Plasma Protein Signatures of a Murine Venous Thrombosis Model and Slc44a2 Knockout Mice Using Quantitative-Targeted Proteomics
Bibliographic record
Abstract
Abstract The plasma compartment of the blood holds important information on the risk to develop cardiovascular diseases such as venous thrombosis (VT). Mass spectrometry-based targeted proteomics with internal standards quantifies proteins in multiplex allowing generation of signatures associated with a disease or a condition. Here, to demonstrate the method, we investigate the plasma protein signatures in mice following the onset of VT, which was induced by RNA interference targeting the natural anticoagulants antithrombin and protein C. We then study mice lacking Slc44a2, which was recently characterized as a VT-susceptibility gene in human genome-wide association studies. We use a recently developed panel of 375 multiplexed mouse protein assays measured by mass spectrometry. A strong plasma protein siganture was observed when VT was induced. Discriminators included acute phase response proteins, and proteins related to erythrocyte function. In mice lacking Slc44a2, protein signature was primarily overruled by the difference between sexes and not by the absent gene. Upon separate analyses for males and females, we were able to establish a signature for Slc44a2 deficiency, in which glycosylation-dependent cell adhesion molecule-1 and thrombospondin-1 were shared by both sexes. The minimal impact of Slc44a2 deficiency on the measured plasma proteins suggests that the main effect of Slc44a2 on VT does not lay ultimately in the plasma compartment. This suggests further investigation into the role of this VT-susceptibility gene should perhaps also question the possible involvement in cellular mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".