UNRAVELINGTHE MOLECULAR MECHANISM BEHIND METABOLIC REPROGRAMMING CAUSED BY ALTERATIONS OF THE ENZYME PI3-KINASE

Oncogenes and tumor suppressor genes play a key role in cancer induction and progression. They directly or indirectly regulate critical metabolic pathways, phosphatidylinositol-3 kinase pathway being frequently activated pathway in cancer. The catalytic subunit of phosphatidylinositol-4,5bisphosphate 3-kinase (PI3K), p110α, is the most frequently mutated kinase in human cancer, E542K, E545K, and H1047R mutations being the most common. Expression of hepatic E545K and H1047R p110α mutants in vivo shows marked and rapid increase in hepatic lipid and glycogen accumulation in mice with developmental (chronic) liver-specific deletion of p110α, which was not seen in mice when wildtype p110α is overexpressed. To investigate the logical pathways that could explain the lipid accumulation in mutant expressing mice, RNA sequencing from wildtype, knockout and mutated mouse livers was performed. Read alignment and count quantification was done using the Rsubread package and the statistical analyses are performed using the DeSeq2 package. Differentially expressed genes were identified with adjusted p-value of 0.05. Gene ontology analysis was performed on the differentially expressed genes using clusterProfiler, an R package to identify several key pathways which were upregulated and downregulated among the different sample groups. Signaling pathways related to cell cycle processes were mainly upregulated in the mutated samples when compared with the wildtype as well as knockout samples while signaling pathways related to many metabolic processes seem to be downregulated in mutated samples, even though these mutants showed upregulated metabolism by accumulation of lipids and glycogen physiologically. To confirm the results of gene expression data the results have to be cross validated with the gold standard quantitative Real Time Polymerase Chain Reaction. Popular scientific abstract “Cancer” is a broad term which we all are familiar with and its diagnosis can be scary and overwhelming. In simple words, cancer is described as a disease which results due to cellular changes that cause uncontrolled growth and division of cells which crowd the normal cells thus making it hard for the body to function the way it should. The genetic diversity of cancer and the ability of cancerous cells to adapt and evolve makes it extremely difficult to treat the disease. In order to fight the disease, one has to work towards identifying new ways to cure the disease and that can only be done by understanding the complexities behind cancer metabolism. Cells, basic building blocks of all living things, contain numbers of genes which they selectively turn on and off in order to become specialized. Any alteration or abnormal change in the genetic material of a cell can affect the cell in many ways. These mutational effects can be beneficial, harmful or neutral depending upon locations. Cancer being one of the leading causes of death is mainly due to interaction of many factors but all the causes leading to cancer exert their effects on a class of genes called “proto-oncogenes” which on mutation changes into its cancer causing “oncogenes”. Another class of genes associated with cancer are “tumor suppressor genes”, these genes are responsible for programmed cell death, that is, it tells the cells when to die. Mutation related to these genes leads to uncontrolled cell division thus leading to cancer. Both oncogenes and tumor suppressor genes are intricately linked to metabolic regulation and to alteration of metabolism in cancer cells relative to that of normal cells which helps gain and maintain malignant properties, making cancer metabolic reprogramming one of the cancer hallmarks. Understanding the basic cancer biology and mechanism behind altered metabolism can be incorporated into designing of newer therapeutic strategies and coming up with individual cancer treatment. This personalized approach to treating cancer patients is becoming widely popular with advances in newer technology. Next Generation sequencing with significant improvements over the years, can be customized in order to fit the requirements of a specific study, has gained popularity in field of oncology to help in diagnosis, classify the cancer, predict the risk associated as well as in providing personalized patient care. In this thesis, the most common pathway which is altered in cancer, phosphatidylinositol 3-kinase (PI3K) pathway, is studied in normal as well as mutated mouse liver cells using Next Generation sequencing. This pathway contains enzymes involved in cellular functions such as cell growth, proliferation, differentiation, motility, survival, lipidand glucose metabolism, which in turn are closely involved in survival, growth, invasion of cancer cells. The key molecules involved in this signaling pathway are receptor tyrosine kinase, phosphatidylinositol 3-kinase (PI3K), phosphatidylinositol-4,5-bisphosphate, phosphatidylinositol-3,4,5-bisphosphate and AKT/protein kinase B. PI3K has a catalytic domain p110 and a regulatory domain p85. Abnormalities in the PI3K pathway are common in cancer, with PI3K being a frequent target of mutational activation. The most frequent mutations are seen in the oncogene PIK3CA gene encoding catalytic subunit isoform p110α. The differential gene expression profiles from the different sample groups belonging to liver cells of mice was studied using RNA sequencing, a method of RNA profiling using next generation sequencing. This technique enables us to investigate all the RNAs present in all the samples at the same time in high resolution, producing millions of reads, which are then mapped to a reference genome, mouse genome in this case, to give measure of different levels of gene expression. The differentially expressed genes are linked to the biological process which in turn helps us understand the molecular mechanism behind the metabolic reprogramming caused by altered PI3-kinase enzyme. List of Abbreviations ABD Adaptor-binding domain Akt/PkB Protein Kinase B BAM file Binary Alignment Map file β2m beta-2 microglobulin cDNA Complimentary DNA CA Cholic acid CDCA Chenodeoxycholic acid DEGs Differentially Expressed Genes ERK1/2 Extracellular signal-regulated kinase1/2 FDR False Discovery Rate FM index Ferragina-Manzini (FM) index GTF Gene Transfer Format GO Gene Ontology GC Guanine-Cytosine HISAT 2 Hierarchical Indexing for Spliced Alignment of Transcripts Hmbs Hydroxymethylbilane synthase IGF-1 Insulin-like growth factor 1 KO Knockout mTOR Mammalian target of rapamycin MAPK Mitogen-activated protein kinase NGS Next-generation Sequencing PCA Principal Component Analysis PI3K Phosphatidylinositol 3-kinase PIP2 phosphatidylinositol (4,5)-bisphosphate PIP3 phosphatidylinositol (3,4,5)-trisphosphate PKC Protein Kinase C RBD Ras-binding domain RT-qPCR Real time quantitative polymerase chain reaction SRA Sequence Read Archive WT Wildtype Table of