Geographic microevolution of <i>Mycobacterium ulcerans</i> sustains Buruli ulcer extension, Australia
Bibliographic record
Abstract
Abstract The reason why severe cases of Buruli ulcers caused by Mycobacterium ulcerans are emerging in some South Australia counties has not been determined. In this study, we measured the diversity of M. ulcerans complex whole genome sequences (WGS) and reported a marker of this diversity. Using this marker as a probe, we compared WGS diversity in Buruli ulcer-epidemic South Australia counties versus non-epidemic Australian counties and further refined comparisons at the level of counties where severe Buruli ulcer cases have been reported. Analyzing 218 WGS (35 complete and 183 reconstructed WGS, including 174 Australian WGS) yielded 15 M. ulcerans complex genotypes, including three genotypes specific to Australia and one genotype specific to South Australia. A 1,068-bp PPE family protein gene exhibiting genotype-specific sequence variations was employed to further probe 13 minority clones hidden in sequence reads. The repartition of these clones significantly differed between South Australia and the rest of Australia. In addition, a significantly higher prevalence of 3/13 clones was observed in South Australia counties of the Mornington Peninsula, Melbourne and Bellarine Peninsula than in other South Australia counties. The data presented in this report suggest that the microevolution of three M. ulcerans complex clones drove the emergence of severe Buruli ulcer cases in some South Australia counties. Sequencing one specific PPE gene served to efficiently probe M. ulcerans complex clones. Further functional studies may balance the environmental adaptation and virulence of these clones.
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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 | 0.000 |
| 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) | 0.001 | 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".