67. cardiac-diseases-based gene regulatory network construction and application
Name: Shulin Cao
Grad Year: 2020
Many cardiac diseases have been associated with heart fibrosis. More specifically, cardiac fibroblasts are activated, accumulate and augment deposition of extracellular matrix (ECM) proteins, increasing fibrotic ECM production and stiffness. This hampers diastolic function of the heart and induces pathological signaling within cardiomyocytes, which may ultimately result in failure of the heart. Increased mechanical stress is one of the triggers for the fibrotic response; however it is not clear which exact signaling pathways are involved. In the current project we wish to clarify which mechanotransduction pathways promote fibrosis. Low efficiency and high cost of studying single signaling pathways one at a time makes it a less operable way for studying large complex gene regulatory networks. Furthermore, the choice of pathways to study will often be based on previously identified pathways, rendering the discovery of novel signaling networks less likely. Considering the fact that the physical and chemical changes within cells are a result of gene regulation and upstream biochemical pathways, an alternate method is to investigate activities of genes by measuring their respective transcription levels in response to mechanical stress. With Next-Generation high-throughput RNA-Sequencing, it is possible to rapidly acquire information about the entire transcriptome of a cell. The data from RNA-Sequencing experiments are of enormous size and need to be processed before results can be interpreted, i.e. trimming, normalization and advanced statistical analyses. This procedure requires high performance of working station, which usually urges high processing capacity and special working environments. Computational models can assist with the interpretation of whole cell transcriptome output. Thus the main goal for this project is to construct an integrated gene regulation network for cardiac fibrosis and use this network to identify pathways that are activated in our RNA-sequencing data set. Previous works have provided us a basic frame while a lot more are still in shadow. We hope to complete the jigsaw using our bioinformatics tools to discover the large quantities of unknown signaling nodes. With a powerful and detailed network, it will enable identification of activated mechanosensitive signaling pathways involved in fibrosis. In addition, optimization for the network model will also be accomplished for more accurate predictions and further applications in related areas.
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