Autoimmune targeting of key components of RNA interference
Bibliographic record
Abstract
RNA interference (RNAi) is an evolutionarily conserved mechanism that is involved in the post-transcriptional silencing of genes. This process elicits the degradation or translational inhibition of mRNAs based on the complementarity with short interfering RNAs (siRNAs) or microRNAs (miRNAs). Recently, differential expression of specific miRNAs and disruption of the miRNA synthetic pathway have been implicated in cancer; however, their role in autoimmune disease remains largely unknown. Here, we report that anti-Su autoantibodies from human patients with rheumatic diseases and in a mouse model of autoimmunity recognize the human Argonaute (Ago) protein, hAgo2, the catalytic core enzyme in the RNAi pathway. More specifically, 91% (20/22) of the human anti-Su sera were shown to immunoprecipitate the full-length recombinant hAgo2 protein. Indirect immunofluorescence studies in HEp-2 cells demonstrated that anti-Su autoantibodies target cytoplasmic foci identified as GW bodies (GWBs) or mammalian P bodies, structures recently linked to RNAi function. Furthermore, anti-Su sera were also capable of immunoprecipitating additional key components of the RNAi pathway, including hAgo1, -3, -4, and Dicer. Together, these results demonstrate an autoimmune response to components of the RNAi pathway which could potentially implicate the involvement of an innate anti-viral response in the pathogenesis of autoantibody production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".