Proteomics signatures of cerebrovascular pathology in TGFß1 overexpressing mice
Bibliographic record
Abstract
Abstract Background Vascular cognitive impairment and dementia (VCID), the second most prevalent of the age‐related dementias, develops as a consequence of various types of cerebrovascular insults that damage brain function (Corriveau et al. 2016). Accumulating lines of evidence point to a link between VCID, both sporadic (Kim et al. 2006) and genetic (Hara et al. 2009, Zellner et al. 2018) forms, and the transforming growth factor beta (TGFB) family signaling. Transgenic mice overexpressing a constitutively active form of TGFβ1 in the brain (TGF mice) recap the cerebrovascular pathology seen in VCID (Wyss‐Coray et al. 2000, Tong et al. 2015), and develop VCID when submitted to a comorbid cardiovascular risk factor for dementia (Trigiani et al. 2020). Our aim was to characterize the cerebrovascular proteome of TGF mice using mass spectrometry‐based quantitative proteomics. Method Eighteen, 6‐month‐old‐TGF and ‐wildtype (WT) mice (N = 9/group) were transcardially perfused and pial arteries harvested under a dissecting microscope. Arterial proteins were extracted, trypsin‐digested, fractionated by strong cation exchange (SCX, gel‐free method) and analyzed by nanoLC‐MS/MS using nanoAcquity UPLC and ESI‐LTQ Orbitrap. For total and/or differentially‐expressed proteins (≥2‐fold change, p≤0.01) we i) performed characterization of proteins, and demonstrated presence of ii) protein RNA‐transcript in mouse and human brain vascular cells using transcriptomics datasets, and iii) identified proteins present in human‐extracellular‐vesicles (EVs) using Vesiclepedia. Result We identified 3602 proteins in brain vessels of WT and TGF mice, including canonical vascular proteins (e.g. Claudin‐5), and 103 direct (N = 103) and indirect (N = 1,942) interactors of TGFβ1 (Fig1). We also identified 83 proteins demonstrating significantly altered levels in TGF mice. Level dysregulation in these proteins point to perturbations in brain vessel vasomotricity, remodeling, and inflammation. We further demonstrated that several of the differentially‐expressed mouse proteins are i) expressed in the human brain vasculature, and ii) found as cargo proteins in EVs. Conclusion We characterized the deleterious impact of TGFβ1 overproduction on the cerebrovascular proteome. Given the growing popularity of extracellular vesicles in blood as a novel and minimally invasive biomarker discovery platform for the age‐related dementias, including VCID, several of the proteins identified by us can serve as protein biomarkers in human.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".