Combinatorial active‐site variants confer sustained clavulanate resistance in <scp>B</scp>laC β‐lactamase from <scp><i>M</i></scp><i>ycobacterium tuberculosis</i>
Bibliographic record
Abstract
Bacterial resistance to β-lactam antibiotics is a global issue threatening the success of infectious disease treatments worldwide. Mycobacterium tuberculosis has been particularly resilient to β-lactam treatment, primarily due to the chromosomally encoded BlaC β-lactamase, a broad-spectrum hydrolase that renders ineffective the vast majority of relevant β-lactam compounds currently in use. Recent laboratory and clinical studies have nevertheless shown that specific β-lactam-BlaC inhibitor combinations can be used to inhibit the growth of extensively drug-resistant strains of M. tuberculosis, effectively offering new tools for combined treatment regimens against resistant strains. In the present work, we performed combinatorial active-site replacements in BlaC to demonstrate that specific inhibitor-resistant (IRT) substitutions at positions 69, 130, 220, and/or 234 can act synergistically to yield active-site variants with several thousand fold greater in vitro resistance to clavulanate, the most common clinical β-lactamase inhibitor. While most single and double variants remain sensitive to clavulanate, double mutants R220S-K234R and S130G-K234R are substantially less affected by time-dependent clavulanate inactivation, showing residual β-lactam hydrolytic activities of 46% and 83% after 24 h incubation with a clinically relevant inhibitor concentration (5 μg/ml, 25 µM). These results demonstrate that active-site alterations in BlaC yield resistant variants that remain active and stable over prolonged bacterial generation times compatible with mycobacterial proliferation. These results also emphasize the formidable adaptive potential of inhibitor-resistant substitutions in β-lactamases, potentially casting a shadow on specific β-lactam-BlaC inhibitor combination treatments against M. tuberculosis.
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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 | 0.001 |
| 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".