<scp>TLR7</scp>/8 Activation in Immune Cells and Muscle by <scp>RNA</scp>‐Containing Immune Complexes: Role in Inflammation and the Pathogenesis of Myositis
Bibliographic record
Abstract
OBJECTIVE: Activation of endosomal toll-like receptors (TLRs) is one possible driver of inflammation in idiopathic inflammatory myopathies (IIM). We investigated the potential contribution of TLR7 and TLR8 to IIM pathogenesis. METHODS: Activation of TLR7/8 in healthy donor peripheral blood mononuclear cells (PBMCs) by immune complexes from patients with IIMs and lupus was tested. Autoantibody profiling of patient IgG samples was performed using a 1581 antigen array. TLR7 and/or TLR8 activation by RNA molecules associated with autoantibodies was assessed. Gene expression in human myoblasts and satellite cells following treatment with supernatants from TLR7/8-activated PBMCs was evaluated by NanoString. C57BL/6 mice were dosed intramuscularly with the TLR7/8 agonist R848 and single-cell RNA-sequencing was performed on the muscle to ascertain the cell types responding to TLR7/8 activation and the downstream effects. RESULTS: Overall, 69 patients with IIMs were included with representation of dermatomyositis, polymyositis, and inclusion body myositis subsets. Immune complexes from patients with IIMs, as well as autoantibody-associated RNAs histidyl-transfer RNA, Y1, Y4, and U1, activated PBMCs to produce interferon-α and IL-6 via TLR7/8. Several canonical (Ro60, Ro52, and HIST1H4A) and novel (IL-36RN) autoreactivities correlated highly with TLR7/8 activation. Supernatants from TLR7/8-activated PBMCs had a negative impact on human myoblasts and satellite cells. Endothelial cells were activated by R848 in mouse muscle in vivo in addition to immune cells such as monocytes and macrophages. CONCLUSION: Our results suggest that patients with IIMs have autoantibodies in their blood causing TLR7/8 activation, which leads to inflammation in muscles with potential deleterious effects.
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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 | 0.000 | 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 | 0.000 |
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".