Data_Sheet_1_Deciphering Risperidone-Induced Lipogenesis by Network Pharmacology and Molecular Validation.PDF

BackgroundRisperidone is an atypical antipsychotic that can cause substantial weight gain. The pharmacological targets and molecular mechanisms related to risperidone-induced lipogenesis (RIL) remain to be elucidated. Therefore, network pharmacology and further experimental validation were undertaken to explore the action mechanisms of RIL.MethodsRILs were systematically analyzed by integrating multiple databases through integrated network pharmacology, transcriptomics, molecular docking, and molecular experiment analysis. The potential signaling pathways for RIL were identified and experimentally validated using gene ontology (GO) enrichment and Kyoto encyclopedia of genes and genomes (KEGG) analysis.ResultsRisperidone promotes adipocyte differentiation and lipid accumulation through Oil Red O staining and reverse transcription-polymerase chain reaction (RT-PCR). After network pharmacology and GO analysis, risperidone was found to influence cellular metabolism. In addition, risperidone influences adipocyte metabolism, differentiation, and lipid accumulation-related functions through transcriptome analysis. Intersecting analysis, molecular docking, and pathway validation analysis showed that risperidone influences the adipocytokine signaling pathway by targeting MAPK14 (mitogen-activated protein kinase 14), MAPK8 (mitogen-activated protein kinase 8), and RXRA (retinoic acid receptor RXR-alpha), thereby inhibiting long-chain fatty acid β-oxidation by decreasing STAT3 (signal transducer and activator of transcription 3) expression and phosphorylation.ConclusionRisperidone increases adipocyte lipid accumulation by plausibly inhibiting long-chain fatty acid β-oxidation through targeting MAPK14 and MAPK8.</p

Table_1_Deciphering Risperidone-Induced Lipogenesis by Network Pharmacology and Molecular Validation.docx

BackgroundRisperidone is an atypical antipsychotic that can cause substantial weight gain. The pharmacological targets and molecular mechanisms related to risperidone-induced lipogenesis (RIL) remain to be elucidated. Therefore, network pharmacology and further experimental validation were undertaken to explore the action mechanisms of RIL.MethodsRILs were systematically analyzed by integrating multiple databases through integrated network pharmacology, transcriptomics, molecular docking, and molecular experiment analysis. The potential signaling pathways for RIL were identified and experimentally validated using gene ontology (GO) enrichment and Kyoto encyclopedia of genes and genomes (KEGG) analysis.ResultsRisperidone promotes adipocyte differentiation and lipid accumulation through Oil Red O staining and reverse transcription-polymerase chain reaction (RT-PCR). After network pharmacology and GO analysis, risperidone was found to influence cellular metabolism. In addition, risperidone influences adipocyte metabolism, differentiation, and lipid accumulation-related functions through transcriptome analysis. Intersecting analysis, molecular docking, and pathway validation analysis showed that risperidone influences the adipocytokine signaling pathway by targeting MAPK14 (mitogen-activated protein kinase 14), MAPK8 (mitogen-activated protein kinase 8), and RXRA (retinoic acid receptor RXR-alpha), thereby inhibiting long-chain fatty acid β-oxidation by decreasing STAT3 (signal transducer and activator of transcription 3) expression and phosphorylation.ConclusionRisperidone increases adipocyte lipid accumulation by plausibly inhibiting long-chain fatty acid β-oxidation through targeting MAPK14 and MAPK8.</p

Deciphering the Interactome of Histone Marks in Living Cells via Genetic Code Expansion Combined with Proximity Labeling

Deciphering the endogenous interactors of histone post-translational modifications (hPTMs, also called histone marks) is essential to understand the mechanisms of epigenetic regulation. However, most of the analytical methods to determine hPTM interactomes are in vitro settings, lacking interrogating native chromatin. Although lysine crotonylation (Kcr) has recently been considered an important hPTM for the regulation of gene transcription, the interactors of Kcr still remain to be explored. Herein, we present a general approach relying upon a genetic code expansion system, APEX2 (engineered peroxidase)-mediated proximity labeling, and quantitative proteomics to profile interactomes of the selected hPTMs in living cells. We genetically fused APEX2 to the recombinant histone H3 with a crotonyl lysine inserted site specifically to generate APEX2–H3K9cr that incorporated into native chromatin. Upon activation, APEX2 triggered in vivo biotin labeling of H3K9cr interactors that can then be enriched with streptavidin beads and identified by mass spectrometry. Proteomic analysis further revealed the endogenous interactomes of H3K9cr and confirmed the reliability of the method. Moreover, DPF2 was identified as a candidate interactor, and the binding interaction of DPF2 to H3K9c was further characterized and verified. This study provides a novel strategy for the identification of hPTM interactomes in living cells, and we envision that this is key to elucidating epigenetic regulatory pathways

Deciphering the Interactome of Histone Marks in Living Cells via Genetic Code Expansion Combined with Proximity Labeling

Deciphering the endogenous interactors of histone post-translational modifications (hPTMs, also called histone marks) is essential to understand the mechanisms of epigenetic regulation. However, most of the analytical methods to determine hPTM interactomes are in vitro settings, lacking interrogating native chromatin. Although lysine crotonylation (Kcr) has recently been considered an important hPTM for the regulation of gene transcription, the interactors of Kcr still remain to be explored. Herein, we present a general approach relying upon a genetic code expansion system, APEX2 (engineered peroxidase)-mediated proximity labeling, and quantitative proteomics to profile interactomes of the selected hPTMs in living cells. We genetically fused APEX2 to the recombinant histone H3 with a crotonyl lysine inserted site specifically to generate APEX2–H3K9cr that incorporated into native chromatin. Upon activation, APEX2 triggered in vivo biotin labeling of H3K9cr interactors that can then be enriched with streptavidin beads and identified by mass spectrometry. Proteomic analysis further revealed the endogenous interactomes of H3K9cr and confirmed the reliability of the method. Moreover, DPF2 was identified as a candidate interactor, and the binding interaction of DPF2 to H3K9c was further characterized and verified. This study provides a novel strategy for the identification of hPTM interactomes in living cells, and we envision that this is key to elucidating epigenetic regulatory pathways

Deciphering the Interactome of Histone Marks in Living Cells via Genetic Code Expansion Combined with Proximity Labeling

Deciphering the endogenous interactors of histone post-translational modifications (hPTMs, also called histone marks) is essential to understand the mechanisms of epigenetic regulation. However, most of the analytical methods to determine hPTM interactomes are in vitro settings, lacking interrogating native chromatin. Although lysine crotonylation (Kcr) has recently been considered an important hPTM for the regulation of gene transcription, the interactors of Kcr still remain to be explored. Herein, we present a general approach relying upon a genetic code expansion system, APEX2 (engineered peroxidase)-mediated proximity labeling, and quantitative proteomics to profile interactomes of the selected hPTMs in living cells. We genetically fused APEX2 to the recombinant histone H3 with a crotonyl lysine inserted site specifically to generate APEX2–H3K9cr that incorporated into native chromatin. Upon activation, APEX2 triggered in vivo biotin labeling of H3K9cr interactors that can then be enriched with streptavidin beads and identified by mass spectrometry. Proteomic analysis further revealed the endogenous interactomes of H3K9cr and confirmed the reliability of the method. Moreover, DPF2 was identified as a candidate interactor, and the binding interaction of DPF2 to H3K9c was further characterized and verified. This study provides a novel strategy for the identification of hPTM interactomes in living cells, and we envision that this is key to elucidating epigenetic regulatory pathways