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      Microinvasion by Streptococcus pneumoniae induces epithelial innate immunity during colonisation at the human mucosal surface

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          Abstract

          Control of Streptococcus pneumoniae colonisation at human mucosal surfaces is critical to reducing the burden of pneumonia and invasive pneumococcal disease, interrupting transmission, and achieving herd protection. Here, we use an experimental human pneumococcal carriage model (EHPC) to show that S. pneumoniae colonisation is associated with epithelial surface adherence, micro-colony formation and invasion, without overt disease. Interactions between different strains and the epithelium shaped the host transcriptomic response in vitro. Using epithelial modules from a human epithelial cell model that recapitulates our in vivo findings, comprising of innate signalling and regulatory pathways, inflammatory mediators, cellular metabolism and stress response genes, we find that inflammation in the EHPC model is most prominent around the time of bacterial clearance. Our results indicate that, rather than being confined to the epithelial surface and the overlying mucus layer, the pneumococcus undergoes micro-invasion of the epithelium that enhances inflammatory and innate immune responses associated with clearance.

          Abstract

          Streptococcus pneumoniae is a common coloniser of the human nasopharynx, but it also causes severe diseases. Here, Weight et al. use an experimental human pneumococcal carriage model to show that bacterial colonisation is associated with invasion of the epithelium and enhancement of immune responses.

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          Most cited references70

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          Near-optimal probabilistic RNA-seq quantification.

          We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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            Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

            High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package ( tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.
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              SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data

              Background Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods. Results SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of an R package and two R script templates (for DESeq2 and edgeR respectively). Tuning a small number of parameters and executing one of the R scripts, users have access to the full results of the analysis, including lists of differentially expressed genes and a HTML report that (i) displays diagnostic plots for quality control and model hypotheses checking and (ii) keeps track of the whole analysis process, parameter values and versions of the R packages used. Conclusions SARTools provides systematic quality controls of the dataset as well as diagnostic plots that help to tune the model parameters. It gives access to the main parameters of DESeq2 and edgeR and prevents untrained users from misusing some functionalities of both packages. By keeping track of all the parameters of the analysis process it fits the requirements of reproducible research.
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                Author and article information

                Contributors
                +44 20 3108 2351 , c.weight@ucl.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 July 2019
                16 July 2019
                2019
                : 10
                : 3060
                Affiliations
                [1 ]ISNI 0000000121901201, GRID grid.83440.3b, Division of Infection and Immunity, , University College London, ; London, UK
                [2 ]ISNI 0000 0004 1936 9764, GRID grid.48004.38, Department of Clinical Sciences, , Liverpool School of Tropical Medicine, ; Liverpool, UK
                [3 ]ISNI 0000000121901201, GRID grid.83440.3b, Department of Respiratory Medicine, , University College London, ; London, UK
                Author information
                http://orcid.org/0000-0001-6235-550X
                http://orcid.org/0000-0002-7746-3279
                http://orcid.org/0000-0002-4835-1032
                http://orcid.org/0000-0001-9129-5569
                http://orcid.org/0000-0002-4774-0853
                http://orcid.org/0000-0002-5650-5361
                http://orcid.org/0000-0002-0594-0902
                http://orcid.org/0000-0003-4573-449X
                Article
                11005
                10.1038/s41467-019-11005-2
                6635362
                31311921
                54e9234d-d0bb-4655-bc8d-c0f028d63f02
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 August 2018
                : 4 June 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 106846/Z/15/Z
                Award ID: 104936/Z/14/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Award ID: MR/M011569/1
                Award ID: G0900950
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000865, Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation);
                Award ID: OPP1117728
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                pathogens,respiratory tract diseases,infection,innate immunity
                Uncategorized
                pathogens, respiratory tract diseases, infection, innate immunity

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