The Unique Efg1 Fungal Virulence Regulon in the Catheterized Bladder Environment
Bibliographic record
Abstract
ABSTRACT Urinary catheterization, a common procedure in hospitals and nursing home facilities, is a primary driver of hospital-acquired infections (HAI). These devices frequently lead to catheter-associated urinary tract infections (CAUTIs), which often progress to severe complication, sepsis, and ultimately death. The fungus Candida albicans has emerged as the second most common causative agent of CAUTIs; yet, its pathogenesis is poorly understood, which complicates development of efficient treatments. Previously, we identified the transcription factor Efg1 as a critical virulence driver in C. albicans CAUTIs. However, its specific downstream targets within the unique bladder microenvironment remained unknown. This study identifies, for the first time, the complete Efg1 regulon that is active during growth in human urine. We confirmed the clinical relevance of this discovery, finding that many of these Efg1-regulated factors are present and significantly upregulated in catheter samples from patients with C. albicans infections. Furthermore, we characterized two of these key factors, ECE1 and EED1 , validating their roles both in vitro in urine conditions and in vivo using a CAUTI mouse model. Identifying the tissue-specific downstream targets of Efg1 elucidates the precise mechanism of fungal CAUTI. This knowledge provides a new roadmap for developing targeted therapeutics, offering vital antimicrobial-sparing strategies to combat these life-threatening infections. SIGNIFICANCE Catheter-associated urinary tract infections (CAUTIs) are common hospital-acquired infections that can lead to severe complications and death. Although most are caused by bacteria, the fungus Candida albicans is an increasingly prevalent cause, yet the pathogenesis of fungal CAUTIs is poorly understood. Previous research identified Efg1 as necessary for CAUTI, and now this study defines the urine-specific Efg1 regulon, validating its clinical relevance in catheter samples from infected patients. We further assessed how key downstream factors, Ece1 and Eed1, contribute to bladder infection. This first report of the urine-specific EFG1 network provides new targets for diagnosing and treating these life-threatening infections.
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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 | 0.001 | 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".