Whole genome sequencing improves the discrimination between Mycobacterium bovis strains on the southern border of Kruger National Park, South Africa
Bibliographic record
Abstract
Mycobacterium bovis forms part of the Mycobacterium tuberculosis complex and has an extensive host range and zoonotic potential. Various genotyping methods (e.g., spoligotyping) have been used to describe the molecular epidemiology of M. bovis. Advances in whole genome sequencing (WGS) have increased resolution to enable detection of genomic variants to the level of single nucleotide polymorphisms. This is especially relevant to One Health research on tuberculosis which benefits by being able to use WGS to identify epidemiologically linked cases, especially recent transmission. The use of WGS in molecular epidemiology has been extensively used in humans and cattle but is limited in wildlife. This approach appears to overcome the limitations of conventional genotyping methods due to lack of genetic diversity in M. bovis. This pilot study investigated the spoligotype and WGS of M. bovis isolates (n = 7) from wildlife in Marloth Park (MP) and compared these with WGS data from other South African M. bovis isolates. In addition, the greater resolution of WGS was used to explore the phylogenetic relatedness of M. bovis isolates in neighbouring wildlife populations. The phylogenetic analyses showed the closest relatives to the seven isolates from MP were isolates from wildlife in Kruger National Park (KNP), which shares a border with MP. However, WGS data indicated that the KNP and MP isolates formed two distinct clades, even though they had similar spoligotypes and identical in silico genetic regions of difference profiles. Mycobacterium bovis isolates from MP were hypothesized to be directly linked to KNP wildlife, based on spoligotyping. However, WGS indicated more complex epidemiology. The presence of two distinct clades which were genetically distinct (SNP distance of 19–47) and suggested multiple transmission events. Therefore, WGS provided new insight into the molecular epidemiology of the M. bovis isolates from MP and their relationship to isolates from KNP. This approach will facilitate greater understanding of M. bovis transmission at wildlife-livestock-human interfaces and advances One Health research on tuberculosis, especially across different host species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".