Anti-Human CEBPB Polyclonal Antibody

Background: Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results: We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions: The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

Background: Pulmonary arterial hypertension (PAH) is a lethal disease with pronounced narrowing of pulmonary vessels due to abnormal cell proliferation. The platelet-derived growth factor BB (PDGF-BB) is well known as a potent mitogen for smooth muscle cell proliferation. To better understand how this growth factor regulates pulmonary arterial smooth muscle cells (PASMCs) proliferation, we sought to characterize the response to PDGF-BB stimulation at system-wide levels, including the transcriptome and proteome. Results: In this study, we identified 1611 mRNAs (transcriptome), 207 proteins (proteome) differentially expressed in response to PDGF-BB stimulation in PASMCs based on RNA-sequencing and isobaric tags for relative and absolute quantification (iTRAQ) assay. Transcription factor (TF)-target network analysis revealed that PDGF-BB regulated gene expression potentially via TFs including HIF1A, JUN, EST1, ETS1, SMAD1, FOS, SP1, STAT1, LEF1 and CEBPB. Among them, SMAD1-involved BMPR2/SMADs axis plays a significant role in PAH development. Interestingly, we observed that the expression of BMPR2 was decreased in both mRNA and protein level in response to PDGF-BB. Further study revealed that BMPR2 is the direct target of miR-376b that is up-regulated upon PDGF-BB treatment. Finally, EdU incorporation assay showed that miR-376b promoted proliferation of PASMCs. Conclusion: This integrated analysis of PDGF-BB-regulated transcriptome and proteome was performed for the first time in normal PASMCs, which revealed a crosstalk between PDGF signaling and BMPR2/SMADs axis. Further study demonstrated that PDGF-BB-induced miR-376b upregulation mediated the downregulation of BMPR2, which led to expression change of its downstream targets and promoted proliferation of PASMCs.