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Record W4211125020 · doi:10.1038/s42003-022-03076-7

Adaptive laboratory evolution in S. cerevisiae highlights role of transcription factors in fungal xenobiotic resistance

2022· article· en· W4211125020 on OpenAlexafffund
Sabine Ottilie, Madeline R. Luth, Erich Hellemann, Gregory M. Goldgof, Eddy Vigil, Prianka Kumar, Andrea L. Cheung, Miranda Song, Karla P. Godinez‐Macias, Krypton Carolino, Jennifer H. Yang, Gisel Lopez, Matthew Abraham, Maureen Tarsio, Emmanuelle V. LeBlanc, Luke Whitesell, Jake Schenken, Felicia Gunawan, Reysha Patel, Joshua D. Smith, Melissa S. Love, Roy M. Williams, Case W. McNamara, William H. Gerwick, Trey Ideker, Yo Suzuki, Dyann F. Wirth, Amanda K. Lukens, Patricia M. Kane, Leah E. Cowen, Jacob D. Durrant, Elizabeth A. Winzeler

Bibliographic record

VenueCommunications Biology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Toronto
FundersUniversity of California, San DiegoU.S. Department of Health and Human ServicesNational Institutes of HealthNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchNational Institute of Allergy and Infectious DiseasesUniversity of PittsburghBill and Melinda Gates Foundation
KeywordsGeneBiologySaccharomyces cerevisiaeTranscription factorGeneticsPhenotypeXenobioticTranscription (linguistics)Computational biologyGenomeFunction (biology)Drug resistanceBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Abstract In vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in Saccharomyces cerevisiae . Sequence analysis identified many genes contributing to the resistance phenotype as well as numerous amino acids in potential targets that may play a role in compound binding. Our work shows that compound-target pairs can be conserved across multiple species. The set of 25 most frequently mutated genes was enriched for transcription factors, and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function SNVs found in a 170 amino acid domain in the two Zn 2 C 6 transcription factors YRR1 and YRM1 ( p < 1 × 10 −100 ). This remarkable enrichment for transcription factors as drug resistance genes highlights their important role in the evolution of antifungal xenobiotic resistance and underscores the challenge to develop antifungal treatments that maintain potency.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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 designObservational
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

Citations23
Published2022
Admission routes2
Has abstractyes

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