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      M-CSF, IL-6, and TGF-β promote generation of a new subset of tissue repair macrophage for traumatic brain injury recovery

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          Abstract

          The combination use of TGF-β, M-CSF, and IL-6 represents a promising therapy to repair traumatic brain injury.

          Abstract

          Traumatic brain injury (TBI) leads to high mortality rate. We aimed to identify the key cytokines favoring TBI repair and found that patients with TBI with a better outcome robustly increased concentrations of macrophage colony-stimulating factor, interleukin-6, and transforming growth factor–β (termed M6T) in cerebrospinal fluid or plasma. Using TBI mice, we identified that M2-like macrophage, microglia, and endothelial cell were major sources to produce M6T. Together with the in vivo tracking of mCherry+ macrophages in zebrafish models, we confirmed that M6T treatment accelerated blood-borne macrophage infiltration and polarization toward a subset of tissue repair macrophages that expressed similar genes as microglia for neuroprotection, angiogenesis and cell migration. M6T therapy in TBI mice and zebrafish improved neurological function while blocking M6T-exacerbated brain injury. Considering low concentrations of M6T in some patients with poor prognostic, M6T treatment might repair TBI via generating a previously unidentified subset of tissue repair macrophages.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

            Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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              Is Open Access

              The M1 and M2 paradigm of macrophage activation: time for reassessment

              Macrophages are endowed with a variety of receptors for lineage-determining growth factors, T helper (Th) cell cytokines, and B cell, host, and microbial products. In tissues, macrophages mature and are activated in a dynamic response to combinations of these stimuli to acquire specialized functional phenotypes. As for the lymphocyte system, a dichotomy has been proposed for macrophage activation: classic vs. alternative, also M1 and M2, respectively. In view of recent research about macrophage functions and the increasing number of immune-relevant ligands, a revision of the model is needed. Here, we assess how cytokines and pathogen signals influence their functional phenotypes and the evidence for M1 and M2 functions and revisit a paradigm initially based on the role of a restricted set of selected ligands in the immune response.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                March 2021
                12 March 2021
                : 7
                : 11
                : eabb6260
                Affiliations
                [1 ]Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China.
                [2 ]Neurosurgical Institute, Fudan University, Shanghai 200040 China.
                [3 ]State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.
                [4 ]Experimental Immunology Branch, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA.
                [5 ]School of Life Sciences, Xiamen University, Xiamen, Fujian, China.
                [6 ]School of Life Sciences, Shanghai University, Shanghai 200444, China.
                [7 ]Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China.
                [8 ]Cancer Center, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China.
                [9 ]Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
                [10 ]Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China.
                [11 ]Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China.
                [12 ]Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China.
                [13 ]School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
                Author notes
                [*]

                These authors contributed equally to this work.

                []Corresponding author. Email: hongyanwang@ 123456sibcb.ac.cn (H.W.); hujin_dana@ 123456126.com (J.H.); weibinwhy@ 123456shu.edu.cn (B.W.)
                Author information
                http://orcid.org/0000-0002-0731-9685
                http://orcid.org/0000-0002-0276-1968
                http://orcid.org/0000-0001-7050-7585
                http://orcid.org/0000-0003-3420-5111
                http://orcid.org/0000-0001-5130-2277
                http://orcid.org/0000-0003-3151-5843
                http://orcid.org/0000-0001-6394-2773
                http://orcid.org/0000-0002-4730-3549
                http://orcid.org/0000-0002-4305-4223
                http://orcid.org/0000-0002-0014-2792
                http://orcid.org/0000-0002-0938-603X
                http://orcid.org/0000-0001-9281-3612
                http://orcid.org/0000-0003-0887-3183
                http://orcid.org/0000-0001-5278-5522
                http://orcid.org/0000-0002-1844-9178
                http://orcid.org/0000-0002-2731-489X
                http://orcid.org/0000-0001-9401-1031
                http://orcid.org/0000-0002-5781-3745
                Article
                abb6260
                10.1126/sciadv.abb6260
                7954455
                33712456
                7b691d86-146f-40e6-8f05-165e015a75e4
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 10 March 2020
                : 18 December 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81930038, 81825011, 81630043, 81571617, 81571552
                Funded by: the Ministry of Science and Technology of China;
                Award ID: 2018YFA0800702, 2016YFD0500207, 2016YFC0902200, 2016YFD0500407, 2016YFC0905902
                Funded by: The Strategic Priority Research Program of the Chinese Academy of Sciences;
                Award ID: XDB19000000
                Funded by: the Hundred Talents Program of the Chinese Academy of Sciences;
                Funded by: the State Key Laboratory of Cell Biology, SIBCB, CAS;
                Award ID: SKL CBKF2013003
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Immunology
                Life Sciences
                Immunology
                Custom metadata
                Mjoy Azul

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