Argonaute, Vault, and Ribosomal Proteins Targeted by Autoantibodies in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To expand, in an unbiased manner, our knowledge of autoantigens and autoantibodies in patients with systemic lupus erythematosus (SLE) and evaluate their associations with serological and clinical variables. METHODS: Human proteome arrays (> 21,000 proteins) were screened with serum from patients with SLE (n = 12) and healthy controls (n = 6) for IgG and IgA binding. Top hits were validated with 2 cohorts of patients with SLE (cohort 1, n = 49; cohort 2, n = 46) and other rheumatic diseases by ELISA. Clinical associations of the autoantibodies were tested. RESULTS: Ro60 was the top hit in the screen, and the 10 following proteins included 2 additional known SLE autoantigens plus 8 novel autoantigens involved in microRNA processing (Argonaute protein 1 [AGO1], AGO2, and AGO3), ribosomes (ribosomal protein lateral stalk subunit P2 and ovarian tumor deubiquitinase 5 [OTUD5]), RNA transport by the vault (major vault protein), and the immune proteasome (proteasome activator complex subunit 3). Patient serum contained IgG reactive with these proteins and IgA against the AGO proteins. Using the 95th percentile of healthy donor reactivity, 5-43% were positive for the novel antigens, with OTUD5 and AGO1 showing the highest percentages of positivity. Autoantibodies against AGO1 proteins were more prevalent in patients with oral ulcers in a statistically significant manner. IgG autoantibodies against AGO proteins were also seen in other rheumatic diseases. CONCLUSION: We discovered new autoantigens existing in cytosolic macromolecular protein assemblies containing RNA (except the proteasome) in cells. A more comprehensive list of autoantigens will allow for a better analysis of how proteins are targeted by the autoimmune response. Future research will also reveal whether specific autoantibodies have utility in the diagnosis or management of SLE.
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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) | 0.001 | 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".