Promotion of TLR7-MyD88-dependent inflammation and autoimmunity in mice through stem-loop changes in Lnc-Atg16l1
Bibliographic record
Abstract
Uncontrolled TLR signaling can cause inflammatory immunopathology and trigger autoimmune diseases. For example, TLR7 promotes pathogenesis of systemic lupus erythematosus. However, whether RNA structural changes affect nucleic acids-sensing TLRs signaling and impact disease progression is unclear. Here by iCLIP-seq we identify a TLR7-binding long non-coding RNA, Lnc-Atg16l1, and find that it promotes TLR7 and other MyD88-dependent TLRs signaling in various types of immune cells. Depletion of Lnc-Atg16l1 attenuates development of TLR7-linked autoimmune phenotypes in the mouse SLE model. Mechanistically, we find that Lnc-Atg16l1 binds to TLR7 at bases near U84 and MyD88 at bases around A129. The analysis of Lnc-Atg16l1 in situ structures show that it strengthens the interaction between TIR domain of TLR7 and MyD88 through specific stem-loop structure changes as a molecular scaffold after TLR7 activation to promote TLR7 downstream signaling. Therefore, we discover a mechanism for host RNA regulation of innate signaling and autoimmune disease through its structural changes. These findings provide insights into the pro-inflammatory function of self RNA in a structure-dependent manner and suggest a potential target for TLR-related autoimmune disorders. TLR7 has been shown to promote inflammatory autoimmunity. Here the authors investigate whether changes in RNA structure affect TLR signaling and show that a TLR7 binding lncRNA Lnc-Atg16l1 promotes development of autoimmunity in the mouse SLE model through enhancement of TLR7-MyD88 interactions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".