Understanding Metabolic Interactions in the Human Microbiome using Multi-omics data, Machine learning, and Genome-scale models

Microorganisms form complex communities across multiple human body sites, including the gut, skin, vaginal, and oral microbiomes, where they coexist and exchange essential metabolites required for survival. It is not just microbe-microbe interactions that matter; factors such as nutrient availability or disease also control host-microbe interactions. These microbial communities are dynamic and undergo significant shifts in composition during disease conditions such as obesity, type-2 diabetes, inflammatory disorders, infections, cancer, and site-specific dysbiosis. Despite having vast amounts of microbiome sequencing data and knowing which species constitute a specific microbiome, our mechanistic understanding of these metabolic interactions remains limited. How a microbe competes or cooperates with others for a metabolite, how diet intervenes, and how these dynamics get perturbed during dysbiosis ultimately determine the functions of these crucial metabolic interactions. Therefore, investigating these interactions at a systems level is essential to advancing microbiome research and its translational applications.Multi-omics data from a microbiome can comprise metatranscriptomics (metabolic gene expression), metaproteomics (metabolic enzyme levels), metagenomics (microbial DNA), and metabolomics (metabolites exchanged among microbes). Although these data are readily available in various microbiome repositories, gaining deep insights into metabolic interactions can be challenging. Microbiome composition, species abundance, and metabolite or gene expression levels vary not only among individuals but also within a single microbiome. Despite this complexity, microbiome multi-omics data harbors a wealth of biological information that needs to be extracted and analyzed systematically in greater depth. This Research Topic seeks to promote machine learning (ML) models built to untangle the complex multi-omics data of microbiomes. Users can apply these models to human microbiomes to identify and comprehend their novel metabolic interactions.Another goal of this Research Topic is to advance a mechanistic understanding of how metabolic interactions drive human microbiome functions during healthy and diseased conditions using systems biology approaches. The topic aims to highlight omics-integrated GEMs and ML models that can simulate microbial coexistence patterns, predict host-environmental conditions or globally visualize the metabolic pathways involved in any human microbiome. Additionally, the topic expects ML models to leverage diverse multi-omics information and feature importance to identify key reactions, genes, pathways, metabolites, and metabolic states during metabolic interactions in both disease and healthy scenarios.This Research Topic welcomes original research articles, reviews, methods papers, perspectives, and brief research reports that systemically address metabolic interactions in human microbiomes. Topics of interest include, but are not limited to:- Machine learning models built for analyzing heterogeneous multi-omics microbiome data.- Systems-level analysis and visualization of host-microbe and microbe-microbe interactions in healthy or diseased human microbiomes.- Genome-scale modeling and Flux balance analysis (FBA) of microbial communities to identify metabolic interactions driving microbial coexistence.- Modeling dietary or environmental influences on human microbiomes to predict host-microbe interactions using machine learning methods.- Reporting novel biomarkers for drug-targeting, improving microbiome-associated disease therapeutics.Studies may focus on any human microbiome site or adopt a comparative perspective. Research investigating microbiomes at the population or community level is also highly welcomed. Experimental contributions that validate in silico predictions will be encouraged. Topic editors: Arnaud John KOMBE KOMBE, Pratip Rana, DEBASREE SARKAR, Abhishek Subramanian Source: https://www.frontiersin.org/research-topics/78491/understanding-metabolic-interactions-in-the-human-microbiome-using-multi-omics-data-machine-learning-and-genome-scale-models