Proteome profiling of brain vessels in a mouse model of cerebrovascular pathology
Bibliographic record
Abstract
ABSTRACT A cerebrovascular pathology that involves altered protein levels or signaling of the transforming growth factor beta (TGFβ) family has been associated with various forms of dementia, including Alzheimer disease (AD) and vascular cognitive impairment and dementia (VCID). Transgenic mice overexpressing TGFβ1 in the brain (TGF mice) recap VCID-associated cerebrovascular pathology and develop cognitive deficits in old age or when submitted to comorbid cardiovascular risk-factors for dementia. Here, we characterized the cerebrovascular proteome of TGF mice using mass-spectrometry (MS) based quantitative proteomics. Cerebral arteries were surgically removed from 6-month-old-TGF and wild-type mice, proteins extracted and analyzed by gel-free nanoLC-MS/MS. We identified 3,602 proteins in brain vessels, with 20 demonstrating robust altered levels in TGF mice. For total and/or differentially-expressed proteins ( p ≤0.01, ≥2-fold change), using multiple databases, we performed protein characterization, and identified proteins demonstrating RNA-transcripts in both mouse and human cerebrovascular cells, and known to be present in human-extracellular-vesicles (EVs). Dysregulated proteins point to perturbed brain vessel vasomotricity, remodeling, and inflammation. Given that blood-isolated EVs are novel, attractive and a minimally invasive biomarker discovery platform for the age-related dementias, several proteins identified in this study can potentially serve as VCID markers in humans.
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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 | 0.000 | 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".