, in biomedical applications, on different -omics views. In fact, multiple microbiome studies often encourage integration of multi-omics data [ 8 , 9 , 10 , 43 ] in a single model. One of the previous studies [ 43 ] combined multiple -omics views using stacking, but unlike in our study, authors used exactly the same linear model across all views instead of applying different models.…”

“…Such methods have their limitations since they neither include complex non-linear interactions among species, nor do they provide a prognostic value for a new unseen dataset [ 7 ]. Hence, most of the modern human microbiome studies rely on machine learning models to identify biomarkers of health and disease [ 7 , 8 ]. Machine learning also provides tools to integrate microbial data with other -omics datasets and to identify the most important species involved in health and disease [ 9 , 10 ].…”

Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All

“…The complexity of IBD is also manifested by the heterogeneity of disease presentation and behavior [11][12][13][14] . Heterogeneity in IBD is not only attributed to the complex phenotypes and the etiological drivers, but also to the plethora of diverse molecules 15-18 19-22 , microbes, cell-types 23,24 25 and the interactions 26 among them.…”