PROTEOMIC ASSESSMENT OF CIRCULATING MICROPARTICLES IN HYPERTENSION AND DIABETES
Bibliographic record
Abstract
Objective: It is estimated that 415 million individuals are living with diabetes and an estimated 1.13 billion individuals worldwide have hypertension. Hypertension and diabetes are both associated with increased cardiovascular risk however the molecular changes to the vasculature associated these conditions are not fully understood. In the present study we examined the protein composition of circulating microparticles (MPs) in hypertensive, diabetic and healthy mice to identify common and disease-specific molecular changes. Design and method: Protein content was assessed in microparticles isolated from platelet-poor plasma of transgenic mice expressing human renin in the liver (TtRhRen, hypertension model), OVE26 type 1 diabetic mice and wild-type (WT) FVBN mice. Microparticles were isolated by differential centrifugation and protein content was analyzed using liquid chromatography mass spectrometry (LC-MS/MS). Results: We identified a total of 297 independent proteins with at least 2 peptides per protein. Of these, 163 proteins were found in all groups studied, 48 were exclusive to WT mice, 23 were exclusive to OVE26 mice and 4 were exclusive to TtRhRen mice. In addition, 22 proteins were observed with > 1.5 fold change (FC) compared to wild-type mice, and 68 proteins were reduced by > 50%. Amongst the top ten differentially expressed proteins, fibrinogen was upregulated in both OVE26 and TtRhRen mice compared with wild-type controls. Similarly Trem-like transcript 1, sarcoplasmic/ endoplasmic reticulum calcium ATPase 3 and junction plakoglobin were all downregulated in both OVE26 mice and TtRhRen mice. Conversely, arginase was up-regulated in diabetic, but not hypertensive mice while carboxypeptidase was up-regulated in hypertensive but not diabetic mice. Gene ontology (GO) analysis of 96 proteins unique or enriched in MPs from OVE26 mice indicated the presence of proteins involved in arginine biosynthesis and blood coagulation. GO analysis of TtRhRen-enriched proteins indicated presence of proteins associated with integrin signaling and inflammation. Conclusions: In summary, assessment of MP protein composition revealed common and disease-specific changes in diabetes and hypertension. Further analysis of these changes may lead to the identification of novel pathways associated with the pathogenesis of vascular injury in hypertension and diabetes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".