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Record W3138233583 · doi:10.1038/s41467-021-22175-3

Coagulase-negative staphylococci release a purine analog that inhibits Staphylococcus aureus virulence

2021· article· en· W3138233583 on OpenAlexafffund
Denny Chin, Mariya I. Goncheva, Ronald S. Flannagan, Shayna R. Deecker, Verónica Guariglia-Oropeza, Alexander W. Ensminger, David E. Heinrichs

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsAmgen (Canada)MaRSWestern University
FundersCanadian Institutes of Health ResearchUniversity of TorontoGovernment of Canada
KeywordsStaphylococcus aureusMicrobiologyCoagulaseOperonVirulenceBiologyPurineStaphylococcal infectionsStaphylococcusGeneBacteriaBiochemistryEnzymeGeneticsEscherichia coli

Abstract

fetched live from OpenAlex

Coagulase-negative staphylococci and Staphylococcus aureus colonize similar niches in mammals and conceivably compete for space and nutrients. Here, we report that a coagulase-negative staphylococcus, Staphylococcus chromogenes ATCC43764, synthesizes and secretes 6-thioguanine (6-TG), a purine analog that suppresses S. aureus growth by inhibiting de novo purine biosynthesis. We identify a 6-TG biosynthetic gene cluster in S. chromogenes and other coagulase-negative staphylococci including S. epidermidis, S. pseudintermedius and S. capitis. Recombinant S. aureus strains harbouring this operon produce 6-TG and, when used in subcutaneous co-infections in mice with virulent S. aureus USA300, protect the host from necrotic lesion formation. Used prophylactically, 6-TG reduces necrotic skin lesions in mice infected with USA300, and this effect is mediated by abrogation of toxin production. RNAseq analyses reveal that 6-TG downregulates expression of genes coding for purine biosynthesis, the accessory gene regulator (agr) and ribosomal proteins in S. aureus, providing an explanation for its effect on toxin production.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.029
GPT teacher head0.317
Teacher spread0.288 · 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.

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

Citations68
Published2021
Admission routes2
Has abstractyes

Explore more

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