Influence of Fat on Differential Receptor Interacting Serine/Threonine Protein Kinase 1 Activity Leading to Apoptotic Cell Death in Murine Liver Ischemia Reperfusion Injury Through Caspase 8
Bibliographic record
Abstract
Current understanding is that receptor interacting serine/threonine protein kinase 1 (RIPK1) can lead to two distinct forms of cell death: RIPK3‐mediated necroptosis or caspase 8 (Casp8)‐mediated apoptosis. Here, we report that RIPK1 signaling is indispensable for protection from hepatocellular injury in a steatotic liver undergoing ischemia reperfusion injury (IRI) but not in the lean liver. In lean liver IRI, RIPK1‐mediated cell death is operational, leading to protection in RIP1 kinase‐dead knock‐in (RIPK1K45A) mice and necrostatin‐1s (Nec1s)‐treated lean wild‐type (WT) mice. However, when fed a high‐fat diet (HFD), RIPK1K45A‐treated and Nec1s‐treated WT mice undergoing IRI demonstrate exacerbated hepatocellular injury along with decreased RIPK1 ubiquitylation. Furthermore, we demonstrate that HFD‐fed RIPK3–/–/Casp8–/– mice show protection from IRI, but HFD‐fed RIPK3–/–/Casp8–/+ mice do not. We also show that blockade of RIPK1 leads to increased Casp8 activity and decreases mitochondrial viability. Conclusion: Although more studies are required, we provide important proof of concept for RIPK1 inhibition leading to distinctive outcomes in lean and steatotic liver undergoing IRI. Considering the rising incidence of nonalcoholic fatty liver disease (NAFLD) in the general population, it will be imperative to address this critical difference when treating patients with RIPK1 inhibitors. This study also presents a new target for drug therapy to prevent hepatocellular injury in NAFLD.
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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) | 0.003 | 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".