<p>  </p> <p>Shikonin is 1,4-naphthoquinones produced exclusively in Boraginaceae species. The compound and its derivatives are predominantly made in roots where they function in mediating plant-plant (allelopathic) and plant-microbe interactions. Moreover, this compound has been a target for drug development due to its strong anti-cancer properties. Our genome assembly and analysis of <em>Lithospermum erythrorhizon</em> uncovered metabolic innovation events that contributed to the evolution of the shikonin biosynthesis. This metabolic innovation also reveals the evolutionary link between shikonin biosynthesis and ubiquinone biosynthesis, one of the central metabolism functions in aerobic cellular respiration. To explore additional links between these two pathways, we used a transcriptome-based network analysis which uncovered a shikonin gene network model that predicts strong associations between primary metabolic pathway genes and known shikonin biosynthesis genes, as well as links with uncharacterized genes. <em>L. erythrorhizon</em> geranyldiphosphate (GPP) synthase (<em>LeGPPS</em>) is one of the candidates predicted by the network analysis, of which encodes a cytoplasmic enzyme shown in vitro to produce GPP. Knocking down of <em>LeGPPS</em> in <em>L. erythrorhizon </em>hairy roots (<em>LeGPPSi </em>lines) results in reduced shikonin content. This result provides functional evidence that cytoplasmic LeGPPS supplies GPP precursor to the shikonin biosynthesis. <em>LeGPPSi </em>lines also increased ubiquinone content, further supporting our hypothesis on the metabolic and evolutionary connection between shikonin and ubiquinone biosynthesis. Further RNA-seq analysis of the <em>LeGPPSi</em> line showed that downregulating <em>LeGPPS</em> significantly reduces the expression of benzenoid/phenylpropanoid genes, indicating the presence of factors that coordinately regulate the pathways providing the 4-hydroxybenzoic acid and GPP precursors to the shikonin pathway. In addition to <em>LeGPPS</em>, we also found<em> ubiquinone biosynthesis protein COQ4-like </em>gene (<em>LeCOQ4-L</em>) which provided another evolutionary link between shikonin and ubiquinone biosynthesis. The enzymatic activity of canonical COQ4 is unknown. In yeast, the protein is essential for ubiquinone biosynthesis and its metabolon formation. With the existing connections between shikonin and ubiquinone biosynthesis, if LeCOQ4 functions in the same manner as yeast COQ4, it is possible that <em>LeCOQ4-L </em>has an analogous function in shikonin biosynthesis as a structural protein for stabilizing biosynthesis metabolon. This leads us to the characterization of<em> COQ4</em> ortholog in Arabidopsis (<em>AtCOQ4</em>) to gain insight into its functional mechanism. Characterization of <em>atcoq4 </em>T-DNA mutant line showed that reduced <em>AtCOQ4</em> expression resulted in reduced ubiquinone. Further subcellular localization study revealed that AtCOQ4 and <em>LeCOQ4-L</em> localize in mitochondria without conventional transit peptide. We also performed pull-down assay to identify AtCOQ4 interactors which might be the missing enzymes that cannot be identified based on homology. 80 potential AtCOQ4 interactors were found including proteins like AtCHLM, GRIM-19, and AtSSLs. However, further study is needed to verify the protein interactions captured by pull-down assay. Taken all together, our study sheds light on the metabolic innovations that give rise to shikonin biosynthesis from ubiquinone biosynthesis and provide insight into the dynamics of the metabolic networks.</p>

<p>The prediction of chromatin accessibility for cancer and neuron cell lines using genomic sequences is quite challenging. Advances in machine learning and deep learning techniques allow such challenges to be addressed. This thesis investigates the use of both the transfer learning and the multi-task learning techniques. In particular, this research demonstrates the potential of transfer learning and multi-task learning in improving the prediction accu?racy for twenty-three cancer types in human and neuron cell lines. Three different network architectures are used: the Basset network, the network, and the DeepSEA network. In addition, two transfer learning techniques are also used. In the first technique data relevant to the desired prediction task is not used during the pre-training stage while the second technique includes limited data about the desired prediction task in the pre-training phase. The preferred performance evaluation metric used to evaluate the performance of the models was the AUPRC due to the numerous negative samples. Our results demonstrate an average improvement of 4% of the DeepSEA network in predicting all twenty-three cancer cell line types when using the first technique, a decrease of 0.42% when using the second technique, and an increase of 0.40% when using multi-task learning. Also, it had an average improvement of 3.09% when using the first technique, 1.16% when using the second technique and 4.60% for the multi-task learning when predicting chromatin accessibility for the 14 neuron cell line types. The DanQ network had an average improvement of 1.18% using the first transfer learning technique, the second transfer learning technique showed an average decrease of 1.93% and also, a decrease of 0.90% for the multi-task learning technique when predicting for the different cancer cell line types. When predicting for the different neuron cell line types the DanQ had an average improvement of 1.56% using the first technique, 3.21% when using the second technique, and 5.35% for the multi-task learning techniques. The Basset network showed an average improvement of 2.93% using the first transfer learning technique and an average decrease of 0.02%, and 0.63% when using the second technique and multi-task learning technique respectively. Using the Basset network for prediction of chromatin accessibility in the different neuron types showed an average increase of 2.47%, 9 3.80% and 5.50% for the first transfer learning technique, second transfer learning technique and the multi-task learning technique respectively. The results show that the best technique for the cancer cell lines prediction is the first transfer learning model as it showed an improvement for all three network types, while the best technique for predicting chromatin accessibility in the neuron cell lines is the multi-task learning technique which showed the highest average improvement among all networks. The DeepSEA network showed the greatest improvement in performance among all techniques when predicting the different cancer cell line types. Also, it showed the greatest improvement when using the first transfer learning technique for predicting chromatin accessibility for neuron cell lines in the brain. The basset network showed the greatest improvement for the multi-task learning technique and the second transfer learning technique when predicting the accessibility for neuron cell lines. </p>

<p>Heterosis, or hybrid vigor, is a phenomenon observed in both plant and animal systems where hybrid offspring perform better when compared to their parents. For hybrid plants, this can result in increased biomass, crop yields, and vigor when compared to the inbred parents. Even though heterosis has been used in agriculture for over a century, the molecular mechanisms that result in hybrid vigor remain elusive even after years of investigation. A molecular understanding of heterosis is desirable because it will speed up the process of breeding compatible inbred lines for developing hybrid seeds, and it will provide us with the knowledge to potentially engineer inbred lines that can mimic the beneficial phenotypic effects of heterosis, eliminating the need for farmers to buy new hybrid seeds every year. The goal of this research project is to identify genes that are required for heterotic phenotypes in maize. Our working hypothesis is that a mutation in genes that are essential for heterosis will cause an altered heterotic phenotype in hybrid maize plants. To test this hypothesis, we applied combined approaches of EMS mutagenesis, trait phenotyping in field and controlled conditions, bulk segregant analysis, whole genome sequencing, and bioinformatics analysis. First, we applied a forward genetics approach to identify mutant hybrids with altered heterosis and detected potential causal genes <em>via</em> whole genome sequencing. We identified one mutation occurring in a protein coding gene (gene ID <em>Zm00001eb305590</em>) located in a region of interest on chromosome 7, whose genotypes across various samples assayed fit the observed segregation pattern of hybrid traits. This mutation leads to a moderate or high-level codon change, indicating that this gene may play a role in mediating heterosis in maize. By investigating this gene with further studies, the learned knowledge could speed up the process of hybrid maize breeding by selecting compatible inbred lines through sequencing or by engineering hybrids that have favorable alleles for this gene.</p>

<p>  </p> <p>Microbiome studies are varied and involve the examination of microorganisms at different levels: individual cells to determine individual functions, populations of specific microorganisms to determine interactions between organisms, and/or communities of microorganisms for a broader investigation of interactions between organism and environment. These studies are typically done within the context of a particular niche or environment. There are two parts to this dissertation, separated by the types of research involved. First, the analysis of bacterial communities using 16S rRNA sequencing and analysis. In this first part the bacterial communities of the reproductive tract of bulls and the gastrointestinal tract of weanling pigs were studied. The reproductive organs of the male, domestic species had not been studied from an ecological perspective prior to the study. As such, the research was mainly focused on characterizing the bacterial communities found within the prepuce of bulls that were considered to be healthy, or that the breeding soundness exam was satisfactory and the bulls had no clinical disease in the urogenital tract. Through this study two distinct types of bacterial communities were found based on the diversity of the observed taxa; the groups were split into a low diversity group identified by the presence of <em>Bradyrhizobium</em> and a high diversity group distinguished by the abundance of mucosal-associated bacteria found in oral, respiratory, and vaginal communities of cattle. Second, the effects of supplementary, soluble fiber on the intestinal bacterial communities of piglets pre- and/or post-weaning were studied. The rationale behind this study was to determine if pre-weaning fiber could alter the microbiome prior to weaning and the change of diet from liquid to solid. Pre-weaning, supplementary, soluble fiber was found to increase short-chain fatty acid concentrations and bacterial taxa potentially involved in their production. Additionally, bacterial taxa implicated in an increased inflammatory response were reduced in groups fed supplementary fiber. Taken together, the two bacterial community studies highlight the gaps in knowledge for reproductive communities in male animals as well as the potential for reducing weaning stress in pigs. Part two of this dissertation focuses on whole genome sequence analysis as a way to study bacterial populations associated with bovine respiratory disease (BRD), a common and potentially fatal disease in cattle. Identification of BRD has low accuracy and the presence of antibiotic resistant bacteria increases the chance of treatment failure. Using machine learning, the prediction of antibiotic resistance in bacterial isolates from animals with BRD was performed to find potential sequences for use in future molecular assays. While using known resistance genes was helpful for some antibiotics, several of the antibiotics used in treating BRD were better predicted using the machine learning models. Model output sequences should be further tested using molecular methods to determine function and importance before using as an assay target. Put together, the contents of this dissertation should serve as an introduction to bacterial ecology as well as how the concepts can be applied to food animal production systems.</p>

<p dir="ltr">Neurotrauma encompasses a broad category of injuries affecting the central nervous system (CNS), which includes both the traumatic brain injury (TBI) and spinal cord injury (SCI). These injuries can result from various causes, including accidents, falls, sports-related incidents, and other traumatic events, affecting millions of individuals annually. Traumatic injuries are the leading cause of disability, and moreover are associated with elevated risk of developing cognitive impairments and neurodegenerative diseases (ND) such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). The elevated ND risk arising from neurotrauma poses significant burdens on healthcare systems and affect life quality of affected individuals, emphasizing the critical need for research aimed at understanding the underlying mechanisms conferring ND risk from the lesion center to CNS. The goal of my thesis is to understand persistent molecular changes post SCI associated with ND using a combination of a rat animal model and neuronal cultures derived from human induced pluripotent stem cells.</p><p dir="ltr">I started with Sprague-Dawley rats with T10 spinal cord contusive injury; and assessed immediate and persistent changes in transcriptomic and epigenetic markers via next generation sequencing (NGS) at primary lesion site and distal spinal cord tissue. Along with global changes in chromatin arrangements and DNA methylation, we observed significant transcriptomic changes enriched for pathways of inflammatory responses, and synaptogenesis. These changes were further verified using immunohistochemistry and super resolution microscopy. To further understand the long-term brain abnormality linked to SCI, we investigated persistent alterations in the composition and molecular profiles of both the male and female motor cortex 30 days after injury. Immunohistochemistry revealed that SCI leads to neuronal loss and changes in synaptic density and morphology; and significant alterations in the neuron-astrocyte ratio and astrocyte morphology, in male motor cortex supporting our hypothesis that SCI may increase the risk of neurodegeneration by affecting the motor cortex. Comparison of transcriptomic data collected at a sub-acute stage in male rats, namely 7 days post injury, with 30 days post injury, identified persistent and de novo changes that occur primarily after recovery of spinal cord injury, which are enriched for neuronal and synaptic function related pathways. Interestingly, neuroendocrine-related pathways were prominently implicated at the chronic stage of SCI, with Esr1 identified as a major upstream regulator offering protective effects in females that did not exhibit significant alterations in cellular composition or morphology after SCI. Collectively, our study paved the way towards understanding sexual dimorphism in brains after spinal cord injury and provides a plausible connection between spinal cord injury and neurodegeneration later in life that were further investigated using a humanized culture model.</p><p dir="ltr">We established the feasibility of using hiPSC derived neurons to examine long term neurotoxic mechanism using lead (Pb) as a model chemical with strong associations with elevated AD risks later in life. A similar culture system was then used to assess persistent neurotoxicity of acrolein, a chemical that is known to emerge in brains post traumatic injury. We found that acrolein induced alterations in neuronal network morphology, synaptic density, and excitability. Furthermore, acrolein exposure negatively impacted mitochondrial function and persistently altered neuronal resilience towards a secondary stressor of mitochondria, namely MPP+. Acrolein exposure also alters the expression of tau and tau phosphorylation which collectively result in increased cellular vulnerability toward paired helical filament (PHF-tau) seeding, a known neurotoxin associated with ND. These findings collectively provide molecular insights as to how acrolein can partake alterations in neural function and resilience to stressors; and relay ND risks in neurotrauma patients later in life.</p><p dir="ltr">In conclusion, our comprehensive investigation employing both rat and hiPSC models uncovers plausible molecular pathways connecting SCI to neurodegenerative diseases, providing insights into the enduring consequences of these injuries on affected patients.</p>

<p dir="ltr">Open-access bioinformatics data enables accessible public health research for a variety of stakeholders, including teachers and low-resourced researchers. This project outlines two case studies utilizing open-access bioinformatics data sets and analysis software as proofs of concept for the types of research projects that can be adapted for workforce development purposes. The first case study is a spatial temporal analysis of Lyme disease rates in the United States from 2008 to 2020 using freely available data from the United States Department of Agriculture and Centers for Disease Control and Prevention to determine how urbanization and other changes in land use have impacted Lyme disease rates over time. The second case study conducts a pangenome analysis using bacteriophage data from the Actinobacteriophage Database to determine conserved gene regions related to host specificity.</p>

<p dir="ltr">Given the relatively poor visual acuity of compound eyes, many insects have developed alternative means for navigating their environment. For example, insects often rely on chemosensation to find food, mates, and inter- and intraspecific communication. Eusocial insects in particular have developed complex systems of pheromone communication to organize their colonies, enabling them to partition labor for foraging, brood care, and colony defense tasks to different portions of the colony. A variety of genes coding for proteins are involved in detecting these chemicals, including gustatory receptors, ionotropic receptors, and odorant receptors (ORs). Eusocial insects, and especially ants, have evolved an expanded clade of ORs in their genome, likely due to an increased reliance on pheromones compared to other insects. The ability to recognize nestmates from non-nestmates is one of the vital functions performed by these ORs, which detect hydrocarbons present on the cuticle to distinguish friend from foe. However, research into the details of nestmate recognition has been stymied due to difficulties in manipulating OR genes. Despite advances in genetic sequencing and manipulation technologies, strict reproductive divisions within most ant lineages make generating transgenic ants nearly impossible, and so we have been left with limited options to further investigate these receptors. To narrow down the ORs that could be involved in nestmate recognition in the Argentine ant (Mayr, 1868), I took a multi-pronged approach of generating tissue transcriptomes to identify ORs that are selectively upregulated in the antennae, as well as conducting a phylostratigraphic analysis to identify which OR genes arose more recently in the Argentine ant genome. While conducting these analyses, it became necessary to reannotate the set of Argentine ant OR genes, due to current published annotations not containing the full breadth of <i>L. humile</i> ORs. Finally, I orally administered fluorescently-labelled dsRNA to workers, and tracked the extent to which ingested dsRNA is capable of traversing the tissues of ant workers, to investigate whether RNAi is a viable method for investigating gene function for genes showing tissue-selective expression. I discovered a subset of OR genes that are highly expressed in the antennae and confirmed that dsRNA is able to reach the antennae and knock down OR gene expression through ingestion, meaning that RNA interference is a viable method for the practical study of ant OR genes and can be used to further explore how individual ORs regulate nestmate recognition.</p>

<p dir="ltr">The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects but does not necessarily indicate the initial point of interference within a network. The objective of this project is to take advantage of large scale and genome-wide perturbational or Perturb-Seq datasets by using them to pre-train a generalist machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of single cell RNA sequencing data collected from CRISPR knock out screens in cell culture. The advent of generative machine learning algorithms, particularly transformers, make it an ideal time to re-assess large scale data libraries in order to grasp cell and even organism-wide genomic expression motifs. By tailoring an algorithm to learn the downstream effects of the genetic perturbations, we present a pre-trained generalist model capable of predicting the effects of multiple perturbations in combination, locating points of origin for perturbation in new datasets, predicting the effects of known perturbations in new datasets, and annotation of large-scale network motifs. We demonstrate the utility of this model by identifying key perturbational signatures in RNA sequencing data from spaceflown biological samples from the NASA Open Science Data Repository.</p>