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      RNA-sequencing reveals molecular and regional differences in the esophageal mucosa of achalasia patients

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          Abstract

          Achalasia is an esophageal motility disorder characterized by the functional loss of myenteric plexus ganglion cells in the distal esophagus and lower esophageal sphincter. Histological changes have been reported in the esophageal mucosa of achalasia, suggesting its involvement in disease pathogenesis. Despite recent advances in diagnosis, our understanding of achalasia pathogenesis at the molecular level is very limited and gene expression profiling has not been performed. We performed bulk RNA-sequencing on esophageal mucosa from 14 achalasia and 8 healthy subjects. 65 differentially expressed genes (DEGs) were found in the distal esophageal mucosa of achalasia subjects and 120 DEGs were identified in proximal esophagus. Gene expression analysis identified genes common or exclusive to proximal and distal esophagus, highlighting regional differences in the disease. Enrichment of signaling pathways related to cytokine response and viral defense were observed. Increased infiltration of CD45+ intraepithelial leukocytes were seen in the mucosa of 38 achalasia patients compared to 12 controls. Novel insights into the molecular changes occurring in achalasia were generated in this transcriptomic study. Some gene changes observed in the mucosa of achalasia may be associated with esophagitis. Differences in DEGs between distal and proximal esophagus highlight the importance of better understanding regional differences in achalasia.

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          Most cited references44

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                marie-pier.tetreault@northwestern.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 November 2022
                30 November 2022
                2022
                : 12
                : 20616
                Affiliations
                GRID grid.16753.36, ISNI 0000 0001 2299 3507, Department of Medicine, Gastroenterology and Hepatology Division, , Northwestern University Feinberg School of Medicine, ; M-336 McGaw Building, 240 East Huron, Chicago, IL 60611-3010 USA
                Article
                25103
                10.1038/s41598-022-25103-7
                9712691
                36450816
                c8c2b6f8-b17c-4c0e-b055-640afeb08f51
                © The Author(s) 2022

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 June 2022
                : 24 November 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NIH NIDDK P01 117824
                Award ID: NIH MIGMS T32 GM008061
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                transcriptomics,gastroenterology
                Uncategorized
                transcriptomics, gastroenterology

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