The isolation and characterization of tescalcin, a novel gene differentially expressed in the mouse testis during the early stages of gonadal differentiation

Gonadal development is an ideal model to study organogenesis because a variety of developmental processes can be studied during the differentiation of the bipotential primordium into testis or ovary. To better understand this process, Representational Difference Analysis of cDNA was used to identify genes that are differentially expressed in mouse gonads at 13.5 days post-coitus. The analysis led to the identification of three testis specific genes and a sequence that was only expressed in the ovary. The male genes identified: renin, Col9a3, and a novel gene termed tescalcin had patterns of expression that suggested a role in testis determination. ^ Studies of the tescalcin gene revealed that it is organized into eight exons and seven introns. The gene was located at 64 cM in mouse chromosome 5, where it spans approximately 35 Kb. Three mRNA variants resulting from alternative splicing of intron 5 were identified in mouse tissues. Gel mobility shift assays demonstrated that Sp1 and Sp3 from Y-1, msc-1, and MIN-6 cells nuclear extracts bind the GC-boxes within the tescalcin proximal promoter. Bisulfite sequencing analysis of tescalcin CpG island revealed that it is differentially methylated in male and female mouse embryonic gonads, and that hypermethylation of this region represses expression of tescalcin in the β-TC3 cell line. ^ The major tescalcin mRNA encodes a protein with 214 amino acids that contains a consensus EF-hand Ca2+-binding domain and an N-myristoylation motif. The amino acid sequence of tescalcin is highly conserved among various species, and it showed the highest homology with calcineurin B homologous proteins 1 and 2, and calcineurin B. Western blot analysis using antibodies generated against the tescalcin protein confirmed its presence in specific mouse tissues and cell lines. Immunohistochemical analysis of mouse embryos confirmed the pattern of expression of tescalcin mRNA in fetal testis. Using pull-down assays, glyceraidehydes-3-phosphate dehydrogenase was identified as an interacting and potential functional partner of tescalcin. ^ The identification and characterization of tescalcin as a novel embryonic testicular marker will contribute to the elucidation of the genetic pathways involved in testis development and likely to the understanding of pathological conditions such as sex reversal and infertility. ^

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.