<p>Repetitive elements, including tandem repeats and transposable elements (TE), are genetic features of all plant and animal genomes. Despite their abundance and the phylogenetic breadth of host genomes, factors that control the genome-wide distribution of repetitive elements are not well understood. Here we have evaluated the correlation between various genomic predictor variables such as gene expression level, distance from genes, and GC content, with the presence of TEs and non-TE repeats in two kilobase windows of the complete genome sequence of the Western Clawed Frog (<em>Silurana tropicalis</em>). We found that the distributions of different classes of TEs and repeats have distinct correlations with these predictor variables, including a generally strong negative correlation with proximity to exons and GC content. We also found that DNA transposons, but not retrotransposons, are preferentially inserted or preferentially retained near germline-expressed genes. Retrotransposons and simple repeats are found more often in or near conserved regions than expected by chance. These results offer insights into various models that have been proposed to account for heterogeneity in the genomic distribution of repetitive elements, most notably for the “gene disruption model” which posits that TE insertion and repeat presence near or in genes imposes costs to host fitness. In general, multiple lines of evidence suggests that the nature of natural selection on TE and other repetitive element evolution in this frog appears to be similar to that acting on TE and other repetitive elements in the human genome. This is possibly related to the similar size and level of complexity of the genomes of both of these species.</p> / Master of Science (MSc)

The cost required for the process of sequencing genomes has decreased drastically in the last few years. The knowledge of full genomes has increased the pace of the advancements in the field of functional genomics. Computational genomics, which analyses these sequences, has seen a similar growth. The multitude of sequencing technologies has resulted in various formats for storing the sequences. This has resulted in the creation of many tools for DNA analysis. There are various tools for sorting, indexing, analyzing read groups and other tasks. The analysis of genomics often requires the creation of pipelines, which processes the DNA sequences by chaining together many tools. This results in the creation of complex scripts that glue together these tools and pass the output from one stage to the other. Also, there are tools which allow creation of these pipelines with a graphical user interface. But these are complex to use and it is difficult to quickly add the new tools being developed to existing workflows. To solve these issues, we developed BioFlow; a web based genomic workflow management system. The use of BioFlow does not require any programming skills. The integrated workflow designer allows creation and saving workflows. The pipeline is created by connecting the tools with a visual connector. BioFlow provides an easy and simple interface that allows users to quickly add tools for use in any workflow. Audit logs are maintained at each stage, which helps users to easily identify errors and fix them. / Master of Science

<p dir="ltr">Spotted Turtle (Clemmys guttata) populations are declining dramatically across their range primarily due to habitat alteration, fragmentation, and reduction. Fragmented habitats have the potential to affect a population’s genetic diversity and size through the direct loss of individuals and the reduction of gene flow. Understanding genetic variation in Spotted Turtles can provide insight into population dynamics, the geographic distribution of genetic variants, and conservation needs. I examined the genetic variation in Spotted Turtle populations across the western portion of their geographic range including localities in Illinois, Indiana, Ohio, Michigan, and Ontario, Canada. Using blood samples collected during the 2022 and 2023 field seasons as well as previously collected tissues, I genotyped 611 individuals across 17 or more localities using 16 microsatellite loci. Five of 17 sites across the geographic extent of the sample suggested the presence of inbreeding (positive Fis values). Although the precision of estimates was low in most localities (10 of 17 with incalculable confidence intervals), the remaining localities in Ohio were estimated to have effective population sizes of < 20 individuals. Model-based and ordination-based clustering were conducted to assess population structure. Both types of clustering approaches identified four genetic clusters within the dataset. The two Illinois sites fell distinctly into their own cluster, whereas all other sites show a pattern of admixture. Despite these clustering results, incorporation of spatial information in principal component analysis (sPCA), shows that genetic composition gradually changes from west to east across the landscape, a pattern supported by isolation by distance using a Mantel test of the correlation between genetic and geographic distances. My results show that several Spotted Turtle populations have low levels of genetic variation and could benefit from augmentation. The observed pattern of isolation by distance 9 suggests that any translocations of turtles to support populations should be attempt to draw from viable populations that are in closer proximity.</p>

<p dir="ltr">In this dissertation, we investigated the genetic background of milk consumption, feeding behavior, disease resistance, and calf resilience in North American Holstein dairy calves using precision livestock farming (PLF) technologies and genetic modeling. Genomic and phenotypic information obtained from automatic milk feeding machines were obtained from 10,072 pre-weaned Holstein calves and used to derive and genetically evaluate novel traits such as daily milk consumption, calf resilience, and incidence of bovine respiratory disease (BRD). Heritability estimates for milk consumption and feeding behavior traits were found to be low but improved with specific statistical models, suggesting potential for genetic improvement if included in selection schemes. Random regression models captured greater amounts of genetic variability among calves for longitudinal milk feeding and behavior traits, with moderate negative (favorable) genetic correlations between milk consumption and BRD, indicating potential for genetic selection to enhance calf health outcomes and performance based on milk intake data. Various quantitative trait loci (QTL) for milk consumption, drinking duration traits, feeding behavior, and disease susceptibility were identified, linking key genes involved in metabolic processes, growth, and overall health. The same datasets were used to derive resilience indicators based on cumulative milk consumption. Genetic parameters for resilience traits, including amplitude, perturbation time, and recovery time, were estimated, highlighting substantial phenotypic and genetic variability. Significant genomic regions for six resilience traits were identified, with key genes such as <i>ABCB8</i>,<i> ABCF2</i>, and <i>AGAP3</i> linked to resilience traits, impacting mitochondrial function, cellular stress responses, and homeostasis. Pathway analyses revealed critical biological processes for stress response, including nucleotide binding and hormone activity. Genes such as <i>EPC1</i>, <i>ASB10</i>, and <i>ASIC3</i> were associated with recovery time, while <i>DPP6</i>, <i>GBX1</i>, and <i>GIMAP5</i> were linked to other resilience traits. These findings underscore the importance of genetic tools and breeding strategies in enhancing health, resilience, and productivity, offering potential new traits to genetically improve health and resilience in dairy cattle, and consequently, improve the sustainability of the dairy cattle industry.</p>

BACKGROUND:Sugarcane breeding has significantly progressed in the last 30 years, but achieving additional yield gains has been difficult because of the constraints imposed by the complex ploidy of this crop. Sugarcane cultivars are interspecific hybrids between Saccharum officinarum and Saccharum spontaneum. S. officinarum is an octoploid with 2n=80 chromosomes while S. spontaneum has 2n=40 to 128 chromosomes and ploidy varying from 5 to 16. The hybrid genome is composed of 70-80%S. officinaram and 5-20%S. spontaneum chromosomes and a small proportion of recombinants. Sequencing the genome of this complex crop may help identify useful genes, either per se or through comparative genomics using closely related grasses. The construction and sequencing of a bacterial artificial chromosome (BAC) library of an elite commercial variety of sugarcane could help assembly the sugarcane genome.RESULTS:A BAC library designated SS_SBa was constructed with DNA isolated from the commercial sugarcane variety SP80-3280. The library contains 36,864 clones with an average insert size of 125 Kb, 88% of which has inserts larger than 90 Kb. Based on the estimated genome size of 760-930 Mb, the library exhibits 5-6 times coverage the monoploid sugarcane genome. Bidirectional BAC end sequencing (BESs) from a random sample of 192 BAC clones sampled genes and repetitive elements of the sugarcane genome. Forty-five per cent of the total BES nucleotides represents repetitive elements, 83% of which belonging to LTR retrotransposons. Alignment of BESs corresponding to 42 BACs to the genome sequence of the 10 sorghum chromosomes revealed regions of microsynteny, with expansions and contractions of sorghum genome regions relative to the sugarcane BAC clones. In general, the sampled sorghum genome regions presented an average 29% expansion in relation to the sugarcane syntenic BACs.CONCLUSION:The SS_SBa BAC library represents a new resource for sugarcane genome sequencing. An analysis of insert size, genome coverage and orthologous alignment with the sorghum genome revealed that the library presents whole genome coverage. The comparison of syntenic regions of the sorghum genome to 42 SS_SBa BES pairs revealed that the sorghum genome is expanded in relation to the sugarcane genome.

The mammalian immune system is capable of detecting and responding to different infectious conditions with specificity at the adaptive level, however whether this ability extends to individual cells of the innate immune system is unclear. The hypothesis of this thesis is that macrophages, as individual cells, can distinguish between productive and non-productive virus infections and respond differently at the gene expression and secreted protein level. To test the hypothesis, mouse bone marrow derived macrophages (BMDMs) were infected in parallel with either a productive (live) and non-productive (attenuated) strain of murine cytomegalovirus (MCMV) and profiled temporally using a range of techniques. Both productive and non-productive MCMV infection resulted in strong type I IFN induction in BMDMs, however induction was significantly more rapid in response to productive infection. In addition, chemoattractant and pro-inflammatory cytokines TNFα, IL-6, RANTES, MIG and MIP-2 were secreted to significantly higher levels in response to productive MCMV infection, and curtailed in response to non-productive MCMV infection. Furthermore, genome-wide microarray profiling revealed a number of co-expressed gene networks regulated differentially in response to the two conditions. This consisted of macrophage gene networks targeted for modulation by de novo MCMV proteins, and late macrophage response genes regulated specifically in response to productive MCMV infection. To further explore the mechanisms of transcriptional regulation during macrophage antiviral response, BMDMs from mice lacking either the type I IFN receptor (Ifnar1) or the IFNβ (Ifnb1) gene were profiled using a similar approach. The resulting genome-wide transcriptional data provided a unique insight into the relationship between type I IFN regulation and the macrophage transcriptome in response to MCMV infection. Overall, the study utilizes a combination of genetic mutants from both host and pathogen to investigate mechanisms of virus detection and host transcriptional regulation during the innate immune response to MCMV infection in macrophages.

published_or_final_version / Microbiology / Doctoral / Doctor of Philosophy

Maize anthracnose, caused by the fungus Colletotrichum graminicola, is an economically important species contributing to major yield losses. C. graminicola is a hemibiotroph; initially it invades its host while it is alive, and then it switches to destructive necrotrophic growth and the host is killed. Establishment of compatible interactions by biotrophic pathogens is usually associated with suppression of host defenses and cell death, while necrotrophic pathogens typically secrete phytotoxic compounds and induce cell death. To understand the relationship of hemibiotrophy in C. graminicola to biotrophy and necrotrophy, I compared a compatible and an incompatible interaction, utilizing a non-pathogenic mutant strain that is very similar to the wild type in vitro. I developed an assay to visualize in detail living fungal and host cells during pathogenic and nonpathogenic interactions. My results provided evidence that C. graminicola produces diffusible substances during colonization that predispose nearby living host cells for fungal invasion. My observations further suggested that the mutant is nonpathogenic because it fails to produce these substances. To explore the possibility that the C. graminicola mutant is impaired in the production and/or secretion of one or more secondary metabolites (SM), I characterized the range of SM-associated genes in C. graminicola. C. graminicola has a large and diverse repetoire of these genes, indicating significant capacity for the production of SM. I then characterized the global expression of fungal genes during different developmental phases in both compatible and incompatible interactions. I found that SM-associated genes are expressed during early and late stages of maize infection. Secreted proteins and putative effectors were overrepresented among differentially regulated predicted gene products. There were relatively few differences in expression between the mutant and wild type, suggesting that differences between them may relate to post-transcriptional events. The transcriptional analysis indicated that the mutant was defective very early in biotrophy. This study indicates that biotrophy and necrotrophy coexist in this pathosystem in different cells, and that arrays of differentially regulated and locally expressed genes are involved in maintaining this balance. Understanding the nature of induced susceptibility may lead to new therapeutic targets for management of this damaging disease.

In the past decade, automatisation has led to an immense increase of data in biology. Next generation sequencing techniques will produce a vast amount of sequences across all species in the coming years. In many cases, identifying the function and biological role of a protein from its sequence can be a complicated and time-intensive task. The identification of a protein's interaction partners is a tremendous help for understanding the biological context in which it is involved. In order to fully characterise a protein-protein interaction (PPIs), it is necessary to know the three-dimensional structure of the interacting partners. Despite optimisation efforts from projects such as the Protein Structure Initivative, determining the structure of a protein through crystallography remains a time- and cost-intensive procedure. The primary aim of the research described in this dissertation was to produce a World Wide Web resource that facilitates visual exploration and validation (or questioning) of data derived from functional genomics experiments, by building upon existing structural information about direct physical PPIs. Secondary aims were (i) to demonstrate the utility of the new resource, and (ii) its application in biological research. We created a database that emphasises specifically the intersection between the PPIs-results emerging from the structural biology and functional genomics communities. The BISC database holds BInary SubComplexes and Modellable Interactions in current functional genomics databases (BICS-MI). It is publicly available at hyyp://bisc.cse.ucsc.edu. BISC is divided in three sections that deliver three types of information of interest to users seeking to investigate or browse PPIs. The template section (BISCHom and BISCHet) is devoted to those PPIs that are characterised in structural detail, i.e. binary SCs extracted from experimentally determined three-dimensional structures. BISCHom and BISCHet contain the homodimeric (13,583 records) and heterodimeric (5612 records) portions of these, respectively. Besides interactive, embedded Jmol displays emphasising the interface, standard information and links are provided, e.g. sequence information and SPOP classification for both partners, interface size and energy scores (PISA). An automated launch of the MolSurfer program enables the user to investigate electrostatic and hydrophobic correlation between the partners, at the inter-molecular interface. The modellable interactions section (BISC0MI) identifies potentially modellable interactions in three major functional genomics interaction databases (BioGRID), IntAct, HPRD). To create BISC-MI all PPIs that are amenable to automated homology modelling based on conservative similarity cut-offs and whose partner protein sequences have recrods in the UniProt database, have been extracted. The modellable interaction services (BISC-MI Services) section offers, upon user request, modelled SC-structures for any PPIs in BISC-MI. This is enabled through an untomated template-based (homology) modelling protocol using the popular MODELLER program. First, a multiple sequence alignment (MSA) is generated using MUSCLE, between the target and homologous proteins collected from UniProt (only reviewed proteins from organisms whose genome has been completely sequenced are included to find putative orthologs). Then a sequence-to-profile alignment is generated to integrate the template structure in the MSA. All models are produced upon user request to ensure that the most recent sequence data for the MSAs are used. Models generated through this protocol are expected to be more accurate generally than models offered by other automated resources that rely on pairwise alignments, e.g. ModBase. Two small studies were carried out to demonstrate the usability and utility of BISC in biological research. (1) Interaction data in functional genomics databases often suffers from insufficient experimental and reporting standards. For example, multiple protein complexes are typically recorded as an inferred set of binary interactions. Using the 20S core particle of the yeast proteasome as an example, we demonstrate how the BISC Web resource can be used as a starting point for further investigation of such inferred interactions. (2) Malaria, a mosquito-borne disease, affects 3500-500 million people worldwide. Still very little is known about the malarial parasites' genes and their protein functions. For Plasmodium falciparum, the most lethal among the malaria parasites, only one experimentally derived medium scale PPIs set is available. The validity of this set has been doubted in the the malarial biologist community. We modelled and investigated eleven binary interactions from this set using the BISC modelling pipeline. Alongside we compared the BISC models of the individual partners to those obtained from ModBase.

Microsatellites are short (1-6bp long) highly polymorphic tandem repeats, found in all genomes analyzed so far. Popular genetic markers for many applications including population genetics, pedigree analysis, genetic mapping and linkage analysis, some microsatellites also can cause a variety of human neurodegenerative diseases and may act as agents of adaptive evolution through the regulation of gene expression. As a consequence of these diverse uses and functions, the mutational and evolutionary dynamics of microsatellite sequences have gained much attention in recent years. Mostly, the focus of studies investigating microsatellite evolution has been to develop more refined evolutionary models for estimating parameters such as genetic distance or linkage disequilibrium. However, there is an incentive in using our understanding of the evolutionary processes that affect these sequences to examine the functional implications of microsatellite evolution. What has emerged from nearly two decades of study are highly complex mutational dynamics, with mutation rates varying across species, loci and alleles, and a multitude of potential influences on these rates, most of which are not yet fully understood. The increasing availability of whole genome sequences has immensely extended the scope for studying microsatellite evolution. For example, where once it was common to examine single loci, it is now possible to examine microsatellites using genome wide approaches. In the first part of my dissertation I discuss approaches and issues associated with detecting microsatellites in genomic data. In Chapter 2 I undertook a meta-analysis of studies investigating the distribution of microsatellites in yeast and showed that studies comparing the distribution of microsatellites in genomic data can be fraught due to the application of different definitions for microsatellites by different investigators. In particular, I found that variation in how investigators choose the repeat unit size of a microsatellite, handle imperfections in the array and especially the choice of minimum array length used, leads to a large divergence in results and can distort the conclusions drawn from such studies, particularly where inter-specific comparisons are being made. In a review of the currently available suite of bioinformatics tools (Chapter 3), I further showed that this bias extends beyond a solely theoretical controversy into a methodological issue because most software tools not only incorporate different definitions for the key parameters used to define microsatellites, but also employ different strategies to search and filter for microsatellites in genomic data. In this chapter I provide an overview of the available tools and a practical guide to help other researchers choose the appropriate tool for their research purpose. In the second part of my thesis, I use the analytical framework developed from the previous chapters to explore the biological significance of microsatellites exploiting the well annotated genome of the model organism Saccharomyces cerevisiae (baker’s yeast). Several studies in different organisms have indicated spatial associations between microsatellites and individual genomic features, such as transposable elements, recombinational hotspots, GC-content or local substitution rate. In Chapter 4, I summarized these studies and tested some of the underlying hypotheses on microsatellite distribution in the yeast genome using Generalized Linear Models (GLM) and wavelet transformation. I found that microsatellite type and distribution within the genome is strongly governed by local sequence composition and negative selection in coding regions, and that microsatellite frequency is inversely correlated with SNP density reflecting the stabilizing effect point mutations have on microsatellites. Microsatellites may also be markers for recent genome modifications, due to their depletion in regions nearby LTR transposons, and elements of potential structural importance, since I found associations with features such as meiotic double strand breaks, regulatory sites and nucleosomes. Microsatellites are subject to local genomic influences, particularly on small (1-2kb) scales. Although, these local scale influences might not be as dominant as other factors on a genome-wide scale they are certainly of importance with respect to individual loci. Analysis of locus conservation across 40 related yeast strains (Chapter 5) showed no bias in the type of microsatellites conserved, only a negative influence of coding sequences, which supports again the idea that microsatellites evolve neutrally. Polymorphism was rare, and despite a positive correlation with array length, there was no relationship with either genomic fraction or repeat size. However, the analysis also revealed a non-random distribution of microsatellites in genes of functionally distinct groups. For example, conserved microsatellites (similar to general microsatellites in yeast) are mostly found in genes associated with the regulation of biological and cellular processes. Polymorphic loci show further an association with the organization and biogenesis of cellular components, morphogenesis, development of anatomical structures and pheromone response, which, is absent for monomorphic loci. Whether this distribution is an indication of functionality or simply neutral mutation (e.g. genetic hitch-hiking) is debatable since most conserved microsatellites, particularly variable loci, are located within genes that show low selective constraints. Overall, microsatellites appear as neutrally evolving sequences, but owing to the sheer number of loci within a single genome, individual loci may well acquire some functionality. More work is definitely needed in this area, particularly experimental studies, such as reporter-gene expression assays, to confirm phenotypic effects.