Identification of Antibodies to DQβ:DRα Interisotypic Heterodimers in Human Sera
Bibliographic record
Abstract
BACKGROUND: HLA class II antigens, DR, DQ, and DP, comprised an α and β chains, which typically combine, within the same isotype, to form the major histocompatibility complex:peptide complex. Interisotypic pairing is not commonly observed. Although reports of DQβ:DRα heterodimers exist, the pairing was reported to be unstable and, therefore, not studied to any extent. METHODS: DQβ:DRα single antigens were produced through transfectant cell lines and used to identify and characterize positive reactive human sera by a multiplex bead-based assay. RESULTS: Stable DQβ:DRα transfectants were constructed. Cell surface staining with class II-specific monoclonal antibodies revealed that some DQB1 alleles appear to be more efficient in expressing DQβ:DRα heterodimers. Interestingly, alleles within the same serological group varied in their efficiency of forming dimers on the cell surface. For example, DQβ0601:DRα had the highest transfection and cell membrane expression efficiency among 16 common DQB1 alleles tested. In contrast, DQβ0603:DRα-positive transfectants demonstrated minimal surface expression. Assembly of DQβ0601:DRα was not affected by the presence of a DQα chain. DQβ0601:DRα and DQβ0603:DRα single-antigen beads were used to screen human sera. Positive sera were identified that reacted to the unique epitopes of DQβ0601:DRα protein on the cell surface of the transfectants. CONCLUSIONS: Our studies have demonstrated that unique DQβ:DRα heterodimers can be formed and are stably expressed on the cell surface. Such antigenic combinations, presented on single-antigen beads, demonstrated that patient sera can react with such heterodimers. Investigations on the potential clinical roles of antibodies against such interisotypic heterodimers are now possible.
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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 | 0.001 | 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 | 0.001 |
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".