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Spatial molecular and cellular determinants of STAT3 activation in liver fibrosis progression in non-alcoholic fatty liver disease

Open AccessPublished:November 21, 2022DOI:https://doi.org/10.1016/j.jhepr.2022.100628

      Highlights

      • Advanced liver fibrosis is the main determinant of mortality in patients with NASH
      • Non-hepatocytes pSTAT3 in NAFLD liver biopsies correlated with fibrosis severity, inflammation and progression to NASH
      • pSTAT3 was enriched in HPCs and SECs as determined by digital spatial profiling of NASH biopsies
      • STAT3 inhibition in mice resulted in reduced liver fibrosis and depletion of HPCs, Kupffer cells and plasmacytoid DCs
      • In conclusion, STAT3 activation in HPCs results in their expansion and may mediate fibrogenesis in NAFLD

      Background & Aims

      The prevalence of non-alcoholic fatty liver disease (NAFLD) and its severe form, non-alcoholic steatohepatitis (NASH), is increasing. Subjects with NASH often develop liver fibrosis and advanced liver fibrosis is the main determinant of mortality in NASH patients. We and others have reported that STAT3 contributes to liver fibrosis and hepatocellular carcinoma in mice.

      Methods

      Here, we explored whether STAT3 activation in hepatocytes and in non-hepatocytes areas, measured by phospho-STAT3 (pSTAT3), is associated with liver fibrosis progression in 133 patients with NAFLD. We further characterized the molecular and cellular determinants of STAT3 activation by integrating spatial distribution and transcriptomic changes in fibrotic NAFLD liver.

      Results

      pSTAT3 scores in non-hepatocytes areas progressively increased with fibrosis severity (r=0.53, p<0.001). Correlation analyses between pSTAT3 scores and expression of 1540 immune- and cancer-associated genes, revealed a large effect of STAT3 activation on gene expression changes in non-hepatocytes areas and confirmed a major role for STAT3 activation in fibrogenesis. Digital spatial transcriptomic profiling was also performed on 13 regions selected in hepatocytes and non-hepatocytes areas from four NAFLD liver biopsies with advanced fibrosis, using a customized panel of markers including pSTAT3, PanCK+CK8/18, and CD45. The regions were further segmented based on pSTAT3 positive or negative staining. Cell deconvolution analysis revealed that activated STAT3 was enriched in hepatic progenitor cells (HPCs) and sinusoidal endothelial cells. Regression of liver fibrosis upon STAT3 inhibition in mice with NASH, resulted in a reduction of HPCs, demonstrating a direct role for STAT3 in HPCs expansion.

      Conclusion

      Increased understanding of the spatial dependence of STAT3 signaling in NASH and liver fibrosis progression could lead to novel targeted treatment approaches.

      Lay Summary

      Advanced liver fibrosis is the main determinant of mortality in patients with NASH. This study showed using liver biopsies from 133 NAFLD patients, that STAT3 activation in non-hepatocytes areas is strongly associated with fibrosis severity, inflammation, and progression to NASH. STAT3 activation was enriched in HPCs and SECs, as determined by innovative technologies interrogating the spatial distribution of pSTAT3. Finally, STAT3 inhibition in mice resulted in reduced liver fibrosis and depletion of HPCs, suggesting that STAT3 activation in HPCs contributes to their expansion and fibrogenesis in NAFLD.

      Graphical abstract

      Keywords

      Abbreviations:

      DSP (digital spatial profiler), HCC (hepatocellular carcinoma), HPC (hepatic progenitor cells), HSC (hepatic stellate cells), LSECs (liver sinusoidal endothelial cells), NAFLD (nonalcoholic fatty liver disease), NASH (nonalcoholic steatohepatitis), pSTAT3 (phospho-STAT3), SEC (sinusoidal endothelial cells), STAT (signal transducer and activator of transcription)

      Introduction

      The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing in the United States (U.S.).
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      Signal transducer and activator of transcription 3 (STAT3) is a transcription factor that belongs to the Janus Kinase-STAT pathway. Phosphorylation at tyrosine Y705 is a key event in canonical STAT3 activation, with multiple upstream inputs that lead to the activation of STAT3.
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      fibrosis/cirrhosis,
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      We and others have shown that STAT3 activation contributes to HCC development and growth in NASH- and obesity-related mouse models.
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      We reported the therapeutic effect of a small-molecule STAT3 inhibitor, TTI-101 (previously named C188-9), in a preclinical model of NASH-related HCC.
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      Multifunctional Effects of a Small-Molecule STAT3 Inhibitor on NASH and Hepatocellular Carcinoma in Mice.
      STAT3 has therefore emerged as a promising target for pharmacological intervention in HCC and TTI-101 is currently in Phase I clinical trial for the treatment of HCC and other solid tumors. We also reported that the tumor growth inhibition was concomitant with improvement in steatosis, inflammation, fibrosis, and liver injury markers.
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      Multifunctional Effects of a Small-Molecule STAT3 Inhibitor on NASH and Hepatocellular Carcinoma in Mice.
      Decreased liver fibrosis by other STAT3 inhibitors in preclinical models was also reported.
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      • Liu C.J.
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      Sorafenib and its derivative SC-1 exhibit antifibrotic effects through signal transducer and activator of transcription 3 inhibition.
      ,
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      • Long J.
      • Zhang H.
      The STAT3 inhibitor S3I-201 suppresses fibrogenesis and angiogenesis in liver fibrosis.
      However, there has been to date no study evaluating the activation of STAT3 and the spatial expression of activated STAT3 in liver fibrosis in NAFLD patients. Thus, we explored whether STAT3 activation, estimated by levels of STAT3 phosphorylated on Y705 (pSTAT3), is associated with liver fibrosis severity, in a cohort of patients with histologically characterized NAFLD. We further integrated pSTAT3 spatial distribution and transcriptomic changes in fibrotic NAFLD liver to characterize the molecular and cellular determinants mediating STAT3-induced liver fibrosis.

      Patients and Methods

      Patients and liver biopsy samples

      Archived formalin-fixed paraffin-embedded (FFPE) liver biopsies from 133 patients with NAFLD, obtained through the percutaneous route using an 18‐gauge core needle, were used for this study. Participants were recruited from The University of Texas Medical Branch at Galveston and written informed consent was obtained from each participant in the study. Demographic and clinical parameters from these patients are shown in Table S1. At collection, biopsies were immediately placed into 10% buffered formalin and processed using a TissueTekVIP tissue processor (Sakura Finetek, Torrance, CA) and paraffin-embedded. Each biopsy was evaluated for fibrosis stage using the criteria reported by Brunt et al.
      • Kleiner D.E.
      • Brunt E.M.
      • Van Natta M.
      • Behling C.
      • Contos M.J.
      • Cummings O.W.
      • et al.
      Design and validation of a histological scoring system for nonalcoholic fatty liver disease.
      (F0: no fibrosis; F1: mild/moderate zone three perisinusoidal fibrosis or portal/periportal fibrosis only; F2: perisinusoidal and portal/periportal fibrosis; F3: bridging fibrosis; F4: cirrhosis) and components of NAFLD Activity Score (NAS): steatosis (S0: <5%, S1: 5-33%, S2: >33-66%, S3: >66%), lobular inflammation (I0: no foci; I1: <2 foci per 200x field; I2: 2-4 foci per 200x field; I3: >4 foci per 200x field) and hepatocyte ballooning (B0: none; B1: few ballooning cells; B2: many cells with prominent ballooning). The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the institutional review boards of the participating institutions.

      Immunohistochemical (IHC) analysis for pSTAT3

      IHC analysis was performed on an automated immunostainer (Leica Bond III IHC Stainer, San Diego, CA). Tissue sections (3-μm) were deparaffinized and underwent heat-induced antigen retrieval using the Tris-EDTA buffer for 20 minutes solution for 20 min. Phospho-STAT3 on Tyr705 (pSTAT3) antibody (Cell Signaling #9145) was used at 1:100 dilution. Digital images were captured at 20X magnification using a whole slide scanner (Leica Aperio ImageScope software) and saved in SVS format (Aperio). Portal triads and lobular inflammatory foci were annotated as non-hepatocytes areas. Liver lobular areas excluding inflammatory foci were annotated as hepatocytes areas. Quantification of pSTAT3 (pSTAT3 score) was developed in a CLIA laboratory, using nuclear V.9 algorithm (Aperio) and product of the staining intensity multiplied by the percentage of nuclei [(3×% of 3+ cells) + (2×% of 2+ cells) + (1× % of 1+ cells)].

      RNA isolation and targeted gene expression profiling

      FFPE tissue blocks were sectioned at a thickness of 3μm (2-3 sections per block). RNA was extracted using the High Pure FFPET RNA Isolation kit (Roche). The concentration of the extracted RNA samples was measured with Qubit and quality control was performed on a bioanalyzer using RNA6000 pico assay. The percentage of RNA fragments above 200 nucleotides was used to adjust RNA input. Gene expression was interrogated using the PanCancer Immune Profiling and PanCancer Pathways panels (NanoString Technologies) in the nCounter® SPRINT platform. Gene expression data were analyzed using NanoString's software nSolver V.4.0 with Advanced Analysis 2.0 plugin. Data were normalized using the Advanced Analysis tool which draws on the NormqPCR R package.
      • Perkins J.R.
      • Dawes J.M.
      • McMahon S.B.
      • Bennett D.L.
      • Orengo C.
      • Kohl M.
      ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT-qPCR quantification cycle (Cq) data.

      GeoMx Digital Spatial Profiler (DSP) whole transcriptome workflow

      Slides (4μm) for four NAFLD liver biopsies with advanced fibrosis were submitted for DSP whole transcriptome sequencing (NanoString). The panel of morphology markers was custom-designed to include pSTAT3 in addition to SYTO13 nuclear stain, PanCK+CK8/18, and CD45. Slides were stained with RNAscope probes and GeoMx DSP oligo-conjugated RNA detection probes and incubated overnight at 37°C. Slides were then washed with equal parts of 4x saline sodium citrate (SSC) and 100% formamide, and dipped into 2xSSC-T (20xSSC, 10% tween-20) to allow coverslips to slide off. Slides were then washed with 2xSSC and transferred to a humidity chamber for antibody staining. Slides were covered with 200μl Buffer W (GeoMx RNA slide prep kit, Nanostring) and incubated at room temperature for 30 min. Buffer W was removed and 200μl of morphology markers solution was applied to each tissue. Slides were stained for one hour in the humidity chamber at room temperature and then washed with 2xSSC. Slides were immediately loaded onto the GeoMX DSP slide holder with 6mL of Buffer S (GeoMx RNA slide prep kit, Nanostring). Files were configured to associate RNA targets and GeoMx readout barcodes. The appropriate scan type and focus channel were selected to populate fluorescence exposure settings. Scan areas were defined for high magnification. A total of 13 regions of interest (ROIs) were selected including 5 non-hepatocytes and 8 hepatocytes areas. The ROIs were further segmented based on pSTAT3 positive or negative staining. The DSP barcodes were UV-cleaved and collected for each ROI and were subsequently dispensed into a 96-well plate and counted. During the library preparation, the DSP barcodes were tagged with their specific ROI location and RNA target identification sequence, thus matching them to their in situ hybridization probes and a unique molecular identifier to deduplicate reads. Sequenced oligonucleotides were then processed and imported back into the GeoMx DSP platform for integration with the slide images and ROI selections for spatially-resolved RNA expression. FASTQ sequencing files were processed into digital count conversion (DCC) digital files using Nanostring’s GeoMx NGS Pipeline software. Quality control checks and data analysis were performed in the GeoMx DSP Data Analysis suite. Data were filtered by the limit of quantitation (LOQ2) and then normalized by the third quartile of all counts.

      Cell deconvolution analyses

      Cell deconvolution analyses were generated in the GeoMx DSP control center, using the spatialdecon geoscript (v1.1, updated April 2021) available at Nanostring’s Geoscript Hub [https://nanostring.com/products/geomx-digital-spatial-profiler/geoscript-hub/]. The analyses were run using the landscape adult liver 10x matrix [https://github.com/Nanostring-Biostats/CellProfileLibrary].
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      • Innes B.T.
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      • Gage B.K.
      • et al.
      Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations.
      CIBERSORTx [https://cibersortx.stanford.edu/] was used to generate cell deconvolution analyses using the adult liver mouse matrix [https://github.com/Nanostring-Biostats/CellProfileLibrary]
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      • Wang R.
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      • Fei L.
      • Sun H.
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      • et al.
      Mapping the Mouse Cell Atlas by Microwell-Seq.
      and RNA-sequencing data from HepPTEN- NASH mouse livers treated by the STAT3 inhibitor C188-9 or placebo for 4 weeks and reported in.
      • Jung K.H.
      • Yoo W.
      • Stevenson H.L.
      • Deshpande D.
      • Shen H.
      • Gagea M.
      • et al.
      Multifunctional Effects of a Small-Molecule STAT3 Inhibitor on NASH and Hepatocellular Carcinoma in Mice.

      Statistical analyses

      Principal component analysis (PCoA) was performed with Euclidian-based distances matrix, generated in R using log2-transformed gene expression values and log10 transformed expression values alongside permutational multivariate analysis of variance (PERMANOVA) test for statistical significance. QIAGEN’s Ingenuity® Pathway Analysis (IPA) core analysis was performed on fibrosis-correlated genes associated with STAT3 activation. Scatter plots and cell deconvolution plots were generated in Graph Prism 9.0.0.

      Results

      Characteristics of the NAFLD subjects

      The demographic and clinical parameters of the 133 NAFLD subjects included in the study are summarized in Table S1. The patients had a median age of 51 years old and were predominantly females (65%). The large majority of subjects were obese (89%) with a median body mass index (BMI) of 41.5, and 48% had type 2 diabetes. The majority of subjects were White with 67% non-Hispanic White and 25% Hispanics. The distribution of liver fibrosis stages was 2% F0, 33% F1, 28% F2, 14% F3, and 23% F4. Thirty-seven % of the subjects had mild steatosis (S1), 43% had moderate steatosis (S2) and 15% had marked steatosis (S3). Of note, 4% of the subjects had cirrhosis and burned-out NASH. The proportion of patients with no ballooning (B0), few ballooning cells (B1), and prominent ballooning (B2) was 32%, 51%, and 15%, respectively. The proportion of patients with no lobular inflammation (I0), mild inflammation (I1), moderate inflammation (I2), and strong inflammation (I3) was 3%, 47%, 44%, and 5%, respectively.

      Hepatic STAT3 activation and liver fibrosis severity

      We first examined the relationship between liver histology features and hepatic levels of STAT3 phosphorylation at tyrosine 705 (pSTAT3), a measurement of STAT3 activation. pSTAT3 staining was observed in both hepatocytes and non-hepatocytes areas (Fig. 1). pSTAT3 staining was successfully scored in hepatocytes and non-hepatocytes areas, in 126 biopsies. While pSTAT3 scores in hepatocytes did not significantly differ between liver fibrosis stages with a median ranging from 2.25 to 10.53, a small positive correlation was observed between hepatocytes pSTAT3 scores and liver fibrosis stages (r=0.24, p=0.034) (Fig. 2A, upper panel). In contrast, non-hepatocytes pSTAT3 scores significantly increased with fibrosis severity (Fig. 2A, lower panel). The median non-hepatocytes pSTAT3 scores were 4.68 in F1, 8.52 in F2, 11.33 in F3, and 13.69 in F4 (p<0.001). These results were further confirmed by a strong positive correlation between non-hepatocytes pSTAT3 scores and liver fibrosis stages (r=0.53, p<0.001). Non-hepatocytes pSTAT3 scores were also significantly higher in NASH patients compared to NAFLD patients without NASH (11.04 vs 5.72, p=0.017) (Fig. 2B, lower panel). This result was confirmed by Spearman correlation analysis, as NAS and non-hepatocytes pSTAT3 scores positively correlated (r=0.22, p=0.05). While no significant changes in non-hepatocytes pSTAT3 scores were observed with steatosis severity (Fig. 2C, lower panel), non-hepatocytes pSTAT3 scores positively correlated with inflammation severity (r=0.34, p=0.002) (Fig. 2D, lower panel) and ballooning (r=0.25, p=0.02) (Fig. 2E, lower panel). Non-hepatocytes pSTAT3 scores increased with inflammation severity from 5.37 in subjects with inflammation scores I0-1, to 11.04 in subjects with I2 and 18.94 in subjects with I3 (p=0.005) (Fig. 2D, lower panel). Patients with prominent ballooning (B2) had higher non-hepatocytes pSTAT3 scores (16.88) compared to those with no ballooning (B0, 5.78) or mild ballooning (B1, 9.04) (p=0.020) (Fig. 2E, lower panel). There was no significant difference in hepatocytes pSTAT3 scores with NASH or severity of steatosis, ballooning, and inflammation (Fig. 2B-E, upper panels).
      Figure thumbnail gr1
      Fig. 1Nuclear staining of pSTAT3. (A) in hepatocytes, and (B) in non-hepatocytes areas, of representative liver biopsies from patients with NAFLD. Scale bar: 200μM.
      Figure thumbnail gr2
      Fig. 2Quantification of pSTAT3 nuclear staining in hepatocytes (top) and non-hepatocytes areas (bottom) in liver biopsies (A) with F1 to F4 fibrosis stages. (B) with or without NASH. (C) with different degrees of steatosis. (D) with different degrees of inflammation. (E) with different degrees of ballooning. Log2-transformed pSTAT3 scores were used. Error bar: interquartile range. Statistical significance was done using Kruskal–Wallis test (more than two groups) or Mann-Whitney test (two groups).

      Hepatic gene expression changes associated with STAT3 activation in NAFLD patients

      Using the targeted PanCancer Immune Profiling and PanCancer Pathways panels, the expression of 770 immune-related genes and of 770 genes related to cancer-associated canonical pathways was successfully measured in RNA extracted from 101 of the same archived liver biopsies. To determine the contribution of STAT3 activation in the fibrogenic process in NAFLD, Spearman correlation analyses were performed between expression levels of these genes and pSTAT3 scores. A total of 120 genes and 319 genes positively correlated with hepatocytes and non-hepatocytes pSTAT3 scores, respectively, including 54 genes in common (Table S2, Fig. 3). In hepatocytes, a strong positive correlation (r>0.5, p<0.001) was observed for TGF beta receptor 2 (TGFBR2), an upstream activator of STAT3, the proto-oncogene c-MYC, a direct target of STAT3 activation, thrombomodulin (THBD), C-X-C motif chemokine receptor 4 (CXCR4/CD184), interleukin 15 receptor A (IL15RA), IL1 receptor-associated kinase 3 (IRAK3), suppressor of cytokine signaling 1 and 3 (SOCS1, SOCS3), interferon-gamma receptor 1 (IFNGR1), fibroblast growth factor receptor 1 (FGFR1) and IL4R. Other upstream activators of STAT 3 or direct downstream targets of STAT3 activation that positively correlated with pSTAT3 scores in hepatocytes are marked in Table S2. In non-hepatocytes areas, a strong positive correlation (r>0.5, p<0.001) was observed for 48 genes, including the direct downstream targets of STAT3 activation: Bcl-2 and hepatocyte growth factor (HGF), the upstream activators of STAT3: platelet-derived growth factor receptor beta (PDGFRB), colony stimulating factor 1 (CSF1) and the proto-oncogene LYN, the collagens col1A2, col3A1 and col5A2, the integrins ITGA5, ITGA9, ITGAX and ITGB1, hepatic progenitor cell markers: activated leukocyte cell adhesion molecule (ALCAM), osteopontin (SPP1) and interleukin enhancer binding factor 3 (ILF3), genes involved in TGFβ1 pathway: SMAD family member 2 (SMAD2), the proto-oncogene ETS1, insulin-like growth factor II (IGFR2), C-X-C motif chemokine ligand 6 (CXCL6) and galectin-3 (LGALS3), cell surface markers CD46, CD47, CD53, CD58 and CD9, pro-fibrotic chemokines: CXCL10 and CXCL12, and the endothelial marker PECAM1. Other upstream activators of STAT3 that positively correlated with non-hepatocytes pSTAT3 scores are marked in Table S2.
      Figure thumbnail gr3
      Fig. 3Correlation between expression levels of selected genes and pSTAT3 scores. (A) with hepatocytes pSTAT3 scores. (B) with non-hepatocytes pSTAT3 scores. r, Spearman’s correlation coefficient. Log10 transformed pSTAT3 scores and log2 transformed gene expressions data were used.
      A similar analysis was performed for genes that negatively correlated with pSTAT3 scores. A total of 62 genes and 283 genes negatively correlated with hepatocytes and non-hepatocytes pSTAT3 scores, respectively, including 32 genes in common (Table S3, Fig. 3). In hepatocytes, strong negative correlations (r<-0.5, p<0.001) were observed for S-phase kinase-associated protein 2 (SKP2) and TNF superfamily member 10 (TNFSF10/TRAIL). In non-hepatocytes areas, strong negative correlations (r<-0.5, p<0.001) were observed for 30 genes, including cell surface markers CEACAM8/CD66b and CD160, interleukins IL10, IL12A, IL15, IL23A and IL27, TIR domain-containing adaptor protein (TIRAP), leukocyte receptor tyrosine kinase (LTK), ETS transcription factor ELK1, and interferon-regulatory factors IRF3 and IRF7.
      IPA analysis confirmed that genes associated with STAT3 activation in non-hepatocytes areas were strongly enriched in the hepatic fibrosis signaling pathway (p=2.0x10-85). Other enriched canonical pathways associated with STAT3 activation in non-hepatocytes areas were PI3K/AKT signaling (p=3.2x10-58); macrophages, fibroblasts, and endothelial cells in inflammation (p=2.5x10-66); Th1 and Th2 activation pathway (p=5.0x10-52); PTEN signaling (p=4.0x10-49); epithelial-mesenchymal transition (p=1.0x10-52); and natural killer cell signaling (6.3x10-44) (Table 1). Upstream regulators included in addition to STAT3, TNF (p=1.6x10-69), IFNγ (p=1.5x10-72), TGFβ1 (p=2.3x10-58), IL-2 (p=1.4x10-52), IL10 (p=2.8x10-51), CDKN2A (p=3.8x10-47), CD3 (p=2.4x10-46), NR3C1 (p=3.5x10-45), IL-17A (p=1.4x10-39), IL-4 (p=1.1x10-38), and IL6 (p=1.9x10-38) (Table 1).
      Table 1IPA core analysis identified top canonical pathways and upstream regulators from pSTAT3 correlated genes in non-hepatocytes areas. Statistical significance was calculated using a right-tailed fisher’s exact test.
      Canonical Pathwaysp values
      Hepatic Fibrosis Signaling Pathway2.0 x 10-85
      Macrophages, Fibroblasts and Endothelial Cells in Inflammation2.5 x 10-66
      Glucocorticoid Receptor Signaling2.0 x 10-63
      PI3K/AKT Signaling3.2 x 10-58
      Epithelial Mesenchymal Transition1.0 x 10-52
      Th1 and Th2 Activation Pathway5.0 x 10-52
      Pattern Recognition Receptors (Bacteria and Viruses)2.0 x 10-49
      PTEN Signaling4.0 x 10-49
      PKR in Interferon Induction and Antiviral Response3.2 x 10-46
      Tumor Microenvironment Pathway2.5 x 10-44
      Natural Killer Cell Signaling6.3 x 10-44
      Senescence Pathway4.0 x 10-43
      Upstream Regulators
      IFNG1.5 x 10-72
      TNF1.6 x 10-69
      TGFB12.3 x 10-58
      IL21.4 x 10-52
      IL102.8x 10-51
      CDKN2A3.8 x 10-47
      CD32.4 x 10-46
      NR3C13.5 x 10-45
      NFkB2.2 x 10-42
      STAT31.0 x 10-40
      IL17A1.4 x 10-39
      CSF15.8 x 10-39
      STAT16.5 x 10-39
      IL41.1 x 10-38
      IL61.9 x 10-38

      Digital spatial transcriptome profiling of hepatocytes and non-hepatocytes areas segmented by pSTAT3 staining

      Spatial transcriptomic profiling was performed on 13 ROIs from four NAFLD liver biopsies with advanced fibrosis, using a custom panel of morphology markers composed of pSTAT3, PanCK+CK8/18, and CD45 (Fig. 4A). The 13 ROIs were further segmented based on pSTAT3 positive or negative staining. Following normalization and filtering by limit of quantitation, expression data were obtained for 13,174 genes. PCoA plots showed a clear separation between hepatocytes and non-hepatocytes areas (Fig. 4B). While pSTAT3+ and pSTAT3- cells within these areas did not fully separate, their expression profiles were still significantly different (p<0.001).
      Figure thumbnail gr4
      Fig. 4Digital spatial transcriptome profiling of ROIs from NASH liver biopsies with advanced fibrosis. (A) Representative ROIs using the following color fluorescence: panCK+CK8/18 (cyan), CD45 (yellow), pSTAT3 (red), and DNA (blue) in hepatocytes (top) and non-hepatocytes/hepatocytes areas (bottom). Scale bar, 100μm. (B) PCoA of gene expression data from whole transcriptome atlas based on hepatocytes and non-hepatocytes areas and pSTAT3 staining. Ellipses were drawn using the standard deviation of point scores. Log10-transformed gene expression data were used.
      Cell deconvolution analysis using an adult liver matrix estimated cell type distribution in pSTAT3+ and pSTAT3- hepatocytes areas (Fig. 5A). As anticipated, hepatocytes were the main cell type with no significant change between pSTAT3+ and pSTAT3- (81.3% vs 82.2%). Similarly, no difference was observed for hepatic stellate cells (HSCs) (3.0% in pSTAT3+ and 3.7% in pSTAT3-). In contrast, the proportion of hepatic progenitor cells (HPCs) was significantly higher in pSTAT3+ hepatocytes areas (1.2%) compared to pSTAT3- hepatocytes areas (0.15%) (FC 8.0, p=0.006). Central venous sinusoidal endothelial cells (SECs) were also enriched in pSTAT3+ hepatocytes areas (9.4%) compared to pSTAT3- hepatocytes areas (5.9%), although not significantly. Central venous SECs were significantly enriched in pSTAT3+ non-hepatocytes areas (8.9%) compared to pSTAT3- non-hepatocytes areas (2.7%) (FC=3.3, p=0.048) (Fig. 5B). Periportal SECs were also enriched in pSTAT3+ non-hepatocytes areas compared to pSTAT3- non-hepatocytes areas (15.3% vs 5%), although not significantly. For immune cells, αβT cells were significantly depleted in pSTAT3+ non-hepatocytes areas (0.6% vs 22.1%, FC=-36.7, p=0.018), suggesting no detection of pSTAT3 in these cells. pSTAT3 was detected but not significantly enriched in γδT cells, Kupffer cells (inflammatory and non-inflammatory macrophages), mature B cells, NK cells and plasma B cells (Fig. 5B).
      Figure thumbnail gr5
      Fig. 5Cell deconvolution plots using adult liver matrix and ROIs transcriptome profiles show changes of cell proportions in (A) hepatocytes and (B) non-hepatocytes areas. The mean of each cell proportion was calculated for hepatocytes and non-hepatocytes areas. Statistical significance was calculated using an unpaired t-test.

      Cell distribution changes in mouse NASH liver upon STAT3 inhibition

      To determine whether STAT3 activation contributed to self-renewal of HPCs or liver SECs in NASH liver, cell deconvolution analysis using a mouse liver matrix was performed on transcriptomic profiles we previously reported,
      • Kleiner D.E.
      • Brunt E.M.
      • Van Natta M.
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      • Contos M.J.
      • Cummings O.W.
      • et al.
      Design and validation of a histological scoring system for nonalcoholic fatty liver disease.
      from NASH liver of HepPTEN- mice treated for 4 weeks by C188-8, a STAT3 inhibitor, or placebo. While no difference was observed for liver SECs (8.1% vs 8.7%), HPCs were significantly depleted upon C188-9 treatment (3.7% vs 5.8%) (FC=-1.6, p=0.048) (Fig. 6). Kupffer cells (2.7% vs 5.4%; FC=-2.0, p<0.001) and plasmacytoid dendritic cells (0.5% vs 1.8%; FC=-3.6, p<0.001) were also depleted upon C188-8 treatment. In addition, while the overall percentage of hepatocytes was unchanged, C188-9 treatment resulted in the reduction of pericentral hepatocytes (30.1% vs 38.6%) (FC=-1.3, p<0.001) and concomitant expansion of periportal hepatocytes (52.6% vs 37.7%) (FC=1.4, p<0.001).
      Figure thumbnail gr6
      Fig. 6Cell deconvolution analysis using mouse liver matrix and transcriptomic profiles show changes of cell proportions in NASH liver between HepPTEN- mice treated by C188-9 or placebo. The mean of each cell proportion was calculated for treated mice and placebo. Statistical significance was calculated using an unpaired t-test.

      Discussion

      STAT3 is strongly associated with liver injury, inflammation, regeneration, and HCC development.
      • Hu Z.
      • Han Y.
      • Liu Y.
      • Zhao Z.
      • Ma F.
      • Cui A.
      • et al.
      CREBZF as a Key Regulator of STAT3 Pathway in the Control of Liver Regeneration in Mice.
      ,
      • He G.
      • Karin M.
      NF-kappaB and STAT3 - key players in liver inflammation and cancer.
      We have also reported a role of STAT3 in liver fibrosis in mice.
      • Jung K.H.
      • Yoo W.
      • Stevenson H.L.
      • Deshpande D.
      • Shen H.
      • Gagea M.
      • et al.
      Multifunctional Effects of a Small-Molecule STAT3 Inhibitor on NASH and Hepatocellular Carcinoma in Mice.
      While it was suggested that STAT3 signaling drives HSC activation to promote liver fibrosis,
      • Meng F.
      • Wang K.
      • Aoyama T.
      • Grivennikov S.I.
      • Paik Y.
      • Scholten D.
      • et al.
      Interleukin-17 signaling in inflammatory, Kupffer cells, and hepatic stellate cells exacerbates liver fibrosis in mice.
      ,
      • Xiang D.M.
      • Sun W.
      • Ning B.F.
      • Zhou T.F.
      • Li X.F.
      • Zhong W.
      • et al.
      The HLF/IL-6/STAT3 feedforward circuit drives hepatic stellate cell activation to promote liver fibrosis.
      molecular and cellular spatial studies of STAT3 in human NASH are lacking. Here, we demonstrated that STAT3 activation strongly correlates with liver fibrosis severity in patients with NAFLD. The correlation was stronger for STAT3 activation in cells other than hepatocytes.
      A major role of STAT3 activation on hepatic fibrosis was further confirmed by IPA of genes whose expression correlated with STAT3 activation in non-hepatocytes areas. Very strong positive correlations with STAT3 activation were observed for several collagens (col1A2, col3A1, col5A2), integrins (ITGA5, ITGA9, ITGAX, ITGB1), and pro-fibrotic chemokines (CXCL10, CXCL12). IPA also suggested that activation of TGFβ signaling was a main mediator of pro-fibrotic STAT3 activity. Indeed, strong correlations were observed with many genes of this pathway. These included TGFBR2, SMAD2, ETS1, IGFR2, CXCL6, and galectin-3. Aberrant TGFβ signaling in conjunction with transdifferentiation of HSCs into fibrogenic myofibroblasts plays a central role in liver fibrosis.
      • Fabregat I.
      • Caballero-Diaz D.
      Transforming Growth Factor-beta-Induced Cell Plasticity in Liver Fibrosis and Hepatocarcinogenesis.
      SMAD proteins are pivotal intracellular effectors of TGFβ in hepatic fibrosis.
      • Xu F.
      • Liu C.
      • Zhou D.
      • Zhang L.
      TGF-beta/SMAD Pathway and Its Regulation in Hepatic Fibrosis.
      Galectin-3 is upregulated in human fibrotic liver disease.
      • Henderson N.C.
      • Mackinnon A.C.
      • Farnworth S.L.
      • Poirier F.
      • Russo F.P.
      • Iredale J.P.
      • et al.
      Galectin-3 regulates myofibroblast activation and hepatic fibrosis.
      In galectin-3 null mice, hepatic fibrosis following liver injury is reduced and TGFβ fails to activate HSCs.
      • Jeftic I.
      • Jovicic N.
      • Pantic J.
      • Arsenijevic N.
      • Lukic M.L.
      • Pejnovic N.
      Galectin-3 Ablation Enhances Liver Steatosis, but Attenuates Inflammation and IL-33-Dependent Fibrosis in Obesogenic Mouse Model of Nonalcoholic Steatohepatitis.
      It has been previously reported that cooperation of STAT3 and TGFβ1 in HSCs exacerbates liver injury and fibrosis
      • Xu M.Y.
      • Hu J.J.
      • Shen J.
      • Wang M.L.
      • Zhang Q.Q.
      • Qu Y.
      • et al.
      Stat3 signaling activation crosslinking of TGF-beta1 in hepatic stellate cell exacerbates liver injury and fibrosis.
      and that STAT3 activation is essential to TGFβ activation of HSCs.
      • Liu Y.
      • Liu H.
      • Meyer C.
      • Li J.
      • Nadalin S.
      • Konigsrainer A.
      • et al.
      Transforming growth factor-beta (TGF-beta)-mediated connective tissue growth factor (CTGF) expression in hepatic stellate cells requires Stat3 signaling activation.
      • Tang L.Y.
      • Heller M.
      • Meng Z.
      • Yu L.R.
      • Tang Y.
      • Zhou M.
      • et al.
      Transforming Growth Factor-beta (TGF-beta) Directly Activates the JAK1-STAT3 Axis to Induce Hepatic Fibrosis in Coordination with the SMAD Pathway.
      • Cierpka R.
      • Weiskirchen R.
      • Asimakopoulos A.
      Perilipin 5 Ameliorates Hepatic Stellate Cell Activation via SMAD2/3 and SNAIL Signaling Pathways and Suppresses STAT3 Activation.
      In our study, STAT3 activation was detected in approximately half of the HSCs and this activation was not required for their proliferation or survival. Future experiments should further characterize these two HSCs subpopulations.
      Two cell surface markers that strongly correlated with STAT3 activation in non-hepatocytes areas were the tetraspanin CD9 and the innate immune regulator CD47. Anti-CD47 antibody treatment attenuates liver inflammation and fibrosis in NASH mouse models.
      • Gwag T.
      • Ma E.
      • Zhou C.
      • Wang S.
      Anti-CD47 antibody treatment attenuates liver inflammation and fibrosis in experimental non-alcoholic steatohepatitis models.
      CD9 is an important cell surface marker associated with liver fibrosis. Single-cell analysis of cirrhotic liver identified a scar-associated TREM2+CD9+ subpopulation of macrophages, which expands in liver fibrosis and is pro-fibrogenic.
      • Ramachandran P.
      • Dobie R.
      • Wilson-Kanamori J.R.
      • Dora E.F.
      • Henderson B.E.P.
      • Luu N.T.
      • et al.
      Resolving the fibrotic niche of human liver cirrhosis at single-cell level.
      In non-hepatocytes areas, strong negative correlations with STAT3 activation in non-hepatocytes areas were observed for IL-10 and IL-15, two major anti-inflammatory and anti-fibrotic cytokines in the liver. IL-10 was also found as an upstream regulator by IPA analysis suggesting an important role in STAT3-mediated liver fibrosis. Mesenchymal stem cells overexpressing IL-10 inhibited liver fibrosis in mice.
      • Choi J.S.
      • Jeong I.S.
      • Han J.H.
      • Cheon S.H.
      • Kim S.W.
      IL-10-secreting human MSCs generated by TALEN gene editing ameliorate liver fibrosis through enhanced anti-fibrotic activity.
      Growing evidence suggests an important role of B cells in the development of NAFLD. B cell harboring but antibody-deficient IgMi mice were completely protected from the development of hepatic steatosis, inflammation, and fibrosis upon a high-fat diet (HFD). HFD reduced the number of regulatory B cells and IL-10 production in the liver.
      • Karl M.
      • Hasselwander S.
      • Zhou Y.
      • Reifenberg G.
      • Kim Y.O.
      • Park K.S.
      • et al.
      Dual roles of B lymphocytes in mouse models of diet-induced nonalcoholic fatty liver disease.
      We found a strong correlation between expression of the endothelial cell marker PECAM1 and STAT3 activation in non-hepatocytes areas. Digital spatial profiling confirmed strong levels of activated STAT3 in LSECs. LSECs are known actors in the fibrogenic response to injury. Activated HSCs, LSECs, and Kupffer cells are responsible for sinusoidal capillarization and perisinusoidal matrix deposition, promoting fibrogenesis.
      • Terkelsen M.K.
      • Bendixen S.M.
      • Hansen D.
      • Scott E.A.H.
      • Moeller A.F.
      • Nielsen R.
      • et al.
      Transcriptional Dynamics of Hepatic Sinusoid-Associated Cells After Liver Injury.
      ,
      • Ramirez-Pedraza M.
      • Fernandez M.
      Interplay Between Macrophages and Angiogenesis: A Double-Edged Sword in Liver Disease.
      STAT3 activation also strongly correlated with the expression of HPCs markers such as ALCAM, SPP1, ILF3, and CXCR4/CD184. Interestingly, activated STAT3 was specifically enriched in HPCs as shown by digital spatial profiling, suggesting that STAT3 activation may lead to either dedifferentiation of hepatocytes or increased cell expansion of HPCs. In the context of HCC, several reports described that STAT3 signaling promotes the expansion of tumor-initiating cells self-renewal and increases the stemness of HCC stem cells.
      • Wan S.
      • Zhao E.
      • Kryczek I.
      • Vatan L.
      • Sadovskaya A.
      • Ludema G.
      • et al.
      Tumor-associated macrophages produce interleukin 6 and signal via STAT3 to promote expansion of human hepatocellular carcinoma stem cells.
      • Chen Z.Z.
      • Huang L.
      • Wu Y.H.
      • Zhai W.J.
      • Zhu P.P.
      • Gao Y.F.
      LncSox4 promotes the self-renewal of liver tumour-initiating cells through Stat3-mediated Sox4 expression.
      • Wang X.
      • Sun W.
      • Shen W.
      • Xia M.
      • Chen C.
      • Xiang D.
      • et al.
      Long non-coding RNA DILC regulates liver cancer stem cells via IL-6/STAT3 axis.
      • Won C.
      • Kim B.H.
      • Yi E.H.
      • Choi K.J.
      • Kim E.K.
      • Jeong J.M.
      • et al.
      Signal transducer and activator of transcription 3-mediated CD133 up-regulation contributes to promotion of hepatocellular carcinoma.
      • Uthaya Kumar D.B.
      • Chen C.L.
      • Liu J.C.
      • Feldman D.E.
      • Sher L.S.
      • French S.
      • et al.
      TLR4 Signaling via NANOG Cooperates With STAT3 to Activate Twist1 and Promote Formation of Tumor-Initiating Stem-Like Cells in Livers of Mice.
      To determine whether STAT3 activation contributes to HPCs and LSECs expansion in NASH liver, we performed deconvolution analysis using a mouse adult liver matrix of transcriptomic data we previously generated on the liver from HepPTEN- mice treated for four weeks with C188-9, a STAT3 inhibitor, or placebo.
      • Jung K.H.
      • Yoo W.
      • Stevenson H.L.
      • Deshpande D.
      • Shen H.
      • Gagea M.
      • et al.
      Multifunctional Effects of a Small-Molecule STAT3 Inhibitor on NASH and Hepatocellular Carcinoma in Mice.
      This analysis revealed that C188-9 treatment results in depletion of HPCs, demonstrating that STAT3 activation in HPCs induces their expansion. We and others previously showed that the proliferation of HPCs is important in HSC activation. It was suggested that increased fibrosis likely occurs by primary progenitor expansion/proliferation and secondary fibrotic myofibroblast expansion, in close contact with progenitors.
      • Greenbaum L.E.
      • Wells R.G.
      The role of stem cells in liver repair and fibrosis.
      ,
      • Grzelak C.A.
      • Martelotto L.G.
      • Sigglekow N.D.
      • Patkunanathan B.
      • Ajami K.
      • Calabro S.R.
      • et al.
      The intrahepatic signalling niche of hedgehog is defined by primary cilia positive cells during chronic liver injury.
      We reported that anti-miR-21 treatment reduced liver fibrosis with a concomitant reduction of CD24+ liver progenitor cells.
      • Zhang J.
      • Jiao J.
      • Cermelli S.
      • Muir K.
      • Jung K.H.
      • Zou R.
      • et al.
      miR-21 Inhibition Reduces Liver Fibrosis and Prevents Tumor Development by Inducing Apoptosis of CD24+ Progenitor Cells.
      Kupffer cells and plasmacytoid dendritic cells were also depleted upon C188-8 treatment. Kupffer cells, labeled as inflammatory and non-inflammatory macrophages in the human liver matrice we used for deconvolution analysis, were not enriched for pSTAT3 in the human liver biopsies. Plasmacytoid dendritic cells were not represented in the human liver matrice. Major limitations of the analysis of immune cells are the lack of canonical markers and standardized classification of immune cells in liver. Translating mice immune cells data to human is also challenging. Finally, there is only partial overlap of the immune cells included in the currently available human and mouse liver matrices.
      While the overall percentage of hepatocytes was unchanged, C188-9 treatment resulted in the reduction of pericentral hepatocytes and concomitant expansion of periportal hepatocytes. Certain liver injuries are zone-dependent, with NAFLD for example, originating in pericentral regions of the lobule in adults.
      • Cunningham R.P.
      • Porat-Shliom N.
      Liver Zonation - Revisiting Old Questions With New Technologies.
      NAFLD and NASH/cirrhosis initially develop in pericentral cells and progress toward periportal regions. Pericentral hepatocytes have increased expression of HPCs markers and can replace most hepatocytes in the lobule during homeostatic renewal. LSECs in pericentral regions are also more susceptible to damage associated with liver cirrhosis compared to periportal LSECs.
      Whether these findings are specific to NAFLD patients or reflect a general mechanism of disease progression should be investigated in follow-up studies. Understanding in particular, the common and distinct molecular and cellular determinants of liver fibrosis progression in the context of NAFLD, alcoholic steatohepatitis and viral hepatitis, would be highly valuable.
      This study strongly increases our understanding of the spatial dependence of main signaling pathways such as STAT3 in NASH and liver fibrosis progression. It also increases our understanding of the role of specific cell types such as HPCs, in liver fibrosis progression. Such information could improve targeted treatment approaches. In addition, future digital spatial profiler experiments would benefit from using different sets of liver cell type markers, such as HSCs, HPCs and SECs markers.

      Financial support

      This study was supported in part by NCI R01CA204665 to L. Beretta and by the MD Anderson Cancer Center support grant CA016672.

      Conflict of interest

      None to disclose

      Author contributions

      J.J. and J.I.S. were responsible for the study concept and design, acquisition of data, interpretation of data, drafting of the manuscript, and statistical analysis; O.S. and H.L.S. were responsible for the collection of samples; L.M.S., D.J.T., and D.M.M. were responsible for technical support; L.B was responsible for study concept and design, interpretation of data, drafting of the manuscript, obtaining funding and study supervision.

      Data Availability Statement

      All data relevant to the study are included in the article or uploaded as Supplementary Material.

      Acknowledgments

      The authors would like to thank for their technical assistance, Ms. Haidee Chancoco from the MD Anderson Cancer Center Biospecimen Extraction Facility, Ms. Indu Raman from the Microarray Core Facility at The University of Texas Southwestern, Dr. Liang Zhang from Nanostring Technologies Inc., Mr. Victor Ortega, Director of the Immunohistochemistry laboratory in the Department of Pathology at MD Anderson Cancer Center as well as Drs. Sharia Hernandez, Frank Rojas Alvarez, and Wei Lu in the Department of Translational Molecular Pathology at MD Anderson Cancer Center.

      Appendix A. Supplementary data

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