Identification of a protective antigen reveals the trade-off between iron acquisition and antigen exposure in a global fungal pathogen
Bibliographic record
Abstract
Systemic infections caused by Cryptococcus claim over 161,000 lives annually, with global mortality rate close to 70% despite antifungal therapies. Currently, no vaccine is available. To develop an effective multivalent vaccine against this free-living opportunistic eukaryotic pathogen, it is critical to identify protective antigens. We previously discovered ZNF2 oe strains elicit protective host immune responses and increase the abundance of antigens present in the capsule, which is required for its immunoprotection. Capsule is a defining feature of Cryptococcus species and composed of polysaccharides and mannoproteins. Here, we found increased levels of exposed mannoproteins in ZNF2 oe cells. As mannoproteins are the primary components recognized by anticryptococcal cell-mediated immune responses and few have been characterized, we systemically screened all 49 predicted GPI-mannoproteins in Cryptococcus neoformans for enhanced host recognition. We identified those highly present in ZNF2 oe cells and found Cig1 to be a protective antigen against cryptococcosis either as a recombinant protein vaccine or an mRNA vaccine. Cig1 is induced by iron limitation and is highly expressed by this fungus in infected mice and in patients with cryptococcal meningitis. Remarkably, iron restriction by the host induces cryptococcal cells to express iron-uptake proteins including Cig1, which act as cryptococcal antigens and in turn enhance host detection. Our results highlight an arms race between the pathogen and the host centered on iron competition, and the trade-off between cryptococcal iron acquisition and antigen exposure. These findings demonstrate the potential of leveraging this host–pathogen interaction for vaccine development.
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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".