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Record W4406085229 · doi:10.1038/s44259-024-00072-1

The role of gene copy number variation in antimicrobial resistance in human fungal pathogens

2025· review· en· W4406085229 on OpenAlexafffund
David Jordan, Aleeza C. Gerstein, Christian R. Landry

Bibliographic record

Venuenpj Antimicrobials and Resistance · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversity of ManitobaUniversité LavalPROTEO
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsCopy-number variationBiologyGene dosageGeneGeneticsDrug resistanceAntifungal drugAdaptation (eye)Gene duplicationGenomeResistance (ecology)AntifungalAntibiotic resistanceComputational biologyMicrobiologyCandida albicansGene expressionAntibiotics

Abstract

fetched live from OpenAlex

Faced with the burden of increasing resistance to antifungals in many fungal pathogens and the constant emergence of new drug-resistant strains, it is essential to assess the importance of various resistance mechanisms. Fungi have relatively plastic genomes and can tolerate genomic copy number variation (CNV) caused by aneuploidy and gene amplification or deletion. In many cases, these genomic changes lead to adaptation to stressful conditions, including those caused by antifungal drugs. Here, we specifically examine the contribution of CNVs to antifungal resistance. We undertook a thorough literature search, collecting reports of antifungal resistance caused by a CNV, and classifying the examples of CNV-conferred resistance into four main mechanisms. We find that in human fungal pathogens, there is little evidence that gene copy number plays a major role in the emergence of antifungal resistance compared to other types of mutations. We discuss why we might be underestimating their importance and new approaches being used to study them.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.250
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations16
Published2025
Admission routes2
Has abstractyes

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