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      Early peripheral blood gene expression associated with good and poor 90-day ischemic stroke outcomes

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          Abstract

          Background

          This study identified early immune gene responses in peripheral blood associated with 90-day ischemic stroke (IS) outcomes.

          Methods

          Peripheral blood samples from the CLEAR trial IS patients at ≤ 3 h, 5 h, and 24 h after stroke were compared to vascular risk factor matched controls. Whole-transcriptome analyses identified genes and networks associated with 90-day IS outcome assessed using the modified Rankin Scale (mRS) and the NIH Stroke Scale (NIHSS).

          Results

          The expression of 467, 526, and 571 genes measured at ≤ 3, 5 and 24 h after IS, respectively, were associated with poor 90-day mRS outcome (mRS ≥ 3), while 49, 100 and 35 genes at ≤ 3, 5 and 24 h after IS were associated with good mRS 90-day outcome (mRS ≤ 2). Poor outcomes were associated with up-regulated genes or pathways such as IL-6, IL-7, IL-1, STAT3, S100A12, acute phase response, P38/MAPK, FGF, TGFA, MMP9, NF-kB, Toll-like receptor, iNOS, and PI3K/AKT. There were 94 probe sets shared for poor outcomes vs. controls at all three time-points that correlated with 90-day mRS; 13 probe sets were shared for good outcomes vs. controls at all three time-points; and 46 probe sets were shared for poor vs. good outcomes at all three time-points that correlated with 90-day mRS. Weighted Gene Co-Expression Network Analysis (WGCNA) revealed modules significantly associated with 90-day outcome for mRS and NIHSS. Poor outcome modules were enriched with up-regulated neutrophil genes and with down-regulated T cell, B cell and monocyte-specific genes; and good outcome modules were associated with erythroblasts and megakaryocytes. Finally, genes identified by genome-wide association studies (GWAS) to contain significant stroke risk loci or loci associated with stroke outcome including ATP2B, GRK5, SH3PXD2A, CENPQ, HOXC4, HDAC9, BNC2, PTPN11, PIK3CG, CDK6, and PDE4DIP were significantly differentially expressed as a function of stroke outcome in the current study.

          Conclusions

          This study suggests the immune response after stroke may impact functional outcomes and that some of the early post-stroke gene expression markers associated with outcome could be useful for predicting outcomes and could be targets for improving outcomes.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12974-022-02680-y.

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          Most cited references56

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              Causal analysis approaches in Ingenuity Pathway Analysis

              Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.
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                Author and article information

                Contributors
                hamini@ucdavis.edu
                bsstamova@ucdavis.edu
                Journal
                J Neuroinflammation
                J Neuroinflammation
                Journal of Neuroinflammation
                BioMed Central (London )
                1742-2094
                23 January 2023
                23 January 2023
                2023
                : 20
                : 13
                Affiliations
                [1 ]GRID grid.413079.8, ISNI 0000 0000 9752 8549, Department of Neurology, , University of California at Davis, ; MIND Institute Biosciences Building Room 2417, 2805 50th Street, Sacramento, CA USA
                [2 ]GRID grid.17089.37, ISNI 0000 0001 2190 316X, Division of Neurology, , University of Alberta, ; Edmonton, AB Canada
                [3 ]GRID grid.241167.7, ISNI 0000 0001 2185 3318, Wake Forest University School of Medicine, ; Winston Salem, NC USA
                Article
                2680
                10.1186/s12974-022-02680-y
                9869610
                36691064
                6844ea93-dfd5-4caa-9898-aae4b6f15758
                © The Author(s) 2023

                Open AccessThis 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/. 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 in a credit line to the data.

                History
                : 1 November 2022
                : 21 December 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Funded by: FundRef http://dx.doi.org/10.13039/100004411, Heart and Stroke Foundation of Canada;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001804, Canada Research Chairs;
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NS106950
                Award ID: NS075035
                Award ID: NS097000
                Award ID: NS101718
                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Neurosciences
                ischemic stroke,outcomes,gene expression,transcriptome,wgcna
                Neurosciences
                ischemic stroke, outcomes, gene expression, transcriptome, wgcna

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