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Record W3165563222 · doi:10.1038/s41467-021-23402-7

Ecology and molecular targets of hypermutation in the global microbiome

2021· article· en· W3165563222 on OpenAlexafffund
Simon Roux, Blair G. Paul, Sarah C. Bagby, Stephen Nayfach, Michelle A. Allen, Graeme T. Attwood, Ricardo Cavicchioli, Ludmila Chistoserdova, Robert J. Gruninger, Steven Hallam, Maria Hernandez, Matthias Hess, Wen‐Tso Liu, Tim A. McAllister, Michelle O’Malley, Xuefeng Peng, Virginia I. Rich, S. R. Saleska, Emiley A. Eloe‐Fadrosh

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsGenome British ColumbiaUniversity of British ColumbiaAgriculture and Agri-Food Canada
FundersLawrence Berkeley National LaboratoryCalifornia NanoSystems InstituteBiological and Environmental ResearchAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaUniversity of California, Santa BarbaraOffice of ScienceBeef Cattle Research CouncilGenome British ColumbiaNational Energy Research Scientific Computing CenterJoint Genome InstituteGenome CanadaU.S. Department of EnergyAgriculture and Agri-Food CanadaNational Science FoundationCompute CanadaAlberta Beef ProducersMarine Biological LaboratoryGovernment of Canada
KeywordsBiologySomatic hypermutationOrganismGenomeGeneMicrobiomeGeneticsEvolutionary biologyComputational biologyModel organismMolecular evolution

Abstract

fetched live from OpenAlex

Changes in the sequence of an organism's genome, i.e., mutations, are the raw material of evolution. The frequency and location of mutations can be constrained by specific molecular mechanisms, such as diversity-generating retroelements (DGRs). DGRs have been characterized from cultivated bacteria and bacteriophages, and perform error-prone reverse transcription leading to mutations being introduced in specific target genes. DGR loci were also identified in several metagenomes, but the ecological roles and evolutionary drivers of these DGRs remain poorly understood. Here, we analyze a dataset of >30,000 DGRs from public metagenomes, establish six major lineages of DGRs including three primarily encoded by phages and seemingly used to diversify host attachment proteins, and demonstrate that DGRs are broadly active and responsible for >10% of all amino acid changes in some organisms. Overall, these results highlight the constraints under which DGRs evolve, and elucidate several distinct roles these elements play in natural communities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.274
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations85
Published2021
Admission routes2
Has abstractyes

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