Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery

Bernard, Guillaume; Pathmanathan, Jananan S.; Lannes, Romain; Lopez, Philippe; Bapteste, Éric

doi:10.1093/gbe/evy031

Cited by 92 publications

(95 citation statements)

References 89 publications

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“…An ever-increasing number of genomes and metagenomes are unlocking uncharted regions of microbial diversity 1,8,9 , providing new perspectives on the evolution of life 10,11 . However, our rapidly growing inventories of new genes have a glaring issue: between 40% and 60% cannot be assigned to a known function [12][13][14][15] . Current analytical approaches for genomic and metagenomic data [16][17][18][19][20] generally do not include this uncharacterized fraction in downstream analyses, constraining their results to conserved pathways and housekeeping functions 17 .…”

Section: Introductionmentioning

confidence: 99%

Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2020

Preprint

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AbstractBridging the gap between the known and the unknown coding sequence space is one of the biggest challenges in molecular biology today. This challenge is especially extreme in microbiome analyses where between 40% to 60% of the coding sequences detected are of unknown function, and ignoring this fraction limits our understanding of microbial systems. Discarding the uncharacterized fraction is not an option anymore. Here, we present an in-depth exploration of the microbial unknown fraction through the lenses of a conceptual framework and a computational workflow we developed to unify the microbial known and unknown coding sequence space. Our approach partitions the coding sequence space in gene clusters and contextualizes them with genomic and environmental information. We analyzed 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes putting into perspective the extent of the unknown fraction, its diversity, and its relevance in a genomic and environmental context. With the identification of a target gene of unknown function for antibiotic resistance, we demonstrate how a contextualized unknown coding sequence space provides a robust framework for the generation of hypotheses that can be used to augment experimental data.

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Section: Introductionmentioning

confidence: 99%

Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2020

Preprint

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“…Our definition is much stricter than previous definitions of the metagenomic dark matter which accepts remote homology to known sequences [23]. The need to include metagenomic dark matter in comprehensive analyses of the gut microbiome matches the arguments presented by Bernard et al in their recent manuscript on microbial dark matter where they opine that "unraveling the microbial dark matter should be identified as a central priority for biologists" [24].…”

Section: Introductionmentioning

confidence: 70%

Baseline human gut microbiota profile in healthy people and standard reporting template

King

Desai

Sylvetsky

et al. 2018

Preprint

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A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individual’s microbiome to the growing knowledgebase of “normal” microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and abundances the study also generated a list of contigs of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBI’s Short Read Archive.

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“…Culture-based approaches to characterizing the subgingival microbiome spawned a slew of studies on individual bacteria, their responses to environmental shifts and their roles in disease causation as independent operators [42][43][44][45][46]. However, cultivation independent methods have revealed that polymicrobial diseases are caused by the collective actions and interactions of the microbiome [47,48], and that the "invisible" members of a microbial community have important contributions to these interactions [49,50]. When we used a top-down approach to map the genomic content of and the biological pathways encoded by the microbiomes associated with chronic, localized and generalized aggressive periodontitis, a surprising picture of the three phenotypes emerged.…”

Section: Discussionmentioning

confidence: 99%

Metagenomic Sequencing of Stage 3 Periodontitis Reveals Phenotype-Specific Taxonomic and Functional Features of the Subgingival Microbiome

Altabtbaei

Maney

Ganesan

et al. 2020

Preprint

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Background: The search for microbial biomarkers of periodontitis has been hampered by our ability to separate disease-specific associations from those that are common to all forms of periodontitis. Here, we present the first functional characterization of the microbiomes of three common clinical phenotypes of this disease: Localized aggressive periodontitis (LAP), generalized aggressive periodontitis (GAP) and chronic periodontitis (CP). Methods: We collected subgingival plaque samples from sites with disease of 59 subjects with Stage 3 periodontitis and 25 periodontally healthy individuals and used shotgun metagenomics to characterize the aggregate of bacterial genes. Results: Beta-dispersion metrics demonstrated that no two individuals with disease, especially those with chronic disease, are alike (the Anna Karenina Principle), indicating that microbial modulation therapies will have to incorporate patient-specific parameters for efficacy. We discovered broad patterns of microbial dysbiosis that spanned the three disease phenotypes, as well as disease-specific indicators unique to each phenotype. Genes common to all forms of periodontitis encoded pathways that facilitate life in an oxygen-poor, protein and heme-rich, pro-oxidant environment, and enhance capacity for attachment and biofilm formation, while genes encoding the acetate switch, c-type cytochrome and molybdenum cofactor biosynthesis, iron-sulfur clusters, and formate dehydrogenase were unique to LAP. These can serve as potential biomarkers for molecular identification of clinical phenotypes and clarify the role of the microbiome in disease pathogenesis. We also discovered that clinical phenotypes of disease resolved variance in the microbiome with greater clarity than the newly established grading system. Importantly, we observed that one-third of the metagenome of LAP is unique to this phenotype while GAP shares significant functional and taxonomic features with both LAP and CP, suggesting either attenuation of an aggressive disease or an early-onset chronic disease. Conclusion: Within the limitations of a small sample size and a cross-sectional study design, the distinctive features of the microbiomes associated with LAP and CP strongly persuade us that these are discrete disease entities, while calling into question whether GAP is a separate disease entity, or an artifact induced by cross-sectional study designs.

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Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery

Cited by 92 publications

References 89 publications

Unifying the known and unknown microbial coding sequence space

Unifying the known and unknown microbial coding sequence space

Baseline human gut microbiota profile in healthy people and standard reporting template

Metagenomic Sequencing of Stage 3 Periodontitis Reveals Phenotype-Specific Taxonomic and Functional Features of the Subgingival Microbiome

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