Learning functional groups in complex microbiomes

TL;DR

This paper presents a data-driven, neural-network based clustering method to identify key functional groups in complex microbiomes, linking microbial composition to community functions across different environments.

Contribution

It introduces a novel neural-network clustering approach that simplifies microbiome complexity and connects microbial groups to functions through interpretable models and experiments.

Findings

Correctly recovers known functional groups in gut microbiomes

Distills gene modules into sparse groups revealing survival strategies

Links microbial groups to metabolic responses in soils

Abstract

From soil to the gut, communities composed of thousands of microbes perform functions such as carbon sequestration and immune system regulation. Here, we introduce a data-driven approach that explains how community function can be traced to just a few groups of microbes or genes. In gut communities, our neural-network based clustering algorithm correctly recovers known functional groups. In the ocean metagenome, it distills ~500 gene modules down to three sparse groups highlighting survival strategies at different depths. In soils, it distills ~4400 bacterial species into two groups that enter a mathematical model of nitrate metabolism. By combining interpretable ML with strain isolation and sequencing experiments, we connect the metabolic specialization of each group to community-wide responses to perturbations. This integrated approach yields simple structure-function maps of microbiomes, allowing the discovery of molecular mechanisms underlying human and environmental health. More broadly, we illustrate how to do function-informed dimensionality reduction in biology.