Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration

TLDR

The gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, but how they interact to influence host pathophysiology remains unclear. The study develops a machine‑learning framework to jointly analyze host transcriptomic and gut microbiome profiles from colonic mucosal samples of colorectal cancer, inflammatory bowel disease, and irritable bowel syndrome patients, aiming to identify host gene–microbiome associations that may influence disease outcomes. The framework uses machine learning to identify associations between gut microbes and host genes, revealing shared and disease‑specific patterns. A common set of host genes and pathways involved in gastrointestinal inflammation, gut barrier protection, and energy metabolism are associated with disease‑specific gut microbes, and microbes such as *Streptococcus* are linked to different host pathways in each disease, indicating that similar microbes can affect host pathophysiology in a disease‑specific manner.

Abstract

Abstract While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic ( n = 208) and gut microbiome ( n = 208) profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease and irritable bowel syndrome. We identified associations between gut microbes and host genes that depict shared as well as disease-specific patterns. We found that a common set of host genes and pathways implicated in gastrointestinal inflammation, gut barrier protection and energy metabolism are associated with disease-specific gut microbes. Additionally, we also found that mucosal gut microbes that have been implicated in all three diseases, such as Streptococcus , are associated with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes. Our framework can be applied to other diseases for the identification of host gene–microbiome associations that may influence disease outcomes.