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Pavlopoulos GA, Baltoumas FA, Liu S, Selvitopi O, Camargo AP, Nayfach S, Azad A, Roux S, Call L, Ivanova NN, Chen IM, Paez-Espino D, Karatzas E, Iliopoulos I, Konstantinidis K, Tiedje JM, Pett-Ridge J, Baker D, Visel A, Ouzounis CA, Ovchinnikov S, Buluç A, Kyrpides NC. Unraveling the functional dark matter through global metagenomics. Nature 2023; 622:594-602. [PMID: 37821698 PMCID: PMC10584684 DOI: 10.1038/s41586-023-06583-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/30/2023] [Indexed: 10/13/2023]
Abstract
Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. 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.
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Affiliation(s)
- Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Science Research Center Alexander Fleming, Vari, Greece.
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
| | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Science Research Center Alexander Fleming, Vari, Greece
| | - Sirui Liu
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Oguz Selvitopi
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Antonio Pedro Camargo
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Stephen Nayfach
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ariful Azad
- Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, USA
| | - Simon Roux
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Lee Call
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Natalia N Ivanova
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - I Min Chen
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - David Paez-Espino
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Science Research Center Alexander Fleming, Vari, Greece
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, Heraklion, Greece
| | | | - James M Tiedje
- Center for Microbial Ecology, Michigan State University, East Lansing, MI, USA
| | - Jennifer Pett-Ridge
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Axel Visel
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christos A Ouzounis
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
- Biological Computation & Computational Biology Group, Artificial Intelligence & Information Analysis Lab, School of Informatics, Aristotle University of Thessalonica, Thessalonica, Greece
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Aydin Buluç
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Nikos C Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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2
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Arikawa K, Hosokawa M. Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics. Comput Struct Biotechnol J 2023; 21:4508-4518. [PMID: 37771751 PMCID: PMC10523443 DOI: 10.1016/j.csbj.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/10/2023] [Accepted: 09/10/2023] [Indexed: 09/30/2023] Open
Abstract
Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications.
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Affiliation(s)
- Koji Arikawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- bitBiome, Inc., 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
| | - Masahito Hosokawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- bitBiome, Inc., 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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3
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Rizos I, Debeljak P, Finet T, Klein D, Ayata SD, Not F, Bittner L. Beyond the limits of the unassigned protist microbiome: inferring large-scale spatio-temporal patterns of Syndiniales marine parasites. ISME COMMUNICATIONS 2023; 3:16. [PMID: 36854980 PMCID: PMC9975217 DOI: 10.1038/s43705-022-00203-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 03/02/2023]
Abstract
Marine protists are major components of the oceanic microbiome that remain largely unrepresented in culture collections and genomic reference databases. The exploration of this uncharted protist diversity in oceanic communities relies essentially on studying genetic markers from the environment as taxonomic barcodes. Here we report that across 6 large scale spatio-temporal planktonic surveys, half of the genetic barcodes remain taxonomically unassigned at the genus level, preventing a fine ecological understanding for numerous protist lineages. Among them, parasitic Syndiniales (Dinoflagellata) appear as the least described protist group. We have developed a computational workflow, integrating diverse 18S rDNA gene metabarcoding datasets, in order to infer large-scale ecological patterns at 100% similarity of the genetic marker, overcoming the limitation of taxonomic assignment. From a spatial perspective, we identified 2171 unassigned clusters, i.e., Syndiniales sequences with 100% similarity, exclusively shared between the Tropical/Subtropical Ocean and the Mediterranean Sea among all Syndiniales orders and 25 ubiquitous clusters shared within all the studied marine regions. From a temporal perspective, over 3 time-series, we highlighted 39 unassigned clusters that follow rhythmic patterns of recurrence and are the best indicators of parasite community's variation. These clusters withhold potential as ecosystem change indicators, mirroring their associated host community responses. Our results underline the importance of Syndiniales in structuring planktonic communities through space and time, raising questions regarding host-parasite association specificity and the trophic mode of persistent Syndiniales, while providing an innovative framework for prioritizing unassigned protist taxa for further description.
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Affiliation(s)
- Iris Rizos
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France.
- Sorbonne Université, CNRS, AD2M-UMR7144 Station Biologique de Roscoff, 29680, Roscoff, France.
| | - Pavla Debeljak
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
| | - Thomas Finet
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
| | - Dylan Klein
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
| | - Sakina-Dorothée Ayata
- Sorbonne Université, Laboratoire d'Océanographie et du Climat: Expérimentation et Analyses Numériques (LOCEAN, SU/CNRS/IRD/MNHN), 75252, Paris Cedex 05, France
| | - Fabrice Not
- Sorbonne Université, CNRS, AD2M-UMR7144 Station Biologique de Roscoff, 29680, Roscoff, France
| | - Lucie Bittner
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
- Institut Universitaire de France, Paris, France
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4
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Vanni C, Schechter MS, Acinas SG, Barberán A, Buttigieg PL, Casamayor EO, Delmont TO, Duarte CM, Eren AM, Finn RD, Kottmann R, Mitchell A, Sánchez P, Siren K, Steinegger M, Gloeckner FO, Fernàndez-Guerra A. Unifying the known and unknown microbial coding sequence space. eLife 2022; 11:e67667. [PMID: 35356891 PMCID: PMC9132574 DOI: 10.7554/elife.67667] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/30/2022] [Indexed: 12/02/2022] Open
Abstract
Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71 M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.
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Affiliation(s)
- Chiara Vanni
- Microbial Genomics and Bioinformatics Research G, Max Planck Institute for Marine MicrobiologyBremenGermany
- Jacobs University BremenBremenGermany
| | - Matthew S Schechter
- Microbial Genomics and Bioinformatics Research G, Max Planck Institute for Marine MicrobiologyBremenGermany
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Silvia G Acinas
- Department of Marine Biology and Oceanography, Institut de Ciències del Mar (CSIC)BarcelonaSpain
| | - Albert Barberán
- Department of Environmental Science, University of ArizonaTucsonUnited States
| | - Pier Luigi Buttigieg
- Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Alfred Wegener InstituteBremerhavenGermany
| | - Emilio O Casamayor
- Center for Advanced Studies of Blanes CEAB-CSIC, Spanish Council for ResearchBlanesSpain
| | - Tom O Delmont
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-SaclayEvryFrance
| | - Carlos M Duarte
- Red Sea Research Centre and Computational Bioscience Research Center, King Abdullah University of Science and TechnologyThuwalSaudi Arabia
| | - A Murat Eren
- Department of Medicine, University of ChicagoChicagoUnited States
- Josephine Bay Paul Center, Marine Biological LaboratoryWoods HoleUnited States
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome CampusHinxtonUnited Kingdom
| | - Renzo Kottmann
- Microbial Genomics and Bioinformatics Research G, Max Planck Institute for Marine MicrobiologyBremenGermany
| | - Alex Mitchell
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome CampusHinxtonUnited Kingdom
| | - Pablo Sánchez
- Department of Marine Biology and Oceanography, Institut de Ciències del Mar (CSIC)BarcelonaSpain
| | - Kimmo Siren
- Section for Evolutionary Genomics, The GLOBE Institute, University of CopenhagenCopenhagenDenmark
| | - Martin Steinegger
- School of Biological Sciences, Seoul National UniversitySeoulRepublic of Korea
- Institute of Molecular Biology and Genetics, Seoul National UniversitySeoulRepublic of Korea
| | - Frank Oliver Gloeckner
- Jacobs University BremenBremenGermany
- University of Bremen and Life Sciences and ChemistryBremenGermany
- Computing Center, Helmholtz Center for Polar and Marine ResearchBremerhavenGermany
| | - Antonio Fernàndez-Guerra
- Microbial Genomics and Bioinformatics Research G, Max Planck Institute for Marine MicrobiologyBremenGermany
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of CopenhagenCopenhagenDenmark
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5
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Robinson SL, Piel J, Sunagawa S. A roadmap for metagenomic enzyme discovery. Nat Prod Rep 2021; 38:1994-2023. [PMID: 34821235 PMCID: PMC8597712 DOI: 10.1039/d1np00006c] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Indexed: 12/13/2022]
Abstract
Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.
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Affiliation(s)
| | - Jörn Piel
- Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland.
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6
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Etzion-Fuchs A, Todd DA, Singh M. dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains. Nucleic Acids Res 2021; 49:e78. [PMID: 33999210 PMCID: PMC8287948 DOI: 10.1093/nar/gkab356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 03/30/2021] [Accepted: 04/22/2021] [Indexed: 01/08/2023] Open
Abstract
Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties of proteins are frequently analyzed based upon whether they contain one of these domains. To date, however, knowledge of whether and how protein domains interact with ligands has been limited to domains that have been observed in co-crystal structures; this leaves approximately two-thirds of human protein domain families uncharacterized with respect to whether and how they bind DNA, RNA, small molecules, ions and peptides. To fill this gap, we introduce dSPRINT, a novel ensemble machine learning method for predicting whether a domain binds DNA, RNA, small molecules, ions or peptides, along with the positions within it that participate in these types of interactions. In stringent cross-validation testing, we demonstrate that dSPRINT has an excellent performance in uncovering ligand-binding positions and domains. We also apply dSPRINT to newly characterize the molecular functions of domains of unknown function. dSPRINT's predictions can be transferred from domains to sequences, enabling predictions about the ligand-binding properties of 95% of human genes. The dSPRINT framework and its predictions for 6503 human protein domains are freely available at http://protdomain.princeton.edu/dsprint.
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Affiliation(s)
- Anat Etzion-Fuchs
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ 08544, USA
| | - David A Todd
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ 08544, USA.,Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA
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Faure E, Ayata SD, Bittner L. Towards omics-based predictions of planktonic functional composition from environmental data. Nat Commun 2021; 12:4361. [PMID: 34272373 PMCID: PMC8285379 DOI: 10.1038/s41467-021-24547-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 05/25/2021] [Indexed: 02/06/2023] Open
Abstract
Marine microbes play a crucial role in climate regulation, biogeochemical cycles, and trophic networks. Unprecedented amounts of data on planktonic communities were recently collected, sparking a need for innovative data-driven methodologies to quantify and predict their ecosystemic functions. We reanalyze 885 marine metagenome-assembled genomes through a network-based approach and detect 233,756 protein functional clusters, from which 15% are functionally unannotated. We investigate all clusters' distributions across the global ocean through machine learning, identifying biogeographical provinces as the best predictors of protein functional clusters' abundance. The abundances of 14,585 clusters are predictable from the environmental context, including 1347 functionally unannotated clusters. We analyze the biogeography of these 14,585 clusters, identifying the Mediterranean Sea as an outlier in terms of protein functional clusters composition. Applicable to any set of sequences, our approach constitutes a step towards quantitative predictions of functional composition from the environmental context.
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Affiliation(s)
- Emile Faure
- Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, Villefranche-sur-Mer, France.
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France.
| | - Sakina-Dorothée Ayata
- Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, Villefranche-sur-Mer, France
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
| | - Lucie Bittner
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
- Institut Universitaire de France, Paris, France
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8
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Liu C, Du MX, Abuduaini R, Yu HY, Li DH, Wang YJ, Zhou N, Jiang MZ, Niu PX, Han SS, Chen HH, Shi WY, Wu L, Xin YH, Ma J, Zhou Y, Jiang CY, Liu HW, Liu SJ. Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank. MICROBIOME 2021; 9:119. [PMID: 34020714 PMCID: PMC8140505 DOI: 10.1186/s40168-021-01064-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/30/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND In gut microbiome studies, the cultured gut microbial resource plays essential roles, such as helping to unravel gut microbial functions and host-microbe interactions. Although several major studies have been performed to elucidate the cultured human gut microbiota, up to 70% of the Unified Human Gastrointestinal Genome species have not been cultured to date. Large-scale gut microbial isolation and identification as well as availability to the public are imperative for gut microbial studies and further characterizing human gut microbial functions. RESULTS In this study, we constructed a human Gut Microbial Biobank (hGMB; homepage: hgmb.nmdc.cn ) through the cultivation of 10,558 isolates from 31 sample mixtures of 239 fresh fecal samples from healthy Chinese volunteers, and deposited 1170 strains representing 400 different species in culture collections of the International Depository Authority for long-term preservation and public access worldwide. Following the rules of the International Code of Nomenclature of Prokaryotes, 102 new species were characterized and denominated, while 28 new genera and 3 new families were proposed. hGMB represented over 80% of the common and dominant human gut microbial genera and species characterized from global human gut 16S rRNA gene amplicon data (n = 11,647) and cultured 24 "most-wanted" and "medium priority" taxa proposed by the Human Microbiome Project. We in total sequenced 115 genomes representing 102 novel taxa and 13 previously known species. Further in silico analysis revealed that the newly sequenced hGMB genomes represented 22 previously uncultured species in the Unified Human Gastrointestinal Genome (UHGG) and contributed 24 representatives of potentially "dark taxa" that had not been discovered by UHGG. The nonredundant gene catalogs generated from the hGMB genomes covered over 50% of the functionally known genes (KEGG orthologs) in the largest global human gut gene catalogs and approximately 10% of the "most wanted" functionally unknown proteins in the FUnkFams database. CONCLUSIONS A publicly accessible human Gut Microbial Biobank (hGMB) was established that contained 1170 strains and represents 400 human gut microbial species. hGMB expands the gut microbial resources and genomic repository by adding 102 novel species, 28 new genera, 3 new families, and 115 new genomes of human gut microbes. Video abstract.
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Affiliation(s)
- Chang Liu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China.
- Environmental Microbiology Research Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China.
| | - Meng-Xuan Du
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
| | - Rexiding Abuduaini
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hai-Ying Yu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
| | - Dan-Hua Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Environmental Microbiology Research Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Yu-Jing Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Nan Zhou
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Environmental Microbiology Research Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Min-Zhi Jiang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
| | - Peng-Xia Niu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Environmental Microbiology Research Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Shan-Shan Han
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
| | - Hong-He Chen
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Environmental Microbiology Research Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Wen-Yu Shi
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Microbial Resources and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Linhuan Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Microbial Resources and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Yu-Hua Xin
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- China General Microorganism Culture Collection, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Juncai Ma
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Microbial Resources and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Yuguang Zhou
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- China General Microorganism Culture Collection, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Cheng-Ying Jiang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China
- Environmental Microbiology Research Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong-Wei Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, No. 1 Beichenxi Road, Chaoyang District, Beijing, 100101, China
| | - Shuang-Jiang Liu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, PR China.
- Environmental Microbiology Research Center, Institute of Microbiology, Chinese Academy of Sciences, No.1 Beichenxi Road, Chaoyang District, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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9
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Life-history strategies of soil microbial communities in an arid ecosystem. THE ISME JOURNAL 2021; 15:649-657. [PMID: 33051582 PMCID: PMC8027408 DOI: 10.1038/s41396-020-00803-y] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/28/2020] [Accepted: 10/01/2020] [Indexed: 01/30/2023]
Abstract
The overwhelming taxonomic diversity and metabolic complexity of microorganisms can be simplified by a life-history classification; copiotrophs grow faster and rely on resource availability, whereas oligotrophs efficiently exploit resource at the expense of growth rate. Here, we hypothesize that community-level traits inferred from metagenomic data can distinguish copiotrophic and oligotrophic microbial communities. Moreover, we hypothesize that oligotrophic microbial communities harbor more unannotated genes. To test these hypotheses, we conducted metagenomic analyses of soil samples collected from copiotrophic vegetated areas and from oligotrophic bare ground devoid of vegetation in an arid-hyperarid region of the Sonoran Desert, Arizona, USA. Results supported our hypotheses, as we found that multiple ecologically informed life-history traits including average 16S ribosomal RNA gene copy number, codon usage bias in ribosomal genes and predicted maximum growth rate were higher for microbial communities in vegetated than bare soils, and that oligotrophic microbial communities in bare soils harbored a higher proportion of genes that are unavailable in public reference databases. Collectively, our work demonstrates that life-history traits can distill complex microbial communities into ecologically coherent units and highlights that oligotrophic microbial communities serve as a rich source of novel functions.
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10
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Tremblay BJM, Lobb B, Doxey AC. PhyloCorrelate: inferring bacterial gene-gene functional associations through large-scale phylogenetic profiling. Bioinformatics 2021; 37:17-22. [PMID: 33416870 DOI: 10.1093/bioinformatics/btaa1105] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/26/2020] [Accepted: 12/29/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Statistical detection of co-occurring genes across genomes, known as "phylogenetic profiling", is a powerful bioinformatic technique for inferring gene-gene functional associations. However, this can be a challenging task given the size and complexity of phylogenomic databases, difficulty in accounting for phylogenetic structure, inconsistencies in genome annotation, and substantial computational requirements. RESULTS We introduce PhyloCorrelate-a computational framework for gene co-occurrence analysis across large phylogenomic datasets. PhyloCorrelate implements a variety of co-occurrence metrics including standard correlation metrics and model-based metrics that account for phylogenetic history. By combining multiple metrics, we developed an optimized score that exhibits a superior ability to link genes with overlapping GO terms and KEGG pathways, enabling gene function prediction. Using genomic and functional annotation data from the Genome Taxonomy Database and AnnoTree, we performed all-by-all comparisons of gene occurrence profiles across the bacterial tree of life, totaling 154,217,052 comparisons for 28,315 genes across 27,372 bacterial genomes. All predictions are available in an online database, which instantaneously returns the top correlated genes for any PFAM, TIGRFAM, or KEGG query. In total, PhyloCorrelate detected 29,762 high confidence associations between bacterial gene/protein pairs, and generated functional predictions for 834 DUFs and proteins of unknown function. AVAILABILITY PhyloCorrelate is available as a web-server at phylocorrelate.uwaterloo.ca as well as an R package for analysis of custom datasets. We anticipate that PhyloCorrelate will be broadly useful as a tool for predicting function and interactions for gene families. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Briallen Lobb
- Department of Biology, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada
| | - Andrew C Doxey
- Department of Biology, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada
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11
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Jiang T, Pu H, Duan Y, Yan X, Huang Y. New Natural Products of Streptomyces Sourced from Deep-Sea, Desert, Volcanic, and Polar Regions from 2009 to 2020. CHINESE J ORG CHEM 2021. [DOI: 10.6023/cjoc202010004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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12
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Lobb B, Tremblay BJM, Moreno-Hagelsieb G, Doxey AC. An assessment of genome annotation coverage across the bacterial tree of life. Microb Genom 2020; 6. [PMID: 32124724 PMCID: PMC7200070 DOI: 10.1099/mgen.0.000341] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Although gene-finding in bacterial genomes is relatively straightforward, the automated assignment of gene function is still challenging, resulting in a vast quantity of hypothetical sequences of unknown function. But how prevalent are hypothetical sequences across bacteria, what proportion of genes in different bacterial genomes remain unannotated, and what factors affect annotation completeness? To address these questions, we surveyed over 27 000 bacterial genomes from the Genome Taxonomy Database, and measured genome annotation completeness as a function of annotation method, taxonomy, genome size, 'research bias' and publication date. Our analysis revealed that 52 and 79 % of the average bacterial proteome could be functionally annotated based on protein and domain-based homology searches, respectively. Annotation coverage using protein homology search varied significantly from as low as 14 % in some species to as high as 98 % in others. We found that taxonomy is a major factor influencing annotation completeness, with distinct trends observed across the microbial tree (e.g. the lowest level of completeness was found in the Patescibacteria lineage). Most lineages showed a significant association between genome size and annotation incompleteness, likely reflecting a greater degree of uncharacterized sequences in 'accessory' proteomes than in 'core' proteomes. Finally, research bias, as measured by publication volume, was also an important factor influencing genome annotation completeness, with early model organisms showing high completeness levels relative to other genomes in their own taxonomic lineages. Our work highlights the disparity in annotation coverage across the bacterial tree of life and emphasizes a need for more experimental characterization of accessory proteomes as well as understudied lineages.
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Affiliation(s)
- Briallen Lobb
- Department of Biology, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
| | | | - Gabriel Moreno-Hagelsieb
- Department of Biology, Wilfrid Laurier University, 75 University Avenue West, Waterloo, ON, Canada
| | - Andrew C Doxey
- Department of Biology, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
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13
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Hockenberry AJ, Wilke CO. Evolutionary couplings detect side-chain interactions. PeerJ 2019; 7:e7280. [PMID: 31328041 PMCID: PMC6622159 DOI: 10.7717/peerj.7280] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 06/09/2019] [Indexed: 12/19/2022] Open
Abstract
Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue–residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10–15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue–residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.
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Affiliation(s)
- Adam J Hockenberry
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Claus O Wilke
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
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