The autoimmunity-associated gene CLEC16A controls HLA-II expression by participating in the molecular machinery of late endosomal maturation (HUM2P.337)
Bibliographic record
Abstract
Abstract In GWA studies, CLEC16A has been identified as a risk gene for several major autoimmune diseases, including multiple sclerosis (MS). We show that CLEC16A is increased in MS and functionally links to HLA-II, and uncovered the molecular mechanism of CLEC16A as key regulator of HLA-II antigen presentation. We used the human melanoma cell line MelJuSo and primary moDC as APC models to silence CLEC16A with different siRNA and lentiviral shRNA. CLEC16A knockdown in MelJuSo cells and moDC resulted in cytoplasmic dispersion and strong accumulation of HLA-II+ late endosomes, suggesting impaired formation of multivesicular late endosomal compartments, i.e. MIIC. Indeed, in CLEC16A shRNA transductants, MIIC contained no or a few abnormally enlarged internal vesicles and showed an increased number of HLA-II molecules as compared to scrambled shRNA transductants of MelJuSo cells. CLEC16A associated with Rab7-interacting lysosomal protein (RILP) and the homotypic fusion and protein sorting (HOPS) complex, two members of the dynein motor complex regulating late endosomal trafficking and maturation. In addition, CLEC16A silencing disrupted RILP-mediated recruitment of HLA-II+ late endosomes to perinuclear regions. We reveal direct involvement of CLEC16A in the machinery regulating late endosomal processing of HLA-II. This novel C-type lectin function supports a pathogenic role of CLEC16A in autoimmune disease by promoting HLA-II antigen presentation via the formation of MIIC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".