1,410 research outputs found

    Advanced sequencing technologies applied to human cytomegalovirus

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    The betaherpesvirus human cytomegalovirus (HCMV) is a ubiquitous viral pathogen. It is the most common cause of congenital infection in infants and of opportunistic infections in immunocompromised patients worldwide. The large double-stranded DNA genome of HCMV (236 kb) contains several genes that exhibit a high degree of variation among strains within an otherwise highly conserved sequence. These hypervariable genes encode immune escape, tropism or regulatory factors that may affect virulence. Variation arising from these genes and from an evolutionary history of recombination between strains has been hypothesised to be linked to disease severity. To investigate this, the HCMV genome has been scrutinised in detail over the years using a variety of molecular techniques, most looking only at one or a few of these genes at a time. The advent of high-throughput sequencing (HTS) technology 20 years ago then started to enable more in-depth whole-genome analyses. My study extends this field by using both HTS and the more recently developed long-read nanopore technology to determine HCMV genome sequences directly from clinical samples. Firstly, I used an Illumina HTS pipeline to sequence HCMV strains directly from formalin-fixed, paraffin-embedded (FFPE) tissues. FFPE samples are a valuable repository for the study of relatively rare diseases, such as congenital HCMV (cCMV). However, formalin fixation induces DNA fragmentation and cross-linking, making this a challenging sample type for DNA sequencing. I successfully sequenced five whole HCMV genomes from FFPE tissues. Next, I developed a pipeline utilising the single-molecule, long-read sequencer from Oxford Nanopore Technologies (ONT) to sequence HCMV initially from high-titre cellcultured laboratory strains and then from clinical samples with high HCMV loads. Finally, I utilised a direct RNA sequencing protocol with the ONT sequencer to characterise novel HCMV transcripts produced during infection in cell culture, demonstrating the existence of transcript isoforms with multiple splice sites. Overall, my findings demonstrate how advanced sequencing technologies can be used to characterise the genome and transcriptome of a large DNA virus, and will facilitate future studies on HCMV prognostic factors, novel antiviral targets and vaccine development

    Human norovirus emergence and circulation in humans and animals

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    Human norovirus emergence and circulation in humans and animals

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    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Recombinant spidroins from infinite circRNA translation

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    Spidroins are a diverse family of peptides and the main components of spider silk. They can be used to produce sustainable, lightweight and durable materials for a large variety of medical and engineering applications. Spiders’ territorial behaviour and cannibalism precludes farming them for silk. Recombinant protein synthesis is the most promising way of producing these peptides. However, many approaches have been unsuccessful in obtaining large titres of recombinant spidroins or ones of sufficient molecular weight. The work described here is focused on expressing high molecular weight spidroins from short circular RNA molecules. Mammalian host cells were transfected with designed circular-RNA-producing plasmid vectors. A backsplicing approach was implemented to successfully circularise RNA in a variety of mammalian cell types. This approach could not express any recombinant spidroins based on a variety of qualitative protein assays. Further experiments investigated the reasons behind this. Additionally, due to the diversity of spidroins in a large number of spider lineages, there are potentially many spidroin sequences left to be discovered. A bioinformatic pipeline was developed that accepts transcriptome datasets from RNA sequencing and uses tandem repeat detection and profile HMM annotation to identify novel sequences. This pipeline was specifically designed for the identification of repeat domains in expressed sequences. 21 transcriptomes from 17 different species, encompassing a wide selection of basal and derived spider lineages, were investigated using this pipeline. Six previously undescribed spidroin sequences were discovered. This pipeline was additionally tested in the context of the suckerin protein family. These proteins have recently been investigated for their potential properties in medicine and engineering including adhesion in wet environments. The computational pipeline was able to double the number of suckerins known to date. Further phylogenetic analysis was implemented to expand on the knowledge of suckerins. This pipeline enables the identification of transcripts that may have been overlooked by more mainstream analysis methods such as pairwise homology searches. The spidroins and suckerins discovered by this pipeline may contribute to the large repertoire of potentially useful properties characteristic of this diverse peptide family

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE

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    In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph

    Computational methods in drug repurposing and natural product based drug discovery

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    For a few decades now, computation methods have been widely used in drug discovery or drug repurposing process, especially when saving time and money are important factors. Development of bioinformatics, chemoinformatics, molecular modelling techniques and machine or deep learning tools, as well as availability of various biological and chemical databases, have had a significant impact on improving the process of obtaining successful drug candidates. This dissertation describes the role of natural products in drug discovery, as well as presents several computational methods used in drug discovery and drug repurposing. Application of these methods is presented with the example of searching for potential drug treatment options for the COVID-19 disease. The disease is caused by the novel coronavirus SARS-CoV-2, which was first discovered in December 2019 and has caused the death of more than 5.6 million people worldwide (until January 2022). Findings from two research projects, which aimed to identify potential inhibitors of main protease of SARS-CoV-2, are presented in this work. Moreover, a summary on COVID-19 treatment possibilities has been included. In the first project, a ligand-based virtual screening of around 360,000 compounds from natural products databases, as well as approved and withdrawn drugs databases was conducted, followed by molecular docking and molecular dynamics simulations. Moreover, computational predictions of toxicity and cytochrome activity profiles for selected candidates were provided. Twelve candidates as SARS-CoV-2 main protease inhibitors were identified - among them novel drug candidates, as well as existing drugs. The second project was focused on finding potential inhibitors from plants (Reynoutria japonica and Reynoutria sachalinensis) and was based on molecular docking studies, followed by in vitro studies of the activity of selected compounds, extract, and fractions from those plants against the enzyme. Several natural compounds were identified as promising candidates for SARS-CoV-2 main protease inhibitors. Additionally, butanol fraction of Ryenoutria rhizomes extracts also showed inhibitory activity on the enzyme. Suggested drugs, natural compounds and plant extracts should be further investigated to confirm their potential as COVID-19 therapeutic options. Presented workflow could be used for investigation of compounds for other biological targets and different diseases in the future research projects.Seit einigen Jahrzehnten werden bei der Entwicklung und Repositionierung von Arzneimitteln rechenintensive computergestützte Methoden eingesetzt, insbesondere da Zeit- und Kostenersparnis wichtige Faktoren sind. Die Weiterentwicklung der Bioinformatik und Chemoinformatik und die damit einhergehende Optimierung von molekularen Modellierungstechniken und Tools für maschinelles sowie tiefes Lernen ermöglicht die Verarbeitung von großen biologischen und chemischen Datenbanken und hat einen erheblichen Einfluss auf die Verbesserung des Prozesses zur Gewinnung erfolgreicher Arzneimittelkandidaten. In dieser Dissertation wird die Rolle von Naturstoffen bei der Entwicklung von Arzneimitteln beschrieben, und es werden verschiedene computergestützte Methoden vorgestellt, die bei der Entdeckung von Arzneimitteln und der Repositionierung von Arzneimitteln eingesetzt werden. Die Anwendung dieser Methoden wird am Beispiel der Suche nach potenziellen medikamentösen Behandlungsmöglichkeiten für die Krankheit COVID-19 vorgestellt. Die Krankheit wird durch das neuartige Coronavirus SARS-CoV-2 ausgelöst, das erst im Dezember 2019 entdeckt wurde und bisher (bis Januar 2022) weltweit mehr als 5,6 Millionen Menschen das Leben gekostet hat. In dieser Arbeit werden Ergebnisse aus zwei Forschungsprojekten vorgestellt, die darauf abzielten, potenzielle Hemmstoffe der Hauptprotease von SARS-CoV-2 zu identifizieren. Außerdem wird ein Überblick über die Behandlungsmöglichkeiten von COVID-19 gegeben. Im ersten Projekt wurde ein ligandenbasiertes virtuelles Screening von rund 360.000 Kleinstrukturen aus Naturstoffdatenbanken sowie aus Datenbanken für zugelassene und zurückgezogene Arzneimittel durchgeführt, gefolgt von molekularem Docking und Molekulardynamiksimulationen. Darüber hinaus wurden für ausgewählte Kandidaten rechnerische Vorhersagen zur Toxizität und zu Cytochrom-P450-Aktivitätsprofilen erstellt. Es wurden zwölf Kandidaten als SARS-CoV-2-Hauptproteaseinhibitoren identifiziert - darunter sowohl neuartige als auch bereits vorhandene Arzneimittel. Das zweite Projekt konzentrierte sich auf die Suche nach potenziellen Inhibitoren aus Pflanzen (Reynoutria japonica und Reynoutria sachalinensis) und basierte auf molekularen Docking-Studien, gefolgt von In-vitro-Studien der Aktivität ausgewählter Verbindungen, Extrakte und Fraktionen aus diesen Pflanzen gegen das Enzym. Mehrere Naturstoffe wurden als vielversprechende Kandidaten für SARS-CoV-2- Hauptproteaseinhibitoren identifiziert. Außerdem zeigte die Butanolfraktion von Ryenoutria Rhizomextrakten ebenfalls eine hemmende Wirkung auf das Enzym. Die vorgeschlagenen Arzneimittel, Naturstoffe und Pflanzenextrakte sollten weiter untersucht werden, um ihr Potenzial als COVID-19-Therapieoptionen zu bestätigen. Der vorgestellte Arbeitsablauf könnte in zukünftigen Forschungsprojekten zur Untersuchung von Verbindungen für andere biologische Ziele und verschiedene Krankheiten verwendet werden

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Multi-dimensional omics approaches to dissect natural immune control mechanisms associated with RNA virus infections

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    In recent decades, global health has been challenged by emerging and re-emerging viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), human immunodeficiency viruses (HIV-1), and Crimean–Congo hemorrhagic fever virus (CCHFV). Studies have shown dysregulations in the host metabolic processes against SARS-CoV2 and HIV-1 infections, and the research on CCHFV infection is still in the infant stage. Hence, understanding the host metabolic re-programming on the reaction level in infectious disease has therapeutic importance. The thesis uses systems biology methods to investigate the host metabolic alterations in response to SARS-CoV2, HIV-1, and CCHFV infections. The three distinct viruses induce distinct effects on human metabolism that, nevertheless, show some commonalities. We have identified alterations in various immune cell types in patients during the infections of the three viruses. Further, differential expression analysis identified that COVID-19 causes disruptions in pathways related to antiviral response and metabolism (fructose mannose metabolism, oxidative phosphorylation (OXPHOS), and pentose phosphate pathway). Up-regulation of OXPHOS and ROS pathways with most changes in OXPHOS complexes I, III, and IV were identified in people living with HIV on treatment (PLWHART). The acute phase of CCHFV infection is found to be linked with OXPHOS, glycolysis, N-glycan biosynthesis, and NOD-like receptor signaling pathways. The dynamic nature of the metabolic process and adaptive immune response in CCHFV-pathogenesis are also observed. Further, we have identified different metabolic flux in reactions transporting TCA cycle intermediates from the cytosol to mitochondria in COVID-19 patients. Genes such as monocarboxylate transporter (SLC16A6) and nucleoside transporter (SLC29A1) and metabolites such as α-ketoglutarate, succinate, and malate were found to be linked with COVID-19 disease response. Metabolic reactions associated with amino acid, carbohydrate, and energy metabolism pathways and various transporter reactions were observed to be uniquely disrupted in PLWHART along with increased production of αketoglutarate (αKG) and ATP molecules. Changes in essential (leucine and threonine) and non-essential (arginine, alanine, and glutamine) amino acid transport were found to be caused by acute CCHFV infection. The altered flux of reactions involving TCA cycle compounds such as pyruvate, isocitrate, and alpha-ketoglutarate was also observed in CCHFV infection. The research described in the thesis displayed dysregulations in similar metabolic processes against the three viral Infections. But further downstream analysis unveiled unique alterations in several metabolic reactions specific to each virus in the same metabolic pathways showing the importance of increasing the resolution of knowledge about host metabolism in infectious diseases
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