Host-microbiome protein-protein interactions reveal mechanisms in human disease

Abstract

Host-microbe interactions are crucial for normal physiological and immune system development and are implicated in a wide variety of diseases, including inflammatory bowel disease (IBD), obesity, colorectal cancer (CRC), and type 2 diabetes (T2D). Despite large-scale case-control studies aimed at identifying microbial taxa or specific genes involved in pathogeneses, the mechanisms linking them to disease have thus far remained elusive. To better identify potential mechanisms linking human-associated bacteria with host health, we leveraged publicly-available interspecies protein-protein interaction (PPI) data to identify clusters of homologous microbiome-derived proteins that bind human proteins. By detecting humaninteracting bacterial genes in metagenomic case-control microbiome studies and applying a tailored machine learning algorithm, we are able to identify bacterial-human PPIs strongly linked with disease. In 9 independent case studies, we discover the microbiome broadly targets human immune, oncogenic, apoptotic, and endocrine signaling pathways, among others. This host-centric analysis strategy illuminates human pathways targeted by the commensal microbiota, provides a mechanistic hypothesis-generating platform for any metagenomics cohort study, and extensively annotates bacterial proteins with novel hostrelevant functions.

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

“…However, these studies do not provide the required mechanistic insights for formulating therapeutic interventions. Beltran and Brito (2019) devised an integrated methodology to unravel the molecular mechanisms underlying the microbe-host interactions associated with various diseases such as colorectal cancer, IBD, obesity and type-2 diabetes. The aforementioned study represents one of the first and few initiatives to use community-wide microbe-host interaction predictions using meta -omic datasets from patients to discover mechanistic interactions driving the clinical phenotypes.…”

“…The aforementioned study represents one of the first and few initiatives to use community-wide microbe-host interaction predictions using meta -omic datasets from patients to discover mechanistic interactions driving the clinical phenotypes. By combining orthology based approaches to extrapolate interactions from experimental PPIs, machine learning and patient derived -omic datasets, the authors identified a subset of inter-species PPIs which are associated with disease phenotypes ( Beltran and Brito, 2019 ). Thiele et al (2020) published a novel study by integrating different levels of information (dietary information, physiological parameters, organ weights, and organ connectivities, etc.)…”