Genetic Factors and Genotype-Environment Interactions Contribute to Variation in Melanin Production in the Fungal Pathogen Cryptococcus neoformans
Bibliographic record
Abstract
Cryptococcosis is a common opportunistic fungal infection that often disseminates into the central nervous system, leading to meningitis. Production of melanin pigments during infections is one of the most important virulence factors of its causal agent, the human pathogenic yeast Cryptococcus neoformans species complex. However, almost nothing is known about the patterns of variation in melanin production among clinical and environmental strains and the potential effects of such variations on virulence. In this study, we assembled a global collection of C. neoformans var. neoformans strains and investigated their patterns of melanin variation and potential contributors to such variations. Our analyses revealed that genetic differences and genotype-environment interactions explained up to 59% and 43% of the population's melanin variance respectively, depending on the tested environments. In comparison, environmental factors alone contributed relatively little to melanin variance. We also identified specific changes within the LAC1 gene, whose protein product catalyzes melanin synthesis, to be associated with variable melanin levels. This study provides fresh insights into the origin and evolution of virulence traits in fungal pathogens while highlighting the complex interplay between genetic and environmental factors that lead to phenotypic variance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".