<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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".