Journal Article

Using cross-species co-expression to predict metabolic interactions in microbiomes

Koetsier, Robert A., van der Hooft, Justin J. J., Chevrette, Marc G., Medema, Marnix H., Reitz, Zachary L., Zhu, Yijun, Handelsman, Jo, Belzer, Clara

2026

Overview

An improved mechanistic understanding of microbial interactions can guide targeted interventions or inform the rational design of microbial communities to optimize them for applications such as pathogen control, food fermentation, and various biochemical processes. Existing methodologies for inferring the mechanisms behind microbial interactions often rely on complex model-building and are, therefore, sensitive to the introduction of biases from the incorporated existing knowledge and model-building assumptions. We highlight the microbial interaction prediction potential of cross-species co-expression analysis, which contrasts with these methods by its data-driven nature. We describe the utility of cross-species co-expression for various types of interactions and thereby inform future studies on use-cases of the approach and the opportunities and pitfalls that can be expected in its application.

• Publisher: American Society for Microbiology
• Subject: antibiotics / Bacteria - classification / Bacteria - genetics / Bacteria - metabolism / coexpression / Community Dynamics / Community Structure and Diversity / Computational Biology / Cross-Feeding and Metabolic Exchange / Gene Expression Regulation, Bacterial / metabolic gene cluster / Metabolic Interactions in Microbial Communities / Metabolic Networks and Pathways / Microbial Co-Occurrence Networks / Microbial Communities / Microbial Consortia and Networks / Microbial Ecology and Evolution / Microbial Interaction Networks / Microbial Interactions / Microbial Metabolism / Microbial Physiology and Genetics / Microbiome Dynamics / Microbiome Research / Microbiota - genetics / Research Article / Rhizosphere / synthetic community