Candida albicans Ssy1 is the extracellular sensor of gut microbiota-derived peptidoglycan fragments mediating invasive hyphal growth in the host
Bibliographic record
Abstract
Gut microbiota-derived peptidoglycan fragments (PGNs) are potent inducers of Candida albicans hyphal growth, a key virulence trait for C. albicans pathogenesis in hosts. Herein, we identify the C. albicans oligopeptide transporter 4 (Opt4) as the long-sought major transporter responsible for internalizing a diverse range of natural PGNs into fungal cells. However, contrary to the conventional view, we reveal that blocking the cellular uptake of PGNs does not prevent C. albicans hyphal growth. Instead, we discover that extracellular sensing of PGNs by C. albicans cell surface protein Ssy1 is essential for activating the downstream cAMP-PKA pathway in hyphal signaling. Importantly, the ssy1Δ/Δ mutant, which is defective in PGN-induced hyphal growth, remains unresponsive to the β-lactam-induced PGN storm in the mouse gut. It predominantly maintains yeast morphology and shows no sign of systemic dissemination. These findings establish Ssy1 as a potential anti-virulence target for preventing PGN-induced invasive growth of C. albicans in hosts. Bacterial peptidoglycan fragments (PGNs) are potent inducers of Candida albicans invasive hyphal growth in the host. Here, the authors reveal that surface sensing of PGNs by Candida albicans cell surface protein Ssy1 is a key prerequisite for PGN-induced hyphal growth, highlighting Ssy1 as a potential promising target for anti-virulence 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".