Dynamics of within-host Mycobacterium tuberculosis diversity and heteroresistance during treatment
Bibliographic record
Abstract
BACKGROUND: Studying within-host genetic diversity of Mycobacterium tuberculosis (Mtb) in patients during treatment may identify adaptations to antibiotic and immune pressure. Understanding the significance of genetic heteroresistance, and more specifically heterozygous resistance-associated variants (RAVs), is clinically important given increasing use of rapid molecular tests and whole genome sequencing (WGS). METHODS: We analyse data from six studies in KwaZulu-Natal, South Africa. Most patients (>75%) had baseline rifampicin resistance. Sputum was collected for culture at baseline and at between two and nine intervals until month six. Positive cultures underwent WGS. Mixed infections and reinfections were excluded from analysis. FINDINGS: Baseline Mtb overall genetic diversity (at treatment initiation or major change to regimen) was associated with cavitary disease, not taking antiretroviral therapy if HIV infected, infection with lineage 2 strains and absence of second-line drug resistance on univariate analyses. Baseline genetic diversity was not associated with six-month outcome. Genetic diversity increased from baseline to weeks one and two before returning to previous levels. Baseline genetic heteroresistance was most common for bedaquiline (6/10 [60%] of isolates with RAVs) and fluoroquinolones (9/62 [13%]). Most patients with heterozygous RAVs on WGS with sequential isolates available demonstrated RAV persistence or fixation (17/20, 85%). New RAVs emerged in 9/286 (3%) patients during treatment. We could detect low-frequency RAVs preceding emergent resistance in only one case, although validation of deep sequencing to detect rare variants is required. INTERPRETATION: In this study of single-strain Mtb infections, baseline within-host bacterial genetic diversity did not predict outcome but may reveal adaptations to host and drug pressures. Predicting emergent resistance from low-frequency RAVs requires further work to separate transient from consequential mutations. FUNDING: Wellcome Trust, NIH/NIAID.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| 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".