Exploring the microbial influence on seasonal nitrous oxide concentration in a full-scale wastewater treatment plant using metagenome assembled genomes.

Nitrous oxide is a highly potent greenhouse gas and one of the main contributors to the greenhouse gas footprint of wastewater treatment plants (WWTP). Although nitrous oxide can be produced by abiotic reactions in these systems, biological N2O production resulting from the imbalance of nitrous oxide production and reduction by microbial populations is the dominant cause. The microbial populations responsible for the imbalance have not been clearly identified, yet they are likely responsible for strong seasonal nitrous oxide patterns. Here, we examined the seasonal nitrous oxide concentration pattern in Avedøre WWTP alongside abiotic parameters, the microbial community composition based on 16S rRNA gene sequencing and already available metagenome-assembled genomes (MAGs). We found that the WWTP parameters could not explain the observed pattern. While no distinct community changes between periods of high and low dissolved nitrous oxide concentrations were determined, we found 26 and 28 species with positive and negative correlations to the seasonal N2O concentrations, respectively. MAGs were identified for 124 species (approximately 31% mean relative abundance of the community), and analysis of their genomic nitrogen transformation potential could explain this correlation for four of the negatively correlated species. Other abundant species were also analysed for their nitrogen transformation potential. Interestingly, only one full-denitrifier (Candidatus Dechloromonas phosphorivorans) was identified. 59 species had a nosZ gene predicted, with the majority identified as a clade II nosZ gene, mainly from the phylum Bacteroidota. A correlation of MAG-derived functional guilds with the N2O concentration pattern showed that there was a small but significant negative correlation with nitrite oxidizing bacteria and species with a nosZ gene (N2O reducers (DEN)). More research is required, specifically long-term activity measurements in relation to the N2O concentration to increase the resolution of these findings.

Novel syntrophic bacteria in full-scale anaerobic digesters revealed by genome-centric metatranscriptomics.

Short-chain fatty acid (SCFA) degradation is an important process in methanogenic ecosystems, and is usually catalyzed by SCFA-oxidizing bacteria in syntrophy with methanogens. Current knowledge of this functional guild is mainly based on isolates or enrichment cultures, but these may not reflect the true diversity and in situ activities of the syntrophs predominating in full-scale systems. Here we obtained 182 medium to high quality metagenome-assembled genomes (MAGs) from the microbiome of two full-scale anaerobic digesters. The transcriptomic response of individual MAG was studied after stimulation with low concentrations of acetate, propionate, or butyrate, separately. The most pronounced response to butyrate was observed for two MAGs of the recently described genus Candidatus Phosphitivorax (phylum Desulfobacterota), expressing a butyrate beta-oxidation pathway. For propionate, the largest response was observed for an MAG of a novel genus in the family Pelotomaculaceae, transcribing a methylmalonyl-CoA pathway. All three species were common in anaerobic digesters at Danish wastewater treatment plants as shown by amplicon analysis, and this is the first time their syntrophic features involved in SCFA oxidation were revealed with transcriptomic evidence. Further, they also possessed unique genomic features undescribed in well-characterized syntrophs, including the metabolic pathways for phosphite oxidation, nitrite and sulfate reduction.

The Signal and the Noise: Characteristics of Antisense RNA in Complex Microbial Communities.

High-throughput sequencing has allowed unprecedented insight into the composition and function of complex microbial communities. With metatranscriptomics, it is possible to interrogate the transcriptomes of multiple organisms simultaneously to get an overview of the gene expression of the entire community. Studies have successfully used metatranscriptomics to identify and describe relationships between gene expression levels and community characteristics. However, metatranscriptomic data sets contain a rich suite of additional information that is just beginning to be explored. Here, we focus on antisense expression in metatranscriptomics, discuss the different computational strategies for handling it, and highlight the strengths but also potentially detrimental effects on downstream analysis and interpretation. We also analyzed the antisense transcriptomes of multiple genomes and metagenome-assembled genomes (MAGs) from five different data sets and found high variability in the levels of antisense transcription for individual species, which were consistent across samples. Importantly, we challenged the conceptual framework that antisense transcription is primarily the product of transcriptional noise and found mixed support, suggesting that the total observed antisense RNA in complex communities arises from the combined effect of unknown biological and technical factors. Antisense transcription can be highly informative, including technical details about data quality and novel insight into the biology of complex microbial communities.IMPORTANCE This study systematically evaluated the global patterns of microbial antisense expression across various environments and provides a bird's-eye view of general patterns observed across data sets, which can provide guidelines in our understanding of antisense expression as well as interpretation of metatranscriptomic data in general. This analysis highlights that in some environments, antisense expression from microbial communities can dominate over regular gene expression. We explored some potential drivers of antisense transcription, but more importantly, this study serves as a starting point, highlighting topics for future research and providing guidelines to include antisense expression in generic bioinformatic pipelines for metatranscriptomic data.