Transcriptional Profiling of the Candida albicans Response to the DNA Damage Agent Methyl Methanesulfonate
Bibliographic record
Abstract
The infection of a mammalian host by the pathogenic fungus Candida albicans involves fungal resistance to reactive oxygen species (ROS)—induced DNA damage stress generated by the defending macrophages or neutrophils. Thus, the DNA damage response in C. albicans may contribute to its pathogenicity. Uncovering the transcriptional changes triggered by the DNA damage—inducing agent MMS in many model organisms has enhanced the understanding of their DNA damage response processes. However, the transcriptional regulation triggered by MMS remains unclear in C. albicans. Here, we explored the global transcription profile in response to MMS in C. albicans and identified 306 defined genes whose transcription was significantly affected by MMS. Only a few MMS-responsive genes, such as MGT1, DDR48, MAG1, and RAD7, showed potential roles in DNA repair. GO term analysis revealed that a large number of induced genes were involved in antioxidation responses, and some downregulated genes were involved in nucleosome packing and IMP biosynthesis. Nevertheless, phenotypic assays revealed that MMS-induced antioxidation gene CAP1 and glutathione metabolism genes GST2 and GST3 showed no direct roles in MMS resistance. Furthermore, the altered transcription of several MMS—responsive genes exhibited RAD53—related regulation. Intriguingly, the transcription profile in response to MMS in C. albicans shared a limited similarity with the pattern in S. cerevisiae, including COX17, PRI2, and MGT1. Overall, C. albicans cells exhibit global transcriptional changes to the DNA damage agent MMS; these findings improve our understanding of this pathogen’s DNA damage response pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".