Identification of pathogen genomic differences that impact human immune response and disease during <i>Cryptococcus neoformans</i> infection
Bibliographic record
Abstract
Abstract Patient outcomes during infection are due to a complex interplay between the quality of medical care, host immunity factors, and the infecting pathogen’s characteristics. To probe the influence of pathogen genotype on human immune response and disease, we examined Cryptococcus neoformans isolates collected during the Cryptococcal Optimal ART Timing (COAT) trial in Uganda. We measured human participants’ immunologic phenotypes, meningitis disease parameters, and survival. We compared this clinical data to whole genome sequences from 38 C. neoformans isolates of the most frequently observed sequence type (ST) ST93 in our Ugandan participant population, and an additional 18 strains from 9 other sequence types representing the known genetic diversity within the Ugandan Cryptococcus clinical isolates. We focused our analyses on 652 polymorphisms that: were variable among the ST93 genomes, were not in centromeres or extreme telomeres, and were predicted to have a fitness effect. Logistic regression and principal component analyses identified 40 candidate Cryptococcus genes and 3 hypothetical RNAs associated with human immunologic response or clinical parameters. We infected mice with 17 available KN99α gene deletion strains for these candidate genes and found that 35% (6/17) directly influenced murine survival. Four of the six gene deletions that impacted murine survival were novel. Such bedside-to-bench translational research provides important candidate genes for future studies on virulence-associated traits in human Cryptococcus infections. Author Summary Even with the best available care, mortality rates in cryptococcal meningitis range from 20-60%. Disease is often due to infection by the fungus Cryptococcus neoformans and involves a complex interaction between the human host and the fungal pathogen. Although previous studies have suggested genetic differences in the pathogen impact human disease, it has proven quite difficult to identify the specific C. neoformans genes that impact the outcome of the human infection. Here, we take advantage of a Ugandan patient cohort infected with closely related C. neoformans strains to examine to role of pathogen genetic variants on several human disease characteristics. Using a pathogen whole genome sequencing approach, we showed that 40 C. neoformans genes are associated with human disease. Surprisingly, many of these genes are specific to Cryptococcus and have unknown functions. We also show deletion of these genes alters disease in a mouse model of infection, confirming their role in disease. These findings are particularly important because they are the first to identify C. neoformans genes associated with human cryptococcal meningitis and lay the foundation for future studies that may lead to new treatment strategies aimed at reducing patient mortality.
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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 | 0.001 |
| 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".