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      Short-term transcriptomic response to plasma membrane injury

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          Abstract

          Plasma membrane repair mechanisms are activated within seconds post-injury to promote rapid membrane resealing in eukaryotic cells and prevent cell death. However, less is known about the regeneration phase that follows and how cells respond to injury in the short-term. Here, we provide a genome-wide study into the mRNA expression profile of MCF-7 breast cancer cells exposed to injury by digitonin, a mild non-ionic detergent that permeabilizes the plasma membrane. We focused on the early transcriptional signature and found a time-dependent increase in the number of differentially expressed (> twofold, P < 0.05) genes (34, 114 and 236 genes at 20-, 40- and 60-min post-injury, respectively). Pathway analysis highlighted a robust and gradual three-part transcriptional response: (1) prompt activation of immediate-early response genes, (2) activation of specific MAPK cascades and (3) induction of inflammatory and immune pathways. Therefore, plasma membrane injury triggers a rapid and strong stress and immunogenic response. Our meta-analysis suggests that this is a conserved transcriptome response to plasma membrane injury across different cell and injury types. Taken together, our study shows that injury has profound effects on the transcriptome of wounded cells in the regeneration phase (subsequent to membrane resealing), which is likely to influence cellular status and has been previously overlooked.

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          Most cited references89

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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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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              Toward understanding the origin and evolution of cellular organisms

              In this era of high‐throughput biology, bioinformatics has become a major discipline for making sense out of large‐scale datasets. Bioinformatics is usually considered as a practical field developing databases and software tools for supporting other fields, rather than a fundamental scientific discipline for uncovering principles of biology. The KEGG resource that we have been developing is a reference knowledge base for biological interpretation of genome sequences and other high‐throughput data. It is now one of the most utilized biological databases because of its practical values. For me personally, KEGG is a step toward understanding the origin and evolution of cellular organisms.
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                Author and article information

                Contributors
                jnl@cancer.dk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 September 2021
                27 September 2021
                2021
                : 11
                : 19141
                Affiliations
                [1 ]GRID grid.417390.8, ISNI 0000 0001 2175 6024, Membrane Integrity, Danish Cancer Society Research Center, ; Strandboulevarden 49, 2100 Copenhagen, Denmark
                [2 ]GRID grid.417390.8, ISNI 0000 0001 2175 6024, Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, ; Strandboulevarden 49, 2100 Copenhagen, Denmark
                [3 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Translational Disease Systems Biology, Faculty of Health and Medical Sciences, , Novo Nordisk Foundation Center for Protein Research University of Copenhagen, ; Blegdamsvej 3B, 2200 Copenhagen N, Denmark
                [4 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, , University of Copenhagen, ; Blegdamsvej 3C, 2200 Copenhagen N, Denmark
                Article
                98420
                10.1038/s41598-021-98420-y
                8476590
                34580330
                07f70d7a-2ddb-4a46-af32-7ff26dfc00b9
                © The Author(s) 2021

                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
                : 1 April 2021
                : 6 September 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cell biology,computational biology and bioinformatics,genetics,molecular biology
                Uncategorized
                cell biology, computational biology and bioinformatics, genetics, molecular biology

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