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Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation

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      Summary

      Macrophage activation is associated with profound transcriptional reprogramming. Although much progress has been made in the understanding of macrophage activation, polarization, and function, the transcriptional programs regulating these processes remain poorly characterized. We stimulated human macrophages with diverse activation signals, acquiring a data set of 299 macrophage transcriptomes. Analysis of this data set revealed a spectrum of macrophage activation states extending the current M1 versus M2-polarization model. Network analyses identified central transcriptional regulators associated with all macrophage activation complemented by regulators related to stimulus-specific programs. Applying these transcriptional programs to human alveolar macrophages from smokers and patients with chronic obstructive pulmonary disease (COPD) revealed an unexpected loss of inflammatory signatures in COPD patients. Finally, by integrating murine data from the ImmGen project we propose a refined, activation-independent core signature for human and murine macrophages. This resource serves as a framework for future research into regulation of macrophage activation in health and disease.

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      Highlights

      • Macrophages react with specific transcriptional programming upon distinct signals
      • Activation by TNF, PGE 2, and P3C activates a STAT4-associated transcriptional program
      • NFKB1, JUNB, and CREB1 are central transcription factors of macrophage activation
      • Inflammatory signatures are lost in alveolar macrophages from COPD patients

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      Most cited references 54

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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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        Exploring the full spectrum of macrophage activation.

        Macrophages display remarkable plasticity and can change their physiology in response to environmental cues. These changes can give rise to different populations of cells with distinct functions. In this Review we suggest a new grouping of macrophage populations based on three different homeostatic activities - host defence, wound healing and immune regulation. We propose that similarly to primary colours, these three basic macrophage populations can blend into various other 'shades' of activation. We characterize each population and provide examples of macrophages from specific disease states that have the characteristics of one or more of these populations.
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          Integration of biological networks and gene expression data using Cytoscape.

          Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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            Author and article information

            Affiliations
            [1 ]Genomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany
            [2 ]Institute of Innate Immunity, University Hospitals, University of Bonn, 53127 Bonn, Germany
            [3 ]Division of Infectious Diseases and Immunology, UMass Medical School, Worcester, MA 01605, USA
            [4 ]German Center of Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
            [5 ]The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Edinburgh, Midlothian EH25 9RG, Scotland, UK
            Author notes
            []Corresponding author j.schultze@ 123456uni-bonn.de
            Contributors
            Journal
            Immunity
            Immunity
            Immunity
            Cell Press
            1074-7613
            1097-4180
            20 February 2014
            20 February 2014
            : 40
            : 2
            : 274-288
            24530056
            3991396
            S1074-7613(14)00034-X
            10.1016/j.immuni.2014.01.006
            © 2014 The Authors

            This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

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            Immunology

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