The Uptake of Lipoprotein-Borne Phylloquinone (Vitamin K1) by Osteoblasts and Osteoblast-Like Cells: Role of Heparan Sulfate Proteoglycans and Apolipoprotein E
Bibliographic record
Abstract
Vitamin K is essential for the gamma-carboxylation of Gla-containing bone proteins such as osteocalcin and a suboptimal vitamin K status has been linked to osteoporosis but nothing is known of how the lipoprotein-borne vitamin accesses the bone matrix. We have studied the mechanism of transport of lipoproteins labeled with [3H]-phylloquinone (vitamin K1 [K1]) into osteoblasts using both tumor-derived cell lines and normal osteoblast-rich cell populations. We also investigated the effect of heparin in this model since long-term heparin treatment causes osteopenia and the anticoagulant is known to impair normal lipoprotein metabolism. Heparinase treatment, which removes heparan sulfate proteoglycans (HSPG), reduced uptake of [3H]-K1 from triglyceride-rich lipoproteins (TRL) and low-density lipoproteins (LDL). The effect of heparin in this model was complex depending on cell type, concentration, and time but, overall, the results were consistent with an inhibition of vitamin K uptake by osteoblasts. Anti-apolipoprotein E (apoE) antiserum reduced uptake of TRL-[3H]-K1 by 55 +/- 4% and LDL-[3H]-K1 uptake by 35 +/- 2%. Exogenous apoE4 increased uptake of TRL-[3H]-K1 by 90 +/- 1% compared with 53 +/- 11% for apoE3 and 52 +/- 5% for apoE2. Our findings show that HSPG on the cell surface and apoE in the lipoprotein particles contribute to lipoprotein-K1 uptake by osteoblasts as is known for lipoprotein uptake by hepatocytes. This mechanism is significant in view of the epidemiological association of both undercarboxylation of osteocalcin and the presence of an apo epsilon4 allele with increased fracture risk and reduced bone mineral density (BMD). The inhibition by heparin of lipoprotein-mediated carriage of vitamin K and possibly other lipids to bone may provide a basis for the future understanding of heparin-induced osteoporosis.
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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".