Renalase inhibition defends against acute and chronic β cell stress by regulating cell metabolism
Bibliographic record
Abstract
OBJECTIVE: Renalase (Rnls) is annotated as an oxidase enzyme. It has been implicated in Type 1 diabetes (T1D) risk via genome-wide association studies (GWAS). We previously discovered through CRISPR screening and validation experiments that Rnls inhibition prevents or delays T1D in multiple mouse models of diabetes in vivo, and protects pancreatic β cells against autoimmune killing, ER and oxidative stress in vitro. The molecular biochemistry and functions of Rnls are largely uncharted. Here we studied the mechanisms of Rnls inhibition that underlie β cell protection during diabetogenic stress. METHODS: Akita mice were treated with oral Pargyline (PG) in vivo to bind and inhibit Rnls, and pancreas or islets were harvested for β cell mass and β cell function analyses. Genetic and pharmacological tools were used to inhibit Rnls in β cell lines. RNA sequencing, metabolomics and metabolic function experiments were conducted in vitro in NIT-1 mouse β cell lines and human stem cell-derived β cells. RESULTS: ) or knockout (Rnls KO) Rnls induced a robust metabolic shift towards glycolysis in both mouse and human β cell lines, in vitro. Stress protection was abolished when glycolysis was blocked with 2-deoxyglucose (2-DG). Pharmacological Rnls inhibition with PG did not strongly mimic these newly identified metabolic mechanisms. CONCLUSIONS: Our work illustrates a role for Rnls in regulating cell metabolism. We show that inhibiting Rnls protects against chronic stress in vivo, and shields against acute stress in β cell lines in vitro by rewiring cell metabolism towards glycolysis.
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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) | 0.002 | 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".