Integration of metatranscriptomics data improves the predictive capacity of microbial community metabolic models.

Microbial consortia play pivotal roles in nutrient cycling across diverse ecosystems, where the functionality and composition of microbial communities are shaped by metabolic interactions. Despite the critical importance of understanding these interactions, accurately mapping and manipulating microbial interaction networks to achieve specific outcomes remains challenging. Genome-scale metabolic models (GEMs) offer significant promise for predicting microbial metabolic functions from genomic data; however, traditional community GEMs typically rely on species abundance information, which may limit their predictive accuracy due to the absence of condition-specific gene expression or protein abundance data. Here, we introduce the Integration of Metatranscriptomes Into Community GEMs (IMIC) approach, which utilizes metatranscriptomic data to construct context-specific community models for predicting individual growth rates and metabolic interactions. By incorporating metatranscriptomic profiles, which reflect both gene expression activity and partially encode abundance information, IMIC could predict condition-specific flux distributions that enable the investigation of metabolite interactions among community members. Our results show that growth rates predicted by IMIC correlate strongly with relative as well as absolute abundance of species and offer a streamlined, automated procedure for estimating the single intrinsic parameter. Specifically, IMIC results in improved predictions of measured metabolite concentration changes compared with other approaches in our case study. We further demonstrate that this improvement is driven by the network-wide adjustment of flux bounds based on gene expression profiles. In conclusion, the IMIC approach enables the accurate prediction of individual growth rates and improves the model performance of predicting metabolite interactions, facilitating a deeper understanding of metabolic interdependencies within microbial communities.