Optimization of plasma-based BioID identifies plasminogen as a ligand of ADAMTS13
Bibliographic record
Abstract
ADAMTS13, a disintegrin and metalloprotease with a thrombospondin type 1 motif, member 13, regulates the length of Von Willebrand factor (VWF) multimers and their platelet-binding activity. ADAMTS13 is constitutively secreted as an active protease and is not inhibited by circulating protease inhibitors. Therefore, the mechanisms that regulate ADAMTS13 protease activity are unknown. We performed an unbiased proteomics screen to identify ligands of ADAMTS13 by optimizing the application of BioID to plasma. Plasma BioID identified 5 plasma proteins significantly labeled by the ADAMTS13-birA* fusion, including VWF and plasminogen. Glu-plasminogen, Lys-plasminogen, mini-plasminogen, and apo(a) bound ADAMTS13 with high affinity, whereas micro-plasminogen did not. None of the plasminogen variants or apo(a) bound to a C-terminal truncation variant of ADAMTS13 (MDTCS). The binding of plasminogen to ADAMTS13 was attenuated by tranexamic acid or ε-aminocaproic acid, and tranexamic acid protected ADAMTS13 from plasmin degradation. These data demonstrate that plasminogen is an important ligand of ADAMTS13 in plasma by binding to the C-terminus of ADAMTS13. Plasmin proteolytically degrades ADAMTS13 in a lysine-dependent manner, which may contribute to its regulation. Adapting BioID to identify protein-interaction networks in plasma provides a powerful new tool to study protease regulation in the cardiovascular system.
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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 | 0.001 | 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".