Uncontrolled transposition following RNAi loss causes hypermutation and antifungal drug resistance in clinical isolates of Cryptococcus neoformans
Bibliographic record
Abstract
Abstract Microorganisms survive and compete within their environmental niches and avoid evolutionary stagnation by stochastically acquiring mutations that enhance fitness. Although increased mutation rates are often deleterious in multicellular organisms, hypermutation can be beneficial for microbes in the context of strong selective pressures. To explore how hypermutation arises in nature and elucidate its consequences, we employed a collection of 387 sequenced clinical and environmental isolates of Cryptococcus neoformans. This fungal pathogen is responsible for ~ 15% of annual AIDS-related deaths and is associated with high mortality rates, attributable to a dearth of antifungal drugs and increasing drug resistance. Isolates were screened for the ability to rapidly acquire antifungal drug resistance, and two robust hypermutators were identified. Insertion of the non-LTR Cnl1 retrotransposon was found to be responsible for the majority of drug-resistant isolates. Long-read whole-genome sequencing revealed both hypermutator genomes have two unique features: 1) hundreds of Cnl1 copies organized in subtelomeric arrays on both ends of almost all chromosomes, and 2) a nonsense mutation in the first exon of ZNF3, a gene encoding an RNAi component involved in silencing transposons. Quantitative trait locus mapping identified a significant genetic locus associated with hypermutation that includes the mutant znf3 allele, and CRISPR-mediated genome editing of the znf3 single-base pair nonsense mutation abolished the hypermutation phenotype and restored siRNA production. In sum, hypermutation and drug resistance in these isolates results from loss of RNAi combined with subsequent accumulation of a large genomic burden of a novel transposable element in C. neoformans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".