Pubmed Record 34081399
Abstract Text
Environmental and host-associated microbial communities are complex ecosystems, of which many members are still unknown. Hence, it is challenging to study community dynamics and important to create model systems of reduced complexity that mimic major community functions. Therefore, we developed MiMiC, a computational approach for data-driven design of simplified communities from shotgun metagenomes. We first built a comprehensive database of species-level bacterial and archaeal genomes (n = 22 627) consisting of binary (presence/absence) vectors of protein families (Pfam = 17 929). MiMiC predicts the composition of minimal consortia using an iterative scoring system based on maximal match-to-mismatch ratios between this database and the Pfam binary vector of any input metagenome. Pfam vectorization retained enough resolution to distinguish metagenomic profiles between six environmental and host-derived microbial communities (n = 937). The calculated number of species per minimal community ranged between 5 and 11, with MiMiC selected communities better recapitulating the functional repertoire of the original samples than randomly selected species. The inferred minimal communities retained habitat-specific features and were substantially different from communities consisting of most abundant members. The use of a mixture of known microbes revealed the ability to select 23 of 25 target species from the entire genome database. MiMiC is open source and available at github.com/ClavelLab/MiMiC.
Pubmed Record 27199914
Abstract Text
How soil microbial communities contrast with respect to taxonomic and functional composition within and between ecosystems remains an unresolved question that is central to predicting how global anthropogenic change will affect soil functioning and services. In particular, it remains unclear how small-scale observations of soil communities based on the typical volume sampled (1-2 g) are generalizable to ecosystem-scale responses and processes. This is especially relevant for remote, northern latitude soils, which are challenging to sample and are also thought to be more vulnerable to climate change compared to temperate soils. Here, we employed well-replicated shotgun metagenome and 16S rRNA gene amplicon sequencing to characterize community composition and metabolic potential in Alaskan tundra soils, combining our own datasets with those publically available from distant tundra and temperate grassland and agriculture habitats. We found that the abundance of many taxa and metabolic functions differed substantially between tundra soil metagenomes relative to those from temperate soils, and that a high degree of OTU-sharing exists between tundra locations. Tundra soils were an order of magnitude less complex than their temperate counterparts, allowing for near-complete coverage of microbial community richness (~92% breadth) by sequencing, and the recovery of 27 high-quality, almost complete (>80% completeness) population bins. These population bins, collectively, made up to ~10% of the metagenomic datasets, and represented diverse taxonomic groups and metabolic lifestyles tuned toward sulfur cycling, hydrogen metabolism, methanotrophy, and organic matter oxidation. Several population bins, including members of Acidobacteria, Actinobacteria, and Proteobacteria, were also present in geographically distant (~100-530 km apart) tundra habitats (full genome representation and up to 99.6% genome-derived average nucleotide identity). Collectively, our results revealed that Alaska tundra microbial communities are less diverse and more homogenous across spatial scales than previously anticipated, and provided DNA sequences of abundant populations and genes that would be relevant for future studies of the effects of environmental change on tundra ecosystems.
