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Integration of proteomic and transcriptomic profiles identifies a novel PDGF-MYC network in human smooth muscle cells
- Integration of proteomic and transcriptomic profiles identifies a novel PDGF-MYC network in human smooth muscle cells
- Yang, W[Yang, Wei]; Ramachandran, A[Ramachandran, Aruna]; You, S[You, Sungyong]; Jeong, H[Jeong, HyoBin]; Morley, S[Morley, Samantha]; Mulone, MD[Mulone, Michelle D.]; Logvinenko, T[Logvinenko, Tanya]; Kim, J[Kim, Jayoung]; Hwang, D[Hwang, Daehee]; Freeman, MR[Freeman, Michael R.]; Adam, RM[Adam, Rosalyn M.]
- DGIST Authors
- Hwang, D[Hwang, Daehee]
- Issue Date
- Cell Communication and Signaling, 12
- Article Type
- Adaptor Proteins, Signal Transducing; Animal Experiment; Animal Model; Bladder; Bladder Injury; Bladder Muscle; Cell Culture; Cell Migration; Cell Motion; Cell Movement; Cell Proliferation; Cell Survival; Cells, Cultured; Cytology; DIAPH3; DIAPH3 Protein, Human; Diaphanous Related Formin 3; Drug Effects; Early Growth Response Factor 1; Female; Gene Expression; Gene Expression Profiling; Gene Targeting; Genetics; Human; Human Cell; Humans; In Vitro Study; In Vivo Study; Lamellipodium; Messenger RNA; Metabolism; Mouse; Myc Protein; Myocytes, Smooth Muscle; Network Analysis; Non-Human; PDGF; Physiology; Platelet-Derived Growth Factor; Protein C Jun; Protein Database; Protein Expression; Protein Interaction Maps; Protein Myb; Protein Protein Interaction; ProteomeXchange PXD000624; Proteomics; Proto-Oncogene Proteins C-JUN; Proto-Oncogene Proteins C-MYC; Regulator Protein; Signal Transducing Adaptor Protein; Smooth Muscle Fiber; Transcription Factor AP-1; Transcription Factor RUNX1; Transcriptomics; Unclassified Drug; Upregulation; Urinary Bladder; Visceral Smooth Muscle
- Background: Platelet-derived growth factor-BB (PDGF-BB) has been implicated in the proliferation, migration and synthetic activities of smooth muscle cells that characterize physiologic and pathologic tissue remodeling in hollow organs. However, neither the molecular basis of PDGFR-regulated signaling webs, nor the extent to which specific components within these networks could be exploited for therapeutic benefit has been fully elucidated. Results: Expression profiling and quantitative proteomics analysis of PDGF-treated primary human bladder smooth muscle cells identified 1,695 genes and 241 proteins as differentially expressed versus non-treated cells. Analysis of gene expression data revealed MYC, JUN, EGR1, MYB, RUNX1, as the transcription factors most significantly networked with up-regulated genes. Forty targets were significantly altered at both the mRNA and protein levels. Proliferation, migration and angiogenesis were the biological processes most significantly associated with this signature, and MYC was the most highly networked master regulator. Alterations in master regulators and gene targets were validated in PDGF-stimulated smooth muscle cells in vitro and in a model of bladder injury in vivo. Pharmacologic inhibition of MYC and JUN confirmed their role in SMC proliferation and migration. Network analysis identified the diaphanous-related formin 3 as a novel PDGF target regulated by MYC and JUN, which was necessary for PDGF-stimulated lamellipodium formation. Conclusions: These findings provide the first systems-level analysis of the PDGF-regulated transcriptome and proteome in normal smooth muscle cells. The analyses revealed an extensive cohort of PDGF-dependent biological processes and connected key transcriptional effectors to their regulation, significantly expanding current knowledge of PDGF-stimulated signaling cascades. These observations also implicate MYC as a novel target for pharmacological intervention in fibroproliferative expansion of smooth muscle, and potentially in cancers in which PDGFR-dependent signaling or MYC activation promote tumor progression. © 2014 Yang et al.; licensee BioMed Central Ltd.
- BioMed Central Ltd.
- Related Researcher
Hwang, Dae Hee
Systems Biology and Medicine Lab
Multilayered spatiotemporal networks; Regulatory motifs or pathways; Metabolite-protein networks; Network stochasticity; Proteomics and informatics
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