Methylmercury (MeHg), a neurotoxic compound biomagnifying in aquatic food webs, can be a threat to human health via fish consumption. However, the compositionand distribution of the microbial communities mediating the methylation of mercury (Hg) to MeHg in marine systems remain largely unknown. In order to fill this knowledge gap, we used the Baltic Sea Reference Metagenome (BARM) dataset to study the abundance and distribution of the genes involved in Hg methylation (the hgcAB gene cluster). We determined the relative abundance of the hgcAB genes and their taxonomic identity in 81 brackish metagenomes that cover spatial,seasonal and redox variability in the Baltic Sea water column. The hgcAB genes were predominantly detected in anoxic water, but some hgcAB genes were alsodetected in hypoxic and normoxic waters. Phylogenetic analysis identified putative Hg methylators within Deltaproteobacteria, in oxygen-deficient water layers, but also Spirochaetes-like and Kiritimatiellaeota-like bacteria. Higher relative quantities of hgcAB genes were found in metagenomes from marine particles compared to free-living communities in anoxic water, suggesting that such particles are hotspot habitats for Hg methylators in oxygen-depleted seawater. Altogether, our work unveils the diversityof the microorganisms with the potential to mediate MeHg production in the BalticSea and pinpoint the important ecological niches for these microorganisms within themarine water column.

The recent recognition of fungi in the oceanic igneous crust challenges the understanding of this environment as being exclusively prokaryotic and forces reconsiderations of the ecology of the deep biosphere. Anoxic provinces in the igneous crust are abundant and increase with age and depth of the crust. The presence of anaerobic fungi in deep-sea sediments and on the seafloor introduces a type of organism with attributes of geobiological significance not previously accounted for. Anaerobic fungi are best known from the rumen of herbivores where they produce molecular hydrogen, which in turn stimulates the growth of methanogens. The symbiotic cooperation between anaerobic fungi and methanogens in the rumen enhance the metabolic rate and growth of both. Methanogens and other hydrogen-consuming anaerobic archaea are known from subseafloor basalt; however, the abiotic production of hydrogen is questioned to be sufficient to support such communities. Alternatively, biologically produced hydrogen could serve as a continuous source. Here, we propose anaerobic fungi as a source of bioavailable hydrogen in the oceanic crust, and a close interplay between anaerobic fungi and hydrogen-driven prokaryotes.

The formyltetrahydrofolate synthetase (FTHFS) gene is a molecular marker of choice to study the diversity of acetogenic communities. However, current analyses are limited due to lack of a high-throughput sequencing approach for FTHFS gene amplicons and a dedicated bioinformatics pipeline for data analysis, including taxonomic annotation and visualization of the sequence data. In the present study, we combined the barcode approach for multiplexed sequencing with unsupervised data analysis to visualize acetogenic community structure. We used samples from a biogas digester to develop proof-of-principle for our combined approach. We successfully generated high-throughput sequence data for the partial FTHFS gene and performed unsupervised data analysis using the novel bioinformatics pipeline "AcetoScan" presented in this study, which resulted in taxonomically annotated OTUs, phylogenetic tree, abundance plots and diversity indices. The results demonstrated that high-throughput sequencing can be used to sequence the FTHFS amplicons from a pool of samples, while the analysis pipeline AcetoScan can be reliably used to process the raw sequence data and visualize acetogenic community structure. The method and analysis pipeline described in this paper can assist in the identification and quantification of known or potentially new acetogens. The AcetoScan pipeline is freely available at https://github.com/abhijeetsingh1704/AcetoScan.