MHC Class II Polymorphisms, Autoreactive T-Cells, and Autoimmunity
Bibliographic record
Abstract
Major histocompatibility complex (MHC) genes, also known as human leukocyte antigen genes (HLA) in humans, are the prevailing contributors of genetic susceptibility to autoimmune diseases such as Type 1 Diabetes (T1D), multiple sclerosis, and rheumatoid arthritis, among others (1-3). Although the pathways through which MHC molecules afford autoimmune risk or resistance remain to be fully mapped out, it is generally accepted that they do so by shaping the central and peripheral T-cell repertoires of the host toward autoimmune proclivity or resistance, respectively. Disease-predisposing MHC alleles would both spare autoreactive thymocytes from central tolerance and bias their development toward a pathogenic phenotype. Protective MHC alleles, on the other hand, would promote central deletion of autoreactive thymocytes and skew their development toward non-pathogenic phenotypes. This interpretation of the data is at odds with two other observations: that in MHC-heterozygous individuals, resistance is dominant over susceptibility; and that it is difficult to understand how deletion of one or a few clonal autoreactive T-cell types would suffice to curb autoimmune responses driven by hundreds if not thousands of autoreactive T-cell specificities. This review provides an update on current advances in our understanding of the mechanisms underlying MHC class II-associated autoimmune disease susceptibility and/or resistance and attempts to reconcile these seemingly opposing concepts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".