Site-specific O-glycosylation of members of the low-density lipoprotein receptor superfamily enhances ligand interactions
Bibliographic record
Abstract
The low-density lipoprotein receptor (LDLR) and related receptors are important for the transport of diverse biomolecules across cell membranes and barriers. Their functions are especially relevant for cholesterol homeostasis and diseases, including neurodegenerative and kidney disorders. Members of the LDLR-related protein family share LDLR class A (LA) repeats providing binding properties for lipoproteins and other biomolecules. We previously demonstrated that short linker regions between these LA repeats contain conserved O -glycan sites. Moreover, we found that O -glycan modifications at these sites are selectively controlled by the GalNAc-transferase isoform, GalNAc-T11. However, the effects of GalNAc-T11–mediated O -glycosylation on LDLR and related receptor localization and function are unknown. Here, we characterized O -glycosylation of LDLR-related proteins and identified conserved O -glycosylation sites in the LA linker regions of VLDLR, LRP1, and LRP2 (Megalin) from both cell lines and rat organs. Using a panel of gene-edited isogenic cell line models, we demonstrate that GalNAc-T11–mediated LDLR and VLDLR O -glycosylation is not required for transport and cell-surface expression and stability of these receptors but markedly enhances LDL and VLDL binding and uptake. Direct ELISA-based binding assays with truncated LDLR constructs revealed that O -glycosylation increased affinity for LDL by ∼5-fold. The molecular basis for this observation is currently unknown, but these findings open up new avenues for exploring the roles of LDLR-related proteins in disease.
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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) | 0.002 | 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".