Deciphering the microbial architecture of pesticide and antibiotics biodegradation

The emergence of ecological functions performed by microorganisms at the community level is a major challenge in microbial ecology. Understanding emerging functions would be a promising tool for engineering microbial communities, for example with the aim of bioremediating a herbicide and an antibiotic. Here, through a top-down approach we obtained compositional variants of degrading communities and further investigated community features associated with their degradation abilities. We first tested whether diversity indices or functional gene abundance could reliably be used as a proxy for this function, and obtained encouraging, albeit variable results. Further, through the use of statistical tools borrowed from the genomic selection literature, we were able to derive an accurate prediction of the mineralisation potential of a bacterial community, based on its composition. However, the parallel between genotype-phenotype and community composition-mineralisation potential suffers a crucial caveat: bacterial abundances vary on a much wider scale than allele dosage at a given locus and are prone to change over time (particularly at the mineralisation scale). Here we observed that using presence/absence data instead of relative abundance can overcome these limitations and provide a clearer functional signal for mineralisation prediction through linear regression models. Random forests can also intrinsically deal with microbial data without transformation and select significant predictors. We suggest drawing inspiration from the tools and concepts used in genotype-phenotype mapping to elucidate microbial functions at the community level, while keeping in mind the significant differences between these two fields.