Hepatic Nrf1 ( <i>Nfe2l1</i> ) promotes VLDL dependent liver defense against sepsis
Bibliographic record
Abstract
Abstract Sepsis causes mortality by triggering organ damage. Interest has emerged in stimulating disease tolerance to reduce organ damage. Liver plays a role in disease tolerance by mediating defensive adaptations, but sepsis-induced liver damage limit these effects. Here, we investigated whether stress defending transcription factors nuclear factor erythroid 2 related factor-1 (Nrf1) and -2 (Nrf2) in hepatocytes protect against sepsis. Using mice, we evaluated responses by hepatic Nrf1 and Nrf2 during sepsis triggered by lipopolysaccharide or Escherichia coli . We also genetically altered hepatic Nrf1 and Nrf2 activity to determine the protective role of these factors in sepsis. Our results show hepatic Nrf1 and Nrf2 activity is reduced in severe sepsis and hepatic Nrf1, but not Nrf2, deficiency predisposes for hypothermia and mortality. In contrast, enhancing hepatic Nrf1 activity protects against hypothermia and improves survival. Moreover, in sepsis hepatic Nrf1 deficiency reduces VLDL secretion whereas enhancing hepatic Nrf1 increases VLDL secretion, and inhibiting VLDL secretion with lomitapide obstructs protective actions of hepatic Nrf1. Gene expression profiles suggest Nrf1 promotes this effect by inducing stress defenses. Hence, we show mortality in sepsis may result from impaired stress defense and that hepatic Nrf1 improves disease tolerance during sepsis by promoting VLDL dependent liver defense.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".