Faculty Opinions recommendation of Endoplasmic reticulum stress sensor IRE1α propels neutrophil hyperactivity in lupus.
Bibliographic record
Abstract
Neutrophils amplify inflammation in lupus through the release of neutrophil extracellular traps (NETs). The endoplasmic reticulum stress sensor inositol-requiring enzyme 1 α (IRE1α) has been implicated as a perpetuator of inflammation in various chronic diseases; however, IRE1α has been little studied in relation to neutrophil function or lupus pathogenesis. Here, we found that neutrophils activated by lupus-derived immune complexes demonstrated markedly increased IRE1α ribonuclease activity. Importantly, in neutrophils isolated from patients with lupus, we also detected heightened IRE1α activity that was correlated with global disease activity. Immune complex-stimulated neutrophils produced both mitochondrial ROS (mitoROS) and the activated form of caspase-2 in an IRE1α-dependent fashion, whereas inhibition of IRE1α mitigated immune complex-mediated NETosis (in both human neutrophils and a mouse model of lupus). Administration of an IRE1α inhibitor to lupus-prone MRL/lpr mice over 8 weeks reduced mitoROS levels in peripheral blood neutrophils, while also restraining plasma cell expansion and autoantibody formation. In summary, these data identify a role for IRE1α in the hyperactivity of lupus neutrophils and show that this pathway is upstream of mitochondrial dysfunction, mitoROS formation, and NETosis. We believe that inhibition of the IRE1α pathway is a novel strategy for neutralizing NETosis in lupus, and potentially other inflammatory conditions. PMID: 33561013 Funding information This work was supported by: NIAMS NIH HHS, United States Grant ID: R01 AR071384 NHLBI NIH HHS, United States Grant ID: R01 HL134846 NIAID NIH HHS, United States Grant ID: R01 AI130025 NIAMS NIH HHS, United States Grant ID: P30 AR075043 NCATS NIH HHS, United States Grant ID: UL1 TR000433
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.524 | 0.301 |
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".