The Effect of an Anti-HLA-B27 Immune Response on CTL Recognition of <i>Chlamydia</i>
Bibliographic record
Abstract
The interplay between triggering bacteria and HLA-B27 in the pathogenesis of the spondyloarthropathies remains one of the most active areas of investigation in the rheumatic diseases. This has proved difficult to study systematically in the clinical setting, and in this study we utilized a rat model to address the influence that B27-related immunity may have on the process of generating anti-Chlamydia immunity. When splenocytes from HLA-B27 DNA-immunized Lewis (LEW) animals received restimulation in vitro with Chlamydia-treated cells from B27-transgenic LEW rats, we observed that in addition to the expected CTL recognition of HLA-B27, there was also anti-Chlamydia CTL killing of Chlamydia-sensitized syngeneic fibroblast targets. This was not seen when responding cells in vitro were naive LEW splenocytes. To confirm the existence of CTLs recognizing both HLA-B27 and Chlamydia, LEW rats were immunized with B27-transgenic LEW cells, instead of the B27 DNA construct. Splenocytes from the immune rats were restimulated in vitro with Chlamydia-treated B27-transgenic LEW cells. In this instance, the CTLs retained the allele-specific recognition of HLA-B27, as well as recognition of Chlamydia-sensitized syngeneic fibroblasts. Thus, if there is prior expansion of an immune response against HLA-B27, then the resulting splenocytes demonstrate a reduced threshold for generating a primary anti-Chlamydia CTL response. These studies implicate a dynamic interrelationship between recognition of HLA-B27 and Chlamydia trachomatis. The results may have implications for deciphering the cellular basis of Chlamydia-induced reactive arthritis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".