Multimerin 1 supports platelet function in vivo and binds to specific GPAGPOGPX motifs in fibrillar collagens that enhance platelet adhesion
Bibliographic record
Abstract
Background Multimerin 1 (human: MMRN1, mouse: Mmrn1) is a homopolymeric, adhesive, platelet and endothelial protein that binds to von Willebrand factor and enhances platelet adhesion to fibrillar collagen ex vivo. Objectives To examine the impact of Mmrn1 deficiency on platelet adhesive function, and the molecular motifs in fibrillar collagen that bind MMRN1 to enhance platelet adhesion. Methods Mmrn1‐deficient mice were generated and assessed for altered platelet adhesive function. Collagen Toolkit peptides, and other triple‐helical collagen peptides, were used to identify multimerin 1 binding motifs and their contribution to platelet adhesion. Results MMRN1 bound to conserved GPAGPOGPX sequences in collagens I, II, and III (including GPAGPOGPI, GPAGPOGPV, and GPAGPOGPQ) that enhanced activated human platelet adhesion to collagen synergistically with other triple‐helical collagen peptides ( P < .05). Mmrn1 −/− and Mmrn1 +/− mice were viable and fertile, with complete and partial platelet Mmrn1 deficiency, respectively. Relative to wild‐type mice, Mmrn1 −/− and Mmrn1 +/− mice did not have overt bleeding, increased median bleeding times, or increased wound blood loss ( P ≥ .07); however, they both showed significantly impaired platelet adhesion and thrombus formation in the ferric chloride injury model ( P ≤ .0003). Mmrn1 −/− platelets had impaired adhesion to GPAGPOGPX peptides and fibrillar collagen ( P ≤ .03) and formed smaller aggregates than wild‐type platelets when captured onto collagen, triple‐helical collagen mimetic peptides, von Willebrand factor, or fibrinogen ( P ≤ .008), despite preserved, low shear, and high shear aggregation responses. Conclusions Multimerin 1 supports platelet adhesion and thrombus formation and binds to highly conserved, GPAGPOGPX motifs in fibrillar collagens that synergistically enhance platelet adhesion.
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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".