The genetic requirements for Influenza virus to infect and adapt to new species is largely unknown. To understand the evolutionary steps required by a virus to become virulent, a human virus (A/HK/1/68) (HK), avirulent in mice, was subjected to 20 and 21 serial lung-to-lung passages in mouse. Sequence analysis revealed the emergence of eleven mutations within the NS1 gene of the new virulent strains, many of which occurred in binding sites for transcriptional and translational cellular factors. In the present study we have rescued viruses containing each of the NS1 mouse adapted mutations onto A/PR/8/34 (PR8) backbone. We found 9 of 16 NS1 mutants were adaptive by inducing mortality, body weight loss in BALB/c mice and enhanced virus replication in MDCK cells with properties of host cell interferon transcription inhibition. Sequence comparisons with the highly pathogenic A/Hong Kong/156/1997 (H5N1) and the most severe pandemic A/Brevig Mission/1/1918 (H1N1) NS1 genes showed convergent evolution with some of the mouse adapted viruses for F103L plus M106I and V226I plus R227K mutations respectively. The F103L and M106I mutations in the HK NS1 gene were shown to be adaptive by assessment with respect to replication, early viral protein synthesis, interferon-β antagonism and tropism in the mouse lung. We extended the study and proved increased virulence associated with F103L+M106I mutations in their respective H5N1 NS1 gene on the PR8 and HK backbones, as well as the PR8 NS1 gene and the H9N2 (A/Ck/Bj/1/95) gene in the PR8 and A/WSN/33 backbones respectively. However the V226I and R227K mutations in their respective HK and 1918 NS1 genes slightly enhanced virulence and viral growth at later stages of infection. This study demonstrates that NS1 is a virulence factor; involved in multiple viral processes including interferon antagonism and viral protein synthesis. Furthermore, NS1 mutations acquired during mouse adaptation are proven to be adaptive in human, mouse and avian NS1 genes.

One of the leading aims of systems biology is the complete delineation of the organization and architecture of signaling networks. Within this aim, characterizing integrated circuits is a particular challenge. Integrated circuits are the sites of information multiplexing, where input from multiple sources are combined into a single output or channel. A number of quantitative methods for analyzing epistasis within integrated pathways have been developed, with limited success. Here I present Expression Component Analysis, a novel approach for determining quantitative epistasis within an integrated signaling circuit, and describe the application of Expression Component Analysis in analyzing an interesting and important integrated signaling circuit in the model eukaryote, S.cerevisiae.

Finally, two main extensions of this approach were considered - linkage approaches to examine more than two loci, and extending the method in this study to include a test of association.

Recent scientific support for the involvement of genetic locus interaction in quantitative trait variation and the widespread use of quantitative trait locus (QTL) mapping has resulted in the need to examine those aspects concurrently. Computer software was written to simulate interacting quantitative trait loci (QTLs) in plant populations. Using this software, interacting QTLs were simulated to examine effects of epistasis on the detection of QTLs and the quality of QTL parameter estimates. Simulations involved doubled haploid populations exhibiting two non-epistatic traits and seven epistatic traits, each trait at four levels of heritability. Detection efficiency of QTL main and interaction effects decreased with decreasing heritability. At a given level of broad-sense heritability, traits differed with respect to the relative quality of main-effect detection and interaction-effect detection. Main-effect detection was notably poor for one epistatic locus that has a relatively small additive effect. Position estimates were accurate but their precision deteriorated with decreasing heritability. The quality of QTL effect estimates declined consistently with decreasing heritability, and loss in the accuracy was associated with losses in power of detection.

Thesis (Ph. D. in Neuroscience)--Vanderbilt University, Dec. 2006. / Title from title screen. Includes bibliographical references.

Thesis (Ph. D.)--UCLA, 2009. / Vita. Includes bibliographical references (leaves 65-70).

Abstract Mitochondria contain a maternally inherited 16,568bp genome (mtDNA) that encodes for 13 out of more than 70 subunits of complexes of the respiratory chain that produce ATP by oxidative phosphorylation (OXPHOS). As a byproduct of OXPHOS, reactive oxygen species (ROS) are formed, which may play a role in ageing. MtDNA has accumulated numerous polymorphisms during evolution, leading to haplogroups characterized by ancient polymorphisms and defined by letters. MtDNA polymorphisms are thought to be neutral, but some may be slightly deleterious or even advantageous and may influence phenotypes of complex traits. Interestingly, several complex traits such as longevity and maximal aerobic power show maternal inheritance. Associations between mtDNA polymorphisms and longevity have been reported, but no systematic study has been made of the role of mtDNA in longevity. In addition, there are no previous reports on mtDNA haplogroups in elite athletic performance. Associations are demonstrated here between mtDNA haplogroups J, K and U and longevity in Finns. Interestingly, subhaplogroup J2 and haplogroup K, which were found in increased frequency among the 225 very old subjects studied, were not found among the 52 endurance athletes but were present in 11% of the 89 sprint athletes Uncoupling of OXPHOS reduces ATP and ROS production. Thus, a mitochondrial genome with a higher level of uncoupling may promote longevity but may not be favourable in situations that require a high level of ATP production, such as elite endurance performance. A more detailed analysis also showed an association between a combination of three common mtDNA polymorphisms and longevity in both the Finns and the Japanese, providing the first epidemiological support for the assumption that the nature of a mutation is determined by interactions with other mutations in mtDNA. In addition, a systematic approach was applied to study the role of mtDNA in longevity. Association analyses of mtDNA allele combinations in longevity revealed that the mtDNA control region, the tRNA and rRNA genes and the nucleotide repeats in mtDNA may play a role in longevity, since the alleles and allele combinations that showed the strongest associations with longevity, either negative or positive, were among these genes. Differences in overall variation in mtDNA between the very old and their controls were also studied, revealing more differences at synonymous (silent) sites than at non-synonymous (amino acid altering) sites. The findings support previous data suggesting that certain mtDNA haplogroups are associated with longevity. In addition, those haplogroups that increased in frequency among the very old Finns were not found among Finnish endurance athletes. Also, a novel systematic approach was applied to study mtDNA alleles, allele combinations and overall sequence variation in longevity, suggesting that there are interactions between various mtDNA positions and that the tRNA and rRNA genes and short tandem repeats in mtDNA may play a role in longevity.

The genetic requirements for Influenza virus to infect and adapt to new species is largely unknown. To understand the evolutionary steps required by a virus to become virulent, a human virus (A/HK/1/68) (HK), avirulent in mice, was subjected to 20 and 21 serial lung-to-lung passages in mouse. Sequence analysis revealed the emergence of eleven mutations within the NS1 gene of the new virulent strains, many of which occurred in binding sites for transcriptional and translational cellular factors. In the present study we have rescued viruses containing each of the NS1 mouse adapted mutations onto A/PR/8/34 (PR8) backbone. We found 9 of 16 NS1 mutants were adaptive by inducing mortality, body weight loss in BALB/c mice and enhanced virus replication in MDCK cells with properties of host cell interferon transcription inhibition. Sequence comparisons with the highly pathogenic A/Hong Kong/156/1997 (H5N1) and the most severe pandemic A/Brevig Mission/1/1918 (H1N1) NS1 genes showed convergent evolution with some of the mouse adapted viruses for F103L plus M106I and V226I plus R227K mutations respectively. The F103L and M106I mutations in the HK NS1 gene were shown to be adaptive by assessment with respect to replication, early viral protein synthesis, interferon-β antagonism and tropism in the mouse lung. We extended the study and proved increased virulence associated with F103L+M106I mutations in their respective H5N1 NS1 gene on the PR8 and HK backbones, as well as the PR8 NS1 gene and the H9N2 (A/Ck/Bj/1/95) gene in the PR8 and A/WSN/33 backbones respectively. However the V226I and R227K mutations in their respective HK and 1918 NS1 genes slightly enhanced virulence and viral growth at later stages of infection. This study demonstrates that NS1 is a virulence factor; involved in multiple viral processes including interferon antagonism and viral protein synthesis. Furthermore, NS1 mutations acquired during mouse adaptation are proven to be adaptive in human, mouse and avian NS1 genes.

Inferring regulatory relationships between genes, including the direction and the nature of influence between them, is the foremost problem in the field of genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. More specifically, a “surprising” situation occurs when the phenotype of a double mutant has a similar, aggravating or alleviating effect compared to the phenotype resulting from the single deletion of either one of the genes. As useful as this broad approach has been, there are limits to its ability to discriminate alternative pathway structures, meaning it is not always possible to infer the relationship between the genes. Here, we explore the possibility of dynamic epistasis analysis. In addition to performing genetic perturbations, we drive a genetic pathway with a dynamic, time-varying upstream signal, where the phenotypic consequence is measured at each time step. We explore the theoretical power of dynamic epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We also explore the identifiability of individual links in the pathway. Through these evaluations, we quantify how helpful the addition of dynamics is. We believe that a dynamic input in addition to epistasis analysis is a powerful tool to discriminate between different networks. Our primary findings show that the use of a dynamic input signal alone, without genetic perturbations, appears to be very weak in comparison with the more traditional genetic approaches based on the deletion of genes. However, the combination of dynamical input with genetic perturbations is far more powerful than the classical epistasis analysis approach. In all cases, we find that even relatively simple input dynamics with gene deletions greatly increases the power of epistasis analysis to discriminate alternative network structures and to confidently identify individual links in a network. Our positive results show the potential value of dynamics in epistasis analysis.

No description available.