Temperature-Regulated Transcription in the Pathogenic Fungus <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
The basidiomycete fungus Cryptococcus neoformans is an opportunistic pathogen of worldwide importance that causes meningitis, leading to death in immunocompromised individuals. Unlike many basidiomycete fungi, C. neoformans is thermotolerant, and its ability to grow at 37°C is considered to be a virulence factor. We used serial analysis of gene expression (SAGE) to characterize the transcriptomes of C. neoformans strains that represent two varieties with different polysaccharide capsule serotypes. These include a serotype D strain of the C. neoformans variety neoformans and a serotype A strain of variety grubii . In this report, we describe the construction and characterization of SAGE libraries from each strain grown at 25°C and 37°C. The SAGE data reveal transcriptome differences between the two strains, even at this early stage of analysis, and identify sets of genes with higher transcript levels at 25°C or 37°C. Notably, growth at the lower temperature increased transcript levels for histone genes, indicating a general influence of temperature on chromatin structure. At 37°C, we noted elevated transcript levels for several genes encoding heat shock proteins and translation machinery. Some of these genes may play a role in temperature-regulated phenotypes in C. neoformans , such as the adaptation of the fungus to growth in the host and the dimorphic transition between budding and filamentous growth. Overall, this work provides the most comprehensive gene expression data available for C. neoformans ; this information will be a critical resource both for gene discovery and genome annotation in this pathogen. [This paper is dedicated to the memory of Michael Smith, founding director of the Biotechnology Laboratory and the British Columbia Genome Sciences Centre. The following individuals kindly provided reagents, samples, or unpublished information as indicated in the paper: Brendan Loftus, Claire Fraser, Richard Hyman, Eula Fung, Don Rowley, Ron Davis , Bruce A. Roe, Doris Kupfer, Jennifer Lewis, Sola Yu, Kent Buchanan, Dave Dyer, and Juneann Murphy.]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".