Possible effect of mutations on serological detection of Borrelia burgdorferi sensu stricto ospC major groups: An in-silico study
Bibliographic record
Abstract
The outer surface protein C (OspC) of the agent of Lyme disease, Borrelia burgdorferi sensu stricto, is a major lipoprotein surface-expressed during early-phase human infections. Antibodies to OspC are used in serological diagnoses. This study explored the hypothesis that serological test sensitivity decreases as genetic similarity of ospC major groups (MGs) of infecting strains, and ospC A (the MG in the strain B31 used to prepare antigen for serodiagnosis assays) decreases. We used a previously published microarray dataset to compare serological reactivity to ospC A (measured as pixel intensity) versus reactivity to 22 other ospC MGs, within a population of 55 patients diagnosed by two-tier serological testing using B. burgdorferi s.s. strain B31 as antigen, in which the ospC MG is OspC A. The difference in reactivity of sera to ospC A and reactivity to each of the other 22 ospC MGs (termed 'reactivity difference') was the outcome variable in regression analysis in which genetic distance of the ospC MGs from ospC A was the explanatory variable. Genetic distance was computed for the whole ospC sequence, and 9 subsections, from Neighbour Joining phylogenetic trees of the 23 ospC MGs. Regression analysis was conducted using genetic distance for the full ospC sequence, and the subsections individually. There was a significant association between the reactivity difference and genetic distance of ospC MGs from ospC A: increased genetic distance reduced reactivity to OspC A. No single ospC subsection sequence fully explained the relationship between genetic distance and reactivity difference. An analysis of single nucleotide polymorphisms supported a biological explanation via specific amino acid modifications likely to change protein binding affinity. This adds support to the hypothesis that genetic diversity of B. burgdorferi s.s. (here specifically OspC) may impact serological diagnostic test performance. Further prospective studies are necessary to explore the clinical implications of these findings.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".