Intrinsic and extrinsic factors affecting the evolution of virulence in the HIV-associated opportunistic human fungal pathogen <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
The fungus Cryptococcus neoformans is considered the leading cause of mortality in immunocompromised patients. While extensive research has unveiled the molecular epidemiology of C. neoformans, the influence of genetic and environmental factors on genotype–phenotype correlations remains poorly understood. Specifically, it remains unclear whether the genetic and environmental variability observed across isolates from diverse sources has significant implications for the pathogen’s virulence. In this study, we analyzed 105 Chinese C. neoformans isolates, including 54 from HIV-infected patients, 44 from HIV-uninfected individuals and seven from a natural environment. Multilocus sequence typing (MLST) revealed that sequence type (ST) 5 predominates across all clinical isolates; however, genotypic diversity was notably higher in isolates from HIV-uninfected individuals and the natural environment, whereas HIV-infected isolates exhibited restricted genetic variation. Furthermore, isolates from HIV-uninfected individuals exhibited significantly enhanced virulence traits, including elevated capsule production, increased melanin production, improved survival in human cerebrospinal fluid (CSF), reduced phagocytic uptake, and higher mortality in both Galleria mellonella and murine models of cryptococcosis. Importantly, these pathogenic phenotypes were correlated with CD4+ T cell counts, highlighting the critical role of host immunity in shaping C. neoformans virulence. Whole-genome sequencing and genome-wide association studies (GWAS) further revealed that variations in genes involved in carbohydrate metabolism, such as CDA3 and GPD1, may drive host-specific virulence evolution. Our results support a genotype–phenotype correlation, demonstrating that both genetic and environmental factors shape the virulence of C. neoformans, with significant implications for understanding host–pathogen interactions and guiding therapeutic strategies.
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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".