Targeting the E3 ligase RLIM to regulate VSMC phenotypic switching in vascular aging: implications for cold stress

Abstract

Objective Cold exposure may impair vascular function and promote cardiovascular diseases (CVDs) by causing vasoconstriction, hemodynamic changes, and sympathetic activation. Vascular aging, a key factor in CVDs, is linked to phenotypic switching of vascular smooth muscle cells (VSMCs), but its regulatory mechanisms are not fully understood. Materials and methods We used aged C57BL/6 mice and D-galactose-induced senescent VSMCs to investigate the role of the E3 ligase RLIM in arterial aging. RLIM knockdown and overexpression in vivo were achieved using adenoassociated virus (AAV) vectors. Vascular aging and stiffness were assessed using β-galactosidase staining, pulse wave velocity (PWV) measurements, and histological staining. Proteomic profiling was conducted to identify key protein alterations associated with vascular dysfunction and to elucidate underlying mechanisms. Results RLIM expression was significantly upregulated in the aortae of aged mice and D-galactose-induced senescent VSMCs. AAV-mediated RLIM knockdown significantly attenuated vascular aging, as evidenced by vascular ultrasound and histological assessments. Conversely, RLIM overexpression exacerbated vascular damage. Proteomic analysis revealed that RLIM knockdown in VSMCs from aged mice resulted in increased expression of smooth muscle contractile proteins and decreased levels of inflammatory markers, indicating a phenotypic shift toward a more contractile state. Conclusion These findings identify RLIM as a key regulator of arterial aging and a promising therapeutic target for age-related cardiovascular diseases.

Keywords

arterial aging / vascular remodeling / ubiquitination / vascular smooth muscle cell / phenotypic switching

Acknowledgements

Not applicable.

Research ethics

Not applicable.

Informed consent

Not applicable.

Author contributions

Jiang W Q performed the experiments and collected data. Guo M D assisted with the experiments. Wang X and Liu X contributed to the study design and methodology. Zhang Y supervised the project and provided funding support.

Use of Large Language Models, AI and Machine Learning Tools

No LLM, AI or machine learning tool was used for any part of the present study.

Conflict of interest

Zhang Y is an Editorial Board Member of Frigid Zone Medicine. The article was subject to the journal's standard procedures, with peer review handled independently of this Member and his research groups.

Data availability

All data used during the study are available from the corresponding author by request.

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