PCSK9 regulates LDL-dependent uptake of bacterial lipids by HepG2 cells through LDL receptor
Bibliographic record
Abstract
Abstract Introduction Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key negative regulator of lipid uptake through low-density lipoprotein (LDL) receptor (LDLR), and has recently been implicated in regulating cytokine production during sepsis. We hypothesize that PCSK9 affects cytokine production by regulating hepatic LDLR-mediated uptake of bacterial lipids, such as lipopolysaccharide (LPS) and lipoteichoic acid (LTA), and that this uptake is LDL-dependent. Methods HepG2 cells were cultured in media containing normal serum or lipoprotein-deficient serum (LPDS), and pre-treated with recombinant human PCSK9 or control, and with control IgG, or anti-LDLR antibody. HepG2 cells were also cultured in LPDS with add-back of increasing LDL concentrations. Cells were then treated for 24 h with fluorescent LPS or LTA, or controls. Media was collected for cytokine assays, and flow cytometry was performed to quantify LPS or LTA uptake by measuring fluorescence. Results HepG2 cells pre-treated with recombinant PCSK9 or anti-LDLR antibody had significantly decreased fluorescent LPS and LTA uptake compared to controls when cultured in normal serum. Furthermore, cells pre-treated with both anti-LDLR antibody and PCSK9 showed similar uptake as cells treated with anti-LDLR antibody alone. Neither anti-LDLR antibody nor PCSK9 had any effect on cells cultured in LPDS, but LDL add-back dose-dependently increased uptake of both LPS and LTA. Varying uptake of LPS and LTA did not affect secretion of IL-6, IL-8, IL-10, or IL-17 by HepG2 cells. Conclusion Bacterial lipid uptake by HepG2 cells through LDLR requires LDL, and is negatively regulated by PCSK9, but does not affect cytokine production by these cells.
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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 | 0.001 |
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".