Factors shaping the abundance and diversity of the gut archaeome across the animal kingdom.

Archaea are common constituents of the gut microbiome of humans, ruminants, and termites but little is known about their diversity and abundance in other animals. Here, we analyse sequencing and quantification data of archaeal and bacterial 16S rRNA genes from 250 species of animals covering a large taxonomic spectrum. We detect the presence of archaea in 175 animal species belonging to invertebrates, fish, amphibians, birds, reptiles and mammals. We identify five dominant gut lineages, corresponding to Methanobrevibacter, Methanosphaera, Methanocorpusculum, Methanimicrococcus and "Ca. Methanomethylophilaceae". Some archaeal clades, notably within Methanobrevibacter, are associated to certain hosts, suggesting specific adaptations. The non-methanogenic lineage Nitrososphaeraceae (Thaumarchaeota) is frequently present in animal samples, although at low abundance, but may have also adapted to the gut environment. Host phylogeny, diet type, fibre content, and intestinal tract physiology are major drivers of the diversity and abundance of the archaeome in mammals. The overall abundance of archaea is more influenced by these factors than that of bacteria. Methanogens reducing methyl-compounds with H2 can represent an important fraction of the overall methanogens in many animals. Together with CO2-reducing methanogens, they are influenced by diet and composition of gut bacteria. Our results provide key elements toward our understanding of the ecology of archaea in the gut, an emerging and important field of investigation.

Comparative genomic analysis of Methanimicrococcus blatticola provides insights into host adaptation in archaea and the evolution of methanogenesis.

Other than the Methanobacteriales and Methanomassiliicoccales, the characteristics of archaea that inhabit the animal microbiome are largely unknown. Methanimicrococcus blatticola, a member of the Methanosarcinales, currently reunites two unique features within this order: it is a colonizer of the animal digestive tract and can only reduce methyl compounds with H2 for methanogenesis, a increasingly recognized metabolism in the archaea and whose origin remains debated. To understand the origin of these characteristics, we have carried out a large-scale comparative genomic analysis. We infer the loss of more than a thousand genes in M. blatticola, by far the largest genome reduction across all Methanosarcinales. These include numerous elements for sensing the environment and adapting to more stable gut conditions, as well as a significant remodeling of the cell surface components likely involved in host and gut microbiota interactions. Several of these modifications parallel those previously observed in phylogenetically distant archaea and bacteria from the animal microbiome, suggesting large-scale convergent mechanisms of adaptation to the gut. Strikingly, M. blatticola has lost almost all genes coding for the H4MPT methyl branch of the Wood-Ljungdahl pathway (to the exception of mer), a phenomenon never reported before in any member of Class I or Class II methanogens. The loss of this pathway illustrates one of the evolutionary processes that may have led to the emergence of methyl-reducing hydrogenotrophic methanogens, possibly linked to the colonization of organic-rich environments (including the animal gut) where both methyl compounds and hydrogen are abundant.

Microbial metagenomes and metatranscriptomes during a coastal phytoplankton bloom.

Metagenomic and metatranscriptomic time-series data covering a 52-day period in the fall of 2016 provide an inventory of bacterial and archaeal community genes, transcripts, and taxonomy during an intense dinoflagellate bloom in Monterey Bay, CA, USA. The dataset comprises 84 metagenomes (0.8 terabases), 82 metatranscriptomes (1.1 terabases), and 88 16S rRNA amplicon libraries from samples collected on 41 dates. The dataset also includes 88 18S rRNA amplicon libraries, characterizing the taxonomy of the eukaryotic community during the bloom. Accompanying the sequence data are chemical and biological measurements associated with each sample. These datasets will facilitate studies of the structure and function of marine bacterial communities during episodic phytoplankton blooms.