Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors.

Phylogenetic analysis of within -host serially-sampled viral data

The primary goal of this dissertation is the study of patterns of viral evolution inferred from serially-sampled sequence data, i.e., sequence data obtained from strains isolated at consecutive time points from a single patient or host. RNA viral populations have an extremely high genetic variability, largely due to their astronomical population sizes within host systems, high replication rate, and short generation time. It is this aspect of their evolution that demands special attention and a different approach when studying the evolutionary relationships of serially-sampled sequence data. New methods that analyze serially-sampled data were developed shortly after a groundbreaking HIV-1 study of several patients from which viruses were isolated at recurring intervals over a period of 10 or more years. These methods assume a tree-like evolutionary model, while many RNA viruses have the capacity to exchange genetic material with one another using a process called recombination. ^ A genealogy involving recombination is best described by a network structure. A more general approach was implemented in a new computational tool, Sliding MinPD, one that is mindful of the sampling times of the input sequences and that reconstructs the viral evolutionary relationships in the form of a network structure with implicit representations of recombination events. The underlying network organization reveals unique patterns of viral evolution and could help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies. In order to comprehensively test the developed methods and to carry out comparison studies with other methods, synthetic data sets are critical. Therefore, appropriate sequence generators were also developed to simulate the evolution of serially-sampled recombinant viruses, new and more through evaluation criteria for recombination detection methods were established, and three major comparison studies were performed. The newly developed tools were also applied to "real" HIV-1 sequence data and it was shown that the results represented within an evolutionary network structure can be interpreted in biologically meaningful ways. ^

Plant biogeography and conservation on a tropical island: Isla del Coco, Costa Rica

Isla del Coco (Cocos Island) is a small volcanic island located in the Pacific 500 km west of Costa Rica. Three collecting trips to Isla del Coco, in addition to herbarium research, were completed in order to assess the floristic diversity of the island. The current flora of Isla del Coco contains 262 plant species of which 37 (19.4%) are endemic. This study reports 58 species as new to the island. Seventy-one species (27.1%) were identified as introduced by humans. In addition, five potentially invasive plant species are identified. Seven vegetation types are identified on the island: bayshore, coastal cliff, riparian, low elevation humid forest, high elevation cloud forest, landslide and islet. ^ The biogeographic affinities of the native and endemic species are with Central America/northern South America and to a lesser extent, the Caribbean. Endemic species in the genus Epidendrum were investigated to determine whether an insular radiation event had produced two species found on Isla del Coco. Phylogenetic analysis of the internal transcribed spacer (ITS) of nuclear ribosomal DNA was not able to disprove that the endemic species in this genus are not sister species. Molecular biogeographic analyses of ITS sequence data determined that the Isla del Coco endemic species in the genera Epidendrum, Pilea and Psychotria are most closely related to Central American/northern South American taxa. No biogeographical links were found between the floras of Isla del Coco and the Galápagos Islands. ^ The native and endemic plant diversity of Isla del Coco is threatened with habitat degradation by introduced pigs and deer, and to a lesser extent, by exotic plant species. The IUCN Red List and RAREplants criteria were used to assess the extinction threat for the 37 endemic plant taxa found on the island. All of the endemic species are considered threatened with extinction at the Critically Endangered (CR) by the IUCN criteria or either CR or Endangered (EN) using RAREplants methodology. ^

The potential role of environmental exposures and genomic signaling in development of central nervous system tumors

The etiology of central nervous system tumors (CNSTs) is mainly unknown. Aside from extremely rare genetic conditions, such as neurofibromatosis and tuberous sclerosis, the only unequivocally identified risk factor is exposure to ionizing radiation, and this explains only a very small fraction of cases. Using meta-analysis, gene networking and bioinformatics methods, this dissertation explored the hypothesis that environmental exposures produce genetic and epigenetic alterations that may be involved in the etiology of CNSTs. A meta-analysis of epidemiological studies of pesticides and pediatric brain tumors revealed a significantly increased risk of brain tumors among children whose mothers had farm-related exposures during pregnancy. A dose response was recognized when this risk estimate was compared to those for risk of brain tumors from maternal exposure to non-agricultural pesticides during pregnancy, and risk of brain tumors among children exposed to agricultural activities. Through meta-analysis of several microarray studies which compared normal tissue to astrocytomas, we were able to identify a list of 554 genes which were differentially expressed in the majority of astrocytomas. Many of these genes have in fact been implicated in development of astrocytoma, including EGFR, HIF-1α, c-Myc, WNT5A, and IDH3A. Reverse engineering of these 554 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme (GBM) were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. Lastly, bioinformatics analysis of environmental databases and curated published results on GBM was able to identify numerous potential pathways and geneenvironment interactions that may play key roles in astrocytoma development. Findings from this research have strong potential to advance our understanding of the etiology and susceptibility to CNSTs. Validation of our ‘key genes’ and pathways could potentially lead to useful tools for early detection and novel therapeutic options for these tumors.

Isolation and characterization of the U1 and U2 snRNAs in the silk gland of the 5th instar larval stage silk moth Bombyx mori

In isolation and characterization studies, expression level U1 and U2 snRNA isoforms were obtained from the 5th instar larval stage silk gland (SG). The DNA content of the SG cells is approximately 200,000-fold higher compared to the usual (2N) somatic cells of B. mori due to endoreduplication. In this study, the existence of U1 and U2 snRNA isoforms in the SG of the organism is investigated. Bombyx mori U1 and U2-specific RT-PCR libraries from the silk gland were generated. Five U1 and eight U2 isoforms were isolated and characterized. Nucleotide differences, structural alterations, as well as protein and RNA interaction sites were analyzed in these variants. For the U1 snRNA variants, they were compared to the previously reported BmN isoforms. In all these U-snRNA variants, polymorphic sites do not predominate at the core of known functional sequences, which were interspecifically conserved. Variant sites and inter-species differences are located in moderately conserved regions. Free energy (ΔG) values for the entire U1 and U2 snRNA secondary structures and for the individual stem/loops domains of the isoforms were generated and compared to determine their structural stability. This will be the first time that U1 and U2 variants are shown specific for a development stage (larval) other than embryonic or adult. ^ Using phylogenetic analysis, evolutionary trees were generated for the U1 and U2 snRNAs using animal, plant, protista and fungal species. The resulting trees were boostrapped for robustness and rooted with the self-splicing RNA group II intron sequence from the cyanobacterium Calothrix. Using phylogenetic analyses, possible structural and functional evolutionary interdependence between the U1 and U2 snRNAs was investigated. ^