For over thirty years, Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) has been a major contributor to morbidity and mortality in the commercial swine industry across the globe. This highly mutagenic RNA virus causes significant economic losses wherever it is prevalent, leading to $664 million in annual losses in the United States. Unfortunately, the current prevention and diagnostic techniques available have proven to be insufficient in controlling the spread of this disease. We describe an alternative diagnostic method exploiting the rapid turnaround time and long-read capacity of Oxford Nanopore Technology's MinION next-generation sequencer. We have developed a novel primer set designed to span Open Reading Frames 3 through 7 of the PRRSV genome, which has allowed for multiplexing of samples, thus reducing individual cost of testing, while yielding significantly more information than previously available. This novel primer pair and sequencing technique have distinguished mixed infections within individual animals and may be used to determine vaccination status. This new approach will help producers and veterinarians make better-informed decisions about co-mingling of animals and vaccination strategies, thus reducing the emergence of new, pathogenic strains of PRRSV. / Master of Science / Porcine reproductive and respiratory syndrome virus (PRRSV) is a common, economically important pathogen in commercial swine production. The virus was first identified in the late 1980's during outbreaks in the United States and Europe. In female pigs, the disease is characterized by abortion storms, and the delivery of mummified fetuses or very weak, ill piglets. Neonates often display signs of pneumonia, respiratory distress, and many die from hypoxia. Surviving piglets are highly susceptible to other diseases and are poor growers compared to other, unaffected piglets. Boars may show signs of respiratory disease and can also have decreased libido and reproductive success for months at a time. The virus is prone to mutating once a pig is infected, preventing herds from mounting sufficient immunity to protect against new, mutant strains. Identifying infected pigs early and accurately is crucial to managing PRRSV outbreaks. Currently available diagnostic tests for PRRSV have many limitations, thus we have developed a new diagnostic test using next-generation sequencing technology. Oxford Nanopore Technology provides a commercially available nanopore sequencer, the MinION, that can read long DNA strands in real-time. With this technology we have expanded the area of the PRRSV genome that can be sequenced, which allows us to better identify and distinguish strains of PRRSV in infected, and vaccinated pigs. This new testing method will allow veterinarians and practitioners across the country to better identify and predict outbreaks in their herds, helping them develop better management strategies against PRRSV.

Cystic fibrosis (CF) is an autosomal recessive genetic disorder involving a mutation in the CF transmembrane conductance regulator protein (CFTR), which causes dysfunctional transport of chloride ions across cell membranes. CF affects multiple body systems and a few of its symptoms include chronic cough, difficulty breathing, obstructive airway disease, bacterial pulmonary infections, maldigestion, malabsorption, pancreatitis, and male infertility. Until recently, treatment options have been limited to alleviating symptoms, but a new classification of drugs, CFTR modulators, provide an opportunity to slow the progression of the disease and improve clinical outcomes. The effect of CFTR modulators may be attributed to the reduction of persistently colonizing bacteria in CF lungs. Though, the effects of modulators on microbial communities colonizing the CF lung remains unknown, specifically with common respiratory pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. Particularly, previous CF studies have been limited in scope due to focusing on only one type of modulator and by using low-yield sequencing techniques. To address this gap, we seek to study the changes in CF respiratory pathogens of patients initiating CFTR modulator therapy at Nemours Hospital using long-read metagenomic sequencing (Oxford Nanopore) of longitudinally collected respiratory samples. We have optimized a protocol for host DNA depletion and microbial metagenomic sequencing to characterize the respiratory microbiome. This study focuses on utilizing these sequencing data to compare the microbiome among two healthy controls to pre-CFTR-treatment microbial communities of two recruited pediatric CF patients.

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京都大学 / 新制・課程博士 / 博士(理学) / 甲第23724号 / 理博第4814号 / 新制||理||1689(附属図書館) / 京都大学大学院理学研究科化学専攻 / (主査)教授 杉山 弘, 教授 深井 周也, 教授 秋山 芳展 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Nanopore sequencing, a recently developed methodfor DNA sequencing, involves applying a constant electricfield over a membrane and translocating single-stranded DNAmolecules through membrane pores. This results in an electricalsignal, which is dependent on the structure of the DNA. The aimof this project is to train and evaluate a non-causal temporalconvolution neural network in order to accurately translate suchelectrical raw signal into the corresponding nucleotide sequence.The training dataset is sampled from the E. coli bacterial genomeand the phage Lambda virus. We implemented and evaluatedseveral different temporal convolutional architectures. Using anetwork with five residual blocks with five convolutional layersin each block yields maximum performance, with a predictionaccuracy of 76.1% on unseen test data. This result indicates thata temporal convolution network could be an effective way tosequence DNA data. / Nanopore sequencing är en nyligen utvecklad metod för DNA-sekvensering som innebär att man applicerar ett konstant elektriskt fält över ett membran och translokerar enkelsträngade DNA-molekyler genom membranporer. Detta resulterar i en elektrisk signal som beror på DNA-strukturen.  Målet med detta projekt är att träna och evaluera icke-kausula ”temporal convolutional networks” som ska kunna översätta denna ofiltrerade elektriska signalen till den motsvarande nukleotidsekvensen. Träningsdatan är ett urval av genomen från bakterien E. coli och viruset phage Lambda. Vi implementerade och utvärderade ett antal olika nätverksstrukturer. Ett nätverk med fem residuala block med fem faltande lager i varje block gav maximal prestation, med en precision på 76.1% på testdata. Detta resultat indikerar att ett ”temporal convolution network” skulle kunna vara ett effektivt sätt att sekvensera DNA. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

p53 is a transcription factor and an important tumor suppressor protein that becomes activated due to DNA damage. Because of its role as a tumor suppressor, mutations in the gene that encodes it are found in over 50% of human cancers. The N-terminal transactivation domain (NTAD) of p53 is intrinsically disordered and modulates the function and interactions of p53 in the cell. Its disordered structure allows it to be controlled closely by post-translation modifications that regulate p53’s ability to bind DNA and interact with regulatory binding partners. p53 is an attractive target for developing cancer therapeutics, but its intrinsically disordered region makes it difficult for traditional experimental techniques to resolve its heterogeneous conformational ensemble. This challenge necessitates the use of techniques that can capture the transient structural features and interactions of p53 to aid in designing effective drugs that can modulate and stabilize its activity. Hybrid-resolution (HyRes) II is a coarse-grained molecular dynamics force field that was parameterized specifically to capture the dynamics of IDPs and can give insight into secondary structure propensity and how post-translational modifications affect the structural ensemble of the protein. Nanopore experiments allow for real-time, single-molecule studies of protein dynamics and interactions with binding partners through characteristic changes in the current that passes through the nanopore. Pairing nanopore experiments with simulations can give insight into the molecular detail of IDP ensembles and interactions, revealing a fuller picture of how p53 is controlled in stressed cell conditions and how its structure is affected due to various modifications and small molecules with therapeutic implications. Herein, we show the HyRes II force field can capture the complex, long-range dynamics of the p53 tetramer and provide molecular-level detail of the p53 autoinhibition mechanism, which is enhanced by the phosphorylation of the NTAD. Secondly, we use the MspA nanopore to capture the differences in events of the wild-type NTAD and a cancer-associated NTAD mutant. Lastly, we detect a small molecule binding to the WT NTAD using nanopore sensing. This approach of integrating MD simulations and nanopore experiments can be applied to the study of other IDPs which are prevalent in biology and integral to human health and disease.

Plant pathogens are one of the biggest threats to plant health and food security worldwide. To effectively contain plant disease outbreaks, classification and precise identification of pathogens is crucial to determine treatment and preventive measurements. Conventional methods of detection such as PCR may not be sufficient when the pathogen in question is unknown. Advances in sequencing technology have made it possible to sequence entire genomes and metagenomes in real-time and at a relatively low cost, opening an opportunity for the development of alternative methods for detection of novel and unknown plant pathogens. Within this dissertation, an integrated approach is used to reclassify a high-impact group of plant pathogens. Additionally, the application of metagenomics and nanopore sequencing using the Oxford Nanopore Technologies (ONT) MinION for fungal and bacterial plant pathogen detection and precise identification are demonstrated. To improve the classification of the strains belonging to the Ralstonia solanacearum species complex (RSSC), we performed a meta-analysis using a comparative genomics and a reverse ecology approach to accurately portray and refine the understanding of the diversity and evolution of the RSSC. The groups identified by these approaches were circumscribed and made publicly available through the LINbase web server so future isolates can be properly classified. To develop a culture-free detection method of plant pathogens, we used metagenomes of various plants and long-read nanopore sequencing to precisely identify plant pathogens to the strain-level and performed phylogenetic analysis with SNP resolution. In the first paper, we used tomato plants to demonstrate the detection power of bacterial plant pathogens. We compared bioinformatics tools for detection at the strain-level using reads and assemblies. In the second paper, we used a read-based approach to test the feasibility of the methodology to precisely detect the fungal pathogen causing boxwood blight. Lastly, with the improvement in nanopore sequencing, we used grapevine petioles to investigate whether we can go beyond detection and identification and do a phylogenetic analysis. We assembled a metagenome-assembled genome (MAG) of almost the same quality as the genomes obtained from cultured isolates and did a phylogenetic analysis with SNP resolution. Finally, for the cases where there may be no related genome in the database like the pathogen in question, we used machine learning and metagenomics to develop a reference-free approach to detection of plant diseases. We trained eight different machine learning models with reads from healthy and infected plant metagenomes and compared the classification accuracy of reads as belonging to a healthy or infected plant. From the comparison, random forest was the best model in terms of computational resources needed while maintaining a high accuracy (> 0.90). / Doctor of Philosophy / Microbes are present in every environment on the planet and have been on Earth for billions of years. While some microbes are beneficial, others can cause diseases. To differentiate the ones causing diseases from those who do not, looking into the evolutionary forces making them different is crucial to classify and identify them correctly. Although microorganisms cause diseases in humans and animals, the ones causing diseases in plants are one of the biggest threats to plant health and food security worldwide. In a perfect world, plant diseases would be diagnosed by eye or simple procedures. However, when a plant disease is present, it is not always obvious which organism, if any, is causing the disease making it hard for outbreaks to be detected and contained promptly. With technological advances, it is now possible to obtain all the genetic information of not only one organism but all the organisms living in an environment at a time. This genetic information can then be used to precisely identify what organism is causing a disease in a plant for faster disease diagnosis and, consequently, more efficient disease prevention and control. In this dissertation, we used the bacterial group, called Ralstonia solanacearum species complex, which can cause different diseases in more than 200 crops, to investigate and understand the evolution and diversity of the members of this group. We also used newly developed technologies to obtain the genetic material of all the organisms living in multiple important plants including tomato, grapevine, and the ornamental bush, boxwood. Using this genetic material, we developed a methodology for the detection of bacteria and a fungus causing plant diseases. While this works well when the suspected organism or a similar one is available for comparison, the detection of plant diseases in cases where this information is not available is challenging. Machine learning models, where computers can learn complex patterns from data, have the potential to detect pathogens without the need to compare the sequences to sequences of other pathogens. Here we also used the genetic material to train and compare different machine learning models to classify plants as either being infected or healthy.

The depletion of conventional oil reservoirs forced companies and consequently researchers to pursue alternatives such as resources that in the past were considered not economically viable, in consequence of the high depth, low porosity and permeability of the play zone. The exploration challenges were overcome mainly by the development of horizontal drilling and hydraulic fracturing. However, the extremely high temperatures and pressures, in association to a complex nanopore structure, in which reservoir fluids are now encountered, instigate further investigation of fluid phase behavior and composition, and challenge conventional macroscale reservoir simulation predictions. Moreover, the unusual high temperatures and pressures have increased the cost as well as the hazardous level for reservoir analyzes by lab experiments. Molecular Dynamics (MD) simulation of reservoirs can be a safe and inexpensive alternative tool to replicate reservoir pore and fluid conditions, as well as to monitor fluid behavior. In this study, a MD simulation of nanoconfinement effect on hydrocarbon fluid phase and compositional behavior in organic shale rocks is presented. Chapter 1 reviews and discusses previous works on MD simulations of geological resources. With the knowledge acquired, a fully atomistic squared graphite pore is proposed and applied to study hydrocarbon fluid phase and compositional behavior in organic shale rocks in Chapter 2. Results demonstrate that nano-confinement increases fluid mass density, which can contribute to phase transition, and heptane composition inside studied pores. The higher fluid density results in an alteration of oil in place (OIP) prediction by reservoir simulations, when nano-confinement effect is not considered. / Master of Science / Petroleum sub products are present in the day to day life of almost any human. The list include gasoline, plastics, perfumes, medications, polyester for clothing. Petroleum is naturally encountered in the void space, known as pores, inside rocks at reservoirs thousands of feet underground. In the past, the pores of oil reservoirs in development were larger and interconnected, which facilitates its extraction and reserve predictions. Most of reservoirs being developed nowadays have pores in the nanoscale and with poor interconnection as well as higher reservoir temperatures and pressure. These "new conditions", instigates further investigation of fluid phase behavior and composition, and challenge macroscale reservoir simulation predictions. In this study, the effect of decrease in pore size, as well as higher temperature and pressure conditions, in fluid behavior and composition is studied. Chapter 1 reviews and discusses previous works on geological resources modeling and simulation. With the knowledge acquired, a fully squared shale pore is proposed and applied to study hydrocarbon fluid phase and compositional behavior in organic shale rocks in Chapter 2. Results demonstrate that pores in the nanoscale region tend to increase fluid mass density, which can contribute to phase transition, and heptane composition inside studied pores. The higher fluid density results in an underestimation of reserves prediction by reservoir simulations, when the change in density is not considered.

One focus goal that science always works towards is an understanding of biological structures, with proteins being one of the main research goals. Sequencing proteins is currently a time-exhausting task, so focus is being put on trying to use nanopores in a similar way as in DNA sequencing for proteins. In this report, the neighbouring amino acids in the same peptide as the amino acid being sequenced are varied and the change in ionic current from the pore based on the neighbouring amino acids is analysed. This was done by using Molecular Dynamics program NAMD. A peptide was placed in the center of different silicon nitride pore structures inside a water box with ions and was simulated with an added electric field. The drop in current was checked for 4 different peptide systems and one check for the empty pores. The results presented in the report show that changing the neighbouring amino acids increases the current measured, therefore making the current blocking worse when mixing nearby amino acids. However, the differences are very small and similar amino acids give wildly different values. A larger evaluation with more computational power seems reasonable for a more definitive result.