Unlocking «Candida albicans» cell cycle secrets using strategies from the fungal mating pathway
Bibliographic record
Abstract
No matter their living environment, the long-term survival of cells requires proliferation. This implies that they have to replicate their genetic material and to divide into two cells. But going through the mitotic division cycle is a major cellular commitment that needs to be balanced between the capacity to do it and the necessity to survive (adapt) against immediate threats. Outside of a few keynote species our understanding of the connections between cell cycle progression and environmental sensing is far from clear. In this thesis, I have explored the control of the cell cycle in Candida albicans, a fungal pathogen that is also growing in importance as a model organism compared with Saccharomyces cerevisiae. To do so, I have used the fungal mating pheromone-triggered pathway which, upon stimulation, is known to influence mitotic cycle progression. Although the mating pathway has been extensively studied in S. cerevisiae, our understanding of this pathway in C. albicans is still at its early stage. In this thesis, I will 1) describe the functional characterization of a “barrier” activity in C. albicans that functions to restrict the impact of pheromone stimulation, 2) characterize the Candida homolog of the mating associate d cyclin-dependent kinase inhibitor Far1p, 3) analyze the mitotic-dependent transcriptional regulation profile and 4) identify a novel protein connecting environmental stress adaptation and virulence to C. albicans cell cycle progression. These findings highlight that one of the biggest molecular biology challenges still remains the identification of each gene's function. Although bioinformatical comparisons are tremendously helpful is this quest, ultimately experimental characterization is required to reveal the subtleties beneath adaptive evolution that have occurred among the species being compared.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".