Profiling of <i>rpoB</i> Mutations and MICs for Rifampin and Rifabutin in Mycobacterium tuberculosis
Bibliographic record
Abstract
Resistance to rifampin (RIF) and rifabutin (RFB) in Mycobacterium tuberculosis is associated with mutations within an 81-bp region of the rpoB gene (RIF resistance-determining region [RRDR]). Previous studies have shown that certain mutations in this region are more likely to confer high levels of RIF resistance, while others may be found in phenotypically susceptible isolates. In this study, we sought to determine the relationship between the MICs of RIF and RFB and rpoB RRDR mutations in 32 multidrug-resistant (MDR), 4 RIF-monoresistant, and 5 susceptible M. tuberculosis clinical isolates. The MICs were determined using the MGIT 960 system. Mutations in the rpoB RRDR were determined by Sanger sequencing. RpoB proteins with mutations S531L (a change of S to L at position 531), S531W, H526Y, and H526D and the double mutation D516A-R529Q were associated with high MICs for RIF and RFB. Five isolates carrying the mutations L511P, H526L, H526N, and D516G-S522L were found to be susceptible to RIF. Several mutations were associated with resistance to RIF and susceptibility to RFB (F514FF, D516V, and S522L). Whole-genome sequencing of two MDR isolates without rpoB RRDR mutations revealed a mutation outside the RRDR (V146F; RIF MIC of 50 μg/ml). The implications of the polymorphisms identified in the second of these isolates in RIF resistance need to be further explored. Our study further establishes a correlation between the mutations and the MICs of RIF and, also, RFB in M. tuberculosis. Several rpoB mutations were identified in RIF- and RFB-susceptible isolates. The clinical significance of these findings requires further exploration. Until then, a combination of phenotypic and molecular testing is advisable for drug susceptibility testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".