Identification and characterization of a fungal-selective glutaminyl tRNA synthetase inhibitor with potent activity against Candida albicans
Why this work is in the frame
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Bibliographic record
Abstract
Candida albicans is the leading cause of systemic candidiasis. Effective treatment is threatened by a dearth of antifungal options and the emergence of resistance. Thus, there is an urgent need to identify novel therapeutic targets to expand our antifungal armamentarium. A promising approach is the discovery of essential genes, as most antimicrobials target essential bioprocesses. Despite detailed characterization of gene essentiality in Saccharomyces cerevisiae,defining essential targets in the pathogen of interest is necessary due to the high level of divergence between these organisms. Thus, using a machine learning algorithm we generated a comprehensive prediction of all genes essential in C. albicans . We leveraged our essentiality predictions with high-throughput screening and chemogenomic datasets to assign the mechanism of action of a previously uncharacterized compound. We identified T-035897 as a molecule with potent bioactivity against C. albicans . Prior chemogenomic profiling in S. cerevisiae suggested that T-035897 targets the glutaminyl tRNA synthetase Gln4, whose homolog in C. albicans was predicted and verified to be required for viability. To confirm the mechanism of T-035897 in C. albicans , we performed haploinsufficiency profiling,which supported Gln4as the target. In parallel, selection of resistant mutants and targeted sequencing uncovered substitutions in the Gln4 catalytic domain. Moreover, T-035897 inhibited translation in afluorescence-based reporter assay. Finally, T-035897 selectively abrogated fungal cell growth in a co-culture model with mammalian cells. Thus, we highlight the power of leveraging essentiality datasets in order to characterize compounds with potent antifungal activity in an effort to unveil novel therapeutic strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it