Fellowship for Qing: Telomerase in Atherosclerosis

  • Bruemmer, Dennis (PI)
  • Qing, Hua (CoI)

Grants and Contracts Details


Macrophages are the major cell type infiltrated into atherosclerotic plaques and play a dominant role in the formation and progression of atherosclerosis, the leading cause of death in the US. Telomerase reverse transcriptase (TERT) is known to be actively involved in arterial remodeling through regulating functions of vascular cells and immune cells. There is no current evidence, however, demonstrating a role for TERT in the progression of atherosclerosis. In our pilot study, a genetic model of TERT and low-density lipoprotein receptor (LDLr) double-null mice (TERT-/-LDLr-/-) displayed less susceptibility to diet-induced atherosclerosis. Consistent with this phenotype, a suppressed inflammatory response was observed in TERT-deficient macrophages. Furthermore, TERT activates signal transducer and activator of transcription 3 (STAT3) and its target inflammatory genes in macrophages. These preliminary data suggest a previously unrecognized role for TERT in macrophage biology during atherosclerosis formation. Therefore, the central hypothesis of this proposal is that TERT activates the STAT3 pathway, inducing pro-inflammatory macrophages and thereby contributing to the development of atherosclerosis. Two specific aims are proposed to examine this hypothesis: 1) Insight into the molecular mechanism through which TERT promotes pro-inflammatory macrophages. 2) Investigation of the mechanism by which TERT-induced pro-inflammatory macrophage populations contribute to atherosclerosis formation. The proposed study is the first to elucidate the function of TERT in the process of atherosclerosis. The results will deepen the understanding of the transcriptional regulations of TERT in macrophages which contribute to atherosclerosis formation, and may be further translated to therapeutic strategies.
Effective start/end date7/1/156/30/17


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.