Multilocus Sequence Typing of Borrelia burgdorferi Suggests Existence of Lineages with Differential Pathogenic Properties in Humans
Bibliographic record
Abstract
The clinical manifestations of Lyme disease, caused by Borrelia burgdorferi, vary considerably in different patients, possibly due to infection by strains with varying pathogenicity. Both rRNA intergenic spacer and ospC typing methods have proven to be useful tools for categorizing B. burgdorferi strains that vary in their tendency to disseminate in humans. Neither method, however, is suitable for inferring intraspecific relationships among strains that are important for understanding the evolution of pathogenicity and the geographic spread of disease. In this study, multilocus sequence typing (MLST) was employed to investigate the population structure of B. burgdorferi recovered from human Lyme disease patients. A total of 146 clinical isolates from patients in New York and Wisconsin were divided into 53 sequence types (STs). A goeBURST analysis, that also included previously published STs from the northeastern and upper Midwestern US and adjoining areas of Canada, identified 11 major and 3 minor clonal complexes, as well as 14 singletons. The data revealed that patients from New York and Wisconsin were infected with two distinct, but genetically and phylogenetically closely related, populations of B. burgdorferi. Importantly, the data suggest the existence of B. burgdorferi lineages with differential capabilities for dissemination in humans. Interestingly, the data also indicate that MLST is better able to predict the outcome of localized or disseminated infection than is ospC typing.
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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) | 0.000 | 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".