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Unraveling the functional dark matter through global metagenomics.

ABSTRACT: Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities^1,2. 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 analyse 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 database³. 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 neighbourhood 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.

SUBMITTER: Pavlopoulos GA

PROVIDER: S-EPMC10584684 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Unraveling the functional dark matter through global metagenomics.

Pavlopoulos Georgios A GA Baltoumas Fotis A FA Liu Sirui S Selvitopi Oguz O Camargo Antonio Pedro AP Nayfach Stephen S Azad Ariful A Roux Simon S Call Lee L Ivanova Natalia N NN Chen I Min IM Paez-Espino David D Karatzas Evangelos E Iliopoulos Ioannis I Konstantinidis Konstantinos K Tiedje James M JM Pett-Ridge Jennifer J Baker David D Visel Axel A Ouzounis Christos A CA Ovchinnikov Sergey S Buluç Aydin A Kyrpides Nikos C NC

Nature 20231011 7983

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities<sup>1,2</sup>. 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 f ...[more]

PMID: 37821698

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Project description:Although microorganisms constitute the most diverse and abundant life form on Earth, in many environments, the vast majority of them remain uncultured. As it is based on information gleaned mainly from cultivated microorganisms, our current body of knowledge regarding microbial life is partial and does not reflect actual microbial diversity. That diversity is hidden in the uncultured microbial majority, termed by microbiologists as "microbial dark matter" (MDM), a term borrowed from astrophysics. Metagenomic sequencing analysis techniques (both 16S rRNA gene and shotgun sequencing) compare gene sequences to reference databases, each of which represents only a small fraction of the existing microorganisms. Unaligned sequences lead to groups of "unknown microorganisms" that are usually ignored and rarefied from diversity analysis. To address this knowledge gap, we analyzed the 16S rRNA gene sequences of microbial communities from four different environments-a living organism, a desert environment, a natural aquatic environment, and a membrane bioreactor for wastewater treatment. From those datasets, we chose representative sequences of potentially unknown bacteria for additional examination as "microbial dark matter sequences" (MDMS). Sequence existence was validated by specific amplification and re-sequencing. These sequences were screened against databases and aligned to the Genome Taxonomy Database to build a comprehensive phylogenetic tree for additional sequence classification, revealing potentially new candidate phyla and other lineages. These putative MDMS were also screened against metagenome-assembled genomes from the explored environments for additional validation and for taxonomic and metabolic characterizations. This study shows the immense importance of MDMS in environmental metataxonomic analyses of 16S rRNA gene sequences and provides a simple and readily available methodology for the examination of MDM hidden behind amplicon sequencing results.

Dataset Information

Unraveling the functional dark matter through global metagenomics.

Publications

Unraveling the functional dark matter through global metagenomics.

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