Antibiotic resistance is currently a global threat spanning clinical, environmental, and geopolitical research domains. The environment is increasingly recognized as a key node in the spread of antibiotic resistance genes (ARGs), which confer antibiotic resistance to bacteria. Detecting ARGs in the environment is the first step in monitoring and controlling antibiotic resistance. In recent years, next-generation sequencing of environmental samples (metagenomic sequencing data) has become a prolific tool for the field of surveillance. Metagenomic data are nucleic acid sequences, or nucleotides, of environmental samples. Metagenomic sequencing data has been used over the years to detect and analyze ARGs. An intriguing instance of ARGs is the multi-functional ARG, where one ARG encodes two or more different antibiotic resistance functions. Multi-functional ARGs provide resistance to two or more antibiotics, thus should have evolutionary advantage over ARGs with resistance to single antibiotic. However, there is no tool readily available to detect these multi-functional ARGs in metagenomic data. In this study, we develop a computational pipeline to detect multi-functional ARGs in metagenomic data. The pipeline takes raw metagenomic data as the input and generates a list of potential multi-functional ARGs. A plot for each potential multi-functional ARG is also created, showing the location of the multi-functionalities in the sequence and the sequencing coverage level. We collected samples from three different sources: influent samples of a wastewater treatment plant, hospital wastewater samples, and reclaimed water samples, ran the pipeline, and identified 19, 57, and 8 potentially bi-functional ARGs in each source, respectively. Manual inspection of the results identified three most likely bi-functional ARGs. Interestingly, one bi-functional ARG, encoding both aminoglycoside and tetracycline resistance, appeared in all three data sets, indicating its prevalence in different environments. As the amount of antibiotics keeps increasing in the environment, multi-functional ARGs might become more and more common. The pipeline will be a useful computational tool for initial screening and identification of multi-functional ARGs in metagenomic data. / Master of Science / Antibiotics are the drug to fight against the infection of bacteria. Since the first antibiotic was discovered in 1928, many antibiotic drugs have been developed. At the same time, scientists discovered many genes responsible for the resistance of antibiotic drugs. Nowadays, antibiotic resistance is a global threat. Detecting antibiotic resistance genes in the environment is the first step toward monitoring and controlling antibiotic resistance. In recent years, next-generation sequencing has been widely used to get the DNA sequence from the environment. Metagenomics analysis has been used over the years to detect and analyze ARGs. In the literature, it has been reported that a single gene could carry two parts of sequences corresponding to two different ARGs, thus conferring resistance to two different antibiotics. This fusion might have some evolutionary advantages. In this study, we developed a novel computational tool to detect multi-functional ARGs. We collected data from three sources: the treatment plant water, the hospital wastewater, and the reclaimed water, and identified 19, 57, and 8 potential bi-functional ARGs in each source, respectively. After we manually inspected the result, we found three most likely bi-functional ARGs. We also found one bi-functional ARG that appears in all three datasets. The gene is responsible for aminoglycoside and tetracycline resistance. The tool will serve as the initial screening step to detect multi-functional ARGs.

Metagenomics has become a reliable tool for the analysis of the microbial diversity and the molecular mechanisms carried out by microbial communities. By the use of next generation sequencing, metagenomic studies can generate millions of short sequencing reads that are processed by computational tools. However, with the rapid adoption of metagenomics a large amount of data has been generated. This situation requires the development of computational tools and pipelines to manage the data scalability, accessibility, and performance. In this thesis, several strategies varying from command line, web-based platforms to machine learning have been developed to address these computational challenges. Interpretation of specific information from metagenomic data is especially a challenge for environmental samples as current annotation systems only offer broad classification of microbial diversity and function. Therefore, I developed MetaStorm, a public web-service that facilitates customization of computational analysis for metagenomic data. The identification of antibiotic resistance genes (ARGs) from metagenomic data is carried out by searches against curated databases producing a high rate of false negatives. Thus, I developed DeepARG, a deep learning approach that uses the distribution of sequence alignments to predict over 30 antibiotic resistance categories with a high accuracy. Curation of ARGs is a labor intensive process where errors can be easily propagated. Thus, I developed ARGminer, a web platform dedicated to the annotation and inspection of ARGs by using crowdsourcing. Effective environmental monitoring tools should ideally capture not only ARGs, but also mobile genetic elements and indicators of co-selective forces, such as metal resistance genes. Here, I introduce NanoARG, an online computational resource that takes advantage of the long reads produced by nanopore sequencing technology to provide insights into mobility, co-selection, and pathogenicity. Sequence alignment has been one of the preferred methods for analyzing metagenomic data. However, it is slow and requires high computing resources. Therefore, I developed MetaMLP, a machine learning approach that uses a novel representation of protein sequences to perform classifications over protein functions. The method is accurate, is able to identify a larger number of hits compared to sequence alignments, and is >50 times faster than sequence alignment techniques. / Doctor of Philosophy / Antimicrobial resistance (AMR) is one of the biggest threats to human public health. It has been estimated that the number of deaths caused by AMR will surpass the ones caused by cancer on 2050. The seriousness of these projections requires urgent actions to understand and control the spread of AMR. In the last few years, metagenomics has stand out as a reliable tool for the analysis of the microbial diversity and the AMR. By the use of next generation sequencing, metagenomic studies can generate millions of short sequencing reads that are processed by computational tools. However, with the rapid adoption of metagenomics, a large amount of data has been generated. This situation requires the development of computational tools and pipelines to manage the data scalability, accessibility, and performance. In this thesis, several strategies varying from command line, web-based platforms to machine learning have been developed to address these computational challenges. In particular, by the development of computational pipelines to process metagenomics data in the cloud and distributed systems, the development of machine learning and deep learning tools to ease the computational cost of detecting antibiotic resistance genes in metagenomic data, and the integration of crowdsourcing as a way to curate and validate antibiotic resistance genes.

With the great promise that metagenomics holds in exploring virome composition and discovering novel virus species, there is a pressing demand for comprehensive and up-to-date reference databases to enhance the downstream bioinformatics analysis. In this study, a RNA virus reference database (RVRD) was developed by manual and computational curation of RNA virus genomes downloaded from the three major virus sequence databases including NCBI, ViralZone, and ViPR. To reduce viral sequence redundancy caused by multiple identical or nearly identical sequences, sequences were first clustered and all sequences except one in a cluster that have more than 98% identity to one another were removed. Other identity cutoffs were also examined, and Hepatitis C virus genomes were studied in detail as an example. Using the 98% identity cutoff, sequences obtained from ViPR were combined with the unique RNA virus references from NCBI and ViralZone to generate the final RVRD. The resulting RVRD contained 23,085 sequences, nearly 5 times the size of NCBI RNA virus reference, and had a broad coverage of RNA virus families, with significant expansion on circular ssRNA virus and pathogenic virus families. Compared to NCBI RNA virus reference in performance evaluation, using RVRD as reference database identified more RNA virus species in RNAseq data derived from wastewater samples. Moreover, using RVRD as reference database also led to the discovery of porcine rotavirus as the etiology of unexplained diarrhea observed in pigs. RVRD is publicly available for enhancing RNA virus metagenomics. / Master of Science / Next-generation sequencing technology has demonstrated capability for the detection of viruses in various samples, but one challenge in bioinformatics analysis is the lack of well-curated reference databases, especially for RNA viruses. In this study, a RNA virus reference database (RVRD) was developed by manual and computational curation from the three commonly used resources: NCBI, ViralZone, and ViPR. While RVRD was managed to be comprehensive with broad coverage of RNA virus families, clustering was performed to reduce redundant sequences. The performance of RVRD was compared with NCBI RNA virus reference database using the pipeline FastViromeExplorer developed by our lab recently, the results showed that more RNA viruses were identified in several metagenomic datasets using RVRD, indicating improved performance in practice.

The timely identification of pathogens responsible for disease outbreaks is crucial for implementing effective control measures and minimizing the spread of infectious diseases. Conventional methods of identification are limited to specific pathogen species because they require prior knowledge and pure cultures of the pathogen. Therefore, these methods cannot detect new pathogens responsible for newly emerging diseases. Computational methods that rely on sequencing data have the potential to overcome these limitations. However, the diverse phenotypes among microbial species and strains within the same species pose a challenge in accurately identifying the specific pathogen responsible for the disease. This dissertation highlights the importance of strain-level detection for the identification and characterization of pathogens by employing computational methods that rely on genomic and metagenomic sequencing data. To realize that computational goal, a comparison of different tools, currently used for metagenome classification, was done to illustrate effective detection of bacterial pathogens. To develop computational methods for characterization, genomes of the plant pathogen Ralstonia solanacearum were studied to understand the basis of virulence at cool temperatures. Finally, a new tool was developed that combines accurate detection and characterization at the strain level, through the use of taxonomic databases constructed using genome similarity thresholds. This dissertation work is a contribution to the development of improved approaches to detect and contain disease outbreaks in plants with possible applications in human and animal diseases as well. / Doctor of Philosophy / Detecting and identifying pathogens is crucial for controlling disease outbreaks in humans, animals, and plants. However, currently used methods are limited to identifying only those pathogens that can be grown in a lab. An ideal method for pathogen detection should be broadly applicable to many pathogens. A newer technique called metagenome sequencing allows us to identify known as well as unknown pathogens, including the ones that cannot be grown in a lab. This makes it possible to detect new pathogens from newly emerging diseases. Computational tools that accurately analyze the sequencing data are needed. This dissertation highlights the importance of accurately identifying specific strains of pathogens using computational techniques based on genomic and metagenomic sequencing data. As a result, different tools were evaluated for classifying metagenomes for the successful detection of bacterial pathogens. For the characterization of specific traits responsible for causing disease, genomes of Ralstonia solanacearum, a plant pathogen, were studied to understand how some strains remain harmful at lower temperatures. The dissertation also introduces a novel metagenomic classification tool that combines accurate detection and characterization of pathogen strains by using genome similarity thresholds to create taxonomic databases. This approach improves our ability to identify and understand pathogens at a more specific level. Overall, this research aims to enhance our ability to identify and understand pathogens, allowing for more effective measures to control and prevent disease outbreaks.

Yes / Background. Bacterial bloodstream infection (bBSI) is one of the leading causes of death in critically ill patients and accurate diagnosis is therefore crucial. We here report a 16S metagenomics approach for diagnosing and understanding bBSI. Methodology/Principal Findings. The proof-of-concept was delivered in 75 children (median age 15 months) with severe febrile illness in Burkina Faso. Standard blood culture and malaria testing were conducted at the time of hospital admission. 16S metagenomics testing was done retrospectively and in duplicate on the blood of all patients. Total DNA was extracted from the blood and the V3–V4 regions of the bacterial 16S rRNA genes were amplified by PCR and deep sequenced on an Illumina MiSeq sequencer. Paired reads were curated, taxonomically labeled, and filtered. Blood culture diagnosed bBSI in 12 patients, but this number increased to 22 patients when combining blood culture and 16S metagenomics results. In addition to superior sensitivity compared to standard blood culture, 16S metagenomics revealed important novel insights into the nature of bBSI. Patients with acute malaria or recovering from malaria had a 7-fold higher risk of presenting polymicrobial bloodstream infections compared to patients with no recent malaria diagnosis (p-value = 0.046). Malaria is known to affect epithelial gut function and may thus facilitate bacterial translocation from the intestinal lumen to the blood. Importantly, patients with such polymicrobial blood infections showed a 9-fold higher risk factor for not surviving their febrile illness (p-value = 0.030). Conclusions/Significance. Our data demonstrate that 16S metagenomics is a powerful approach for the diagnosis and understanding of bBSI. This proof-of-concept study also showed that appropriate control samples are crucial to detect background signals due to environmental contamination. / This work was supported by the Flemish Ministry of Sciences (EWI, SOFI project IDIS). / This paper has been subject to a correction. Please see Correction file above.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第25148号 / 理博第5055号 / 新制||理||1721(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 緒方 博之, 教授 望月 敦史, 教授 西山 朋子 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

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State-approved membrane filtration (MF) techniques for water quality assessments were contrasted with metagenomic shotgun sequencing (MSS) protocols to evaluate their efficacy in providing precise health-risk indices for surface waters. Using MSS, the relative numerical abundance of pathogenic bacteria, virulence and antibiotic resistance genes revealed the status and potential pollution sources in samples studied. Traditional culture methods (TCM) showed possible fecal contamination, while MSS clearly distinguished between fecal and environmental bacteria contamination sources, and pinpointed actual risks from pathogens. RNA MSS to detect all viable microorganisms and qPCR of fecal biomarkers were used to assess the possible environmental risk between runoff drainage canals and a swamp area with no anthropogenic impact. Results revealed higher levels of pathogenic bacteria, viruses, and virulence and antibiotic resistance genes in the canal samples. The data underscore the potential utility of MSS in precision risk assessment for public and biodiversity health and tracking of environmental microbiome shifts by field managers and policy makers. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

M. Tech. (Department of Biotechnology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / During one of our preliminary studies in 2015, metagenomic DNA extracted from the goat rumen was sequenced and the in silico mining of the biorefining enzyme showed the presence of significant number of different biocatalysts, such as amylases (E.C 3.2.1.1), xylanases (E.C 3.2.1.8), pectinases (E.C 3.2.1.15) and cellulases (E.C 3.2.1.4). Hence, a subsequent study was conducted which is aimed at extracting metagenomic DNA from the goat rumen, constructing the metagenomic library using pCC2-FOS™ plasmid vector (Epicentre®), and eventually screening the constructed library for potential novel amylases using soluble starch as a substrate. Accordingly, rumen digesta was aseptically collected from four compartments of each goat and pulled before extraction of metagenomic DNA. The conventional CTAB protocol was modified to extract the metagenomic DNA from the rumen digesta. As a result, high molecular weight DNA was obtained and used to construct the metagenomic fosmid library. Since the host (Escherichia coli EPI 300-T1r) supplied with CopyControlHTP Fosmid Library Production Kit has background amylase expression we opted for a knockout E. coli strain with deleted starch hydrolysis (amylase expression) pathway. The library was subsequently screened for the presence of amylase isoforms using soluble starch as a substrate. Therefore, for the purpose of this study, four fosmids clones showing amylase activity were selected, recombinant vector isolated and MiSeq-sequenced. Out of four recombinant proteins, only one (pET30a(+)-amy-vut12) was successfully expressed. Subsequently, pET30a(+)-amyvut12 was further characterize physicochemically. Interestingly, the recombinant enzyme showed maximum activity in the pH and temperature ranges of 6.0 - 8.0 and 70 - 90oC, respectively. Hence, this implies that novel recombinant protein has sound activity from acidic to alkaline pH range and potently thermostable. Further work should be done to optimize and improve the solubility of three other recombinant proteins (pET30a(+)-amy-vut2, pET30a(+)- amy-vut9 and pET30a(+)-amy-vut14) studied, which might harbour important traits. Most importantly, immobilization as well as crystallographic studies of pET30a(+)-amy-vut12 and downstream applications should further be investigated.

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