京都大学 / 新制・課程博士 / 博士(医学) / 甲第24783号 / 医博第4975号 / 新制||医||1066(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 波多野 悦朗, 教授 小川 誠司 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

yes / The next-generation short-read sequencing technologies that generate comprehensive, whole-genome data with single-nucleotide resolution have already advanced tuberculosis diagnosis, treatment, surveillance and source investigation. Their high costs, tedious and lengthy processes, and large equipment remain major hurdles for research use in high tuberculosis burden countries and implementation into routine care. The portable next-generation sequencing devices developed by Oxford Nanopore Technologies (ONT) are attractive alternatives due to their long-read sequence capability, compact low-cost hardware, and continued improvements in accuracy and throughput. A systematic review of the published literature demonstrated limited uptake of ONT sequencing in tuberculosis research and clinical care. Of the 12 eligible articles presenting ONT sequencing data on at least one Mycobacterium tuberculosis sample, four addressed software development for long read ONT sequencing data with potential applications for M. tuberculosis. Only eight studies presented results of ONT sequencing of M. tuberculosis, of which five performed whole-genome and three did targeted sequencing. Based on these findings, we summarize the standard processes, reflect on the current limitations of ONT sequencing technology, and the research needed to overcome the main hurdles. Summary: The low capital cost, portable nature and continued improvement in the performance of ONT sequencing make it an attractive option for sequencing for research and clinical care, but limited data is available on its application in the tuberculosis field. Important research investment is needed to unleash the full potential of ONT sequencing for tuberculosis research and care.

Equine herpesvirus type 1 (EHV-1) is a common pathogen of horses that typically causes upper respiratory disease, however is also associated with late-term abortion, neonatal foal death and neurologic disease. Once a horse is infected, the virus concentrates to local lymphoid tissue, where it becomes latent. The virus can recrudesce during times of stress, which can lead to the initiation of devastating outbreaks. Some variants of EHV-1 have been associated with more severe disease outcomes. Appropriate outbreak management focuses on minimizing the movement of potentially exposed horses. This approach lacks a strategy for prevention at the level of latency largely due to a knowledge paucity in regards to carriage rate of latent EHV-1. Biosecurity decisions are also dependent on awaiting currently-available diagnostic testing that often take several days for results. Thus, our work has been focused on understanding the carriage rate of the latent virus in different geographic regions as well as improving diagnostic efficiency, both of which are essential for improving the management of EHV-1 disease. Loop mediated isothermal amplification (LAMP) is a method that amplifies nucleic acid rapidly at a constant temperature and is minimally affected by inhibitors that are often found in clinical samples. This procedure can be followed by multiple detection methods. A new, efficient sequencing method, called nanopore sequencing, has been developed in a handheld device, called MinION, that provides thorough output in a timely manner. When combined with LAMP, it has been referred to as LAMPore. The first objective of our work was to estimate the prevalence of latent EHV-1 and compare the frequency of each variant in the submandibular lymph nodes from horses in Virginia. Our second objective was to perform direct DNA sequencing of EHV-1 using the mobile MinION sequencer in combination with LAMP viral enrichment. Our findings demonstrated a low apparent prevalence of latent EHV-1 DNA in submandibular lymph nodes in this population of horses in Virginia as well as successful detection and identification of EHV-1 in equine nasal swab samples using LAMPore sequencing. / Doctor of Philosophy / Horses can develop disease from a virus called equine herpesvirus type 1 (EHV-1). Symptoms can vary from mild respiratory signs to the inability to rise leading to death or euthanasia. Horses transmit this virus to other nearby horses; however, the virus also becomes dormant once a horse is infected, meaning the virus is not infectious but is present within the animal. When the horse undergoes stress, such as during travel or competition, the virus can become active again, leading to the spread to other horses. This results in outbreaks, many of which are devastating to the equine industry. In order to minimize the risks of this virus spreading and causing disease, management is currently focused on minimizing the movement of horses that may have been exposed to the virus. There is little information regarding the number of horses that harbor the dormant virus and the current methods to detect the infectious virus can take multiple days for results. These limit decision-making during the management of an outbreak. Our work seeks to determine the number of horses in a region that harbor EHV-1 and also to test a new, efficient diagnostic method to identify the virus in samples from horses. Our findings showed a low number of horses in Virginia harbor dormant EHV-1 in the lymph nodes under their mandible, a common site of dormancy. Further, we found that our new method of detection was effective in identifying the virus in samples from nasal secretions from horse.

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

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 genus Rhodotorula includes basidiomycetous oleaginous yeast species. Rhodotorula babjevae can produce compounds of biotechnological interest such as lipids, carotenoids, and biosurfactants from low value substrates such as lignocellulose hydrolysate. High-quality genome assemblies are needed to develop genetic tools and to understand fungal evolution and genetics. Here, we combined short- and long-read sequencing to resolve the genomes of two R. babjevae strains, CBS 7808 (type strain) and DBVPG 8058, at chromosomal level. Both genomes are 21 Mbp in size and have a GC content of 68.2%. Allele frequency analysis indicates that both strains are tetraploid. The genomes consist of a maximum of 21 chromosomes with a size of 0.4 to 2.4 Mbp. In both assemblies, the mitochondrial genome was recovered in a single contig, that shared 97% pairwise identity. Pairwise identity between most chromosomes ranges from 82 to 87%. We also found indications for strain-specific extrachromosomal endogenous DNA. A total of 7591 and 7481 protein-coding genes were annotated in CBS 7808 and DBVPG 8058, respectively. CBS 7808 accumulated a higher number of tandem duplications than DBVPG 8058. We identified large translocation events between putative chromosomes. Genome divergence values between the two strains indicate that they may belong to different species.

Nanopore sequencing is the next generation ofsequencing methods which promises to deliver cheaper andmore portable genome sequencing capabilities. A single DNAor RNA strand is passed through a nanopore nested in anartificial membrane with an electric potential applied across it.The nucleotide bases of the helix then interact with the ioniccurrent in the nanopore, resulting in a unique signal that canbe translated into the correct corresponding nucleotide sequence.This project investigated whether features of the raw signal datacould be used as predictive indicators of the duration time ofeach nucleotide base in the nanopore. This is done in orderto segment the signal before translation. The training data setused came from the sequenced DNA molecules of an E. Colibacterium. Distribution candidates were fitted to a histogram ofthe duration data of the training set. Features of the currentsignal and distribution parameters were correlated in orderinvestigate if a linear predictive model could be created. Theresults indicate that the feature zero-crossings is not an optimaloption for construction of a linear model, while the large jumpsand moving variance features often generate linear patterns. The parameter of the Log-logistic distribution had the best fit withthe lowest relative root mean square deviation (rRMSD) of 2.7%. / Nanopore sequencing är nästa generationensmetod för DNA sekvensering som kommer att bidra medbilligare och mer portabla sekvenseringsmöjligheter. Metodeninnebär att en enkelsträngad DNA eller RNA molekyl passerargenom porer i nanostorlek, placerade i ett artificiellt membransamtidigt som en elektrisk potential appliceras över membranet.Nukleotiderna i genmolekylen interagerar med jonströmmen iporen, vilket resulterar i en unik signal som kan översättas tillden korresponderande sekvensen av nukleotider som passerat.Detta projekt gick ut på att undersöka om egenskaper frånsignalen kan användas som predikativa indikatorer för varaktighetensom varje nukleotid befinner sig i membranporen. Dettaför att sedan kunna segmentera signalen före översättningen tillDNA sekvensen. Träningsdata som användes är sekvenserad DNAfrån en E. Coli bakterie. Kandiderande sannolikhetsfördelningaranpassades till ett histogram som beskriver varaktigheten.Egenskaperna och parametrar från fördelningarna korreleradesför att skapa en linjär modell. Resultatet visade att antaletskärningar i x-axeln som signalegenskap inte är det optimalavalet för konstruktion av en linjär modell. Skillnaden mellan två signalvärden som är mindre än en varierbar konstant ochglidande variansen som signalegenskaper genererar ofta linjäramönster. Resultatet visade även att sannolikhetsfördelningen Loglogistichade lägst relativ medelkvadratavvikelse (rRMSD) på 2.7%. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Nanopore sequencing is a relatively new DNA sequencing method which measures the current over a nanopore in a membrane as each nucleotide of the DNA passes through the nanopore. From the resulting current signal it is possible to determine the sequence of nucleotides in the DNA by using a base caller. The goal of this project was to create a machine learning model which could estimate the accuracy rate (identity score) of the sequenced DNA using the electric current signal and other data available through nanopore sequencing. The dataset that the machine learning models were trained on were samples from E. coli bacteria that had been sequenced through nanopore sequencing. In this project a linear regression model was created as well as several neural networks. The best performing model was a neural network which had a mean square error (MSE) of 6.12 ∙ 10-4, compared to a variance in the dataset of 2.11 ∙ 10-3. The low MSE indicates that the model can effectively predict identity scores. / Nanopore sequencing är en relativt ny DNA-sekvenseringsmetod som mäter strömmen över en nanoskopisk por i ett membran samtidigt som varje DNA-nukleotid passerar genom poren. Från den resulterande elektriska signalen så är det möjligt att bestämma sekvensen av nukleotider i DNA:t genom att använda en base caller. Målet med det här projektet var att skapa en maskininlärningsmodell som kunde bestämma graden av noggrannhet av det sekvenserade DNA:t genom att använda den elektriska strömsignalen och andra typer av data tillgängliga av Nanopore sequencing. Datamängden som maskininlärningsmodellerna använde för träning bestod av samples från en E. coli bakterie som sekvenserats med nanopore sequencing. I det här projektet har en linjär regressions-modell skapats samt flera olika neurala nätverk. Den bäst presterande modellen var ett neuralt nätverk, som hade ett minstakvadratfel (MSE) på 6.12 ∙ 10-4, jämfört med datamängdens varians på 2.11 ∙ 10-3. Det låga MSE-värdet visar på att modellen effektivt kan skatta noggrannhetsgraden av den avlästa DNA-sekvensen. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Many environmental compartments are acknowledged transmission routes for infectious diseases, antibiotic resistance, and anthropogenic pollution. The need for environmental sensing has consistently been stressed as a means to minimize public health threats caused by such contaminants. Many analytical detection techniques have been developed and applied for environmental sensing. However, these techniques are often reliant upon centralized facilities and require intensive resources. For these reasons their use can be challenging under resource-constrained conditions characterized by poor water, sanitation, and hygiene (WASH) services. In this dissertation, we developed biotechnology- and/or nanotechnology-advanced analytical tools for environmental sensing that have potential for future application in regions with poor WASH services. First, loop-mediated isothermal amplification (LAMP) and nanopore sequencing were applied to develop assays for the detection of SARS-CoV-2, the causative agent of COVID-19, in wastewater samples. Second, surface-enhanced Raman spectroscopy (SERS) was applied for environmental detection of a range of analytes. Gold nanoparticle (AuNP)-based SERS substrates were fabricated by droplet evaporation-induced aggregation on a hydrophobic substrate. These SERS substrates were then applied for the detection of antibiotic resistance genes (ARGs) and other environmental contaminants (e.g., dye or hydrophobic organic contaminants). In a separate study, Au nanostructured SERS substrates were fabricated and applied for pH sensing in a range of environmental media. Finally, the environmental impact of an AuNP-based colorimetric detection assay was assessed via life cycle assessment. / Doctor of Philosophy / Environmental sensing is an important means to intervene against public health threats of infectious diseases and environmental contaminants. However, currently available analytical tools for environmental samples often require intensive resources that are not available in low- and middle-income countries. In this dissertation, we developed biotechnology and/or nanotechnology advanced analytical tools for environmental sensing that have potential future application applied under resource-constrained conditions. First, we applied loop-mediated isothermal amplification (LAMP) and nanopore sequencing to develop detection assays for SARS-CoV-2, the causative agent of COVID-19, in wastewater samples. Second, we applied surface-enhanced Raman spectroscopy (SERS) to develop assays for environmental analytes. We fabricated SERS substrates by evaporation-induced aggregation of gold nanoparticles (AuNPs) on a hydrophobic substrate and applied these for the detection of antibiotic resistance genes (ARGs) and other environmental contaminants. In addition, Au nanostructured SERS substrates were fabricated and applied for pH sensing in a range of environmental media. Finally, we used life cycle assessment to quantitatively evaluate the environmental impacts of an AuNP-based sensing applications.

Le rythme impressionnant auquel les technologies de séquençage progressent est alimenté par leur promesse de révolutionner les soins de santé et la recherche biomédicale. Le séquençage par nanopores est devenu une technologie attrayante pour résoudre des lacunes des technologies précédentes, mais aussi pour élargir nos connaissances sur le transcriptome en générant des lectures longues qui simplifient l’assemblage et la détection de grandes variations structurelles. Au cours du processus de séquençage, les nanopores mesurent les signaux de courant électrique représentant les bases (A, C, G, T) qui se déplacent à travers chaque nanopore. Tous les nanopores produisent simultanément des signaux qui peuvent être analysés en temps réel et traduits en bases par le processus d’appel de bases. Malgré la réduction du coût de séquençage et la portabilité des séquenceurs, le taux d’erreur de l’appel de base entrave leur mise en oeuvre dans la recherche biomédicale. Le but de ce mémoire est de classifier des séquences d’ARNm individuelles en différents groupes d’isoformes via l’élucidation de motifs communs dans leur signal brut. Nous proposons d’utiliser l’algorithme de déformation temporelle dynamique (DTW) pour l’alignement de séquences combiné à la technologie nanopore afin de contourner directement le processus d’appel de base. Nous avons exploré de nouvelles stratégies pour démontrer l’impact de différents segments du signal sur la classification des signaux. Nous avons effectué des analyses comparatives pour suggérer des paramètres qui augmentent la performance de classification et orientent les analyses futures sur les données brutes du séquençage par nanopores. / The impressive rate at which sequencing technologies are progressing is fueled by their promise to revolutionize healthcare and biomedical research. Nanopore sequencing has become an attractive technology to address shortcomings of previous technologies, but also to expand our knowledge of the transcriptome by generating long reads that simplify assembly and detection of large structural variations. During the sequencing process, the nanopores measure electrical current signals representing the bases (A, C, G, T) moving through each nanopore. All nanopores simultaneously produce signals that can be analyzed in real time and translated into bases by the base calling process. Despite the reduction in sequencing cost and the portability of sequencers, the base call error rate hampers their implementation in biomedical research. The aim of this project is to classify individual mRNA sequences into different groups of isoforms through the elucidation of common motifs in their raw signal. We propose to use the dynamic time warping (DTW) algorithm for sequence alignment combined with nanopore technology to directly bypass the basic calling process. We explored new strategies to demonstrate the impact of different signal segments on signal classification. We performed comparative analyzes to suggest parameters that increase classification performance and guide future analyzes on raw nanopore sequencing data.