PPAR (peroxisome-proliferator-activated receptor) γ, a nuclear receptor, can be conjugated with SUMO (small ubiquitin-like modifier), which results in the negative regulation of its transcriptional activity. In the present study, we tested whether de-SUMOylation of PPARγ affects the expression of PPARγ target genes in mouse muscle cells and investigated the mechanism by which de-SUMOylation increases PPARγ transcriptional activity. We found that the SUMO-specific protease SENP2 [SUMO1/sentrin/SMT3 (suppressor of mif two 3 homologue 1)-specific peptidase 2] effectively de-SUMOylates PPARγ-SUMO conjugates. Overexpression of SENP2 in C2C12 cells increased the expression of some PPARγ target genes, such as FABP3 (fatty-acid-binding protein 3) and CD36 (fatty acid translocase), both in the absence and presence of rosiglitazone. In contrast, overexpression of SENP2 did not affect the expression of another PPARγ target gene ADRP (adipose differentiation-related protein). De-SUMOylation of PPARγ increased ChIP (chromatin immunoprecipitation) of both a recombinant PPRE (PPAR-response element) and endogenous PPREs of the target genes CD36 and FABP3, but ChIP of the PPRE in the ADRP promoter was not affected by SENP2 overexpression. In conclusion, these results indicate that SENP2 de-SUMOylates PPARγ in myotubes, and de-SUMOylation of PPARγ selectively increases the expression of some PPARγ target genes.
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Arch Pathol Lab Med
January 2025
the Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles (Petersen, Stuart, He, Ju, Ghezavati, Siddiqi, Wang).
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Objective.
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Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.
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State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
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School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
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Department of Electronic Engineering, Tsinghua University, 100084 Beijing, China.
Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships.
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