The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
An autocrine signaling circuit in hepatic stellate cells underlies advanced fibrosis in nonalcoholic steatohepatitis
Open accessAbstract
Advanced hepatic fibrosis, driven by the activation of hepatic stellate cells (HSCs), affects millions worldwide and is the strongest predictor of mortality in nonalcoholic steatohepatitis (NASH); however, there are no approved antifibrotic therapies. To identify antifibrotic drug targets, we integrated progressive transcriptomic and morphological responses that accompany HSC activation in advanced disease using single-nucleus RNA sequencing and tissue clearing in a robust murine NASH model. In advanced fibrosis, we found that an autocrine HSC signaling circuit emerged that was composed of 68 receptor-ligand interactions conserved between murine and human NASH. These predicted interactions were supported by the parallel appearance of markedly increased direct stellate cell-cell contacts in murine NASH. As proof of principle, pharmacological inhibition of one such autocrine interaction, neurotrophic receptor tyrosine kinase 3–neurotrophin 3, inhibited human HSC activation in culture and reversed advanced murine NASH fibrosis. In summary, we uncovered a repertoire of antifibrotic drug targets underlying advanced fibrosis in vivo. The findings suggest a therapeutic paradigm in which stage-specific therapies could yield enhanced antifibrotic efficacy in patients with advanced hepatic fibrosis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Recent single-cell human liver studies have largely focused on chronic liver disease rather than ALF, with emphasis on non-parenchymal cell populations7–9”
From Multimodal decoding of human liver regeneration (2024) · cites this paper as [9]
The cited source says what the claim attributes to it.
In the citing paper
“the FAT-MASH model was used, as this model reproducibly recapitulates the stages of progression of human MASH that include advanced fibrosis and tumors along with changes in the gut microbiome.14,33,34”
From EHBP1 suppresses liver fibrosis in metabolic dysfunction-associated steatohepatitis (2025) · cites this paper as [33]
In the citing paper
“We recently used single nuclear RNA-seq in murine MASH models and patients with MASH to demonstrate enrichment of pathways in myofibroblasts that regulate extracellular matrix production and cell migration, including matrix metalloproteinase (MMPs), tissue inhibitors of metalloproteinases (TIMPs), and lysyl oxidases (LOXLs) (47-49), similar to earlier studies of mouse fibrosis and human cirrhosis”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“Our profiling of FAT-MASH macrophages using single nucleus RNA-seq showed no enrichment of ECM-related pathways, only induction of a few select matrix metalloproteinases (MMP2, matrix metalloproteinase 12 (MMP12), matrix metalloproteinase 14 (MMP14)) and decreased collagen type 18 alpha 1 (COL18A1) (48).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“The detection of both quiescent and activated HSCs in mouse liver injury models using single cell RNA-seq reinforces HSC activation as a driver of fibrosis common across etiologies that includes MASH (46-50).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“Although activated HSCs are the primary fibrogenic cells in MASH, these methods have identified an expanded list of fibrogenic drivers expressed by activated HSCs that could represent antifibrotic drug targets (42, 48, 49, 125).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“These different communication networks are reflected in the differential expression of ligand-receptor signaling pairs, suggesting that the repertoire of disease drivers – and therefore the underlying signaling events – may change as the disease advances, such that advanced fibrotic stages may respond to different pharmacotherapies than earlier stages (48, 49).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“Applying recent developments in informatic tools and databases (130, 131) has uncovered an altered cell-cell communication network in MASH based on the differential expression of ligands and receptors in different cell types (48, 49).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“It is worth noting that the FAT-MASH mice exhibit transcriptomic similarities to human MASH patients (48), and their microbiome changes also resemble human MASH pathology (197).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“To improve the quantitative measurement of fibrosis in liver biopsies, several robust platforms have been established that leverage machine or deep learning to precisely quantify not only the amount of scar, but also structural features of the extracellular matrix that may reflect progression or regression (48, 258, 290, 291).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“3) Identification of HSC subpopulations that are the main fibrosis drivers will lead to more targeted, safer therapies (42, 48).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [48]
In the citing paper
“In advanced (human or rodent) hepatic fibrosis, HSC directly interacts with surrounding HSCs, driven by autocrine pathways that amplify fibrogenesis.112”
From An integrated view of anti-inflammatory and antifibrotic targets for the treatment of NASH (2023) · cites this paper as [112]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mechanisms of NAFLD development and therapeutic strategies
Friedman, S. L., Neuschwander-Tetri, B. A., Rinella, M., & Sanyal, A. J. (2018) · Nature Medicine · doi:10.1038/s41591-018-0104-9 · Cited by 4,354
Self-citationEfremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449
Angulo, P., Kleiner, D. E., Dam-Larsen, S., Adams, L. A., Bjornsson, E. S., Charatcharoenwitthaya, P., Mills, P. R., Keach, J. C., Lafferty, H. D., Stahler, A., et al. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.04.043 · Cited by 2,987
Mechanisms and disease consequences of nonalcoholic fatty liver disease
Loomba, R., Friedman, S. L., & Shulman, G. I. (2021) · Cell · doi:10.1016/j.cell.2021.04.015 · Cited by 2,002
Self-citationiDISCO: A Simple, Rapid Method to Immunolabel Large Tissue Samples for Volume Imaging
Renier, N., Wu, Z., Simon, D. J., Yang, J. H., Ariel, P., & Tessier‐Lavigne, M. (2014) · Cell · doi:10.1016/j.cell.2014.10.010 · Cited by 1,856
Cross-tissue organization of the fibroblast lineage
Buechler, M. B., Pradhan, R., Krishnamurty, A. T., Cox, C. B., Calviello, A. K., Wang, A. W., Yang, Y. A., Tam, L., Caothien, R. H., Roose‐Girma, M., et al. (2021) · Nature · doi:10.1038/s41586-021-03549-5 · Cited by 1,100
Human hepatic stellate cell lines, LX-1 and LX-2: new tools for analysis of hepatic fibrosis
Xu, L., Hui, A. Y., Albanis, E., Arthur, M. J., O’Byrne, S. M., Blaner, W. S., Mukherjee, P., Friedman, S. L., & Eng, F. J. (2004) · Gut · doi:10.1136/gut.2004.042127 · Cited by 1,020
Self-citationSpatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches
Guilliams, M., Bonnardel, J., Haest, B., Vanderborght, B., Wagner, C., Remmerie, A., Bujko, A., Martens, L., Thoné, T., Browaeys, R., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.018 · Cited by 985
Vilar-Gomez, E., Calzadilla-Bertot, L., Wai-Sun Wong, V., Castellanos, M., Aller-de la Fuente, R., Metwally, M., Eslam, M., Gonzalez-Fabian, L., Alvarez-Quiñones Sanz, M., Conde-Martin, A. F., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.04.034 · Cited by 806
Xiong, X., Kuang, H., Ansari, S., Liu, T., Gong, J., Wang, S., Zhao, X.-Y., Ji, Y., Li, C., Guo, L., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.07.028 · Cited by 784
Self-citationA single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors
Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0844-1 · Cited by 758
Mechanisms of Fibrosis Development in Nonalcoholic Steatohepatitis
Schwabe, R. F., Tabas, I., & Pajvani, U. B. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.11.311 · Cited by 671
Lassailly, G., Caiazzo, R., Ntandja-Wandji, L.-C., Gnemmi, V., Baud, G., Verkindt, H., Ningarhari, M., Louvet, A., Leteurtre, E., Raverdy, V., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.06.006 · Cited by 622 · cited 2×
“The whole-tissue fibrosis phenotype was described for its collagen content and structure (12 traits), the morphometric traits of the segmented collagen fibers (12), and fibrosis architecture traits (7).”
BackgroundTsuchida, T., Lee, Y. A., Fujiwara, N., Ybanez, M., Allen, B., Martins, S., Fiel, M. I., Goossens, N., Chou, H.-I., Hoshida, Y., et al. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2018.03.011 · Cited by 583
Self-citationDrilon, A. E. D. C., Nagasubramanian, R., Blake, J. F., Ku, N. C., Tuch, B. B., Ebata, K. T., Smith, S., Lauriault, V., Kolakowski, G. R., Brandhuber, B. J., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0507 · Cited by 460
Circuit Design Features of a Stable Two-Cell System
Zhou, X., Franklin, R. A., Adler, M., Jacox, J. B., Bailis, W., Shyer, J. A., Flavell, R. A., Mayo, A., Alon, U., & Medzhitov, R. (2018) · Cell · doi:10.1016/j.cell.2018.01.015 · Cited by 430
Self-citationSingle-nucleus cross-tissue molecular reference maps toward understanding disease gene function
Eraslan, G., Drokhlyansky, E., Anand, S. K., Fiškin, E., Subramanian, A., Slyper, M., Wang, J., Wittenberghe, N. V., Rouhana, J. M., Waldman, J., et al. (2022) · Science · doi:10.1126/science.abl4290 · Cited by 406
Niki, T., Pekny, M., Hellemans, K., Bleser, P. D., Berg, K. V. D., Vaeyens, F., Quartier, E., Schuit, F. C., & Geerts, A. E. (1999) · Hepatology · doi:10.1002/hep.510290232 · Cited by 281
Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
Ramachandran, P., Matchett, K. P., Dobie, R., Wilson-Kanamori, J. R., & Henderson, N. C. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253
Self-citationHuman and Rat Hepatic Stellate Cells Express Neurotrophins and Neurotrophin Receptors
Cassiman, D. M., Denef, C., Desmet, V. J., & Roskams, T. (2001) · Hepatology · doi:10.1053/jhep.2001.20793 · Cited by 250
Regulation of Hepatic Stellate Cell Differentiation by the Neurotrophin Receptor p75 NTR
Passino, M. A., Adams, R. A., Sikorski, S. L., & Akassoglou, K. (2007) · Science · doi:10.1126/science.1137603 · Cited by 181
Principles of Cell Circuits for Tissue Repair and Fibrosis
Adler, M., Mayo, A., Zhou, X., Franklin, R. A., Meizlish, M. L., Medzhitov, R., Kallenberger, S. M., & Alon, U. (2020) · iScience · doi:10.1016/j.isci.2020.100841 · Cited by 177
Self-citationGlial fibrillary acidic protein - a cell type specific marker for Ito cells in vivo and in vitro
Neubauer, K., Knittel, T. E., Aurisch, S., Fellmer, P., & Ramadori, G. P. (1996) · Journal of Hepatology · doi:10.1016/s0168-8278(96)80269-8 · Cited by 175
Lef1 expression in fibroblasts maintains developmental potential in adult skin to regenerate wounds
Phan, Q. M., Fine, G. M., Salz, L., Herrera, G. G., Wildman, B., Driskell, I. M., & Driskell, R. R. (2020) · eLife · doi:10.7554/elife.60066 · Cited by 171
Synaptophysin: A Novel Marker for Human and Rat Hepatic Stellate Cells
Cassiman, D. M., van Pelt, J. F., Vos, R. D., Lommel, F. V., Desmet, V. J., Yap, S. H., & Roskams, T. (1999) · American Journal Of Pathology · doi:10.1016/s0002-9440(10)65501-0 · Cited by 147
Knittel, T. E., Aurisch, S., Neubauer, K., Eichhorst, S. T., & Ramadori, G. P. (1996) · Europe PMC (PubMed Central) · Cited by 138
Lee, Y. A., & Friedman, S. L. (2014) · Antiviral Research · doi:10.1016/j.antiviral.2014.03.012 · Cited by 132
Self-citationSuzuki, K., Tanaka, M., Watanabe, N., Saito, S., Nonaka, H., & Miyajima, A. (2008) · Gastroenterology · doi:10.1053/j.gastro.2008.03.075 · Cited by 91
Positional Stability and Membrane Occupancy Define Skin Fibroblast Homeostasis In Vivo
Marsh, E. D., Gonzalez, D. G., Lathrop, E., Boucher, J. D., & Greco, V. (2018) · Cell · doi:10.1016/j.cell.2018.10.013 · Cited by 81
Carter, J. K., Bhattacharya, D., Borgerding, J. N., Fiel, M. I., Faith, J. J., & Friedman, S. L. (2021) · PLOS ONE · doi:10.1371/journal.pone.0244763 · Cited by 52
Self-citationTucker, R. D., Ciofoaia, V., Nadella, S., Gay, M. D., Cao, H., Huber, M., Safronenka, A., Shivapurkar, N., Kallakury, B., Kruger, A. J., et al. (2019) · Digestive Diseases and Sciences · doi:10.1007/s10620-019-05722-3 · Cited by 19
Flagged first, then by citations
Cited by
Sorted by citations
Hepatic inflammatory responses in liver fibrosis
Hammerich, L., & Tacke, F. (2023) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-023-00807-x · Cited by 803
The NLRP3 inflammasome: contributions to inflammation-related diseases
Chen, Y., Ye, X., Escames, G., Lei, W., Zhang, X., Li, M., Jing, T., Yao, Y., Qiu, Z., Wang, Z., et al. (2023) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-023-00462-9 · Cited by 492
An integrated view of anti-inflammatory and antifibrotic targets for the treatment of NASH
Tacke, F., Puengel, T., Loomba, R., & Friedman, S. L. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2023.03.038 · Cited by 321
Self-citationThe tumor microenvironment shows a hierarchy of cell-cell interactions dominated by fibroblasts
Mayer, S., Milo, T., Isaacson, A., Halperin, C., Miyara, S., Stein, Y., Lior, C., Pevsner‐Fischer, M., Tzahor, E., Mayo, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41518-w · Cited by 194
Self-citationMolecular mechanisms in MASLD/MASH-related HCC
Wang, X., Zhang, L., & Dong, B. (2024) · Hepatology · doi:10.1097/hep.0000000000000786 · Cited by 193
Metadata
- Authors
- Wang, S., Li, K., Pickholz, E., Dobie, R., Matchett, K. P., Henderson, N. C., Carrico, C., Driver, I., Borch Jensen, M., Chen, L., Petitjean, M., Bhattacharya, D., Fiel, M. I., Liu, X., Kisseleva, T., Alon, U., Adler, M., Medzhitov, R., & Friedman, S. L.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Centre for Inflammation Research (GB)
- Gordian Biotechnology, San Francisco, CA 94107, USA.
- PharmaNest Inc., Princeton, NJ 08540, USA.
- University of California San Diego (US)
- Weizmann Institute of Science (IL)
- Yale University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Friedman, Scott L.
- ORCID
- Venue
- Science Translational Medicine
- Published
- 2023-01-04
- Volume
- 15
- Issue
- 677
- Pages
- eadd3949-eadd3949
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- PMC
- PMC10686705
- OpenAlex
- W4313544888
- Cited by
- 176
- References
- 31
- Citations by year
- 2026: 43 · 2025: 51 · 2024: 40 · 2023: 41
- FWCI
- 29.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Gastroenterological Association
- Wellcome Trust (219542/Z/19/Z)
- Cancer Research UK (A27145, 27145, C19767/A27145)
- National Institutes of Health (DK120515, UL1TR004419)
- National Institute on Alcohol Abuse and Alcoholism (U01AA029019, R01AA028550, P50AA011999)
- National Cancer Institute (P30CA196521)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK111866, R01DK056621, R01DK128289, R01DK136016, P30DK120515, R01DK101737, R01DK099205)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Hepatology › Liver physiology and pathology
- Health Sciences › Medicine › Hepatology › Liver Disease Diagnosis and Treatment
- Health Sciences › Medicine › Hepatology › Liver Diseases and Immunity
- Keywords
- hepatic stellate cells, nonalcoholic steatohepatitis, liver fibrosis, autocrine signaling, ligand-receptor interactions, NTRK3, neurotrophin-3, stellate cell activation, single-nucleus RNA sequencing, tissue clearing
- MeSH terms
- Animals, Fibrosis, Humans, Liver, Liver Cirrhosis, Autocrine Communication, Mice, Hepatic Stellate Cells, Non-alcoholic Fatty Liver Disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
