THE ROLE OF p/CIP ONCOGENE IN MOUSE EMBRYONIC STEM CELL PLURIPOTENCY

p/CIP is a transcriptional coactivator that binds liganded nuclear hormone receptors, as well as other transcription factors, and facilitates transcription through direct recruitment of accessory factors. p/CIP is highly expressed in undifferentiated mouse embryonic stem (mES) cells and is downregulated during differentiation. Using siRNA- mediated knockdown of p/CIP in mES cells in combination with microarray analysis I have identified a contingent of essential pluripotency genes which are significantly downregulated including Klf4, Tbx3 and Dax-1. Subsequent chromatin immuno­ précipitation (ChIP) analysis demonstrated that Tbx3 and Dax-1 are direct transcriptional targets of p/CIP. Using the piggyBac transposase system, a mouse ES cell line that expresses Flag-p/CIP in a doxycycline-dependent manner was generated. p/CIP overexpression increased pluripotency gene expression and promoted the formation of undifferentiated ES cell colonies. Collectively, these results indicate that p/CIP contributes to the maintenance of ES cell pluripotency through direct, as well as indirect, regulation o f essential pluripotency genes

DiGeorge Syndrome Phenotypes Reflect Disrupted Interaction Between Inductive Signals and 22q11 Genes

We asked whether similar phenotypes that result from diminished 22q11 gene dosage and altered Sonic Hedgehog (Shh), Fibroblast Growth Factor (Fgf), Retinoic Acid (RA) or Bone morphogenetic protein (Bmp) signaling reflect interactions between 22q11 genes and these cardinal morphogenetic signals. When Shh, RA, Fgf, or Bmp signaling is disrupted, expression levels, but not patterns, of several 22q11 genes change in mid-gestation mouse embryos, with most substantial changes associated with altered Shh signaling. When 22q11 gene expression is diminished in mouse embryos by a deletion similar to that in DiGeorge/22q11 Deletion Syndrome (22q11DS), expression of a subset of Shh-, RA-, and Bmp-, but not Fgf-related signaling molecules is altered, with several RA intermediates most substantially changed. Shh and RA signaling, quantified using reporter mice, is altered in the brain or heart of 22q11 deleted, but not Tbx1+/-embryos, even though diminished Tbx1 dosage has been suggested as essential for 22q11DS phenotypes. Brief pharmacological disruption of Shh signaling in mid-gestation 22q11-deleted or wild type, embryos leads to severe dysmorphology. Disrupted RA signaling introduces or enhances brain and heart phenotypes in 22q11-deleted but not wild type or Tbx1+/- embryos. Thus, early heart and brain morphogenesis depends on interactions between Shh and RA signaling and 22q11 gene dosage. Apparently, 22q11 gene dosage sustains normal morphogenesis by maintaining a dynamic range of signaling that, when altered, may intensify cardiovascular and CNS phenotypes in 22q11DS.Doctor of Philosoph

Condition-specific differential subnetwork analysis for biological systems

Indiana University-Purdue University Indianapolis (IUPUI)Biological systems behave differently under different conditions. Advances in sequencing technology over the last decade have led to the generation of enormous amounts of condition-specific data. However, these measurements often fail to identify low abundance genes/proteins that can be biologically crucial. In this work, a novel text-mining system was first developed to extract condition-specific proteins from the biomedical literature. The literature-derived data was then combined with proteomics data to construct condition-specific protein interaction networks. Further, an innovative condition-specific differential analysis approach was designed to identify key differences, in the form of subnetworks, between any two given biological systems. The framework developed here was implemented to understand the differences between limb regeneration-competent Ambystoma mexicanum and –deficient Xenopus laevis. This study provides an exhaustive systems level analysis to compare regeneration competent and deficient subnetworks to show how different molecular entities inter-connect with each other and are rewired during the formation of an accumulation blastema in regenerating axolotl limbs. This study also demonstrates the importance of literature-derived knowledge, specific to limb regeneration, to augment the systems biology analysis. Our findings show that although the proteins might be common between the two given biological conditions, they can have a high dissimilarity based on their biological and topological properties in the subnetwork. The knowledge gained from the distinguishing features of limb regeneration in amphibians can be used in future to chemically induce regeneration in mammalian systems. The approach developed in this dissertation is scalable and adaptable to understand differential subnetworks between any two biological systems. This methodology will not only facilitate the understanding of biological processes and molecular functions which govern a given system but also provide novel intuitions about the pathophysiology of diseases/conditions

A Novel Method for Integrative Biological Studies

DNA microarray technology has been extensively utilized in the biomedical field, becoming a standard in identifying gene expression signatures for disease diagnosis/prognosis and pharmaceutical practices. Although cancer research has benefited from this technology, challenges such as large-scale data size, few replicates and complex heterogeneous data types remain; thus the biomarkers identified by various studies have a small proportion of overlap because of molecular heterogeneity. However, it is desirable in cancer research to consider robust and consistent biomarkers for drug development as well as diagnosis/prognosis. Although cancer is a highly heterogeneous disease, some mechanism common to developing cancers is believed to exist; integrating datasets from multiple experiments increases the accuracy of predictions because increasing the sample size improves and enhances biomarkers detection. Therefore, integrative study is required for compiling multiple cancer data sets when searching for the common mechanism leading to cancers. Some critical challenges of integration analysis remain despite many successful methods introduced. Few is able to work on data sets with different dimensionalities. More seriously, when the replicate number is small, most existing algorithms cannot deliver robust predictions through an integrative study. In fact, as modern high-throughput technology matures to provide increasingly precise data, and with well-designed experiments, variance across replicates is believed to be small for us to consider a mean pattern model. This model assumes that all the genes (or metabolites, proteins or DNA copies) are random samples of a hidden (mean pattern) model. The study implements this model using a hierarchical modelling structure. As the primary component of the system, a multi-scale Gaussian (MSG) model, designed to identify robust differentially-expressed genes to be integrated, was developed for predicting differentially expressed genes from microarray expression data of small replicate numbers. To assure the validity of the mean pattern hypothesis, a bimodality detection method that was a revision of the Bimodality index was proposed