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      A human multi-lineage hepatic organoid model for liver fibrosis

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          Abstract

          To investigate the pathogenesis of a congenital form of hepatic fibrosis, human hepatic organoids were engineered to express the most common causative mutation for Autosomal Recessive Polycystic Kidney Disease (ARPKD). Here we show that these hepatic organoids develop the key features of ARPKD liver pathology (abnormal bile ducts and fibrosis) in only 21 days. The ARPKD mutation increases collagen abundance and thick collagen fiber production in hepatic organoids, which mirrors ARPKD liver tissue pathology. Transcriptomic and other analyses indicate that the ARPKD mutation generates cholangiocytes with increased TGFβ pathway activation, which are actively involved stimulating myofibroblasts to form collagen fibers. There is also an expansion of collagen-producing myofibroblasts with markedly increased PDGFRB protein expression and an activated STAT3 signaling pathway. Moreover, the transcriptome of ARPKD organoid myofibroblasts resemble those present in commonly occurring forms of liver fibrosis. PDGFRB pathway involvement was confirmed by the anti-fibrotic effect observed when ARPKD organoids were treated with PDGFRB inhibitors. Besides providing insight into the pathogenesis of congenital (and possibly acquired) forms of liver fibrosis, ARPKD organoids could also be used to test the anti-fibrotic efficacy of potential anti-fibrotic therapies.

          Abstract

          Autosomal recessive polycystic kidney disease (ARPKD) is a genetic disorder which is associated with kidney and liver pathology, including liver fibrosis. Here the authors develop and characterize human liver organoids with a ARPKD mutation, and find that they show aspects of the pathology, including fibrosis.

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          Most cited references82

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          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            KEGG: kyoto encyclopedia of genes and genomes.

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            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                gpeltz@stanford.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                22 October 2021
                22 October 2021
                2021
                : 12
                : 6138
                Affiliations
                [1 ]Department of Anesthesiology, Pain and Perioperative Medicine, Stanford, CA 94305 USA
                [2 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Materials Science and Engineering, , Stanford University, ; Stanford, CA 94305 USA
                [3 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Pathology, , Institute of Stem Cell Biology and Regenerative Medicine (ISCBRM), Stanford University School of Medicine, ; Stanford, CA 94305 USA
                [4 ]GRID grid.482251.8, ISNI 0000 0004 0633 7958, Shih-Yu Chen, Institute of Biomedical Sciences, Academia Sinica, ; Taipei, 11529 Taiwan
                [5 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Perelman School of Medicine at The University of Pennsylvania, ; Philadelphia, PA 19104 USA
                Author information
                http://orcid.org/0000-0003-2000-1353
                http://orcid.org/0000-0002-7418-1313
                http://orcid.org/0000-0002-3531-8102
                http://orcid.org/0000-0001-5918-4543
                http://orcid.org/0000-0001-6191-7697
                Article
                26410
                10.1038/s41467-021-26410-9
                8536785
                34686668
                227a8c87-01dd-4868-9151-903cbc6cdb0d
                © 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
                : 19 March 2021
                : 28 September 2021
                Funding
                Funded by: Y.G. and G.P. were partially supported by awards (1R01DK102182-01A1 NIDDK, 5U01DA04439902 NIDA) made to GP. A.E. and S.C.H. were partially supported by a Stanford Bio-X Interdisciplinary Initiatives Seed Grant and by awards (R01EB027171, R01HL142718) made to SCH. Y.G., G.P. and S.C.H. were partially supported by awards (National Science Foundation CBET 2033302) made to GP and SCH.
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                disease model,bile duct disease,experimental models of disease
                Uncategorized
                disease model, bile duct disease, experimental models of disease

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