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. ^

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^