HLA Class II Polymorphisms Determine Responses to Bacterial Superantigens
Bibliographic record
Abstract
The excessive immunological response triggered by microbial superantigens has been implicated in the etiology of a wide range of human diseases but has been most clearly defined for the staphylococcal and streptococcal toxic shock syndromes. Because MHC class II presentation of superantigens to T cells is not MHC-restricted, the possibility that HLA polymorphisms could influence superantigenicity, and thus clinical susceptibility to the toxicity of individual superantigens, has received little attention. In this study, we demonstrate that binding of streptococcal and staphylococcal superantigens to HLA class II is influenced by allelic differences in class II. For the superantigen streptococcal pyrogenic exotoxin A, class II binding is dependent on DQ alpha-chain polymorphisms such that HLA-DQA1*01 alpha-chains show greater binding than DQA1*03/05 alpha-chains. The functional implications of differential binding on T cell activation were investigated in various experimental systems using human T cells and murine Vbeta8.2 transgenic cells as responders. These studies showed quantitative and qualitative differences resulting from differential HLA-DQ binding. We observed changes in T cell proliferation and cytokine production, and in the Vbeta specific changes in T cell repertoire that have hitherto been regarded as a defining feature of an individual superantigen. Our observations reveal a mechanism for the different outcomes seen following infection by toxigenic bacteria.
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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 | 0.001 |
| 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 | 0.000 |
| 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".