TMEM98, a novel secretory protein, promotes endothelial cell adhesion as well as vascular smooth muscle cell proliferation and migration
Bibliographic record
Abstract
) is a novel gene, and its function has not been well investigated. In a prior study, we have shown that siRNA-mediated knockdown of TMEM98 inhibited interleukin-8 (IL-8) promoted endothelial cell (EC) adhesion, as well as vascular smooth muscle cell (VSMC) proliferation and migration in the vascular endothelial and smooth muscle cell dysfunction. Herein, we used gain- and loss-of-function approaches combined with biochemical techniques to further explore the role of TMEM98 in the vascular wall cell. The expression and secretion of TMEM98 was increased in cultured human umbilical vein endothelial cells (HUVECs) and VSMCs treated with IL-8 and platelet-derived growth factor-BB (PDGF-BB). Also, PDGF-BB secretion was increased in TMEM98-treated HUVECs and VSMCs. Thus, it appears that TMEM98 and PDGF-BB form a positive feedback loop in potentiation of EC adhesion, as well as VSMC proliferation and migration. Knockdown of TMEM98 mediated by siRNA inhibited PDGF-BB-promoted EC adhesion by downregulating the expression of ICAM-1 and VCAM-1, as well as impaired the proliferation and migration of VSMCs by suppressing the AKT/GSK3β/cyclin D1 signaling pathway and reducing the expression of β-catenin. Hence, TMEM98 promoted EC adhesion by inducing the expression of ICAM-1/VCAM-1 and triggered VSMC proliferation and migration by activating the ERK and AKT/GSK3β signaling pathways. Taken together, TMEM98 may serve as a potential therapeutic target for the clinical treatment of vascular endothelial and smooth muscle cell dysfunction.
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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) | 0.001 | 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".