Drug tolerance facilitates the evolution of drug resistance in Candida albicans
Bibliographic record
Abstract
Background For Candida albicans and Candidiasis, drug resistance is sometimes due to the pre-existence of genetic polymorphisms that bypass the mode of action of the drug, thus conferring a long-term survival benefit. In other cases, resistance is acquired via the evolution of de novo genetic polymorphisms. There is evidence that C. albicans possess a drug tolerance response which “buys time” for individuals to evolve beneficial mutations. Our goal here is to characterize this poorly understood epigenetic cytoprotective program at the single cell molecular level. Methods We developed a nano-litre droplet based Candida single cell sequencing platform capable of transcriptionally profiling several thousand individual cells in an efficient manner. We exploit this platform to profile both untreated and drug exposed (incl. fluconazole, caspofungin and nystatin) populations at early time points post-treatment (tolerance) and late time points (resistance) in order to understand survival trajectories. The profile are compared with the matched sequenced genomes. Results We show that untreated Candida populations exhibit “bet hedging”, stochastically expressing cytoprotective transcriptional programs, and drug tolerant individuals partition into distinct subpopulations, each with a unique survival strategy involving different transcriptional programs. We observe a burst of chromosomal aberrations at two days post-treatment that differ between survivor subpopulation. Discussion Our single cell approach highlights that survivor subpopulations pass through a tolerance phase that involves a multivariate transcriptional response including upregulation of efflux pumps, chaperones and transport mechanisms, and cell wall maintenance. Together this suggests that targeting the tolerance response concomitantly with standard therapies could represent an efficient approach to ablating clinical persistence.
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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".