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      Human Norovirus Efficiently Replicates in Differentiated 3D-Human Intestinal Enteroids

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          Abstract

          The human norovirus (HNoV) clinical and socio-economic impact calls for immediate action in the development of anti-infectives. Physiologically relevant in vitro models are hence needed to study HNoV biology, tropism, and mechanisms of viral-associated disease, and also as a platform to identify antiviral agents.

          ABSTRACT

          Human norovirus (HNoV) accounts for one-fifth of all acute viral gastroenteritis worldwide and an economic burden of ~$60 billion globally. The lack of treatment options against HNoV is in part due to the lack of cultivation systems. Recently, a model of infection in biopsy-derived human intestinal enteroids (HIE) has been described: 3D-HIE are first dispersed in 2D-monolayers and differentiated prior to infection, resulting in a labor-intensive, time-consuming procedure. Here, we present an alternative protocol for HNoV infection of 3D-HIE. We found that 3D-HIE differentiated as efficiently as 2D-monolayers. In addition, immunofluorescence-based quantification of UEA-1, a lectin that stains the villus brush border, revealed that ~80% of differentiated 3D-HIE spontaneously undergo polarity inversion, allowing for viral infection without the need for microinjection. Infection with HNoV GII.4-positive stool samples attained a fold-increase over inoculum of ~2 Log 10 at 2 days postinfection or up to 3.5 Log 10 when ruxolitinib, a JAK1/2-inhibitor, was added. Treatment of GII.4-infected 3D-HIE with the polymerase inhibitor 2′- C -Methylcytidine (2CMC) and other antivirals showed a reduction in viral infection, suggesting that 3D-HIE are an excellent platform to test anti-infectives. The transcriptional host response to HNoV was then investigated by RNA sequencing in infected versus uninfected 3D-HIE in the presence of ruxolitinib to focus on virus-associated signatures while limiting interferon-stimulated gene signatures. The analysis revealed upregulated hormone and neurotransmitter signal transduction pathways and downregulated glycolysis and hypoxia-response pathways upon HNoV infection. Overall, 3D-HIE have proven to be a highly robust model to study HNoV infection, screen antivirals, and to investigate the host response to HNoV infection.

          IMPORTANCE The human norovirus (HNoV) clinical and socio-economic impact calls for immediate action in the development of anti-infectives. Physiologically relevant in vitro models are hence needed to study HNoV biology, tropism, and mechanisms of viral-associated disease, and also as a platform to identify antiviral agents. Biopsy-derived human intestinal enteroids are a biomimetic of the intestinal epithelium and were recently described as a model that supports HNoV infection. However, the established protocol is time-consuming and labor-intensive. Therefore, we sought to develop a simplified and robust alternative model of infection in 3D enteroids that undergoes differentiation and spontaneous polarity inversion. Advantages of this model are the shorter experimental time, better infection yield, and spatial integrity of the intestinal epithelium. This model is potentially suitable for the study of other pathogens that infect intestinal cells from the apical surface but also for unraveling the interactions between intestinal epithelium and indigenous bacteria of the human microbiome.

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          Most cited references27

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
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                Journal
                Journal of Virology
                J Virol
                American Society for Microbiology
                0022-538X
                1098-5514
                November 23 2022
                November 23 2022
                : 96
                : 22
                Affiliations
                [1 ]Institute for Virology and Cell Biology, University of Lübeck, Germany
                [2 ]Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
                [3 ]KU Leuven, Department of Microbiology, Immunology & Transplantation, Rega Institute, Laboratory of Virology & Chemotherapy, Leuven, Belgium
                [4 ]Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
                [5 ]Department of Environmental Health Sciences, University of Michigan, School of Public Health, Ann Arbor, Michigan, USA
                [6 ]College of Pharmacology, University of Michigan, Ann Arbor, Michigan, USA
                Article
                10.1128/jvi.00855-22
                36342297
                f90b08c3-4d63-4c0b-af8e-6738cd3a73ae
                © 2022

                https://doi.org/10.1128/ASMCopyrightv2

                https://journals.asm.org/non-commercial-tdm-license

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