Identification of ERGIC-53 as an intracellular transport receptor of α1-antitrypsin
Bibliographic record
Abstract
Secretory proteins are exported from the endoplasmic reticulum (ER) by bulk flow and/or receptor-mediated transport. Our understanding of this process is limited because of the low number of identified transport receptors and cognate cargo proteins. In mammalian cells, the lectin ER Golgi intermediate compartment 53-kD protein (ERGIC-53) represents the best characterized cargo receptor. It assists ER export of a subset of glycoproteins including coagulation factors V and VIII and cathepsin C and Z. Here, we report a novel screening strategy to identify protein interactions in the lumen of the secretory pathway using a yellow fluorescent protein-based protein fragment complementation assay. By screening a human liver complementary DNA library, we identify alpha1-antitrypsin (alpha1-AT) as previously unrecognized cargo of ERGIC-53 and show that cargo capture is carbohydrate- and conformation-dependent. ERGIC-53 knockdown and knockout cells display a specific secretion defect of alpha1-AT that is corrected by reintroducing ERGIC-53. The results reveal ERGIC-53 to be an intracellular transport receptor of alpha1-AT and provide direct evidence for active receptor-mediated ER export of a soluble secretory protein in higher eukaryotes.
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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 | 0.001 |
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".