Genetically Encoded Sensors for Monitoring Intracellular Redox Health of the Pathogenic Fungus <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Redox sensing and regulation are critical to both the survival and virulence strategies used by the pathogenic fungus Cryptococcus neoformans to evade host immunity and establish infection. However, the precise genetic and biochemical mechanisms driving these redox regulation systems in the context of fungal virulence are unclear. To address this limitation, we designed genetically encoded redox sensors optimized for expression in C. neoformans and linked these sensors to cryptococcal redox proteins for real-time monitoring of intracellular redox status. Using these sensors, we established several fluorescence-based techniques for monitoring dose-responsive changes in the intracellular oxidation status of C. neoformans under stress. Specifically, we demonstrated sensor responsiveness to nontoxic doses of peroxide stress and during different stages of cell growth, and we verified sensor responsiveness in a mutant with known sensitivity to oxidative stress. This approach provides a framework for developing and deploying biosensors in pathogenic fungi and in basidiomycetes─a group of microorganisms with relatively few sophisticated genetic tools for molecular and synthetic biology. Overall, our sensors enable real-time insights into the key redox mechanisms driving growth and survival of a globally important pathogen and pave the way for tool development in other fungi.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".