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      Caspase-1 activates gasdermin A in non-mammals

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          Abstract

          Gasdermins oligomerize to form pores in the cell membrane, causing regulated lytic cell death called pyroptosis. Mammals encode five gasdermins that can trigger pyroptosis: GSDMA, B, C, D, and E. Caspase and granzyme proteases cleave the linker regions of and activate GSDMB, C, D, and E, but no endogenous activation pathways are yet known for GSDMA. Here, we perform a comprehensive evolutionary analysis of the gasdermin family. A gene duplication of GSDMA in the common ancestor of caecilian amphibians, reptiles and birds gave rise to GSDMA-D in mammals. Uniquely in our tree, amphibian, reptile and bird GSDMA group in a separate clade than mammal GSDMA. Remarkably, GSDMA in numerous bird species contain caspase-1 cleavage sites like YVAD or FASD in the linker. We show that GSDMA from birds, amphibians, and reptiles are all cleaved by caspase-1. Thus, GSDMA was originally cleaved by the host-encoded protease caspase-1. In mammals the caspase-1 cleavage site in GSDMA is disrupted; instead, a new protein, GSDMD, is the target of caspase-1. Mammal caspase-1 uses exosite interactions with the GSDMD C-terminal domain to confer the specificity of this interaction, whereas we show that bird caspase-1 uses a stereotypical tetrapeptide sequence to confer specificity for bird GSDMA. Our results reveal an evolutionarily stable association between caspase-1 and the gasdermin family, albeit a shifting one. Caspase-1 repeatedly changes its target gasdermin over evolutionary time at speciation junctures, initially cleaving GSDME in fish, then GSDMA in amphibians/reptiles/birds, and finally GSDMD in mammals.

          One Sentence Summary:

          We demonstrate that amphibians, reptiles and birds engage pyroptosis using caspase-1 and GSDMA, filling an evolutionary gap in which caspase-1 cleaves GSDME in fish and GSDMD in mammals.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: Data CurationRole: Writing - original draftRole: Writing - Review & EditingRole: Visualization
                Role: ConceptualizationRole: InvestigationRole: Writing - Review & Editing
                Role: Investigation
                Role: Investigation
                Role: Formal analysisRole: InvestigationRole: Data CurationRole: Writing - Review & EditingRole: Visualization
                Role: Writing - Review & EditingRole: Funding acquisition
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                20 November 2023
                : 2023.09.28.559989
                Affiliations
                [1 ]Duke University School of Medicine
                [2 ]University of North Carolina at Chapel Hill
                [3 ]National Institutes of Health
                Author notes
                [†]

                These authors contributed equally to this work

                [‡]

                These authors contributed equally to this work

                [§]

                Present address: Departments of: Integrative Immunobiology; Molecular Genetics and Microbiology; Cell Biology; Pathology; Durham, NC, USA;

                [¶]

                Present address: Department of Microbiology and Immunology; Chapel Hill, NC, USA

                [**]

                Present address: Critical Care Medicine Department; Bethesda, MD, USA

                [* ] For correspondence: edward.miao@ 123456duke.com (EAM)
                Author information
                http://orcid.org/0000-0001-7738-5795
                http://orcid.org/0000-0002-2990-2185
                Article
                10.1101/2023.09.28.559989
                10659411
                37987010
                ce6c3e68-f279-4f51-9f11-a30add0b2691

                This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.

                History
                Funding
                Funded by: National Institutes of Health
                Award ID: AI133236
                Award ID: AI139304
                Award ID: AI136920
                Award ID: AI148302
                Award ID: AI175078
                Award ID: AR072694
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