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Grossi, L.

Publications and source records attributed to Grossi, L..

2 recordsLinked to original sources

Annexin A1 as a key modulator of lung inflammation during coronavirus infections

Exacerbated inflammation is a major contributor to tissue damage and mortality in infectious diseases, including SARS-CoV-2. The resolution phase of inflammation is critical for restoring tissue homeostasis following an injury. Annexin A1 (AnxA1) is a ubiquitous protein that plays a fundamental role in the resolution of inflammation, including in preclinical models of infectious disease. Here, we investigated the role of AnxA1 in coronavirus infection and its potential as a host-targeted therapeutic strategy against SARS-CoV-2. Wild-type (WT) and AnxA1 knockout (AnxA1KO) mice were intranasally infected with the murine betacoronavirus MHV-3 to study the endogenous role of AnxA1. Immunohistochemistry and Western blot analyses in the lungs of MHV-3-infected mice revealed increased AnxA1 expression and its cleavage, which was associated with neutrophilic infiltration (Ly6G+ cells) mainly in peribronchiolar and perivascular regions. AnxA1-deficient mice exhibited higher neutrophilic infiltration and lung damage, alongside increased CXCL1 production in the lungs, when compared to WT-infected mice. In a murine model of SARS-CoV-2 infection in K18-hACE2 mice, we found increased AnxA1 cleavage associated with lung inflammation. Treatment of SARS-CoV-2-infected K18-hACE2 mice with the AnxA1-mimetic peptide, Ac2-26, reduced lung damage and lethality, without altering the host ability to deal with viral replication. Notably, Ac2-26-treated mice exhibited similar levels of protection to that afforded by the nucleotide analogue Remdesivir, following SARS-CoV-2 infection. Our findings highlight the protective role of the endogenous AnxA1 in mitigating coronavirus-induced lung inflammation and underscore the therapeutic potential of AnxA1 mimetic Ac2-26 as a host-targeted therapy against SARS-CoV-2.

immunology↗

OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control

MOTIVATIONParticle tracking coupled with time-lapse microscopy is critical for understanding the dynamics of intracellular processes of clinical importance. Spurred on by advances in the spatiotemporal resolution of microscopy and automated computational methods, this field is increasingly amenable to multi-dimensional high-throughput data collection schemes (Snijder et al., 2012). Typically, complex particle tracking datasets generated by individual laboratories are produced with incompatible methodologies that preclude comparison to each other. There is therefore an unmet need for data management systems that facilitate data standardization, meta-analysis, and structured data dissemination. The integration of analysis, visualization, and quality control capabilities into such systems would eliminate the need for manual transfer of data to diverse downstream analysis tools. At the same time, it would lay the foundation for shared trajectory data, particle tracking, and motion analysis standards. RESULTSHere, we present Open Microscopy Environment inteGrated Analysis (OMEGA), a cross-platform data management, analysis, and visualization system, for particle tracking data, with particular emphasis on results from viral and vesicular trafficking experiments. OMEGA provides intuitive graphical interfaces to implement integrated particle tracking and motion analysis workflows while providing easy to use facilities to automatically keep track of error propagation, harvest data provenance and ensure the persistence of analysis results and metadata. Specifically, OMEGA: 1) imports image data and metadata from data management tools such as the Open Microscopy Environment Remote Objects (OMERO; Allan et al., 2012); 2) tracks intracellular particles movement; 3) facilitates parameter optimization and trajectory results inspection and validation; 4) performs downstream trajectory analysis and motion type classification; 5) estimates the uncertainty propagating through the motion analysis pipeline; and, 6) facilitates storage and dissemination of analysis results, and analysis definition metadata, on the basis of our newly proposed FAIRsharing.org complainant Minimum Information About Particle Tracking Experiments (MIAPTE; Rigano and Strambio-De-Castillia, 2016; 2017) guidelines in combination with the OME-XML data model (Goldberg et al., 2005). In so doing, OMEGA maintains a persistent link between raw image data, intermediate analysis steps, the overall analysis output, and all necessary metadata to repeat the analysis process and reproduce its results. Availability and implementationOMEGA is a cross-platform, open-source software developed in Java. Source code and cross-platform binaries are freely available on GitHub at https://github.com/OmegaProject/Omega (doi: 10.5281/zenodo.2535523), under the GNU General Public License v.3. Contactcaterina.strambio@umassmed.edu and alex.rigano@umassmed.edu Supplementary informationSupplementary Material is available at BioRxiv.org

bioinformatics↗