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      Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH)

      1 , 1
      Science Translational Medicine
      American Association for the Advancement of Science (AAAS)

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          Abstract

          Metabolic dysfunction–associated steatohepatitis (MASH) is a severe form of liver disease that poses a global health threat because of its potential to progress to advanced fibrosis, leading to cirrhosis and liver cancer. Recent advances in single-cell methodologies, refined disease models, and genetic and epigenetic insights have provided a nuanced understanding of MASH fibrogenesis, with substantial cellular heterogeneity in MASH livers providing potentially targetable cell-cell interactions and behavior. Unlike fibrogenesis, mechanisms underlying fibrosis regression in MASH are still inadequately understood, although antifibrotic targets have been recently identified. A refined antifibrotic treatment framework could lead to noninvasive assessment and targeted therapies that preserve hepatocellular function and restore the liver’s architectural integrity.

          Abstract

          This article highlights recent mechanistic insights and therapeutic opportunities in MASH fibrosis.

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          Most cited references313

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          Inference and analysis of cell-cell communication using CellChat

          Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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            Mechanisms of NAFLD development and therapeutic strategies

            There has been a rise in the prevalence of nonalcoholic fatty liver disease (NAFLD), paralleling a worldwide increase in diabetes and metabolic syndrome. NAFLD, a continuum of liver abnormalities from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), has a variable course but can lead to cirrhosis and liver cancer. Here we review the pathogenic and clinical features of NAFLD, its major comorbidities, clinical progression and risk of complications and in vitro and animal models of NAFLD enabling refinement of therapeutic targets that can accelerate drug development. We also discuss evolving principles of clinical trial design to evaluate drug efficacy and the emerging targets for drug development that involve either single agents or combination therapies intended to arrest or reverse disease progression.
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              CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

              Cell-cell communication mediated by ligand-receptor complexes is critical to coordinating diverse biological processes, such as development, differentiation and inflammation. To investigate how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions. In contrast to other repositories, our database takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, provide procedures for inferring cell-cell communication networks from single-cell RNA sequencing data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v.2.0 is an updated version of our resource that incorporates additional functionalities to enable users to introduce new interacting molecules and reduces the time and resources needed to interrogate large datasets. CellPhoneDB v.2.0 is publicly available, both as code and as a user-friendly web interface; it can be used by both experts and researchers with little experience in computational genomics. In our protocol, we demonstrate how to evaluate meaningful biological interactions with CellPhoneDB v.2.0 using published datasets. This protocol typically takes ~2 h to complete, from installation to statistical analysis and visualization, for a dataset of ~10 GB, 10,000 cells and 19 cell types, and using five threads.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                October 04 2023
                October 04 2023
                : 15
                : 716
                Affiliations
                [1 ]Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                Article
                10.1126/scitranslmed.adi0759
                37792957
                4bfb97fa-b91a-40c3-9794-1e79fb49529c
                © 2023

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