Polyploid Titan Cells Produce Haploid and Aneuploid Progeny To Promote Stress Adaptation
Bibliographic record
Abstract
UNLABELLED: Cryptococcus neoformans is a major life-threatening fungal pathogen. In response to the stress of the host environment, C. neoformans produces large polyploid titan cells. Titan cell production enhances the virulence of C. neoformans, yet whether the polyploid aspect of titan cells is specifically influential remains unknown. We show that titan cells were more likely to survive and produce offspring under multiple stress conditions than typical cells and that even their normally sized daughters maintained an advantage over typical cells in continued exposure to stress. Although polyploid titan cells generated haploid daughter cell progeny upon in vitro replication under nutrient-replete conditions, titan cells treated with the antifungal drug fluconazole produced fluconazole-resistant diploid and aneuploid daughter cells. Interestingly, a single titan mother cell was capable of generating multiple types of aneuploid daughter cells. The increased survival and genomic diversity of titan cell progeny promote rapid adaptation to new or high-stress conditions. IMPORTANCE: The ability to adapt to stress is a key element for survival of pathogenic microbes in the host and thus plays an important role in pathogenesis. Here we investigated the predominantly haploid human fungal pathogen Cryptococcus neoformans, which is capable of ploidy and cell size increases during infection through production of titan cells. The enlarged polyploid titan cells are then able to rapidly undergo ploidy reduction to generate progeny with reduced ploidy and/or aneuploidy. Under stressful conditions, titan cell progeny have a growth and survival advantage over typical cell progeny. Understanding how titan cells enhance the rate of cryptococcal adaptation under stress conditions may assist in the development of novel drugs aimed at blocking ploidy transitions.
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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 | 0.000 |
| 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".