Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

Abstract

Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of metabolic communities. ImportanceOur study contributes significantly to the field of microbial community modeling through a comprehensive comparison of genome-scale metabolic models (GEMs) generated via various automated tools, including: CarveMe, gapseq, KBase, and a consensus approach. We revealed substantial structural disparities in model outcomes, primarily attributed to variations in the employed databases. A key finding underscored the substantial impact of the reconstruction approach on the set of exchanged metabolites, emphasizing the necessity for enhanced data integration strategies. The consensus models emerge as a powerful solution, exhibiting improved functional capabilities by encompassing a greater number of reactions, metabolites, and genes. This not only offers a more comprehensive representation of metabolic networks within bacterial communities but also shows promise in reducing variability for more accurate predictions of exchange metabolites. Overall, our research provides a critical framework for refining microbial community simulations, impacting fields from ecology to synthetic biology.