Patients with anti-Jo1 antibodies display a characteristic IgG Fc-glycan profile which is further enhanced in anti-Jo1 autoantibodies
Bibliographic record
Abstract
Abstract IgG Fc-glycans affect IgG function and are altered in autoimmune diseases and autoantibodies. Anti-histidyl tRNA synthetase autoantibodies (anti-Jo1) are frequent in patients with idiopathic inflammatory myopathies (IIM) and anti-synthetase syndrome (ASS) with associated interstitial lung disease (ILD). Thus, we hypothesized that the total-IgG Fc-glycans from Jo1+ versus Jo1− patients and anti-Jo1-IgG would show characteristic differences, and that particular Fc-glycan features would be associated with specific clinical manifestations. By proteomics based mass spectrometry we observed a high abundance of agalactosylated IgG1 Fc-glycans in ASS/IIM patients (n = 44) compared to healthy age matched controls (n = 24). Using intra-individual normalization of the main agalactosylated glycan (FA2) of IgG1 vs FA2-IgG2, ASS/IIM and controls were distinguished with an area under the curve (AUC) of 79 ± 6%. For Jo1+ patients (n = 19) the AUCs went up to 88 ± 6%. Bisected and afucosylated Fc-glycans were significantly lower in Jo1+ compared to Jo1− patients. Anti-Jo1-IgG enriched from eleven patients contained even significantly lower abundances of bisected, afucosylated and galactosylated forms compared to matched total-IgG. ASS and ILD diagnosis, as well as lysozyme and thrombospondin correlated with Jo1+ characteristic Fc-glycan features. These results suggest that the anti-Jo1+ patient Fc-glycan profile contains phenotype specific features which may underlie the pathogenic role of Jo1 autoantibodies.
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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 | 0.001 | 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 | 0.000 |
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".