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      Quantitative analysis of phosphoproteome in necroptosis reveals a role of TRIM28 phosphorylation in promoting necroptosis-induced cytokine production

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          Abstract

          Necroptosis is a form of regulated necrotic cell death that promotes inflammation. In cells undergoing necroptosis, activated RIPK1 kinase mediates the formation of RIPK1/RIPK3/MLKL complex to promote MLKL oligomerization and execution of necroptosis. RIPK1 kinase activity also promotes cell-autonomous activation of proinflammatory cytokine production in necroptosis. However, the signaling pathways downstream of RIPK1 kinase in necroptosis and how RIPK1 kinase activation controls inflammatory response induced by necroptosis are still largely unknown. Here, we quantitatively measured the temporal dynamics of over 7000 confident phosphorylation-sites during necroptosis using mass spectrometry. Our study defined a RIPK1-dependent phosphorylation pattern in late necroptosis that is associated with a proinflammatory component marked by p-S473 TRIM28. We show that the activation of p38 MAPK mediated by oligomerized MLKL promotes the phosphorylation of S473 TRIM28, which in turn mediates inflammation during late necroptosis. Taken together, our study illustrates a mechanism by which p38 MAPK may be activated by oligomerized MLKL to promote inflammation in necroptosis.

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          Most cited references38

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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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            The Perseus computational platform for comprehensive analysis of (prote)omics data.

            A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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              The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

              MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.
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                Author and article information

                Contributors
                junying_yuan@sioc.ac.cn
                shanbing@sioc.ac.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                23 October 2021
                23 October 2021
                November 2021
                : 12
                : 11
                : 994
                Affiliations
                [1 ]GRID grid.422150.0, ISNI 0000 0001 1015 4378, Interdisciplinary Research Center on Biology and Chemistry, , Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, ; 100 Haike Road, PuDong District, 201210 Shanghai, China
                [2 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, China
                Author information
                http://orcid.org/0000-0003-1169-5955
                http://orcid.org/0000-0001-5363-9834
                http://orcid.org/0000-0003-2405-6036
                Article
                4290
                10.1038/s41419-021-04290-7
                8542044
                34689152
                b7fe8cec-7219-47a2-a64e-ce14f6cf5987
                © 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 April 2021
                : 13 September 2021
                : 23 September 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002855, Ministry of Science and Technology of the People’s Republic of China (Chinese Ministry of Science and Technology);
                Award ID: 2016YFA0501900
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31530041, 21837004, 91849204, 92049303 and 31701210
                Award ID: 91849109
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003399, Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission);
                Award ID: 2019SHZDZX02
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100007219, Natural Science Foundation of Shanghai (Natural Science Foundation of Shanghai Municipality);
                Award ID: 18JC1420500
                Award Recipient :
                Funded by: the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB39030100)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Cell biology
                cell biology,immunology
                Cell biology
                cell biology, immunology

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