Marked Microevolution of a Unique Mycobacterium tuberculosis Strain in 17 Years of Ongoing Transmission in a High Risk Population
Bibliographic record
Abstract
The transmission and persistence of Mycobacterium tuberculosis within high risk populations is a threat to tuberculosis (TB) control. In the current study, we used whole genome sequencing (WGS) to decipher the transmission dynamics and microevolution of M. tuberculosis ON-A, an endemic strain responsible for an ongoing outbreak of TB in an urban homeless/under-housed population. Sixty-one M. tuberculosis isolates representing 57 TB cases from 1997 to 2013 were subjected to WGS. Sequencing data was integrated with available epidemiological information and analyzed to determine how the M. tuberculosis ON-A strain has evolved during almost two decades of active transmission. WGS offers higher discriminatory power than traditional genotyping techniques, dividing the M. tuberculosis ON-A strain into 6 sub-clusters, each defined by unique single nucleotide polymorphism profiles. One sub-cluster, designated ON-ANM (Natural Mutant; 26 isolates from 24 cases) was also defined by a large, 15 kb genomic deletion. WGS analysis reveals the existence of multiple transmission chains within the same population/setting. Our results help validate the utility of WGS as a powerful tool for identifying genomic changes and adaptation of 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 | 0.001 |
| 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".