Application of whole genome sequence analysis to the study of Mycobacterium tuberculosis in Nunavut, Canada
Bibliographic record
Abstract
Canada has one of the lowest rates of tuberculosis (TB) in the world, however, among certain sub-populations, disease incidence rates approach those observed in sub-Saharan Africa, and other high incidence regions. In this study, we applied mycobacterial interspersed repetitive unit (MIRU) variable number of tandem repeat (VNTR) and whole genome sequencing (WGS) to the analysis of Mycobacterium tuberculosis isolates obtained from Northern communities in the territory of Nunavut. WGS was carried out using the Illumina MiSeq, with identified variants used to infer phylogenetic relationships and annotated to infer functional implications. Additionally, the sequencing data from these isolates were augmented with publically available WGS to evaluate data from the Nunavut outbreak in the broader Canadian context. In this study, isolates could be classified into four major clusters by MIRU-VNTR analysis. These could be further resolved into sub-clusters using WGS. No evidence for antimicrobial resistance, either genetic or phenotypic, was observed in this cohort. Among most subjects with multiple samples, reactivation/incomplete treatment likely contributed to recurrence. However, isolates from two subjects appeared more likely to have occurred via reinfection, based on the large number of genomic single nucleotide variants detected. Finally, although quite distinct from previously reported Canadian MTB strains, isolates obtained from Nunavut clustered most closely with a cohort of samples originating in the Nunavik region of Northern Quebec. This study demonstrates the benefit of using WGS for discriminatory analysis of MTB in Canada, especially in high incidence regions. It further emphasizes the importance of focusing epidemiological intervention efforts on interrupting transmission chains of endemic TB throughout Northern communities, rather than relying on strategies applied in regions where the majority of TB cases result from importation of foreign strains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".