Interkingdom microbial interactions revealed by comparative machine learning guided multi-omics in industrial scale biogas plants

Multi-omics analysis is a powerful tool to detect and study interkingdom interactions between bacterial and archaeal members of complex biogas producing microbial communities. In present study the microbiome of three industrial scale biogas digesters, fed with different substrates, were analyzed by machine-learning guided genome-centric metagenomics complemented with metatranscriptome data. The elucidation the relationship between abundant core methanogenic communities and their syntrophic bacterial partners was in the focus of the study. Overall 297 high-quality, non-redundant metagenome assembled genomes -nrMAGs- were detected. The assembled 16S rRNA gene profiles of these nrMAGs showed that the phylum Firmicutes was represented with the highest copy number, while the representatives of Archaeal domain had the lowest. The investigated three anaerobic microbial communities showed characteristic alterations over time, but remained specific for each industrial scale biogas plant. The relative abundance values of microbes revealed by metagenome data were distinct from their corresponding metatranscriptome activities. Interestingly, Archaea showed considerably higher activities than it was expected from their abundance. 53 nrMAGs were detected in the intersection of the three biogas plant microbiomes with different abundances. The core microbiome was correlated with the main chemical fermentation parameters. None of the individual parameters emerged as a predominant one shaping the communities composition. Various interspecies H2-electron transfer mechanisms were assigned to the hydrogenotrophic methanogens in the biogas plants running on agricultural biomass and wastewater. The analysis of metatranscriptome data revealed that methanogenesis pathways were the most active ones among all main metabolic pathways. These findings highlight the importance of the combination of omics data to confirm and characterize the activity of specific microbes in complex environments.