Isoniazid Activation Defects in Recombinant <i>Mycobacterium tuberculosis</i> Catalase-Peroxidase (KatG) Mutants Evident in InhA Inhibitor Production
Bibliographic record
Abstract
Mycobacterium tuberculosis KatG catalyzes the activation of the antitubercular agent isoniazid to yield an inhibitor targeting enoyl reductase (InhA). However, no firm biochemical link between many KatG variants and isoniazid resistance has been established. In the present study, six distinct KatG variants identified in clinical Mycobacterium tuberculosis isolates resistant to isoniazid were generated by site-directed mutagenesis, and the recombinant mutant proteins (KatG(A110V), KatG(A139P), KatG(S315N), KatG(L619P), KatG(L634F), and KatG(D735A)) were purified and characterized with respect to their catalase-peroxidase activities (in terms of k(cat)/K(m)), rates of free-radical formation from isoniazid oxidation, and, moreover, abilities to activate isoniazid. The A110V amino acid replacement did not result in significant alteration of KatG activities except that the peroxidase activity was enhanced. The other mutations, however, resulted in modestly reduced catalase and peroxidase catalytic efficiencies and, for the four mutants tested, significantly lower activities to oxidize isoniazid. Compared to the wild-type enzyme, the ability of the KatG(L634F), KatG(A139P), and KatG(D735A) variants to activate isoniazid decreased by 36%, 76%, and 73%, respectively, whereas the KatG(S315N) and KatG(L619P) variants completely lost their abilities to convert isoniazid into the InhA inhibitor. In addition, the inclusion of exogenous Mn(2+) to the isoniazid activation reaction mix significantly improved the ability of wild-type and KatG mutants to produce the InhA inhibitor.
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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".