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MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes.

ABSTRACT: 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 https://github.com/ClavelLab/MiMiC.

SUBMITTER: Kumar N

PROVIDER: S-EPMC8313253 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRibonucleotide reductase (RNR), the enzyme responsible for the formation of deoxyribonucleotides from ribonucleotides, is found in all domains of life and many viral genomes. RNRs are also amongst the most abundant genes identified in environmental metagenomes. This study focused on understanding the distribution, diversity, and evolution of RNRs in phages (viruses that infect bacteria). Hidden Markov Model profiles were used to analyze the proteins encoded by 685 completely sequenced double-stranded DNA phages and 22 environmental viral metagenomes to identify RNR homologs in cultured phages and uncultured viral communities, respectively.ResultsRNRs were identified in 128 phage genomes, nearly tripling the number of phages known to encode RNRs. Class I RNR was the most common RNR class observed in phages (70%), followed by class II (29%) and class III (28%). Twenty-eight percent of the phages contained genes belonging to multiple RNR classes. RNR class distribution varied according to phage type, isolation environment, and the host's ability to utilize oxygen. The majority of the phages containing RNRs are Myoviridae (65%), followed by Siphoviridae (30%) and Podoviridae (3%). The phylogeny and genomic organization of phage and host RNRs reveal several distinct evolutionary scenarios involving horizontal gene transfer, co-evolution, and differential selection pressure. Several putative split RNR genes interrupted by self-splicing introns or inteins were identified, providing further evidence for the role of frequent genetic exchange. Finally, viral metagenomic data indicate that RNRs are prevalent and highly dynamic in uncultured viral communities, necessitating future research to determine the environmental conditions under which RNRs provide a selective advantage.ConclusionsThis comprehensive study describes the distribution, diversity, and evolution of RNRs in phage genomes and environmental viral metagenomes. The distinct distributions of specific RNR classes amongst phages, combined with the various evolutionary scenarios predicted from RNR phylogenies suggest multiple inheritance sources and different selective forces for RNRs in phages. This study significantly improves our understanding of phage RNRs, providing insight into the diversity and evolution of this important auxiliary metabolic gene as well as the evolution of phages in response to their bacterial hosts and environments.

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MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes.

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