Coagulation factor XIIIa cross-links amyloid β into dimers and oligomers and to blood proteins
Bibliographic record
Abstract
In cerebral amyloid angiopathy (CAA) and Alzheimer’s disease (AD), the amyloid β (Aβ) peptide deposits along the vascular lumen, leading to degeneration and dysfunction of surrounding tissues. Activated coagulation factor XIIIa (FXIIIa) covalently cross-links proteins in blood and vasculature, such as in blood clots and on the extracellular matrix. Although FXIIIa co-localizes with Aβ in CAA, the ability of FXIIIa to cross-link Aβ has not been demonstrated. Using Western blotting, kinetic assays, and microfluidic analyses, we show that FXIIIa covalently cross-links Aβ40 into dimers and oligomers (kcat/Km = 1.5 × 105 m−1s−1), as well as to fibrin, platelet proteins, and blood clots under flow in vitro. Aβ40 also increased the stiffness of platelet-rich plasma clots in the presence of FXIIIa. These results suggest that FXIIIa-mediated cross-linking may contribute to the formation of Aβ deposits in CAA and Alzheimer’s disease. In cerebral amyloid angiopathy (CAA) and Alzheimer’s disease (AD), the amyloid β (Aβ) peptide deposits along the vascular lumen, leading to degeneration and dysfunction of surrounding tissues. Activated coagulation factor XIIIa (FXIIIa) covalently cross-links proteins in blood and vasculature, such as in blood clots and on the extracellular matrix. Although FXIIIa co-localizes with Aβ in CAA, the ability of FXIIIa to cross-link Aβ has not been demonstrated. Using Western blotting, kinetic assays, and microfluidic analyses, we show that FXIIIa covalently cross-links Aβ40 into dimers and oligomers (kcat/Km = 1.5 × 105 m−1s−1), as well as to fibrin, platelet proteins, and blood clots under flow in vitro. Aβ40 also increased the stiffness of platelet-rich plasma clots in the presence of FXIIIa. These results suggest that FXIIIa-mediated cross-linking may contribute to the formation of Aβ deposits in CAA and Alzheimer’s disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".