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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 Lithospermum erythrorhizon 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. L. erythrorhizon geranyldiphosphate (GPP) synthase (LeGPPS) 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 LeGPPS in L. erythrorhizon hairy roots (LeGPPSi lines) results in reduced shikonin content. This result provides functional evidence that cytoplasmic LeGPPS supplies GPP precursor to the shikonin biosynthesis. LeGPPSi 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 LeGPPSi line showed that downregulating LeGPPS 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 LeGPPS, we also found ubiquinone biosynthesis protein COQ4-like gene (LeCOQ4-L) 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 LeCOQ4-L has an analogous function in shikonin biosynthesis as a structural protein for stabilizing biosynthesis metabolon. This leads us to the characterization of COQ4 ortholog in Arabidopsis (AtCOQ4) to gain insight into its functional mechanism. Characterization of atcoq4 T-DNA mutant line showed that reduced AtCOQ4 expression resulted in reduced ubiquinone. Further subcellular localization study revealed that AtCOQ4 and LeCOQ4-L 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.

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 Bradyrhizobium 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.