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      Molecular signature of different lesion types in the brain white matter of patients with progressive multiple sclerosis

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          Abstract

          To identify pathogenetic markers and potential drivers of different lesion types in the white matter (WM) of patients with progressive multiple sclerosis (PMS), we sequenced RNA from 73 different WM areas. Compared to 25 WM controls, 6713 out of 18,609 genes were significantly differentially expressed in MS tissues (FDR < 0.05). A computational systems medicine analysis was performed to describe the MS lesion endophenotypes. The cellular source of specific molecules was examined by RNAscope, immunohistochemistry, and immunofluorescence. To examine common lesion specific mechanisms, we performed de novo network enrichment based on shared differentially expressed genes (DEGs), and found TGFβ-R2 as a central hub. RNAscope revealed astrocytes as the cellular source of TGFβ-R2 in remyelinating lesions. Since lesion-specific unique DEGs were more common than shared signatures, we examined lesion-specific pathways and de novo networks enriched with unique DEGs. Such network analysis indicated classic inflammatory responses in active lesions; catabolic and heat shock protein responses in inactive lesions; neuronal/axonal specific processes in chronic active lesions. In remyelinating lesions, de novo analyses identified axonal transport responses and adaptive immune markers, which was also supported by the most heterogeneous immunoglobulin gene expression. The signature of the normal-appearing white matter (NAWM) was more similar to control WM than to lesions: only 465 DEGs differentiated NAWM from controls, and 16 were unique. The upregulated marker CD26/DPP4 was expressed by microglia in the NAWM but by mononuclear cells in active lesions, which may indicate a special subset of microglia before the lesion develops, but also emphasizes that omics related to MS lesions should be interpreted in the context of different lesions types. While chronic active lesions were the most distinct from control WM based on the highest number of unique DEGs ( n = 2213), remyelinating lesions had the highest gene expression levels, and the most different molecular map from chronic active lesions. This may suggest that these two lesion types represent two ends of the spectrum of lesion evolution in PMS. The profound changes in chronic active lesions, the predominance of synaptic/neural/axonal signatures coupled with minor inflammation may indicate end-stage irreversible molecular events responsible for this less treatable phase.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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              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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                Author and article information

                Contributors
                zsolt.illes@rsyd.dk
                Journal
                Acta Neuropathol Commun
                Acta Neuropathol Commun
                Acta Neuropathologica Communications
                BioMed Central (London )
                2051-5960
                11 December 2019
                11 December 2019
                2019
                : 7
                : 205
                Affiliations
                [1 ]ISNI 0000 0004 0512 5013, GRID grid.7143.1, Department of Neurology, , Odense University Hospital, ; J.B. Winslowsvej 4, DK-5000 Odense C, Denmark
                [2 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Institute of Clinical Research, BRIDGE, University of Southern Denmark, ; Odense, Denmark
                [3 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Institute of Molecular Medicine, University of Southern Denmark, ; Odense, Denmark
                [4 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Department of Mathematics and Computer Science, , University of Southern Denmark, ; Odense, Denmark
                [5 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Department of Brain Sciences, Imperial College, ; London, UK
                [6 ]ISNI 0000000123222966, GRID grid.6936.a, Research Group Computational Systems Medicine, Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, , Technical University of Munich, ; Freising-Weihenstephan, Germany
                [7 ]ISNI 0000 0004 0512 5013, GRID grid.7143.1, Department of Clinical Genetics, , Odense University Hospital, ; Odense, Denmark
                [8 ]ISNI 0000000123222966, GRID grid.6936.a, Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, , Technical University of Munich, ; Freising-Weihenstephan, Germany
                Author information
                http://orcid.org/0000-0001-9655-0450
                Article
                855
                10.1186/s40478-019-0855-7
                6907342
                31829262
                1ac2d032-2d1d-4461-8adf-6b30b56853fd
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 October 2019
                : 25 November 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003554, Lundbeckfonden;
                Award ID: R118-A11472
                Award ID: R260-2017-1247
                Award ID: R296-2018-2502
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008361, Scleroseforeningen;
                Award ID: R458-A31829-B15690
                Award ID: R487-A33600-B15690
                Funded by: FundRef http://dx.doi.org/10.13039/100010809, Jascha Fonden;
                Award ID: 5589
                Funded by: FundRef http://dx.doi.org/10.13039/501100008242, Direktør Ejnar Jonasson Kaldet Johnsen og Hustrus Mindelegat;
                Award ID: 5609
                Funded by: Region Syddanmark
                Award ID: 14/24200
                Funded by: FundRef http://dx.doi.org/10.13039/501100004196, Odense Universitetshospital;
                Award ID: 29A-1501
                Funded by: FundRef http://dx.doi.org/10.13039/100013995, Sanofi Genzyme;
                Award ID: REG-NOBA-COMPL-SD-017
                Funded by: FIKP
                Award ID: 20765/3/2018/FEKUTSTRAT
                Funded by: FundRef http://dx.doi.org/10.13039/100008398, Villum Fonden;
                Award ID: Young Investigator grant nr. 13154
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100007601, Horizon 2020;
                Award ID: 777111 (REPOTRIAL).
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                multiple sclerosis,secondary progressive,human brain lesions,next-generation rna sequencing,tgf-beta,cd26/dpp4

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