Low-density lipoprotein (LDL)-dependent uptake of Gram-positive lipoteichoic acid and Gram-negative lipopolysaccharide occurs through LDL receptor
Bibliographic record
Abstract
Lipoteichoic acid (LTA) and lipopolysaccharide (LPS) are bacterial lipids that stimulate pro-inflammatory cytokine production, thereby exacerbating sepsis pathophysiology. Proprotein convertase subtilisin/kexin type 9 (PCSK9) negatively regulates uptake of cholesterol by downregulating hepatic lipoprotein receptors, including low-density lipoprotein (LDL) receptor (LDLR) and possibly LDLR-related protein-1 (LRP1). PCSK9 also negatively regulates Gram-negative LPS uptake by hepatocytes, however this mechanism is not completely characterized and mechanisms of Gram-positive LTA uptake are unknown. Therefore, our objective was to elucidate the mechanisms through which PCSK9 regulates uptake of LTA and LPS by investigating the roles of lipoproteins and lipoprotein receptors. Here we show that plasma PCSK9 concentrations increase transiently over time in septic and non-septic critically ill patients, with highly similar profiles over 14 days. Using flow cytometry, we demonstrate that PCSK9 negatively regulates LDLR-mediated uptake of LTA and LPS by HepG2 hepatocytes through an LDL-dependent mechanism, whereas LRP1 and high-density lipoprotein do not contribute to this uptake pathway. Bacterial lipid uptake by hepatocytes was not associated with cytokine production or hepatocellular injury. In conclusion, our study characterizes an LDL-dependent and LDLR-mediated bacterial lipid uptake pathway regulated by PCSK9, and provides evidence in support of PCSK9 inhibition as a potential therapeutic strategy for sepsis.
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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".