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Antibiotic resistance is critical to global health. This thesis examines the relationship between antibiotic resistance and genomic variations in E. coli from wastewater. E. coli is of interest as it causes urinary tract and other infections. Wastewater is a good source because it is a melting pot for E. coli from diverse origins. The research delves into two key aspects: including or excluding antibiotic resistance data and the level of granularity in representing genomic variations. The former is important because there is more genomic data than antibiotic resistance data. Consequently, relying solely on genomic data, this thesis studies positive selection in E. coli to identify mutations and genes favored by evolution. This study demonstrates the preferential selection of known antibiotic resistance genes and mutations, particularly mutations located on functionally important locations of outer membrane porins, and may hence have a direct effect on structure and function. Encouraged by these results, the study was expanded to include antibiotic resistance data and to examine genomic variations at three resolution levels: single mutations, unitigs (genome words) that may contain multiple mutations, and whole coding genome using machine learning classifier models that capture dependencies among multiple mutations and other genomic variations. Representation of single mutations detects well-known resistance mutations as well as potentially novel mechanisms related to biofilm formation and translation. By exploring larger genomic units such as genome words, the analysis confirms the findings from single mutations and additionally uncovers joint mutations in both known and novel genes. Finally, machine learning models, including AI language models, were trained to predict antibiotic resistance based on the whole coding genome. This achieved an accuracy of over 90% in predicting antibiotic resistance when sufficient data were available. Overall, this thesis unveils new antibiotic resistance mechanisms, conducts one of the largest studies of positive selection in E. coli, and stands out as one of the pioneering studies that utilizes AI language models for antibiotic resistance prediction.

京都大学 / 新制・課程博士 / 博士(農学) / 甲第24240号 / 農博第2519号 / 新制||農||1094(附属図書館) / 学位論文||R4||N5411(農学部図書室) / 京都大学大学院農学研究科農学専攻 / (主査)教授 白岩 立彦, 教授 土井 元章, 教授 那須田 周平 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Plant symbiosis with arbuscular mycorrhizal (AM) fungi provides many benefits for plants, including increased nutrient uptake, drought tolerance, and belowground pathogen resistance. In order to have a better understanding of the genetic architecture of mycorrhizal symbiosis, we conducted a genome-wide association study (GWAS) by phenotyping a diversity panel of cultivated sunflower (Helianthus annuus) for root colonization under inoculation with the AM fungus Rhizophagus intraradices. This mapping panel consists of 261 inbred lines that capture approximately 90% of the genetic diversity present in the cultivated sunflower germplasm. Using a mixed linear model approach with a high-density genetic map, we determined regions of the genome that are likely associated with AM colonization in sunflower. Additionally, we used a ‘core 12' set of twelve diverse lines (representing approximately 50% of the genetic diversity in the cultivated germplasm) to assess the effect that inoculation with AM fungi has on dried shoot biomass and macronutrient uptake. Colonization rate among lines in the mapping panel ranged from 0 to 70% and was not correlated with mycorrhizal growth response, shoot P response, or shoot K response among the core 12 lines. Association mapping yielded three SNPs that were significantly associated with AM colonization rate. These SNPs explained 19.0%, 14.4%, and 27.9% of the variance in three different metrics used to measure the degree of root colonization. Three genes of interest identified from the significant regions that contained these SNPs are potentially related to plant defense. Overall, our data suggests that candidate genes involved in plant defense may affect AM colonization rates within cultivated sunflower, and that these genes have a large effect size.

Rice (Oryza sativa) is the staple food for more than 3.5 billion people worldwide. As the population continues to grow, rice yield will need to increase by 1% every year for the next 30 years to keep up with the growth. In the US, Arkansas accounts for more than 50% of rice production. Over the last 68 years, rice production has continued to grow in Mississippi, placing it in fourth place after Arkansas, Louisiana, and California. Due to increasing rice acreage, regionally and worldwide, the need to develop abiotic stress-tolerant rice has increased. Unfortunately, current rice breeding programs lack genetic diversity, and many traits have been lost through the domestication of cultivated rice. Currently, stressors stemming from the continued effects of climate change continue to impact rice. To counteract the impacts of climate change, research has shifted to evaluating wild and weedy relatives of rice to improve breeding techniques. Weedy rice (Oryza sativa ssp.) is a genetically similar, noxious weed in rice with increased competitive ability. Studies have demonstrated that weedy rice has increased genetic variability and inherent tolerance to abiotic stressors. The aims of this study were to 1) screen a weedy rice mini-germplasm for tolerance to cold, heat, and complete submergence-stress, 2) utilize simple sequence repeat (SSR) markers and single nucleotide polymorphisms to evaluate the genetic diversity of the weedy rice population, and 3) use genome-wide association (GWAS) to identify SNPs associated with candidate genes within the population.

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<p dir="ltr">Improving animal health, production, and reproductive performance is of great relevance in breeding programs. The integration of pedigree, large-scale phenotypic data, and genomic information for breeding purposes can lead to a more sustainable livestock industry. In this context, the main objectives of this dissertation were to develop background knowledge for improving livestock welfare and understand the genetic and genomic background of welfare indicators in livestock using quantitative genomic strategies.</p><p dir="ltr">This dissertation includes five original studies. The first study objective aimed to estimate genetic parameters of vaginal temperature (T_V) across time and environments in lactating sows under hot conditions and identify genomic regions associated with T_V variability. We evaluated multiple random regression models. The random regression model fitting fourth order Legendre orthogonal polynomials had the lowest BIC (best model) and required relatively lower computational time. The heritability estimates for T_V ranged from 0.09 to 0.20 across time and environments. Two important genomic regions located on chromosomes 10 (59.370–59.998 Mb) and 16 (21.548–21.966 Mb) were identified.</p><p dir="ltr">The second study objective proposed 15 novel climatic resilience indicators based on variability in automatically recorded T_V and investigated their genomic background. Our findings showed that most of these novel indicators are heritable and could be used for selecting pigs with improved climatic resilience during lactation. We also found that individuals with a higher climatic resilience are more likely to exhibit better physiological responses, have higher body condition score, and enhanced reproductive performance under hot conditions.</p><p dir="ltr">The third study aimed to identify genomic regions and biological mechanisms underlying climatic resilience indicators in lactating sows under heat stress conditions. The genomic regions identified were located at SSC6:16,449,770 bp and SSC7:39,254,889 bp. Many heat tolerance or heat stress related genes were identified, including <i>HSP90AB1</i>, <i>DMGDH</i>, and <i>HOMER1.</i></p><p dir="ltr">The fourth study aimed to systematically investigate population structure and genetic diversity of worldwide Duroc subpopulations and other selected pig breeds based on genomic information. Significant differences were found in the genetic diversity levels, population structure, and ancestry within Duroc subpopulations and between Duroc and other selected pig breeds. High linkage disequilibrium levels were observed in Durocs. The objective of the fifth study was to investigate the genetic relationship between longitudinal trait functional longevity and other economically important traits, including growth, carcass, management and teat health-related traits, in American angus cattle using bivariate random regression model. The genetic correlations between functional longevity and other traits were mostly different in primiparous and multiparous cattle, which indicates parity has important impact on functional longevity. Also, Older animals with longer functional longevity tend to have lower growth performance, more ideal foot health, better temperament, bigger ultrasound fat thickness and ultrasound ribeye area, smaller teat size, and tighter udder suspension would have longer functional longevity.</p><p dir="ltr">This dissertation focuses on improving livestock welfare and sustainability by integrating genomic, phenotypic, and pedigree information in breeding programs. Key findings include the heritability of traits like climatic resilience, vaginal temperature, and functional longevity in pigs and cattle, as well as identifying genomic regions and diversity patterns critical for breeding decisions. These insights contribute to developing more resilient, productive, and welfare-oriented livestock populations.</p>