The genus Prevotella includes more than 50 characterized species that occur in varied natural habitats, although most Prevotella spp. are associated with humans. In the human microbiome, Prevotella spp. are highly abundant in various body sites, where they are key players in the balance between health and disease. Host factors related to diet, lifestyle and geography are fundamental in affecting the diversity and prevalence of Prevotella species and strains in the human microbiome. These factors, along with the ecological relationship of Prevotella with other members of the microbiome, likely determine the extent of the contribution of Prevotella to human metabolism and health. Here we review the diversity, prevalence and potential connection of Prevotella spp. in the human host, highlighting how genomic methods and analysis have improved and should further help in framing their ecological role. We also provide suggestions for future research to improve understanding of the possible functions of Prevotella spp. and the effects of the Western lifestyle and diet on the host-Prevotella symbiotic relationship in the context of maintaining human health.

Acidobacteriota are widespread and often abundant in marine sediments, yet their metabolic and ecological properties are poorly understood. Here, we examined metabolisms and distributions of Acidobacteriota in marine sediments of Svalbard by functional predictions from metagenome-assembled genomes (MAGs), amplicon sequencing of 16S rRNA and dissimilatory sulfite reductase (dsrB) genes and transcripts, and gene expression analyses of tetrathionate-amended microcosms. Acidobacteriota were the second most abundant dsrB-harboring (averaging 13%) phylum after Desulfobacterota in Svalbard sediments, and represented 4% of dsrB transcripts on average. Meta-analysis of dsrAB datasets also showed Acidobacteriota dsrAB sequences are prominent in marine sediments worldwide, averaging 15% of all sequences analysed, and represent most of the previously unclassified dsrAB in marine sediments. We propose two new Acidobacteriota genera, Candidatus Sulfomarinibacter (class Thermoanaerobaculia, "subdivision 23") and Ca. Polarisedimenticola ("subdivision 22"), with distinct genetic properties that may explain their distributions in biogeochemically distinct sediments. Ca. Sulfomarinibacter encode flexible respiratory routes, with potential for oxygen, nitrous oxide, metal-oxide, tetrathionate, sulfur and sulfite/sulfate respiration, and possibly sulfur disproportionation. Potential nutrients and energy include cellulose, proteins, cyanophycin, hydrogen, and acetate. A Ca. Polarisedimenticola MAG encodes various enzymes to degrade proteins, and to reduce oxygen, nitrate, sulfur/polysulfide and metal-oxides. 16S rRNA gene and transcript profiling of Svalbard sediments showed Ca. Sulfomarinibacter members were relatively abundant and transcriptionally active in sulfidic fjord sediments, while Ca. Polarisedimenticola members were more relatively abundant in metal-rich fjord sediments. Overall, we reveal various physiological features of uncultured marine Acidobacteriota that indicate fundamental roles in seafloor biogeochemical cycling.

Simulated metagenomic reads are widely used to benchmark software and workflows for metagenome interpretation. The results of metagenomic benchmarks depend on the assumptions about their underlying ecosystems. Conclusions from benchmark studies are therefore limited to the ecosystems they mimic. Ideally, simulations are therefore based on genomes, which resemble particular metagenomic communities realistically.
We developed Tamock to facilitate the realistic simulation of metagenomic reads according to a metagenomic community, based on real sequence data. Benchmarks samples can be created from all genomes and taxonomic domains present in NCBI RefSeq. Tamock automatically determines taxonomic profiles from shotgun sequence data, selects reference genomes accordingly and uses them to simulate metagenomic reads. We present an example use case for Tamock by assessing assembly and binning method performance for selected microbiomes.
Tamock facilitates automated simulation of habitat-specific benchmark metagenomic data based on real sequence data and is implemented as a user-friendly command-line application, providing extensive additional information along with the simulated benchmark data. Resulting benchmarks enable an assessment of computational methods, workflows, and parameters specifically for a metagenomic habitat or ecosystem of a metagenomic study.
Source code, documentation and install instructions are freely available at GitHub ( https://github.com/gerners/tamock ).