iRhom2 promotes lupus nephritis through TNF-α and EGFR signaling
Bibliographic record
Abstract
Lupus nephritis (LN) often results in progressive renal dysfunction. The inactive rhomboid 2 (iRhom2) is a newly identified key regulator of A disintegrin and metalloprotease 17 (ADAM17), whose substrates, such as TNF-α and heparin-binding EGF (HB-EGF), have been implicated in the pathogenesis of chronic kidney diseases. Here, we demonstrate that deficiency of iRhom2 protects the lupus-prone Fcgr2b-/- mice from developing severe kidney damage without altering anti-double-stranded DNA (anti-dsDNA) Ab production by simultaneously blocking HB-EGF/EGFR and TNF-α signaling in the kidney tissues. Unbiased transcriptome profiling of kidneys and kidney macrophages revealed that TNF-α and HB-EGF/EGFR signaling pathways are highly upregulated in Fcgr2b-/- mice, alterations that were diminished in the absence of iRhom2. Pharmacological blockade of either TNF-α or EGFR signaling protected Fcgr2b-/- mice from severe renal damage. Finally, kidneys from LN patients showed increased iRhom2 and HB-EGF expression, with interstitial HB-EGF expression significantly associated with chronicity indices. Our data suggest that activation of iRhom2/ADAM17-dependent TNF-α and EGFR signaling plays a crucial role in mediating irreversible kidney damage in LN, thereby uncovering a target for selective and simultaneous dual inhibition of 2 major pathological pathways in the effector arm of the disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".