IIIT Hyderabad Publications |
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Systems-level analysis of metabolic disordersAuthor: Shubham Kumar Date: 2019-05-06 Report no: IIIT/TH/2019/45 Advisor:Vinod P K AbstractSystems biology focuses on the integration of experimental, mathematical and computational techniques to develop systemic views and predictive models of biological systems. Transcriptomics has enabled to obtain genome-wide measurements in health and disease. The analysis of transcriptomic data in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays. Biological network reconstructions are important tools in systems biology in order to model the behavior as a whole biological system. Biological networks can also be used as a scaffold for integrative analysis where high throughput data from different conditions can be integrated into the biological network to obtain condition-specific molecular biomarkers and mechanisms. In this thesis, we aimed to gain a systems-level understanding of two obesity-linked metabolic disorders: Non-alcoholic fatty liver disease (NAFLD) and Type 2 Diabetes (T2D) using transcriptomics data obtained from healthy and disease groups. NAFLD is a complex spectrum of diseases ranging from simple steatosis to Non-Alcoholic Steato hepatitis (NASH) with fibrosis, which can progress to cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD is complex, involving crosstalk between multiple organs, cell-types, and environmental and genetic factors. Dysfunction of the adipose tissue plays a central role in NAFLD progression. Here, we analysed transcriptomics data obtained from the Visceral Adipose Tissue (VAT) of NAFLD patients to understand how the VAT metabolism is altered at the genome scale and co regulated with other cellular processes during the progression from obesity to NASH with fibrosis. For this purpose, we constructed weighted gene co-expression network and studied the organization of the disease transcriptome into functional modules of cellular processes and pathways. Our analysis revealed the coordination of metabolic and inflammatory modules (termed ”immunometabolism”) in the VAT of NAFLD patients. We found that genes of arachidonic acid, sphingolipid and glycosphingolipid metabolism were upregulated and co-expressed with genes of proinflammatory signalling pathways and hypoxia in NASH/NASH with fibrosis. We hypothesize that these metabolic alterations might play a role in sustaining VAT inflammation. Furthermore, immunometabolism related genes were also coexpressed with genes involved in Extracellular Matrix (ECM) degradation. Our analysis indicates that upregulation of both ECM degrading enzymes and their inhibitors (incoherent feedforward loop) poten- tially leads to the ECM deposition in the VAT of NASH with fibrosis patients.Further, we studied the pancreatic islets in health and T2D. Studies on how pancreatic islets respond under physiological and pathological conditions are obtained mostly based on the analysis of whole islet transcriptome. However, the measurement from the whole islets quantifies the average behaviour of dominant cell types, thereby making it difficult to understand the cell-type-specific changes. Recently, the advent of single-cell RNA sequencing (scRNA-seq) technique has generated valuable resource on islet biology and T2D. This provides an opportunity to understand the different cell types/states at both the network and individual gene expression levels. Here, we inferred the gene regulatory networks (GRNs) of pancreatic cells from scRNA-seq data in healthy and T2D. Clustering of cells based on GRNs identifies endocrine and exocrine cells and multiple stable cell states in each alpha, beta and ductal cells. The phenotypic variations in cell states due to obesity and T2D are indistinguishable. Therefore, the trajectory of cells in pseudotime was constructed based on the cell-type-specific gene expression. The analysis shows that continuous spectrum of cell states exists with phenotypic-dependent branching and donor cellcell variability in endocrine and exocrine cell types. We characterized the genes that give rise to bifurcation in the trajectory. Our study demonstrates that the network and trajectory inference approaches can be used to better understand the behaviour of pancreatic cells in health and disease. Overall, this thesis generates tissue- and cell-type specific networks that can be further used to understand the underlying pathology of metabolic disorders. Full thesis: pdf Centre for Computational Natural Sciences and Bioinformatics |
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