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      Gut-derived memory γδ T17 cells exacerbate sepsis-induced acute lung injury in mice

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          Abstract

          Sepsis is a critical global health concern linked to high mortality rates, often due to acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). While the gut-lung axis involvement in ALI is recognized, direct migration of gut immune cells to the lung remains unclear. Our study reveals sepsis-induced migration of γδ T17 cells from the small intestine to the lung, triggering an IL-17A-dominated inflammatory response in mice. Wnt signaling activation in alveolar macrophages drives CCL1 upregulation, facilitating γδ T17 cell migration. CD44 + Ly6C IL-7R high CD8 low cells are the primary migratory subtype exacerbating ALI. Esketamine attenuates ALI by inhibiting pulmonary Wnt/β-catenin signaling-mediated migration. This work underscores the pivotal role of direct gut-to-lung memory γδ T17 cell migration in septic ALI and clarifies the importance of localized IL-17A elevation in the lung.

          Abstract

          Sepsis can result in acute lung injury which may involve cross interaction via the gut-lung axis. Here the authors show γδ T17 cell migration from the gut to the lung in a murine model of sepsis and link IL-17A-mediated lung inflammation regulation by Wnt/β-catenin signaling in alveolar macrophages via CCL1.

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          Most cited references42

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).

            Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.
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              A guide to histomorphological evaluation of intestinal inflammation in mouse models.

              Histomorphology remains a powerful routine evaluating intestinal inflammation in animal models. Emphasizing the focus of a given animal study, histopathology can overstate differences between established models. We aimed to systematize histopathological evaluation of intestinal inflammation in mouse models facilitating inter-study comparisons. Samples of all parts of the intestinal tract from well-established mouse models of intestinal inflammation were evaluated from hematoxylin/eosin-stained sections and specific observations confirmed by subsequent immunohistochemistry. Three main categories sufficiently reflected the severity of histopathology independent of the localization and the overall extent of an inflammation: (i) quality and dimension of inflammatory cell infiltrates, (ii) epithelial changes and (iii) overall mucosal architecture. Scoring schemata were defined along specified criteria for each of the three categories. The direction of the initial hit proved crucial for the comparability of histological changes. Chemical noxes, infection with intestinal parasites or other models where the barrier was disturbed from outside, the luminal side, showed high levels of similarity and distinct differences to changes in the intestinal balance resulting from inside events like altered cytokine responses or disruption of the immune cell homeostasis. With a high degree of generalisation and maximum scores from 4-8 suitable scoring schemata accounted specific histopathological hallmarks. Truly integrating demands and experiences of gastroenterologists, mouse researchers, microbiologists and pathologists we provide an easy-to-use guideline evaluating histomorphology in mouse models of intestinal inflammation. Standard criteria and definitions facilitate classification and rating of new relevant models, allow comparison in animal studies and transfer of functional findings to comparable histopathologies in human disease.
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                Author and article information

                Contributors
                shang_you@126.com
                yuan_shiying@163.com
                zhjcheng1@126.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 August 2024
                7 August 2024
                2024
                : 15
                : 6737
                Affiliations
                [1 ]GRID grid.33199.31, ISNI 0000 0004 0368 7223, Department of Critical Care Medicine, Union Hospital, Tongji Medical College, , Huazhong University of Science and Technology, ; Wuhan, PR China
                [2 ]GRID grid.33199.31, ISNI 0000 0004 0368 7223, Institute of Anesthesia and Critical Care Medicine, Union Hospital, Tongji Medical College, , Huazhong University of Science and Technology, ; Wuhan, PR China
                Author information
                http://orcid.org/0000-0003-2465-3797
                http://orcid.org/0009-0005-3138-681X
                http://orcid.org/0009-0007-3992-2845
                http://orcid.org/0000-0002-6415-1604
                http://orcid.org/0000-0002-2820-0851
                Article
                51209
                10.1038/s41467-024-51209-9
                11306781
                39112475
                c21ac30d-bc32-4673-a00f-3e5e811da257
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

                History
                : 11 December 2023
                : 31 July 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 82071480
                Award ID: 82272231
                Award Recipient :
                Categories
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                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                bacterial infection,gammadelta t cells,mucosal immunology
                Uncategorized
                bacterial infection, gammadelta t cells, mucosal immunology

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