Ranaviruses are large dsDNA viruses that are considered emerging pathogens, and they are known to cause mortality events in amphibian and fish populations. This research utilizes experimental and genomic data to elucidate the mechanisms driving the evolution and spread of ranaviruses, with a focus on host switching within the genus. In Chapter 1, we utilize virus challenge assays to examine potential transfer of ranaviruses between cultured juvenile largemouth bass (M. salmoides) and bullfrog tadpoles (Rana catesbeiana). Additionally, a commonly used antiparasitic treatment containing malachite green and formalin (MGF) was utilized to suppress the immune system of largemouth bass to assess the susceptibility of immunocompromised fish to ranaviruses. The results indicate that tadpoles are not susceptible to Largemouth Bass Virus (LMBV), but that bass are susceptible to ranaviruses isolated from amphibians. Furthermore, immunocompromised fish were more susceptible to both LMBV and FV3 infections than immunocompetent fish. In Chapter 2, we used eight sequenced ranavirus genomes and two selection-detection methods (site-based and branch-based) to identify genes that exhibited signatures of positive selection, potentially due to the selective pressures at play during host switching. We found evidence of positive selection acting on four genes via the site-based method, three of which are newly-acquired genes unique to ranavirus genomes. Our results suggest that the group of newly acquired genes in the ranavirus genome may have undergone recent adaptive changes that have facilitated interspecies and interclass host switching. In Chapter 3, we annotated and analyzed the nearly complete genomic sequence of LMBV to determine its taxonomic classification. The available genomic content and phylogenetic evidence suggests that LMBV is more closely related to amphibian-like ranaviruses (ALRVs) than grouper ranaviruses, and this is further supported by greater genomic collinearity between LMBV and ALRVs. This data suggests that the classification of LMBV as a ranavirus is warranted. The results presented here will help to clarify the taxonomic relationships of ranaviruses, and will also be useful in developing management strategies to limit interspecific and intraspecific viral spread. The information garnered from this research will have far-reaching implications in studies of amphibian conservation, disease evolution, and virology. / text

Made available in DSpace on 2014-12-17T15:34:48Z (GMT). No. of bitstreams: 1 KarinK_DISSERT.pdf: 1760441 bytes, checksum: 1f006562e5e54fbb0d69c184d2961828 (MD5) Previous issue date: 2013-06-19 / The main specie of marine shrimp raised at Brazil and in the world is Litopenaeus vannamei, which had arrived in Brazil in the `80s. However, the entry of infectious myonecrosis virus (IMNV), causing the infectious myonecrosis disease in marine shrimps, brought economic losses to the national shrimp farming, with up to 70% of mortality in the shrimp production. In this way, the objective was to evaluate the survival of shrimps Litopenaeus vannamei infected with IMNV using the non parametric estimator of Kaplan-Meier and a model of frailty for grouped data. It were conducted three tests of viral challenges lasting 20 days each, at different periods of the year, keeping the parameters of pH, temperature, oxygen and ammonia monitored daily. It was evaluated 60 full-sib families of L. vannamei infected by IMNV in each viral challenge. The confirmation of the infection by IMNV was performed using the technique of PCR in real time through Sybr Green dye. Using the Kaplan-Meier estimator it was possible to detect significant differences (p <0.0001) between the survival curves of families and tanks and also in the joint analysis between viral challenges. It were estimated in each challenge, genetic parameters such as genetic value of family, it`s respective rate risk (frailty), and heritability in the logarithmic scale through the frailty model for grouped data. The heritability estimates were respectively 0.59; 0.36; and 0.59 in the viral challenges 1; 2; and 3, and it was also possible to identify families that have lower and higher rates of risk for the disease. These results can be used for selecting families more resistant to the IMNV infection and to include characteristic of disease resistance in L. vannamei into the genetic improvement programs / A principal esp?cie de camar?o marinho cultivado no Brasil e no mundo ? o Litopenaeus vannamei, que teve entrada no Brasil nos anos 80. Contudo, a chegada do v?rus da mionecrose infecciosa (IMNV), causador da doen?a da mionecrose infecciosa em camar?es marinhos, trouxe preju?zos econ?micos ? carcinicultura nacional, com mortalidades de at? 70% na produ??o de camar?es. Dessa maneira, objetivou-se avaliar a sobreviv?ncia de camar?es Litopenaeus vannamei infectados pelo v?rus da mionecrose infecciosa utilizando o estimador n?o param?trico de Kaplan-Meier e um modelo de fragilidade para dados grupados. Foram conduzidos tr?s testes de desafios virais com dura??o de 20 dias cada, em diferentes ?pocas do ano, mantendo-se os par?metros de pH, temperatura, oxig?nio e am?nia controlados diariamente. Foram avaliadas 60 fam?lias de irm?os completos de camar?es marinhos L. vannamei infectadas pelo IMNV em cada desafio viral. Foi feita a confirma??o da infec??o pelo IMNV atrav?s da t?cnica de PCR em tempo real utilizando o corante Sybr Green. Atrav?s do Estimador de Kaplan-Meier foi poss?vel detectar diferen?as significativas (p<0,0001) entre as curvas de sobreviv?ncia das fam?lias, entre as curvas de sobreviv?ncia entre os tanques por desafio viral e tamb?m na an?lise conjunta dos desafios virais. Foram estimados, em cada desafio, par?metros gen?ticos como o valor gen?tico de fam?lia e sua respectiva taxa de risco (fragilidade), e a herdabilidade na escala logar?tmica por meio do modelo de fragilidade para dados grupados. A herdabilidade estimada foi respectivamente 0,59; 0,36; e 0,59 nos desafios virais 1; 2; e 3; e tamb?m foi poss?vel identificar as fam?lias que possuem as menores e maiores taxas de risco para a doen?a. Esses resultados podem ser utilizados para sele??o de fam?lias mais resistentes ? infec??o pelo IMNV e a inclus?o da caracter?stica de resist?ncia ? doen?a em programas de melhoramento gen?tico de L. vannamei

Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RM-ANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models.