Forest tree breeding methods rank among the most efficient ways to increase productivity and quality of forests. With the advent of high-throughput genotyping technology, genome-enabled breeding has started to gain importance and may overcome some weaknesses of traditional tree breeding. Genomic Selection (GS), which involves using genome-wide markers to predict breeding values of individuals in a population, has been proposed for animal and plant breeding programs. GS enables very accurate selection decisions through estimation of genomic estimated breeding values (GEBVs). While the goal of GS is to predict phenotype from genotype, it does not identify the underlying genes that have important roles in a trait. Genome-Wide Association Studies (GWAS) approaches are therefore complementary to GS, enabling identification of these genes, which may be useful for marker-assisted selection in some traits. In this study, we first estimated heritability for several adaptive traits (cold hardiness, dbh, bud flush, height, and bud set) in a population of Populus trichocarpa and for height, diameter, and stem straightness in Pinus taeda. GEBVs accuracies were estimated using a ridge regression–best linear unbiased prediction (rrBLUP) model, and these accuracies were compared with estimated heritabilities. GWAS was also performed for the both imputed and non–imputed data of P. taeda population using TASSEL (Trait Analysis by aSSociation Evolution and Linkage) software, as well as rrBLUP and FFBSKAT (Fast Family-Based Sequence Kernel Association Test) packages in R. Heritabilities ranged from 0.34 to 0.56 for P. trichocarpa and 0.14 to 0.37 for P.taeda. GWAS identified 3244 associations for dbh, 4077 associations for stem straightness, and 5280 SNPs for height (p≤0.05) in TASSEL using the reduced model (marker data only), whereas 2729, 3272 and 3531 associations were found with the full model where we also included population structure as a covariate. FFBSKAT showed a similar number of SNP associations (2989, 3046 and 3058). There was an inflation of SNP associations (~20k) found in rrBLUP, which suggests population structure was not effectively controlled. The GEBVs accuracies ranged from 0.09 and 0.22 for P.trichocarpa and 0.09 to 0.23 for P.taeda using rrBLUP method. Testing the effect of repetation on the accuracy of GEBV for poplar showed that there was no significant difference between the number of cycles. Also, there was no significant difference the accuracy of GEBVs in pine between two different imputation methods, the marker mean value and Beagle software. / Master of Science

Pantoea stewartii subsp. stewartii is a bacterial phytopathogen that causes the disease Stewart's wilt in corn. The insect vector Chaetocnema pulicaria, the corn flea beetle, transmits P. stewartii into corn plants through wounds in the leaves. The bacteria can then move to the xylem of the plant where they form a biofilm that inhibits the flow of water. A previous in planta RNA-Seq study resulted in the selection of lrp as a gene of interest for further analyses. A reverse genetics approach was used for the creation of a strain containing the in-frame deletion of lrp, as well as a revertant strain. The strain with the deletion of the lrp gene showed reduced motility and capsule formation when in vitro assays were conducted. It has previously been demonstrated that these characteristics are both important for the bacteria's ability to form a biofilm in the xylem of corn plants and produce disease symptoms. The in planta virulence and competition assays demonstrated that the lrp gene deletion also results in reduced disease symptoms in infected corn plants, as well as an inability to outcompete wildtype P. stewartii in xylem colonization. In a bioinformatics approach, the transcriptional regulator Lrp of P. stewartii was present in the same node of the phylogeny as homologues from other closely related phytopathogens. This demonstrates that Lrp from P. stewartii and such homologues have evolved from a recent common ancestral gene. Examining the genomic islands present in P. stewartii, it is possible to begin to predict where some of the genes which have functions involved in plant colonization may have originated. Overall, the results collected from the studies in this thesis contribute to improving understanding of how P. stewartii is successful at colonizing the xylem of corn plants and cause disease. This research could result in the development of methods to decrease crop susceptibility to infection with P. stewartii. / Master of Science / Stewart's wilt is a disease of corn plants caused by the bacterium Pantoea stewartii subsp. stewartii via the insect vector Chaetocnema pulicaria, the corn flea beetle. This infection has proven to be costly as it impacts the health of corn crops and impedes the export of corn seeds from varieties that are susceptible to infection by P. stewartii. The focus of the research conducted for this thesis has been on learning more about how specific P. stewartii genes impact the ability of the bacterium to colonize corn plants and cause Stewart's wilt disease symptoms. The information collected from this study is important for developing a better understanding of how wilt disease-causing pathogens are able to successfully infect plants, as well as for developing future treatments to prevent further infection of corn plants. In addition, preliminary bioinformatics work has shown that some of the P. stewartii genes of interest share a common ancestor with select genes from other known plant pathogens. Additional preliminary bioinformatics work on regions of the DNA called genomic islands has revealed where some genes of importance to the bacterium's ability to colonize plants may have originated. Overall, the work presented in this thesis contributes to improving our understanding of the roles that different parts of the P. stewartii genome have in allowing the bacterium to successfully colonize and cause disease in corn plants.

No / Metastatic melanoma is the leading cause of skin‑cancer related deaths and while in recent years some progress has been made with targeted therapies, there remains an urgent unmet need for novel therapeutic treatments and reliable diagnostic, prognostic and predictive biomarkers. The emergence of next generation sequencing (NGS) has seen a growing appreciation for the role played by non‑coding genomic transcripts in regulating gene expression and by extension impacting on disease progression. The long non‑coding RNAs (lncRNAs) represent the most enigmatic of these new regulatory molecules. Our understanding of how lncRNAs regulate biological functions and their importance to disease aetiology, while still limited, is rapidly improving, in particular with regards to their role in cancer. Herein we review the identification of several lncRNAs shown to impact on melanoma disease progression and discuss how these molecules are operating at the molecular level.

With the advent of high throughput sequencing technologies over the past decade there has been a surge in the amount of genomic data that needs to be analyzed and interpreted. Despite the availability of software frameworks such as the Genome Analysis Toolkit, data interpretation and analysis still requires human intervention and refinement. Genome browsers enable developers and users of sequence analysis tools to visualize, compare, and better interpret genomic data such as gene expression and functional annotations. We developed a next generation cross platform web-based genome browser, NextBrowse, for visualizing General Feature Format and Binary Alignment Map files. NextBrowse uses advanced visualization techniques such as 3D feature selection and transparency based on mapping quality, and improved Graphical User Interface elements such as individual track searching and textual and graphical reference location. NextBrowse is the first genome browser to allow BAM files to be streamed and visualized, the first genome browser to employ security measures, and the first to use only client side rendering. NextBrowse takes advantage of the open-source community, allowing developers and users to extend the project to fit their needs. NextBrowse along with all documentation is available for use at http://www.nextbrowse.vbi.vt.edu. / Master of Science

Yes / Background. Increased availability of Next Generation Sequencing (NGS) techniques allows, for the first time, to distinguish relapses from reinfections in patients with multiple Buruli ulcer (BU) episodes. Methodology. We compared the number and location of single nucleotide polymorphisms (SNPs) identified by genomic screening between four pairs of Mycobacterium ulcerans isolates collected at the time of first diagnosis and at recurrence, derived from a collection of almost 5000 well characterized clinical samples from one BU treatment center in Benin. Principal Findings. The findings suggest that after surgical treatment—without antibiotics—the second episodes were due to relapse rather than reinfection. Since specific antibiotics were introduced for the treatment of BU, the one patient with a culture available from both disease episodes had M. ulcerans isolates with a genomic distance of 20 SNPs, suggesting the patient was most likely reinfected rather than having a relapse. Conclusions. To our knowledge, this study is the first to study recurrences in M. ulcerans using NGS, and to identify exogenous reinfection as causing a recurrence of BU. The occurrence of reinfection highlights the contribution of ongoing exposure to M. ulcerans to disease recurrence, and has implications for vaccine development. / This work was supported by the UBS Optimus Foundation (Zurich, Switzerland) and the Department of Economy, Science and Innovation of the Flemish Government (Belgium). KV was supported by a VLADOC PhD scholarship of VLIRUOS (Belgium).

Yes / Cancer-causing mutations often arise from inappropriate DNA repair, yet acute exposure to DNA damage is widely used to treat cancer. The challenge remains in how to specifically induce excessive DNA damage in cancer cells while minimizing the undesirable effects of genomic instability in noncancerous cells. One approach is the acute exposure to hyperthermia, which suppresses DNA repair and synergizes with radiotherapy and chemotherapy. An exception, however, is the protective effect of hyperthermia on topoisomerase targeting therapeutics. The molecular explanation for this conundrum remains unclear. Here, we show that hyperthermia suppresses the level of topoisomerase mediated single- and double-strand breaks induced by exposure to topoisomerase poisons. We further uncover that, hyperthermia suppresses hallmarks of genomic instability induced by topoisomerase targeting therapeutics by inhibiting nuclease activities, thereby channeling repair to error-free pathways driven by tyrosyl-DNA phosphodiesterases. These findings provide an explanation for the protective effect of hyperthermia from topoisomerase-induced DNA damage and may help to explain the inverse relationship between cancer incidence and temperature. They also pave the way for the use of controlled heat as a therapeutic adjunct to topoisomerase targeting therapeutics.

Bacterial evolution has been shaped by the acquisition of clusters of genes called genomic islands through means other than vertical inheritance. These gene clusters provide beneficial traits to the recipient bacteria such as virulence, resistance and the ability to utilize different metabolites, thereby facilitating bacterial adaptation to diverse environments and leading to the emergence of multi-drug resistant pathogens. As identification of genomic islands are of immense biomedical importance, we have developed a novel genomic island detection method, DICEP, to robustly identify genomic islands in bacterial genomes. Once genomic islands were identified, we focused on functional characterization of genes harbored by these islands as an essential step towards understanding their role in providing fitness to the recipient bacterium. We have used a gene co-expression network-based approach to gain insights into the functional association of genes within an island. The network analysis revealed novel pathogenicity associated genes and helped in functional characterization of island genes.

This dissertation investigates the utility of deep learning and machine learning approaches for livestock management and quantitative genetic modeling of rice grain size under climate change. Monitoring the live body weight of animals is crucial to support farm management decisions due to its direct relationship with animal growth, nutritional status, and health. However, conventional manual weighing methods are time consuming and can cause potential stress to animals. While there is a growing trend towards the use of three-dimensional cameras coupled with computer vision techniques to predict animal body weight, their validation with deep learning models as well as large-scale data collected in commercial environments is still limited. Therefore, the first two research chapters show how deep learning-based computer vision systems can enable accurate live body weight prediction for dairy cattle and pigs. These studies also address the challenges of managing large, complex phenotypic data and highlight the potential of deep learning models to automate data processing and improve prediction accuracy in an industry-scale commercial setting. The dissertation then shifts the focus to crop resilience, particularly in rice, where the asymmetric increase in average nighttime temperatures relative to the increase in average daytime temperatures due to climate change is reducing grain yield and quality in rice. Through the use of deep learning and machine learning models, the last two chapters explore how metabolic data can be used in quantitative genetic modeling in rice under environmental stress conditions such as high night temperatures. These studies showed that the integration of metabolites and genomics provided an improvement in the prediction of rice grain size-related traits, and certain metabolites were identified as potential candidates for improving multi-trait genomic prediction. Further research showed that metabolic accumulation was low to moderately heritable, and genomic prediction accuracies were consistent with expected genomic heritability estimates. Genomic correlations between control and high night temperature conditions indicated genotype-by-environment interactions in metabolic accumulation and the effectiveness of genomic prediction models for metabolic accumulation varied across metabolites. Joint analysis of multiple metabolites improved the accuracy of genomic prediction by exploiting correlations between metabolite accumulation. Overall, this dissertation highlights the potential of integrating digital technologies and multi-omic data to advance data analytics in agriculture, with applications in livestock management and quantitative genetic modeling of rice. / Doctor of Philosophy / This dissertation explores the application of deep learning and machine learning to computer vision-based livestock management and quantitative genetic modeling of rice grain size under climate change. The first half of the research chapters illustrate how computer vision systems can enable digital phenotyping of dairy cows and pigs, which is critical for informed management decisions and quantitative genetic analysis. These studies address the challenges of managing large-scale, complex phenotypic data and highlight the potential of deep learning models to automate data processing and improve prediction accuracy. Chapter 3 showed that a deep learning-based segmentation, Mask R-CNN, improved the prediction performance of cow body weight from longitudinal depth video data. Among the image features, volume followed by width correlated best with body weight. Chapter 4 showed that efficient deep learning-based supervised learning models are a promising approach for predicting pig body weight from industry-scale depth video data. Although the sparse design, which simulates budget and time constraints by using a subset of the data for training, resulted in some performance loss compared to the full design, the Vision Transformer models effectively mitigated this loss. The second half of the research chapters focuses on integrating metabolomic and genomic data to predict grain traits and metabolic content in rice under climate change. Through the use of machine learning models, these studies investigate how combining genomic and metabolic data can improve predictions, particularly under high night temperature stress in rice. Chapter 5 showed that the integration of metabolites and genomics improved the prediction of rice grain size-related traits, and certain metabolites were identified as potential candidates for improving multi-trait genomic prediction. Chapter 6 showed that metabolic accumulation was low to moderately heritable. Genomic correlations between control and high night temperature conditions indicated genotype-by-environment interactions in metabolic accumulation, and the effectiveness of genomic prediction models for metabolic accumulation varied across metabolites. Joint analysis of multiple metabolites improved the accuracy of genomic prediction by exploiting correlations between metabolite accumulation. Overall, the dissertation provides insight into how cutting-edge methods can be used to improve livestock management and multi-omic quantitative genetic modeling for breeding.

Globally disseminated bacterial pathogens frequently cause epidemics that are of major importance in public health. Of particular significance is the capacity for some of these bacteria to switch into a new environment leading to the emergence of pathogenic clones. Understanding the evolution and epidemiology of such pathogens is essential for designing rational ways for prevention, diagnosis and treatment of the diseases they cause. Whole-genome sequencing of multiple isolates facilitating comparative genomics and phylogenomic analyses provides high-resolution insights, which are revolutionizing our understanding of infectious diseases. In this thesis, a range of population genomic analyses are employed to study the molecular mechanisms and the evolutionary dynamics of bacterial pathogen niche adaptation, specifically between humans, animals and the environment. A large-scale population genomic approach was used to provide a global perspective of the host-switching events that have defined the evolution of Staphylococcus aureus in the context of its host-species. To investigate the genetic basis of host-adaptation, we performed genome-wide association analysis, revealing an array of accessory genes linked to S. aureus host-specificity. In addition, positive selection analysis identified biological pathways encoded in the core genome that are under diversifying selection in different host-species, suggesting a role in host-adaptation. These findings provide a high-resolution view of the evolutionary landscape of a model multi-host pathogen and its capacity to undergo changes in host ecology by genetic adaptation. To further explore S. aureus host-adaptive evolution, we examined the population dynamics of this pathogen after a simulated host-switch event. S. aureus strains of human origin were used to infect the mammary glands of sheep, and bacteria were passaged in multiple animals to simulate onward transmission events. Comparative genomics of passaged isolates allowed us to characterize the genetic changes acquired during the early stages of evolution in a novel host-species. Co-infection experiments using progenitor and passaged strains indicated that accumulated mutations contributed to enhanced fitness, indicating adaptation. Within-host population genomic analysis revealed the existence of population bottlenecks associated with transmission and establishment of infection in new hosts. Computational simulations of evolving genomes under regular bottlenecks supported that the fitness gain of beneficial mutations is high enough to overcome genetic drift and sweep through the population. Overall, these data provide new information relating to the critical early events associated with adaptation to novel host-species. Finally, population genomics was used to study the total diversity of Legionella longbeachae from patient and environmental sources and to investigate the epidemiology of a L. longbeachae outbreak in Scotland. We analysed the genomes of isolates from a cluster of legionellosis cases linked to commercial growing media in Scotland and of non-outbreak-associated strains from this and other countries. Extensive genetic diversity across the L. longbeachae species was identified, associated with intraspecies and interspecies gene flow, and a wide geographic distribution of closely related genotypes. Of note, a highly diverse pool of L. longbeachae genotypes within compost samples that precluded the genetic establishment of an infection source was observed. These data represent a view of the genomic diversity of this pathogen that will inform strategies for investigating future outbreaks. Overall, our findings demonstrate the application of population genomics to understand the molecular mechanisms and the evolutionary dynamics of bacterial adaptation to different ecological niches, and provide new insights relevant to other major bacterial pathogens with the capacity to spread between environments.

A microbiota intestinal é um ecossistema complexo que geralmente vive em harmonia com seu hospedeiro. É essencial para o desenvolvimento e funcionamento adequado do sistema imunológico da mucosa durante o início da vida, um processo que atualmente é conhecido por ser importante para a imunidade de adultos. A microbiota intestinal compreende aproximadamente 1000 espécies, das quais 80% não são cultiváveis. Tendo em vista a importância de se conhecer a microbiota humana e a utilização da ferramenta da construção da biblioteca genômica RNAr 16S ser relativamente recente, esse estudo tem como objetivo analisar diferentes protocolos para avaliar o uso desta ferramenta para o estudo da microbiota intestinal humana. Para a realização dos ensaios experimentais, o DNA extraído de fezes de seis crianças, em diferentes faixas etárias, foi utilizado para a criação de um pool, o qual foi utilizado nos ensaios de PCR. As bibliotecas RNAr 16S foram construídas utilizando 2 pares de iniciadores bactéria-específicos 27F-1492R e 63F-1387R, variando o tempo de desnaturação inicial de cada reação de amplificação do gene RNAr 16S entre 5 e 10 minutos, e 1 par de iniciadores 341F-518R. Os clones foram selecionados aleatoriamente, parcialmente sequenciados e analisados com base em banco de dados do gene RNAr 16S. A diversidade da microbiota foi menor quando os iniciadores 63F-1387R foram utilizados, em comparação aos resultados dos iniciadores 27F-1492R, no entanto, apenas o par de iniciadores 63F-1387R identificou Bifidobacterium sp., gênero importante para o desenvolvimento da microbiota intestinal humana. Não houve diferenças significativas na diversidade quando o tempo de desnaturação inicial da reação de PCR foi estendido para 10 minutos. Com o uso do par de iniciadores 341F-518R mostrou uma diversidade satisfatória, uma maior riqueza, quando comparada com os outros pares de iniciadores e detectou a presença de Bifidobacterium sp. Os dados obtidos sugerem que mais de um par de iniciadores deve ser empregado para o estudo da microbiota fecal quando se utilizar a biblioteca de RNAr 16S como ferramenta. / The intestinal microbiota is a complex ecosystem that usually lives in harmony with its host. It is essential for the development and proper functioning of the mucosal immune system during beginning of the life, a process that is currently known to be important for overall immunity of adults. The intestinal microbiota comprises about 1000 species, 80% of which are not cultivable. Given the importance of understanding the human microbiota and that the use of the tool library construction genomic 16S rRNA is relatively recent, this study aims to analyze different protocols to evaluate the use of this tool to study of the human intestinal microbiota. To perform the experimental tests, the DNA extracted from feces of six children, in different age groups, was used for the creation of a pool, which was used in the PCR assays. The 16S rRNA libraries were constructed using 2 pairs of primers specific-bacterium 27F-1492R and 63F-1387R, varying the denaturation time of each initial amplification reaction between 5 and 10 minutes, and the pair of primers 341F - 518R.The clones were randomly selected, partially sequenced and analyzed based on database 16S rRNA gene. The diversity of the microbiota was lower when the primers 63F - 1387R were used when compared with the results of the primers 27F - 1492R. However, only the pair 63F - 1387R was able to identified Bifidobacterium spp., important genus for the development of the microbiota from human gut. No significant differences in diversity were observed when the time of initial denaturation of the PCR reaction was extended to 10 minutes. By using the pair of primers 341F - 518R a satisfactory diversity, with detection of Bifidobacterium spp., and greater richness were observed, compared with the other pairs of primer. The data suggests that more than one pair of primers should be used for the study of fecal microbiota when using the library of 16S rRNA as a tool.