HDL functionality is dependent on hepatocyte stress defense factors Nrf1 and Nrf2
Bibliographic record
Abstract
High density lipoproteins (HDL) promote homeostasis and counteract stressful tissue damage that underlie cardiovascular and other diseases by mediating reverse cholesterol transport, reducing inflammation, and abrogating oxidative damage. However, metabolically stressful conditions associated with atherosclerosis can impair these effects. Hepatocytes play a major role in the genesis and maturation of circulating HDL, and liver stress elicits marked regulatory changes to circulating HDL abundance and composition, which affect its functionality. The mechanisms linking liver stress to HDL function are incompletely understood. In this study, we sought to determine whether stress defending transcription factors nuclear factor erythroid 2 related factor-1 (Nrf1) and -2 (Nrf2) promote hepatocyte production of functional HDL. Using genetically engineered mice briefly fed a mild metabolically stressful diet, we investigated the effect of hepatocyte-specific deletion of Nrf1, Nrf2, or both on circulating HDL cholesterol, protein composition, and function. Combined deletion, but not single gene deletion, reduced HDL cholesterol and apolipoprotein A1 levels as well as the capacity of HDL to accept cholesterol undergoing efflux from cultured macrophages and to counteract tumor necrosis factor α-induced inflammatory effect on cultured endothelial cells. This coincided with substantial alteration to the HDL proteome, which correlated with liver gene expression profiles of corresponding proteins. Thus, our findings show complementary actions by hepatocyte Nrf1 and Nrf2 play a role in shaping HDL abundance and composition to promote production of functionally viable HDL. Consequently, our study illuminates the possibility that enhancing stress defense programming in the liver may improve atheroprotective and perhaps other health promoting actions of HDL.
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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) | 0.001 | 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".