The cargo receptor SURF4 promotes the efficient cellular secretion of PCSK9
Bibliographic record
Abstract
ABSTRACT Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a secreted protein that plays an important role in regulating plasma cholesterol and cardiovascular disease risk. PCSK9 secretion uniquely depends on the cytoplasmic COPII protein SEC24A, suggesting the presence of a transmembrane ER cargo receptor mediating this interaction. Here, we report a novel approach that combines proximity-dependent biotinylation and proteomics together with genome-scale CRISPR screening to identify proteins that facilitate the efficient secretion of PCSK9 heterologously expressed in HEK293T cells. We first identified 35 candidate proteins that were labeled by BirA* fusions to PCSK9 and either COPII component SAR1A or SAR1B. We then performed genome-scale pooled CRISPR mutagenesis to identify genes whose perturbation resulted in intracellular accumulation of PCSK9-eGFP but not the control A1AT-mCherry. The 4 most enriched sgRNAs in this screen all targeted SURF4 , a homologue of the yeast endoplasmic reticulum (ER) cargo receptor Erv29p and the only candidate also identified by proximity-dependent biotinylation. The functional contribution of SURF4 to PCSK9 secretion was confirmed with multiple independent SURF4 -targeting sgRNAs, clonal SURF4 -deficient cell lines, and functional rescue with SURF4 cDNA. Compatible with a function of SURF4 as a cargo receptor for PCSK9, fluorescence microscopy localized SURF4 to the early secretory pathway, coimmunoprecipitation revealed a physical interaction between SURF4 and PCSK9, and SURF4 deletion resulted in decreased extracellular secretion of PCSK9 and PCSK9 accumulation in the ER. Taken together, these findings support a model in which SURF4 functions as an ER cargo receptor for the efficient cellular secretion of PCSK9.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".