PKC-a plays a role in regulation of membrane associated signal transduction pathways mediated by Ca homeostasis

We also identified a group of small cells that were capable of vigorous proliferation in the same liver samples. Therefore, it is plausible that the abnormally large GFP-positive polygonal cells are derived from the fusion of endogenous hepatocytes exposed to retrorsine and bone marrow cells, whereas the very small polygonal cells are derived by transdifferentiation of bone marrow cells. Thus, we agree with the model proposed by Masson et al. that cell fusion and cell transdifferentiation depends upon the liver environment. A comprehensive study using sex-mismatched liver or bone marrow transplantation is necessary to clarify this issue. In this experiment, we have demonstrated that hepatocytes from GFP-Tg Lewis rats are not able to survive long-term in the syngeneic wild-type Lewis rat liver. Liver is not an immuneprivileged site for hepatocyte transplantation, and multiple factors determine the death or survival of transplanted hepatocytes. It is also notable that the progression of the cell loss phenomenon observed in the current study did not alter when more severe treatment such as 2/3 hepatectomy and 80% hepatectomy with retrorsine treatment was employed. This suggests that an immunological reaction against the transplanted GFP-positive hepatocytes is maintained in this strong liver regeneration model. In conclusion, this study demonstrated the need to consider the host immunological reaction in the hepatocyte transplantation model using GFP-Tg Lewis rats as donors. Transcriptional regulatory network is a directed graph describing regulatory effect of transcriptional factors on genes’ expression by binding to target DNA. Over last decades, several methods of studying regulatory relationship between TFs and genes under a given set of conditions have been proposed and widely used, like ChIP-chip, genome-wide RNA interference and DNase I footprinting assay. Most of these technologies based on the molecular biology or biochemistry are experimental techniques with limitation on mass samples. Therefore, computational biologists have resorted to a forward engineering strategy which is based on searching of transcriptional factor binding sites in the putative target sequences. To reduce the false positive rates of forward engineering method, Yu et al proposed a combinatorial inferring method that integrates forward engineering with reverse engineering of which relationships between TFs and targets are inferred based on expressional correlation. Compared with other networks, TRN has advantages in properties of reflecting regulatory relationship, dynamics and scale-free topological structure. TRN depicts the transcriptional regulation of TFs on target genes which is an important regulatory mechanism of gene expression. Neph S et al studied TRN of 41 diverse cell and tissue types using DNase I footprinting technology and found that human TF networks are highly cell selective. TRN is a scale-free network, in which the number of nodes that make a large number of connections with other nodes is much lower than the number of nodes with few connections, whereby hubs play a central role in directing the cellular response to a specific stimulus. All these features make TRN an irreplaceable tool in disease research. In 2012, Zeng et al found hepatocellular carcinoma metastasis related TF-regulated modules by comparing regulatory network between metastatic and non-metastatic liver cancer. With the development of high-throughput technology, especially the flourish of SNP microarray, combined analysis of genome and transcriptome is becoming increasingly popular, and has greatly promoted our understanding of complex diseases. Copy number variation, an important kind of genomic variation, has gained increasing attention in recent years mainly due to SNP microarray technology which has made studying whole genome fast and economical. The importance of CNVs to occurrence and development of disease has been confirmed in many studies. Until now, most studies of CNVs are focused on CNVs’ impact on expression of genes located in verified regions, like eQTL, a linear-regression based method. Others may combine CNV with network method, like co-expression network to analyze CNVs’ impact on not just genes inside CNV regions but also outside CNV regions that are co-expressed. But there is little work about interpreting influence of genomic variation on expression through its disturbance to TRN. Mutation in TFs can cause huge cascade effects as a TF targets a large amount of genes involving many biological processes. For example, TP53, a well-known tumor suppressor transcription factor, its mutation has been reported associated with cell migration and invasion. In 2012, David et al detailed three mutated transcriptional factors NKX2-5, GATA4, and TBX5 and their affected pathways in congenital heart disease. Essaghir et al introduced an integrated approach to construct minimal connected network to TFs in 305 different human cancer cell lines and found several universal cancer biomarkers. These researches suggest the importance and feasibility of integrating TRN with CNVs. Intrahepatic cholangiocarcinoma is the second most common primary hepatic cancer with the highest occurring rate in Thailand and other eastern Asian areas due to chronic inflammation of bile ducts. In 2013, Sia et al performed gene expression and copy number variation integrated analysis in ICC samples and classified these samples into two groups: proliferation and inflammation. Pathogenesis studies based on gene expression profiling have evolved through several stages: single gene expression profiling; network construction and functional annotation; causal hub discovery and intervention design. Single gene expression profiling is straightforward and simple, numerous gene list signatures have been reported to either diagnose samples or predict outcome or prognosis. However it is hard to explain the functional categories of single genes. Network analysis allows structured grouping of genes, and functional module discovery can often lead to next-step research focus, which is a big progress compared to single gene profiling.