Identification and functional analysis of novel genes expressed in the Anterior Visceral Endoderm

During early vertebrate development, the correct establishment of the body axes is critical. The anterior pole of the mouse embryo is established when Distal Visceral Endoderm (DVE) cells migrate to form the Anterior Visceral Endoderm (AVE). Symmetrical expression of Lefty1, Cer1 and Dkk1 determines the direction of DVE migration and the future anterior side. In addition to the establishment of the Anterior-Posterior axis, the AVE has also been implicated in anterior neural specification. To better understand the role of the AVE in these processes, we have performed a differential screening using Affymetrix GeneChip technology with AVE cells isolated from cer1P-EGFP transgenic mouse embryos. We found 175 genes which were upregulated in the AVE and 36 genes in the Proximal-posterior sample. Using DAVID software, we characterized the AVE cell population regarding cellular component, molecular function and biological processes. Among the genes that were found to be upregulated in the AVE, several novel genes were identified. Four of these transcripts displaying high-fold change in the AVE were further characterized by in situ hybridization in early stages of development in order to validate the screening. From those four selected genes, one, denominated Adtk1, was chosen to be functionally characterized by targeted inactivation in ES cells. Adtk1 encodes for a serine/threonine kinase. Adtk1 null mutants are smaller and present short limbs due to decreased mineralization, suggesting a potential role in chondrogenesis during limb development. Taken together, these data point to the importance of reporting novel genes present in the AVE.

A study of computational methods to analyze gene expression data

The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.

Identification and characterisation of telomere regulatory and signalling pathways after induction of telomere dysfunction

Telomeres are DNA-protein complexes which cap the ends of eukaryotic linear chromosomes. In normal somatic cells telomeres shorten and become dysfunctional during ageing due to the DNA end replication problem. This leads to activation of signalling pathways that lead to cellular senescence and apoptosis. However, cancer cells typically bypass this barrier to immortalisation in order to proliferate indefinitely. Therefore enhancing our understanding of telomere dysfunction and pathways involved in regulation of the process is essential. However, the pathways involved are highly complex and involve interaction between a wide range of biological processes. Therefore understanding how telomerase dysfunction is regulated is a challenging task and requires a systems biology approach. In this study I have developed a novel methodology for visualisation and analysis of gene lists focusing on the network level rather than individual or small lists of genes. Application of this methodology to an expression data set and a gene methylation data set allowed me to enhance my understanding of the biology underlying a senescence inducing drug and the process of immortalisation respectively. I then used the methodology to compare the effect of genetic background on induction of telomere uncapping. Telomere uncapping was induced in HCT116 WT, p21-/- and p53-/- cells using a viral vector expressing a mutant variant of hTR, the telomerase RNA template. p21-/- cells showed enhanced sensitivity to telomere uncapping. Analysis of a candidate pathway, Mismatch Repair, revealed a role for the process in response to telomere uncapping and that induction of the pathway was p21 dependent. The methodology was then applied to analysis of the telomerase inhibitor GRN163L and synergistic effects of hypoglycaemia with this drug. HCT116 cells were resistant to GRN163L treatment. However, under hypoglycaemic conditions the dose required for ablation of telomerase activity was reduced significantly and telomere shortening was enhanced. Overall this new methodology has allowed our group and collaborators to identify new biology and improve our understanding of processes regulating telomere dysfunction.