Antimicrobial resistance (AMR) is a threat to global health, food security, and economic productivity. Infections caused by drug resistant Gram-negative pathogens, such as Escherichia coli, Pseudomonas aeruginosa, and Neisseria gonorrhoeae, are continuously becoming harder to treat due to limited treatment options and long turnaround times for culture-based phenotypic diagnosis. Alternatively, genotypic approaches that exploit whole genome sequencing have the potential to be faster and more accurate. Genotypic approaches rely on using bacterial genomes to predict AMR phenotypes. I generated a rules-based algorithm and machine learning models using known resistance determinants from bacterial genomes to predict resistance or susceptibility. I showed that machine learning was superior to a rules-based algorithm and achieved an average accuracy of 94% and 89% for E. coli and P. aeruginosa, respectively. These machine learning models identified novel AMR genotype-phenotype relationships between known resistance determinants and resistance phenotypes, which were experimentally validated. To identify the parameters that can improve machine learning models, I tested a variety of genetic features, algorithms, and evaluation metrics. I observed an intricate dependency between parameters for AMR prediction performance, illustrating that careful selection of parameters is required to generate accurate AMR prediction models. A limitation of this work was its prediction of resistance and susceptibility categories, as these are interpretations of minimum inhibitory concentrations defined by clinical breakpoint guidelines. Since multiple guidelines exist, these prediction models are not generalizable, so prediction of MIC values was explored. The average accuracy of my MIC prediction models was 86%, 41%, and 98% for E. coli, P. aeruginosa, and N. gonorrhoea, respectively. Despite the multifactorial and intricate nature of the resistome, I was able to accurately predict AMR phenotypes for many antibiotics for these pathogens. This is a step towards advanced diagnostic microbiology methods driven by genomics. / Thesis / Doctor of Philosophy (PhD) / Many surgeries, chemotherapy, and transplantation will be impossible if antibiotic resistance is not addressed. Antibiotic misuse, overuse, and time to definitive therapy exacerbate this global health problem. Phenotypic testing determines definitive therapy, but bacterial culturing is slow. A potentially faster and more accurate approach relies on sequencing the pathogen’s genome. I used machine learning to generate antibiotic resistance prediction models that achieved average accuracies of 94% and 89% for Escherichia coli and Pseudomonas aeruginosa, respectively. These models identified novel relationships between known resistance genes and resistance phenotypes, which were experimentally validated. Resistance and susceptibility are interpretations of a minimum inhibitory concentration (MIC) using a clinical breakpoint guideline. Since there are different guidelines, I generated MIC prediction models with average accuracies of 86%, 41%, and 98% for E. coli, P. aeruginosa, and Neisseria gonorrhoea, respectively. My findings work towards a world where clinical sequencing and genomics-based diagnostics are the gold standard.

Aspergillus fumigatus is a filamentous saprophytic mold that is found abundantly in the biosphere. A. fumigatus is also an airborne human pathogen and is considered the major cause of aspergillosis, infections caused by inhalation of conidia. In immunocompetent individuals, the spores rarely cause any harm as they are cleared by innate pulmonary defences; however, in immunocompromised patients, the host immune system can fail to clear the inhaled conidia and aspergillosis may develop. Indeed, aspergillosis represents a major cause of morbidity and mortality in these populations. Aspergillosis is commonly treated using triazole and amphotericin B (AMB) antifungal agents. However, the increasing prevalence of triazole resistant strains and emergence of AMB resistance has become a challenge in treatment. To further expand our knowledge on the mechanisms of antifungal resistance in the species, we tested previously known or associated genes for antifungal resistance as well as investigated novel mechanisms via multiple genome-wide association studies (GWAS), which used a total of 211 genomes from A. fumigatus strains in 12 countries. Our results identified many novel mutations related to triazole and AMB resistance. Specifically, using stepwise GWAS analyses, we identified 6 and 18 missense variants to be significantly associated with itraconazole and voriconazole resistance, respectively. A linkage disequilibrium analysis identified six additional missense variants associated with triazole resistance, with two of these six being consistently associated with pan-azole resistance across subsets of samples. Furthermore, examination of known mutation sites and genes overexpressed with triazole exposure found a total of 65 SNPs implicated in triazole resistance. For the AMB study, we identified a total of 34 mutations associated with AMB tolerance using a GWAS. Subsequent analysis with 143 progeny strains, generated from a laboratory cross and genotyped with PCR-RFLP, identified epistatic interactions between five of these SNP sites that impacted growth in different concentrations of AMB. With the expanding immunocompromised population and increasing frequency of antifungal resistance, our results will help in investigating novel resistance mechanisms in A. fumigatus and in expanding the molecular diagnostic toolset in resistance screening, to enable rapid and accurate diagnosis and treatment decision-making. / Thesis / Master of Science (MSc)

Many current drugs for stroke act by targeting circulating molecules, yet these have not been exhaustively evaluated for therapeutic potential. A central challenge is that while many molecules correlate with stroke risk, only a subset cause stroke. To disentangle causality from association, a statistical genetics framework called “Mendelian Randomization” can be used by integrating genetic, biomarker, and phenotypic information. In Study 1, we screened 653 circulating proteins using this technique and found evidence supporting causal roles for seven proteins, two of which (SCARA5 and TNFSF12) were not previously implicated in stroke pathogenesis. We also characterized potential side-effects of targeting these molecules for stroke prevention and did not identify any adverse effects for SCARA5. The remaining two studies focused on investigating the role of an emerging marker of mitochondrial activity, leukocyte mitochondrial DNA copy number (mtDNA-CN). Mitochondria have long been known to play a protective role in stroke recovery; however, a mitochondrial basis for stroke protection has not been extensively studied in humans. In Study 2, we first sought to better understand the genetic basis of mtDNA-CN in a series of genetic association studies involving 395,781 UK residents. We identified 71 loci which represents a 40% increase in our knowledge. In Study 3, epidemiological analyses of 3,498 acute stroke demonstrated that low mtDNA-CN was associated with higher risk of subsequent mortality and worse functional outcome 1-month after stroke. Furthermore, Mendelian Randomization analyses corroborated a causative relationship for the first time, implying that interventions that increase mtDNA-CN levels in stroke patients may represent a novel strategy for mitigating post-stroke complications. Ultimately, this work uncovered several novel therapeutic leads for preventing stroke onset and ameliorating its progression. Future investigations are necessary to better understand the underlying biological mechanisms connecting these molecules to stroke and to further interrogate their validity as potential drug targets. / Thesis / Doctor of Philosophy (PhD) / Current stroke medications work by targeting circulating molecules. Our aim was to discover new drug candidates by combining genetic and circulating biomarker data using a technique called “Mendelian Randomization”. In Study 1, we screened 653 circulating proteins and found evidence supporting causal roles for two novel candidates, SCARA5 and TNFSF12. Prior experimental studies suggest an important role for mitochondria in stroke recovery. Accordingly, in Study 2, we characterized the genetic basis of an emerging biomarker, mitochondrial DNA copy number (mtDNA-CN). Analyses of 395,781 participants revealed 71 associated genetic regions, representing a 40% increase in our knowledge. In Study 3, we measured mtDNA-CN in 3,498 acute patients and observed that lower levels predicted elevated risk of worse post-stroke functional outcomes. Furthermore, Mendelian Randomization analysis suggested a likely causal relationship. Overall, this work uncovered several novel therapeutic leads for preventing stroke onset and progression that warrant further investigation to verify therapeutic utility.

Since the completion of the the Human genome project in 2003, the evident complexity of our genome and its regulation has only grown. The idea that having sequenced the human genome would solve this mystery was quickly discarded. With the decreasing costs of DNA sequencing, a plethora of new methods have evolved to further understand the role of non-coding regions of our genome, which makes up 98% its length. Genetic variations in these regions are therefore abundant in the human population, but their e ects are hard to characterize. Many non-coding variants have been linked to complex diseases such as cancer predisposition. This thesis aims to investigate the potential e ects of non-coding variants on drug toxicity, that is, how severe the adverse e ects of a drug are to the treated patients. More specifically it will study the effects of two cancer drugs, Gemcitabine and Carboplatin, on a set of 96 patients with lung cancer. To do this we use spatial data acquired by the promoter-targeting method HiCap as well as expression data obtained from blood cell lines. Using the variants obtained through whole genome sequencing of the patients, a supervised learning approach was attempted to predict the final toxicity experienced by the patients. The large number of variants present among the comparably few patients resulted in poor accuracy. The conclusion was drawn that the resolution of HiCap is too low compared to the density of variants in the non-coding regions. Additional data, such as transcription factor Chip-Seq data, and transcription factor motifs are needed to locate potentially contributing variants within the interactions. / Sedan den första sekvenseringen av det mänskliga genomet 2003 har vår bild av vårt genom och hur det regleras bara blivit mer komplex. Iden om att ha tillgång till ett helt genom skulle losa detta mysterium förkastades snabbt. Med de sjunkande kostnaderna for sekvensering har ett brett utbud av nya metoder utvecklats for att bättre förstå de icke-kodande regionernas roll i v art genom. Då dessa regioner utgör98% av vårt DNA ar innehåller de stor variation bland det mänskliga släktet, men att förutsaga deras effekt är mycket svårt. Många icke-kodande variationer har kopplats till komplexa sjukdomar så som ökad risk för cancer.Denna uppsats syftar till att undersoka de potentiella effekterna av icke-kodande varianter på hur allvarliga biverkningar en patient får av en cancerbehandling. Närmare undersöks två mediciners, Gemcitabins och Carboplatins effekt på 96 lungcancerpatienter. För detta används spatial data samt genuttrycksdata från blodcellinjer.Med utgångspunkt från genetiska varianter bland patienternas sekvenserade genom testades övervakad inlärning för att förutsäga graden av biverkningar hos patienterna. Den stora mängden varianter som bärs av de förhållandevis få patienterna resulterade i låg träffsäkerhet hos prediktorn. Slutsatsen drogs att upplösningen av HiCap är för låg i jämförelse med den höga densiteten av varianter i icke-kodanderegioner. Mer data, så som Chip-Seq data från transkriptionsfaktorer samt deras specifika bindningsekvenser behövs för att lokalisera varianter inom en interaktion, som potentiellt skulle kunna påverka biverkningarna.

Understanding of grass genome structure and evolution has been significantly advanced through comparative genomics. The genomes of most cool-season forage and turf grasses, belonging to the Pooideae subfamily of the grasses, remain understudied. Creeping bentgrass (Agrostis stolonifera) is one of the most important cool-season turfgrasses due to its low mowing tolerance and aggressive growth habit. An RFLP genetic map of creeping bentgrass using 229 RFLP markers derived from cereal and creeping bentgrass EST-RFLP probes was constructed for a comparative genome analysis. This genetic map was compared with those of perennial ryegrass, oat, wheat, and rice. Large-scale chromosomal rearrangements between the map of creeping bentgrass and the respective maps of the Triticeae, oat, and rice were observed. However, no evidence of chromosomal rearrangements between the maps of creeping bentgrass and perennial ryegrass was detected, suggesting that these recently domesticated species might be closely related than expected. Further comparative genome analysis of creeping bentgrass was performed with the genome sequences of Brachypodium distachyon using sequences of the above-mentioned RFLP mapped markers and 8,470 publicly available A. stolonifera EST (AgEST) sequences. A total of 24 syntenic blocks were identified between the Agrostis linkage groups and the B. distachyon chromosomes. Orthologous loci of AgESTs (678) were identified in the B. distachyon genome, and these loci can be utilized in further comparative mapping of Pooideae species. Insights from comparative genomics with B. distachyon will be useful for genetic improvement of Agrostis spp. and provide a better understanding of the evolution of the Pooideae species.

The American chestnut tree (Castanea dentata) is a deciduous tree that largely exists in the eastern United States along the Appalachian Mountain range. Approximately 100 years ago, a fungal pathogen (Cryphonectria parasitica) decimated chestnut populations, resulting in the loss of billions of trees. Disease-resistant American chestnut populations have been developed, but the introgression of wild adaptive diversity into these breeding populations will be necessary to develop locally adapted and disease resistant chestnut trees for reintroduction. In this dissertation, I presented our findings which addressed previous gaps in knowledge regarding the population genomics of wild and backcross American chestnut populations. I 1) estimated the genomic diversity, population structure, and demographic history of remnant wild American chestnut populations; 2) revealed the genomic basis of local climate adaptation in American chestnut, developed a novel method to make tree sampling estimates for germplasm conservation, and defined unique seed zones for American chestnut based on climate and genotype, and 3) determined the amount of wild adaptive diversity captured by the backcross breeding program and made recommendations for their replanting region. These results will inform the development of a breeding plan for the introgression of adaptive diversity into backcross and transgenic chestnut populations. / Doctor of Philosophy / The American chestnut tree (Castanea dentata) is a deciduous tree that largely exists in the eastern United States along the Appalachian Mountain range. Approximately 100 years, a fungal disease (Cryphonectria parasitica) decimated chestnut populations, resulting in the loss of billions of trees. The American Chestnut Foundation developed disease-resistant American chestnut backcross trees by breeding American chestnut trees with Chinese chestnut trees (Castanea mollissima). These trees will need additional breeding with wild American chestnut trees so that their offspring will have both the disease-resistant traits and the adaptations to the local environment where they will be replanted. This is important, because trees that are both disease-resistant and locally adapted will be most likely to survive and thrive in their replanting location. However, a comprehensive evaluation of the genomic basis for local adaptation in American chestnut populations is lacking. In this dissertation, I presented our findings which addressed previous gaps in knowledge regarding the population genomics of wild and backcross American chestnut populations. I 1) estimated the genomic diversity, number of unique populations, and population size changes over time in wild American chestnut; 2) revealed the genes related to local adaptation in American chestnut, developed a novel method to make tree sampling estimates for conserving wild American chestnut diversity, and defined unique seed zones (areas within the species range that have unique adaptations to environment) for American chestnut based on climate (ie, precipitation and temperature values) and genotype (DNA), and 3) determined the amount of wild genomic diversity related to local adaption captured by the backcross breeding program and made recommendations for their replanting region. These results will inform the development of a breeding plan of wild American chestnut with backcross and transgenic chestnut populations to create locally adapted and disease-resistant chestnut populations for reintroduction.

No description available.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24507号 / 医博第4949号 / 新制||医||1064(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 村川 泰裕, 教授 金子 新, 教授 永井 純正 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

No description available.

<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>