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ABSTRACT Here, we report on eight sediment metagenomes obtained from an alkaline hot spring, with their corresponding metagenome-assembled genomes. Samples had been incubated for 48 h with various substrate amendments in conjunction with the amino acid analog l -homopropargylglycine in a study targeted at identifying anabolicly active uncultured thermophilic archaea and bacteria. 

Organic-rich, hydrothermal sediments of the Guaymas Basin are inhabited by diverse microbial communities including many uncultured lineages with unknown metabolic potential. Here we investigated the short-term effect of polysaccharide amendment on a sediment microbial community to identify taxa involved in the initial stage of macromolecule degradation. We incubated anoxic sediment with cellulose, chitin, laminarin, and starch and analyzed the total and active microbial communities using bioorthogonal non-canonical amino acid tagging (BONCAT) combined with fluorescence-activated cell sorting (FACS) and 16S rRNA gene amplicon sequencing. Our results show a response of an initially minor but diverse population of Clostridia particularly after amendment with the lower molecular weight polymers starch and laminarin. Thus, Clostridia may readily become key contributors to the heterotrophic community in Guaymas Basin sediments when substrate availability and temperature range permit their metabolic activity and growth, which expands our appreciation of the potential diversity and niche differentiation of heterotrophs in hydrothermally influenced sediments. BONCAT-FACS, although challenging in its application to complex samples, detected metabolic responses prior to growth and thus can provide complementary insight into a microbial community’s metabolic potential and succession pattern. As a primary application of BONCAT-FACS on a diverse deep-sea sediment community, our study highlights important considerations and demonstrates inherent limitations associated with this experimental approach. 

Metagenomic studies have revolutionized our understanding of the metabolic potential of uncultured microorganisms in various ecosystems. However, many of these genomic predictions have yet to be experimentally tested, and the functional expression of genomic potential often remains unaddressed. In order to obtain a more thorough understanding of cell physiology, novel techniques capable of testing microbial metabolism under close to in situ conditions must be developed. Here, we provide a benchmark study to demonstrate that bioorthogonal non-canonical amino acid tagging (BONCAT) in combination with fluorescence-activated cell sorting (FACS) and 16S rRNA gene sequencing can be used to identify anabolically active members of a microbial community incubated in the presence of various growth substrates or under changing physicochemical conditions. We applied this approach to a hot spring sediment microbiome from Yellowstone National Park (Wyoming, USA) and identified several microbes that changed their activity levels in response to substrate addition, including uncultured members of the phyla Thaumarchaeota, Acidobacteria, and Fervidibacteria. Because shifts in activity in response to substrate amendment or headspace changes are indicative of microbial preferences for particular growth conditions, results from this and future BONCAT-FACS studies could inform the development of cultivation media to specifically enrich uncultured microbes. Most importantly, BONCAT-FACS is capable of providing information on the physiology of uncultured organisms at as close to in situ conditions as experimentally possible.

 

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.