Distinct evolutionary trajectories following loss of RNA interference in <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
While increased mutation rates typically have negative consequences in multicellular organisms, hypermutation can be advantageous for microbes adapting to the environment. Previously, we identified two hypermutator Cryptococcus neoformans clinical isolates that rapidly develop drug resistance due to transposition of a retrotransposon, Cnl1. Cnl1-mediated hypermutation is caused by a nonsense mutation in a gene encoding an RNA interference (RNAi) component, ZNF3 , combined with a tremendous transposon burden. To elucidate adaptive mechanisms following RNAi loss, two bioinformatic pipelines were developed to identify RNAi loss-of-function (LOF) mutations in a collection of 387 sequenced C. neoformans isolates. Remarkably, several RNAi-loss isolates were identified that are not hypermutators and have not accumulated transposons. To test whether these RNAi LOF mutations can cause hypermutation, the mutations were introduced into a nonhypermutator strain with a high transposon burden, which resulted in a hypermutator phenotype. To further investigate whether RNAi-loss isolates can become hypermutators, in vitro passaging was performed. Although no hypermutators were found in two C. neoformans RNAi-loss strains after short-term passage, hypermutation was observed in a passaged Cryptococcus deneoformans strain with an increased transposon burden. Consistent with a two-step evolution, when an RNAi-loss isolate was crossed with an isolate containing a high Cnl1 burden, F1 hypermutator progeny inheriting a high transposon burden were identified. In addition to Cnl1 transpositions, insertions of a gigantic DNA transposon KDZ1 (~11 kb) contributed to hypermutation in the progeny. Our results suggest that RNAi loss is relatively common (7/387, ~1.8%) and enables distinct evolutionary trajectories: hypermutation following transposon accumulation or survival without hypermutation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".