Cryptococcal nutrient acquisition and pathogenesis: dining on the host
Bibliographic record
Abstract
SUMMARY Pathogens must acquire essential nutrients to successfully colonize and proliferate in host tissue. Additionally, nutrients provide signals that condition pathogen deployment of factors that promote disease. A series of transcriptomics experiments over the last 20 years, primarily with Cryptococcus neoformans and to a lesser extent with Cryptococcus gattii , provide insights into the nutritional requirements for proliferation in host tissues. Notably, the identified functions include a number of transporters for key nutrients including sugars, amino acids, metals, and phosphate. Here, we first summarize the in vivo gene expression studies and then discuss the follow-up analyses that specifically test the relevance of the identified transporters for the ability of the pathogens to cause disease. The conclusion is that predictions based on transcriptional profiling of cryptococcal cells in infected tissue are well supported by subsequent investigations using targeted mutations. Overall, the combination of transcriptomic and genetic approaches provides substantial insights into the nutritional requirements that underpin proliferation in the host.
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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) | 0.002 | 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 | 0.001 | 0.000 |
| 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 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".