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Abstract Metagenomic studies on geothermal environments have been central in recent discoveries on the diversity of archaeal methane and alkane metabolism. Here, we investigated methanogenic populations inhabiting terrestrial geothermal features in Yellowstone National Park (YNP) by combining amplicon sequencing with metagenomics and mesocosm experiments. Detection of methyl-coenzyme M reductase subunit A (mcrA) gene amplicons demonstrated a wide diversity of Mcr-encoding archaea inhabit geothermal features with differing physicochemical regimes across YNP. From three selected hot springs we recovered twelve Mcr-encoding metagenome assembled genomes (MAGs) affiliated with lineages of cultured methanogens as well as Candidatus (Ca.) Methanomethylicia, Ca. Hadesarchaeia, and Archaeoglobi. These MAGs encoded the potential for hydrogenotrophic, aceticlastic, hydrogen-dependent methylotrophic methanogenesis, or anaerobic short-chain alkane oxidation. While Mcr-encoding archaea represent minor fractions of the microbial community of hot springs, mesocosm experiments with methanogenic precursors resulted in the stimulation of methanogenic activity and the enrichment of lineages affiliated with Methanosaeta and Methanothermobacter as well as with uncultured Mcr-encoding archaea including Ca. Korarchaeia, Ca. Nezhaarchaeia, and Archaeoglobi. We revealed that diverse Mcr-encoding archaea with the metabolic potential to produce methane from different precursors persist in the geothermal environments of YNP and can be enriched under methanogenic conditions. This study highlights the importance of combining environmental metagenomics with laboratory-based experiments to expand our understanding of uncultured Mcr-encoding archaea and their potential impact on microbial carbon transformations in geothermal environments and beyond. 

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyze 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical, and gene neighborhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.