Sequence analysis of complementary DNAs encoding C3 in the nurse shark ({\it Ginglymostoma cirratum\/})

Mammalian C3 is a pivotal complement protein, encoded for by a single gene. In some vertebrate species multiple C3 isoforms are products of different C3 genes. The goal of this study was to determine whether multiple genes encode for shark C3. A protocol was developed for the isolation of mRNA from shark blood for the isolation of C3 cDNA clones. RT-PCR amplification of mRNA, using sense (GCGEQNM) and antisense (TWLTAYV) primers encoding conserved regions of human C3, yielded 21 clones. The C3-like clones isolated shared 97% similarity with each other and 40% similarity to human C3. RACE-PCR amplification of shark liver RNA, using gene specific primers, yielded products ranging from 1800bp to 3000bp. Deduced amino acid sequence, corresponding to 408bp of the 1800bp fragment, was obtained which showed 51% similarity to human C3. These results suggest that nurse shark C3 might be encoded for by more than one gene. ^

Characterization of a Novel Mouse Phenotype with Marked Hydrocephalus

In multigenic diseases, disorders where mutations in multiple genes affect the expressivity of the disease, genetic interactions play a major role in prevalence and phenotypic severity. While studying the genetic interactions between Pax3 and EdnrB in the melanocyte lineage, a new phenotype was noted in 80% of Pax3 mutants that we believe to be a novel murine model for hydrocephalus. Hydrocephalus, an accumulation of cerebrospinal fluid in the cranial cavity due to obstruction of flow in and out of the cavity, is one of the most common birth defects surpassing Down syndrome. Characteristic to hydrocephalus is a "domed" head appearance, expansion of the ventricles of the brain, and loss of neurons with hyperproliferation of glial cell types all three of which were seen in the mutant mice. The phenotype also consisted of craniofacial deformities coupled with skeletal defects including, but not limited to kyphosis, lordosis, and an apparent shortening of the some limbs. For the cellular analysis of the hydrocephalus phenotype, brains were removed and stained with two antibodies: Glial Fibrillary Acidic Protein (GFAP) and Neurofilament (NF), which are astrocyte- and neuron- specific respectively. A higher number of cells expressing GF AP and a lower number of cells expressing NF were seen in the mutant brain, when compared to control. For skeletal deformity analysis, affected mice skeletons were stained with Alizarin Red and Alcian Blue showing no apparent difference in ossification. Future genetic analysis of these mutant mice has the potential to identify novel gene modifiers involved in the promotion of this particular phenotype.

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.