Expanding the drug discovery space with predicted metabolite\u2013target interactions

Andrea Nuzzo,James R Brown,Joel Tocker,Channa Jayawickreme,Ellen Berg,Somdutta Saha

doi:10.1038/s42003-021-01822-x

Abstract

Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite–host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potential metabolite–target interactions using the Inflammatory Bowel Disease (IBD) cohort dataset of the Human Microbiome Project 2 (HMP2). Using a consensus of multiple machine learning methods, we ranked metabolites based on importance to IBD, followed by virtual ligand-based screening to identify possible human targets and adding evidence from compound assay, differential gene expression, pathway enrichment, and genome-wide association studies. We confirmed known metabolite–target pairs such as nicotinic acid–GPR109a or linoleoyl ethanolamide–GPR119 and inferred interactions of interest including oleanolic acid–GABRG2 and alpha-CEHC–THRB. Eleven metabolites were tested for bioactivity in vitro using human primary cell-types. By expanding the universe of possible microbial metabolite–host protein interactions, we provide multiple drug targets for potential immune-therapies.

Highlights

Metabolites produced in the human gut are known modulators of host immunity
We focused on the metabolomics data collected from stool over the course of one year and bulk transcriptomics data obtained from biopsies of different sections of the gut at the beginning of the study
We greatly expand the number of potential protein–metabolite interactions based on a well-characterized Inflammatory Bowel Disease (IBD) multi-omics dataset from HMP29 by going beyond conventional associative studies

Summary

Introduction

Metabolites produced in the human gut are known modulators of host immunity. large-scale identification of metabolite–host receptor interactions remains a daunting challenge. We employed computational approaches to identify 983 potential metabolite–target interactions using the Inflammatory Bowel Disease (IBD) cohort dataset of the Human Microbiome Project 2 (HMP2). Targeting the interspecies cross-talk between microbial metabolites and human host receptors holds a recognized therapeutic potential[2]. Disentangling these interactions in order to retrieve meaningful information remains highly challenging[3]. We used virtual ligand-based screening to predict the activity of the original query metabolites on multiple targets[12] based upon historical assay databases containing interaction data between similar molecules and specific targets We believe that these predicted interactions will further our understanding of host– microbiome interactions as well as assist in drug discovery for IBD and other diseases

Methods

Results

Conclusion