An anti-tuberculosis compound screen using a zebrafish infection model identifies an aspartyl-tRNA synthetase inhibitor
Bibliographic record
Abstract
Finding new anti-tuberculosis compounds with convincing in vivo activity is an ongoing global challenge to fight the emergence of multidrug-resistant Mycobacterium tuberculosis isolates. In this study, we exploited the medium-throughput capabilities of the zebrafish embryo infection model with Mycobacterium marinum as a surrogate for M. tuberculosis. Using a representative set of clinically established drugs, we demonstrate that this model could be predictive and selective for antibiotics that can be administered orally. We further used the zebrafish infection model to screen 240 compounds from an anti-tuberculosis hit library for their in vivo activity and identified 14 highly active compounds. One of the most active compounds was the tetracyclic compound TBA161, which was studied in more detail. Analysis of resistant mutants revealed point mutations in aspS (rv2572c), encoding an aspartyl-tRNA synthetase. The target was genetically confirmed, and molecular docking studies propose the possible binding of TBA161 in a pocket adjacent to the catalytic site. This study shows that the zebrafish infection model is suitable for rapidly identifying promising scaffolds with in vivo activity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".