Manganese-modulated proteome and phosphoproteome dataset in the opportunistic yeast Candida albicans
Bibliographic record
Abstract
The ability of the opportunistic yeast Candida albicans to acquire and maintain homeostatic levels of manganese (Mn), particularly in the metal-limited host environment, is an important determinant of its fitness. Although significant focus has been given to mechanisms of iron and copper acquisition and their roles in C. albicans fitness, little is known about how this yeast maintains and controls Mn homeostasis. Here, we present a comparative proteomic and phosphoproteomic analysis in C. albicans cells experiencing Mn starvation. Both proteome and phosphoproteome of the C. albicans reference strain SC5314 grown in Mn-deplete medium were compared to those of cells thriving in Mn-replete medium. Samples were collected in three biological replicates at two time-points (5 and 90 min). Mass spectrometry-based proteomics identified approximately 1,500 proteins and over 140 phosphorylated proteins. Proteomic data were analyzed using MaxQuant and Perseus, and all datasets were deposited in the PRIDE repository (accession number PXD064206). Gene ontology analysis was performed to characterize biological processes and signaling pathways affected by Mn availability. This dataset provides a resource for studying Mn homeostasis and regulatory phosphorylation in fungal pathogens. The dataset can be reused for comparative analysis with other fungal species or stress conditions involving transition metal availability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".