Dissecting rifampicin heteroresistance in Mycobacterium tuberculosis: integrating whole-genome sequencing with phenotypic and clonal validation
Bibliographic record
Abstract
Introduction. This study underscores the critical role of identifying heteroresistant infections of Mycobacterium tuberculosis (Mtb) in enhancing the diagnostics of tuberculosis (TB). These conditions complicate diagnostics and treatment, underlining the need for advanced techniques to detect and characterize resistant populations effectively. Hypothesis/Gap statement. Current diagnostics may fail to identify heteroresistance and mixed infections, limiting the understanding of their impact on treatment outcomes. Aim. This pilot study aimed to phenotypically and genotypically characterize rifampicin-heteroresistant clinical isolates and assess their genetic diversity and resistance patterns. Methodology. A retrospective analysis of 2,917 Mtb genomes from Peru (1999–2020) was conducted using MTBseq and TB-Profiler. Techniques included indirect microscopic observation drug susceptibility, MIC determination via tetrazolium microplate assay, agar proportion method and sequencing. From each clinical isolate, three colonies were isolated from both rifampicin-supplemented (1 µg mL −1 ) and drug-free media for subsequent phenotypic and genotypic characterization, including rpoB sequencing. Results. Of the 2,917 genomes analysed, 14.6% were classified as mixed infections, 3.8% exhibited heteroresistance to at least 1 drug between 21 antibiotics analysed and 0.79% were rifampicin-heteroresistant. Colonies from rifampicin-supplemented media displayed high resistance (MIC >1 µg mL −1 ) with mutations such as S450L in the RpoB protein. In contrast, those from drug-free media exhibited sensitivity to rifampicin (MIC <1 µg ml −1 ), harbouring other RpoB mutations including D435Y, L452P and L430P. Notably, some colonies retained WT RpoB sequences, suggesting a diversity of subpopulations within isolates. Conclusion. Whole-genome sequencing and phenotypic analysis confirmed the coexistence of rifampicin-susceptible and rifampicin-resistant Mtb populations within single clinical isolates. Subculturing in drug-free media favoured the selection of sensitive strains, emphasizing the critical need for advanced diagnostic tools to accurately detect and characterize heteroresistant and mixed infections. These findings pave the way for more targeted treatment strategies to combat antimicrobial resistance in TB.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".