{"database":"iProX","file_versions":[],"scores":null,"additional":{"omics_type":["Proteomics"],"submitter":["Bin Zheng"],"species":["Rattus Norvegicus"],"full_dataset_link":["http://www.iprox.org/page/project.html?id=IPX0017809000"],"submitter_email":["doublezb@hebmu.edu.cn"],"submitter_affiliation":["Hebei Medical University"],"sample_protocol":[""],"repository":["iProX"],"data_protocol":[""],"additional_accession":[]},"is_claimable":false,"name":"Pharmacological Destabilization of ZEB2 Attenuates Atherosclerosis via Restoration of Lipid Catabolism","description":"he pathogenesis of atherosclerosis involves phenotypic reprogramming of vascular smooth muscle cells (VSMCs) coupled with profound lipid metabolic dysfunction; however, the critical transcriptional regulators governing this process remain incompletely understood. Here, we identify zinc finger E-box binding homeobox 2 (ZEB2) as a central regulator linking VSMC phenotypic switching to lipid dysregulation in atherosclerosis. Single-cell transcriptomic trajectory analyses revealed progressive induction of ZEB2 during the transition of contractile VSMCs toward foam-like states, a finding validated in both murine and human atherosclerotic plaques. Genetic and pharmacological inhibition of ZEB2 markedly reduced lipid accumulation in VSMCs and attenuated atherosclerotic lesion burden while improving plaque composition in vivo. Next, we found ZEB2 suppressed fatty acid oxidation programs by transcriptionally repressing melanocortin 2 receptor (Mc2r), thereby limiting Mc2r–cAMP signaling and downstream lipid catabolic gene expression. We further developed TB15, a novel small molecule that selectively destabilizes ZEB2 protein by promoting deacetylation-dependent ubiquitination and proteasomal degradation without affecting ZEB2 transcription. To enable precision therapy, an RGD-functionalized mesoporous polydopamine nanoparticle delivery system was engineered to target phenotypically transformed VSMCs within atherosclerotic plaques, achieving superior therapeutic efficacy with minimal systemic toxicity. Notably, machine learning–based drug response modeling (BFReg-NN) predicted optimal anti-atherosclerotic efficacy when TB15 was combined with fluvastatin or pitavastatin, nominating a rational combination strategy. Together, this work defines the ZEB2-Mc2r axis in atherosclerosis, presents a first in class ZEB2 degrader, and demonstrates a targeted nanotherapeutic approach with strong translational potential for precision treatment of atherosclerotic disease.","dates":{"publication":"Tue Jun 16 00:00:00 BST 2026"},"accession":"PXD079803","cross_references":{"TAXONOMY":["10116"]}}