Abstract
Background
Fibrosis is an important pathological process in the development of non-alcoholic steatohepatitis (NASH), and the activation of hepatic stellate cell (HSC) is a central event in liver fibrosis. However, the transcriptomic change of activated HSCs (aHSCs) and resting HSCs (rHSCs) in NASH patients has not been assessed. This study aimed to identify transcriptomic signature of HSCs during the development of NASH and the underlying key functional pathways.
Methods
NASH-associated transcriptomic change of HSCs was defined by single-cell RNA-sequencing (scRNA-seq) analysis, and those top upregulated genes were identified as NASH-associated transcriptomic signatures. Those functional pathways involved in the NASH-associated transcriptomic change of aHSCs were explored by weighted gene co-expression network analysis (WGCNA) and functional enrichment analyses. Key regulators were explored by upstream regulator analysis and transcription factor enrichment analysis.
Results
scRNA-seq analysis identified numerous differentially expressed genes in both rHSCs and aHSCs between NASH patients and healthy controls. Both scRNA-seq analysis and in-vivo experiments showed the existence of rHSCs (mainly expressing a-SMA) in the normal liver and the increased aHSCs (mainly expressing collagen 1) in the fibrosis liver tissues. NASH-associated transcriptomic signature of rHSC (NASHrHSCsignature) and NASH-associated transcriptomic signature of aHSC (NASHaHSCsignature) were identified. WGCNA revealed the main pathways correlated with the transcriptomic change of aHSCs. Several key upstream regulators and transcription factors for determining the functional change of aHSCs in NASH were identified.
Conclusion
This study developed a useful transcriptomic signature with the potential in assessing fibrosis severity in the development of NASH. This study also identified the main pathways in the activation of HSCs during the development of NASH.
Introduction
Non-alcoholic fatty liver disease (NAFLD), as the main liver disease manifestation of metabolic diseases, is closely related to obesity, insulin resistance, type 2 diabetes, and dyslipidemia (1, 2). With the improvement of living standards and changes in lifestyles, the incidence of NAFLD has been increasing worldwide, and it has become one of the most important causes of chronic liver dysfunction (2, 3). NAFLD ranges from simple steatosis to nonalcoholic steatohepatitis (NASH), which is characterized by the ballooning of hepatocytes and inflammation (4, 5, 6). NAFLD is now the most common chronic liver disease with a global prevalence of nearly 25%, while the global prevalence of NASH is estimated at 3% to 5% and NASH is the second leading cause of liver transplantation and poses a heavy financial burden (7, 8, 9).
Fibrosis is a major determinant of clinical outcome in patients with NASH and is associated with an increased risk of cirrhosis and liver cancer (10, 11, 12). In addition, the occurrence of liver fibrosis is a significant predictor of all-cause and liver-related disease mortality in NAFLD, and the risk of liver-related mortality increases exponentially with higher fibrosis stages (13, 14). However, there is lack of effective treatments for fibrosis in NASH (15). Therefore, it is important to explore the mechanisms of liver fibrosis of NASH and uncover the key treatment targets. Liver fibrosis is mainly characterized by the overproduction and deposition of extracellular matrix (ECM) (16). Under normal condition, ECM produced by hepatic stellate cells (HSCs) during liver injury and tissue repair can be hydrolyzed by matrix metalloproteinases (MMPs). However, an imbalance between ECM synthesis and degradation can lead to the development of liver fibrosis (16, 17, 18). Activation of the HSCs is currently regarded as a central event in liver fibrosis (19, 20). Studies over the last two decades have shown that myofibroblasts (MFBs) are the main cells producing ECM during various chronic liver injuries, while HSCs remain the main source of MFBs in various clinical and experimental liver fibrosis models (21, 22, 23). HSCs can transform into MFBs and express α-SMA in large quantities, and this transformation is a key event in the progression of liver fibrosis (24).
NASH-related fibrosis is an important pathological process in the progression of NAFLD, but the risk of developing fibrosis cannot be predicted by liver tissue biopsy. Fibrosis is an important pathological process in the development of NASH, and the activation of HSC is a central event in liver fibrosis. However, the transcriptomic change of activated HSCs (aHSCs) and resting HSCs (rHSCs) in NASH patients has not been assessed. In this study, we analyzed the single-cell RNA-sequencing (scRNA-seq) data of liver tissues from NASH patients and healthy controls. This study aimed to identify the transcriptomic signature of HSCs during the development of NASH and the underlying key functional pathways.
Materials and methods
scRNA-seq data of NASH and healthy control liver tissues
scRNA-seq data of NASH and healthy control liver tissues from Gene Expression Omnibus (GEO) were analyzed in our study. The data were from GSE136103 (Supplementary Table 1, see section on supplementary materials given at the end of this article). The Seurat package was used in the scRNA-seq analyses, and SCTtransform approach was used in integrating data from multiple samples (25). Cells were clustered with both uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) methods, and the types of cell clusters were defined with previously defined makers of distinct cells. The signature genes highly expressed in each cluster but not in the other clusters were identified with Seurat package, and the differentially expressed genes (DEGs) of HSCs between NASH patients and controls were further calculated.
Transcriptomic profile datasets
We searched the transcriptomic profile datasets of liver tissues from NAFLD patients in GEO. Ultimately, GSE49541, GSE126848, and GSE130970 were included as validation datasets of our study, and all those three datasets were transcriptomic profile datasets of liver tissues from NAFLD patients. In addition, we used GSE148849, a transcriptomic dataset of HSC stimulated by TGF-β in vitro, to assess the changes of NASH-related signature gene sets during HSCs activation. DESeq2 of R package was used to determine DEGs for RNA-sequencing (RNA-seq) datasets, and limma package was used to determine DEGs for microarray datasets. We further validated the major findings from scRNA-seq analyses by bulk RNA-seq data containing 206 patients with NAFLD and further evaluated the clinical significance of key transcriptomic signatures (GSE135251). The characteristics of those publicly available transcriptomic datasets used in the analysis are shown Supplementary Table 2.
Gene set variation analysis
Gene set variation analysis (GSVA) is a non-parametric unsupervised analysis method mainly used to evaluate the results of gene set enrichment in transcriptomes (26). To verify the enrichment of the HSC-related signature gene sets in scRNA-seq data, we used GSM4041162 for GSVA. In addition, to verify the enrichment of the signature gene set for TGF-β stimulated HSC activation, we used GSE148849 for GSVA.
Gene set enrichment analysis
Gene set enrichment analysis (GSEA) is a computational method that determines whether an a priori-defined set of genes shows statistically significant and consistent differences between two biological states (27). To determine the enrichment of the signature gene set for TGF-β-stimulated HSC activation, we used GSE148849 for GSEA. In addition, to explore the enrichment of the signature gene sets developed above in the differentially expressed genes (DEGs) list for the progression from NAFL to NASH or fibrosis progression, we also performed GSEA using data from our previous study (28).
Weighted gene co-expression network analysis
To further clarify the possible functional pathways related to the enrichment of signature gene sets, weighted gene co-expression network analysis (WGCNA) was further performed using the R package ‘WGCNA’. WGCNA is a systematic method used to describe the gene co-expression patterns between different samples that can be used to identify highly synergistic co-expression modules (29). GSE49541 was used in this study including 72 patients with NAFLD.
Upstream regulator and transcription factor analyses
In this study, we further explored those key regulators or transcription factor analyses for determining the functional change of aHSCs in NASH. To infer the upstream regulators from the DEGs in aHSCs between NASH and controls, quaternary test statistical analyses were performed with the R package of QuaternaryProd (30). To identify those transcription factors for determining the functional change of aHSCs in NASH, transcriptional regulatory networks of those DEGs in aHSCs between NASH and controls were explored with the Cytoscape plugin of iRegulon, which could detect enriched transcription factor motifs and their direct targets (31).
Experimental animals
C57L/J male mice were obtained from Xiamen University Experimental Animal Center. Mice were fed standard rodent chow ad libitum and housed on woodchip beds. Mice were randomly divided into NASH group and control group at 12 weeks of age. The NASH mice model group was fed a high-fat and choline methionine-deficient diet (HFMCD) (A06071301B16; Research Diets, New Brunswick NJ) for 10 weeks. The control mice were fed standard rodent chow ad libitum.
DBA/2J mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. and were placed in the Xiamen University Experimental Animal Center. Mice were administered intraperitoneally at a dose of 5 μL of 10% CCl4 olive oil solution per gram of body weight. CCl4 injections were started at 12 weeks of age, twice a week for 6 weeks. Olive oil was used in control mice.
Histological examination
The liver specimens of each group were fixed in 10% buffered neutral formalin for 24 h. The fixed specimens were routinely processed to obtain 4–5 μm thick paraffin sections for histological and immunohistochemical evaluation. Sections of each group were stained with Masson trichrome and hematoxylin and eosin (H&E) stain. Collagen 1 (1:200, ab5694, Abcam) and anti-a-SMA (1:200, 72026T, CST) were used for immunohistochemical staining. The samples were then observed and photographed under a microscope. For immunofluorescence staining, liver sections were stained with FITC fluorescent anti-a-SMA antibody (72026T, CST) and cy3 fluorescent anti-collagen 1 antibody (ab5694, Abcam). Nuclei were then stained with DAPI, and the liver sections were then observed and photographed using a fluorescence microscope.
Statistical analysis
Expressions of key genes in liver tissues were presented with median and interquartile range, and difference between advanced fibrosis liver tissues and mild fibrosis liver tissues was determined with Mann-Whitney U test. Difference in the enrichment scores from GSVA between groups was determined with unpaired t-test. The performance of enrichment scores from GSVA in diagnosing fibrosis or NASH among NAFLD patients was explored by receiver operating characteristic (ROC) analysis and area under the curve (AUC) was calculated. R software (version 3.6.1) was used in statistical analyses. P < 0.05 was considered to be significant.
Results
Single-cell transcriptomics in liver identified HSCs subset-specific transcriptomic signatures
A total of six liver samples of single-cell transcriptomics were included in our study, and they were GSM4041151, GSM4041156, GSM4041157, GSM4041159, GSM4041162, and GSM4041163. The main characteristics of these samples are shown in Supplementary Table 1. These six samples contained four healthy controls and two NAFLD samples with fibrosis. After integration by SCTtransform approach, scRNA-seq analyses were subsequently performed. Those cells were classified into 19 clusters by the tSNE or UMAP algorithm (Fig. 1A and B). Two main HSCs subsets including aHSC and rHSC were identified, and they were both identified in the liver tissues of NASH patients and healthy controls (Fig. 1C).
Single-cell transcriptomics of liver tissues identified HSCs subsets and the transcriptomic signatures (A, t-SNE plot of cell clusters in liver tissues; B, UMAP plot of cell clusters in liver tissues; C, Comparative analysis cell clusters in liver tissues between NASH and healthy controls).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
Through scRNA-seq analyses, we further identified the signature genes expressed by each cell cluster. The signature genes of aHSC and rHSC are shown in Table 1. For instance, rHSCs highly expressed RGS5, NDUFA4L2, MYH11, RERGL, etc., and aHSC highly expressed LUM, COL3A1, DPT, PCOLCE, etc. To facilitate the analysis of HSCs alterations using bulk transcriptomic data, we constructed the transcriptomic signature of activated HSC (aHSCsignature) and transcriptomic signature of resting HSC (rHSCsignature) based on the above results. The aHSCsignature consisted of top 54 genes that were highly expressed in aHSCs. rHSCsignature consisted of top 79 genes that were highly expressed in rHSCs (Table 1). Supplementary Fig. 1 shows the genes that were highly expressed in each cell subpopulation in the scRNA-seq analyses (Supplementary Fig. 1).
Gene lists of the signature gene sets developed in this study
| Gene signatures | Gene list |
|---|---|
| rHSCsignature | RGS5, NDUFA4L2, MYH11, RERGL, COX4I2, MAP3K7CL, RCAN2, NET1, PLN, NOTCH3, LMOD1, SERPINI1, PGF, C2orf40, RASD1, NTRK2, CNN1, KLHL23, TBX2, FOXS1, KCNK17, PPP1R12B, HIGD1B, EFHD1, RERG, NRGN, GPR20, GUCY1B3, RRAD, RBPMS2, LGI4, MRGPRF, MRVI1, HRC, CAP2, GRIP2, ACTG2, HEY2, FAM162B, MYOZ1, DMPK, EDNRA, ENPEP, VASN, ITGA7, NT5DC2, CDH6, PRKG1, ARHGEF25, SYNM, RASL12, NTRK3, PDGFA, FAM129A, CLMN, ARPC1A, SMIM10, PCSK7, AOC3, PTK2, MTHFD2, TTLL7, UBA2, GPRC5C, PPP1R12A, TMEM51, PHLDA2, HACD1, KCNMB1, SPECC1, HOXB2, GRK5, OAT, TLE1, ILK, PACSIN2, CCNI, PPP1R15A, ROCK1 |
| aHSCsignature | LUM, COL3A1, DPT, PCOLCE, GGT5, ASPN, FBLN1, OLFML3, COL14A1, ITGBL1, COLEC10, ISLR, ADAMTSL2, PDGFRA, COL5A1, SFRP4, OGN, PODN, THBS2, GPC3, LOXL1, STMN2, LAMA2, ABCA8, COL5A2, CH25H, SAMD11, LAMC3, THY1, ADAMTS13, CXCL14, CYGB, ANGPTL6, CTSK, ADAMTS2, CLEC11A, GPC6, AGTR1, RCN3, COL6A3, CCBE1, CTHRC1, CRABP2, CCL19, PDLIM4, LAMB1, FGF7, FSTL3, FKBP10, CERCAM, QSOX1, CCDC146, CYBRD1, CTSF |
| NASHrHSCsignature | ID3, MALAT1, CRIP1, IGFBP7, MTRNR2L12, NDUFS5, PHLDA1, PLAC9, CRISPLD2, CSRP2, NOTCH3, CCDC102B, HIGD1B, RASD1, CRIP2, NR4A1, NDUFA4L2, UBN2, MTRNR2L8, ANGPTL4, MT2A, ITM2C, PDGFRB, SNCG, COX4I2, FABP5, ADIRF, COL1A2, CCL2 |
| NASHaHSCsignature | IFITM1, MIF, COL1A1, S100A11, COL1A2, LGALS1, PDLIM3, LGALS3, SPON2, NNMT, NBEAL1, CTHRC1, VCAN, SERPINF1, COL3A1, SELM, MGP, CLEC11A, PPIB, PARK7, MTRNR2L12, TIMP1, FN1, FHL2, EMP3, COL5A1, IGFBP7, SERPINE1, NBL1, FAM127A, CRABP2, REXO2, MDK, MXRA8, LMNA, LRP1, SPARC, COL4A2, INAFM1, SPRY1, VIMP, PRDX4, TNFRSF12A, COX7A1, COL5A2, PHPT1, LXN, HCFC1R1, IGFBP6, PFDN4, PDLIM2, ARID5B, COL6A3, CD81, ISLR, ST3GAL4, PDGFRA, TMEM204, PYCR1, LTBP3, THY1 |
Note: The criteria for inclusion of genes in rHSCsignature or aHSCsignature were the log2 of fold change > 0.5 and adjusted P value < 0.05. The criteria for inclusion of genes in NASHrHSCsignature or NASHaHSCsignature were the log2 of fold change > 0.3 and P value < 0.05. Genes with significant findings in other relevant cell subsets were not included. Full names of those gene symbols were shown in the supplementary document.
To verify whether the above-mentioned transcriptomic signatures can be used to define the corresponding HSC subsets, we evaluated the enrichment of the above two transcriptomic signatures in liver tissue of GSM4041162 using GSVA (Supplementary Fig. 2). As shown in Supplementary Fig. 2, rHSCsignature significantly enriched in rHSCs cluster but not in other cell clusters, indicating that the rHSCsignature can be used to represent rHSCs. aHSCsignature significantly enriched in aHSCs cluster but not in other cell clusters, indicating that the aHSCsignature can be used to represent aHSCs (Supplementary Fig. 2).
Single-cell transcriptomics identified NASH-specific transcriptomic signatures in both rHSC and aHSCs
We identified the genes abnormally changed in each HSC subset of NASH patients through scRNA-seq analysis (Fig. 2A and B). As shown in Fig. 2, aHSCs in NASH patients expressed higher levels of IFITM1, MIF, COL1A1, COL1A2, COL3A1, etc. than aHSCs in controls, and rHSCs in NASH patients expressed higher levels of ID3, NOTCH3, CRIP1, etc. than rHSC in controls (Table 1). Single-cell transcriptomics of HSCs reveled that aHSCs and rHSCs both existed in the liver tissues of NASH patients and healthy controls, but there was an obviously increased transition from rHSCs to aHSCs in NASH patients (Fig. 3A). Based on the above findings, we established NASH-associated transcriptomic signature of rHSC (NASHrHSCsignature) and NASH-associated transcriptomic signature of activated HSC (NASHaHSCsignature), respectively (Table 1). GSVA validated that NASHaHSCsignature obviously enriched in the aHSCs cluster of NASH patients, and NASHrHSCsignature obviously enriched in the rHSCs cluster of NASH patients, suggesting that the above two transcriptomic signatures could represent the respective cell subpopulations well (Fig. 3B and C). For instance, NASHaHSCsignature was significantly enriched in aHSCs in NASH patients but not in other cell subpopulations, suggesting that this transcriptomic signature can be used to represent NASH-associated aHSCs (Fig. 3B and C).
Establishment of NASH-associated transcriptomic signatures of activated HSCs and resting HSCs by single-cell RNA-sequencing analysis (A, Heat map showing the expressions of key signature genes in activated HSCs and resting HSCs from NASH patients or healthy controls; B, Violin plots showing the expressions of key signature genes in activated HSCs and resting HSCs from NASH patients or healthy controls).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
Validation of NASH-associated transcriptomic signatures of activated HSCs and resting HSCs by single-cell RNA-sequencing analysis (A, UMAP plot showing comparative analysis of the clusters in activated HSCs and resting HSCs between NASH and healthy controls; B, GSVA revealed that NASHaHSCsignature obviously enriched in the aHSCs cluster of NASH patients, and NASHrHSCsignature obviously enriched in the rHSCs cluster of NASH patients; C, Comparison of the enrichment scores of NASHaHSCsignature and NASHrHSCsignature in each HSCs cluster of NASH patients and healthy controls by violin plots).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
In addition, we also used transcriptomic data of HSCs stimulated by TGF-β (GSE148849) to assess the changes of those transcriptomic signatures above in HSCs activation. GSEA validated that NASHaHSCsignature was significantly increased in TGF-β-induced HSCs activation (Fig. 4A and B). Moreover, the enrichment score of NASHaHSCsignature calculated by GSVA was significantly increased in TGF-β-induced HSCs activation (Fig. 4C). The above outcomes suggested that NASHaHSCsignature could represent the activation state of HSCs.
NASHaHSCsignature was significantly increased in TGF-β-induced HSCs activation and NASH progression and fibrosis of NAFLD patients (A, GSEA analyses validated the significantly increased enrichment of NASHaHSCsignature in TGF-β-induced HSCs activation; B, Bubble plot showing the enrichment of those signature gene sets during TGF-β-induced HSCs activation; C, Difference in the enrichment scores of the above signature gene sets in each HSC sample calculated by GSVA between TGF-β and control groups; D, Bubble plot showing the enrichment of those signature gene sets in the livers of patients with NASH progression; E, Bubble plot showing the enrichment of those signatures in the livers of NAFLD patients with advanced fibrosis; F, GSEA enrichment plots showing the significantly increased enrichment of NASHaHSCsignature in the livers of patients with NASH progression (left) or advanced fibrosis (right)).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
We further validated the enrichment of NASHaHSCsignature in liver tissues of NASH patients or NAFLD patients with advanced fibrosis using the results of our previous study. We found that NASHaHSCsignature was significantly enriched in the liver of NASH patients and NAFLD patients with advanced fibrosis (Fig. 4D and E). It is further illustrated that the enrichment score of NASHaHSCsignature could represent the severity of fibrosis in NAFLD. In addition, aHSCsignature was significantly enriched in NASH patients and NAFLD with advanced fibrosis patients, indicating an increased number of aHSCs in the liver of NASH patients and NAFLD patients with advanced fibrosis (Fig. 4D and E). As shown in Fig. 4F, GSEA enrichment plots showing the significantly increased enrichment of NASHaHSCsignature in the livers of patients with NASH and advanced fibrosis of NAFLD (Fig. 4F).
We next validated the increased enrichment of NASHaHSCsignature in the livers of NAFLD patients with advanced fibrosis in bulk transcriptomic data of GSE49541 and GSE130970, which included patients with NAFLD with or without fibrosis. As shown in Fig. 5, the enrichment scores of NASHaHSCsignature calculated by GSVA in NAFLD patients with advanced fibrosis were significantly higher than that in patients without or with mild fibrosis (P < 0.05; Fig. 5A). ROC analysis revealed that enrichment score of NASHaHSCsignature in patients with NAFLD could help to diagnose advanced fibrosis (Fig. 5B). The outcomes showed that NASHaHSCsignature could effectively predict the advanced fibrosis in NAFLD patients. However, NASHaHSCsignature had limited performance in predicting NASH progression among NAFLD patients (Fig. 5C and D). Moreover, using a NASH mice model and CCl4-included fibrosis mice model, we validated the existence of rHSCs (mainly expressing a-SMA) in the normal liver and the increased aHSCs (mainly expressing collagen 1) in the fibrosis liver tissues (Fig. 6 and 7, Supplementary Fig. 3 and 4).
Validation of the increased enrichment of NASHaHSCsignature in the livers of NAFLD patients with advanced fibrosis (A, Difference in the enrichment scores of NASHaHSCsignature calculated by GSVA between patients with advanced fibrosis and controls; B, ROC analysis revealed enrichment score of NASHaHSCsignature in patients with NAFLD could help to diagnose advanced fibrosis; C, Difference in the enrichment scores of NASHaHSCsignature calculated by GSVA between NAFL patients and NASH patients; D, ROC analysis of GSVA enrichment scores of NASHaHSCsignature in patients with NAFL and NASH).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
Expression of collagen 1 was significantly increased in areas of advanced fibrosis in liver tissue of CCl4-induced DBA/2J mice (A, Representative images of liver sections stained by H&E and Masson staining from control and mice of CCl4-induced liver fibrosis (original magnification, ×200); B, Immunohistochemistry of a-SMA and Collagen 1 expression in each group).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
Change of expression of a-SMA and collagen 1 in control and mice of CCl4-induced liver fibrosis by immunofluorescence in DBA/2J mice (A, Representative images of liver sections stained by immunofluorescence from control and mice of CCl4-induced liver fibrosis (original magnification, ×50); B, Representative images of liver sections stained by immunofluorescence from control and mice of CCl4-induced liver fibrosis (original magnification, ×200)).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
Crucial functional pathways involved in the increased transition from rHSCs to aHSCs in NAFLD patients
To uncover those key genes and functional pathways involved in the increased transition from rHSCs to aHSCs in NAFLD patients, we performed WGCNA analyses of liver transcriptome data in NAFLD patients, in which the enrichment scores of HSCs-relevant transcriptomic signatures were used as clinical traits. Crucial co-expression modules correlated with the enrichment scores of HSCs-relevant transcriptomic signatures were thus explored by WGCNA analyses. As shown in Fig. 8, the co-expression pattern of genes in the liver transcriptome data of NAFLD patients was successfully constructed. The most significant co-expression module correlated with the enrichment score of NASHaHSCsignature was Memagenta module (correlation coefficient = 0.76, P = 2.0E-11), followed by Meturquoise (correlation coefficient = 0.44, P = 0.001) and Megreenyellow module (correlation coefficient = 0.43, P = 0.001). The MEmagenta co-expression module was also associated with advanced fibrosis (correlation coefficient = 0.85, P = 5.0E-16) and aHSCsignature enrichment score (correlation coefficient = 0.87, P = 5.0E-18) (Fig. 8B). Functional annotation analysis of Memagenta module showed that its functions were characterized by multiple ECM-related pathways such as extracellular matrix, extracellular matrix assembly, and extracellular structural organization (Fig. 8D). Functional annotation analysis of MEgreenyellow and MEturquoise co-expression modules showed that their functions were both characterized by immune response-related pathways (Supplementary Fig. 5). Thus, WGCNA analysis confirmed that ECM-related pathways and immune-related pathways were the main functional pathways involved in the activation of HSCs during NAFLD progression.
WGCNA identified crucial functional pathways involved in the increased transition from rHSCs to aHSCs in NAFLD patients (A, Clustering dendrogram showed the co-expression pattern of genes in the liver transcriptome data of NAFLD patients, in which each co-expression gene module was marked with one specific color; B, The heatmap showed the module-trait relationship identified in the WGCNA analysis, in which the coefficient and P values were presented and the transition from green to red indicated the increase in statistical significance; C, The hub genes in Memagenta module were intensively correlated with each other; D, Bubble plot showed those enriched pathways of genes in the MEmagenta co-expression module).
Citation: Endocrine Connections 12, 2; 10.1530/EC-22-0502
Key regulators involved in the transcriptomic change of aHSCs in NASH
We further explored those key regulators or transcription factor analyses for determining the functional change of aHSCs in NASH. As shown in Supplementary Fig. 6A, several key upstream regulators for determining the functional change of aHSCs in NASH were identified such as SDC1, GRP, SDC4, and MUC1. In the transcription factor enrichment analysis, some enriched transcription factor motifs, transcription factors, and their corresponding target genes were identified (Supplementary Table 3). For example, JAZF1 and FOBL1 could regulate the expressions of more than 40 genes upregulated in the aHSCs of NASH patients (Supplementary Fig. 6B). In addition, we found that JunB was one of the most critical transcription factors in NASH-associated aHSC and could regulate the expressions of many signature genes in NASHaHSCsignature (Supplementary Table 3). We used GSE148849, a transcriptomic dataset of HSC stimulated by TGF-β in vitro, to assess the change of JunB during HSCs activation, and we found that the expression of JunB in primary HSCs was significantly increased after TGF-β stimulation (P = 0.008) (Supplementary Fig. 7).
Correlation between key signatures and fibrosis severity of NAFLD patients
We validated the findings with a liver-sequencing dataset containing 206 patients with NAFLD and further evaluated the clinical significance of key transcriptomic signatures (GSE135251). The results showed that the GSVA enrichment scores of aHSCsignature and NASHaHSCsignature were significantly increased in NASH patients (P < 0.05) (Supplementary Fig. 8). GSVA enrichment scores of aHSCsignature and NASHaHSCsignature were significantly increased in the high NAS group (NAS 5–8) than the low NAS group (NAS 1–4) (P < 0.001) (Supplementary Fig. 9), and both signature enrichment scores increased with the increase of the severity of NAFLD patients (P < 0.05) (Supplementary Fig. 10). The GSVA enrichment scores of aHSCsignature and NASHaHSCsignature both increased significantly with the fibrosis severity of NAFLD patients (Supplementary Fig. 11 and 12). The findings suggested that changes in these HSCs-related signatures were able to show a progressive increase in the progression of liver fibrosis as well as NASH progression in common NAFLD patients. In addition, we analyzed the correlation between the expression of the top ten upregulated genes in aHSCsignature and NASHaHSCsignature and the severity of liver fibrosis in NAFLD patients (Supplementary Table 4). The results showed that the expressions of many genes were significantly correlated with the severity of liver fibrosis in NAFLD patients, but several genes were not significantly correlated with the severity of liver fibrosis. Therefore, the transcriptomic signatures had better performance in assessing the severity of fibrosis in NAFLD patients compared to single gene (Supplementary Table 4).
Discussion
Fibrosis in NASH is driven by the activation of HSCs, which transform from quiescent HSCs to myofibroblasts that produce collagen and other types of extracellular matrix (32, 33). Activated HSCs, as the source of hepatic myofibroblasts in NASH, are the most critical factor in producing excessive ECM and causing advanced fibrosis in the liver of NASH patients (22). This study was performed to identify transcriptomic signature of HSCs during the development of NASH and the underlying key functional pathways. This study defined the NASH-associated transcriptomic change of aHSCs and developed a useful transcriptomic signature with the potential .in assessing the fibrosis severity in the development of NASH. This study also identified extracellular matrix-related pathways and immune-related pathways as key players in the activation of HSCs during the development of NASH. Therefore, this study identified NASH-associated transcriptomic signature of HSC and provided new insights into fibrosis progression of NAFLD.
Our study identified differentially expressed genes in rHSC and aHSCs based on single-cell sequencing data and successfully established rHSCsignature and aHSCsignature, respectively. We then defined the NASH-associated transcriptomic changes of aHSCs and developed a useful transcriptomic signature with the potential of representing the degree of HSCs activation and fibrosis severity in NASH. NASHaHSCsignature was significantly enriched during TGF-β-stimulated HSCs activation in vitro, indicating that NASHaHSCsignature could represent the degree of HSCs activation. NASHaHSCsignature also had a strong diagnostic significance in assessing the severity of fibrosis of patients with NAFLD.
Many of those genes in aHSCsignature and NASHaHSCsignature have been reported to be closely associated with extracellular matrix and fibrosis. For instance, Lumican, encoded by LUM, has a key role in collagen assembly (34), and the formation of collagen fibers in the extracellular matrix of several tissues is regulated by lumican (35, 36, 37). Data presented by Anuradha Krishnan et al. suggest that lumican plays an important role in the progression of liver fibrosis by maintaining the stability of collagen fibers during fibrosis (38). Dermatopontin (DPT) has been shown to modulate collagen and fibrin fiber formation, induce cell adhesion, and promote wound healing (39, 40, 41). There is a study that found that the expression of DPT is positively correlated with the severity of liver fibrosis (42). ASPN plays a key role in tissue injury and regeneration (43). It has been found that ASPN expression is increased in the mouse model of pulmonary fibrosis, and ASPN is mainly localized in α-SMA+ myofibroblasts. In vitro experiments demonstrated that ASPN knockout inhibited myofibroblast differentiation (43). However, the mechanisms of other signature genes in the fibrosis of NAFLD patients are unclear and still need to be investigated by more studies.
Apart from ECM-related pathways, we found that immune-related pathways were also key functional pathways involved in the activation of HSCs during NASH progression. There is emerging evidence supporting that innate and adaptive immune activation is the driving force in establishing liver inflammation and fibrosis in NASH (44). Some previous studies have found that immune cells or cytokines have important roles in the development of inflammation and liver fibrosis in NASH (45). In the liver tissue of NAFLD patients, immune cells such as monocytes and macrophages are involved in inflammation, thus promoting the progression of NAFLD to NASH (46, 47, 48). Therefore, immune-related pathways have key roles in the activation of HSCs during NASH progression and may be promising treatment targets.
Our study provides useful information for clinical evaluation of activated HSCs in NASH patients and may help to improve the diagnosis and risk stratification of fibrosis. Activated HSCs are the main effector cells of liver fibrosis and can produce excess extracellular matrix through chronic liver injury. The NASHaHSCsignature developed in our study can represent the degree of HSCs activation and has a strong diagnostic significance for the severity of NAFLD with fibrosis. It may help clinicians to determine the risk of developing liver fibrosis in the future and help to take early interventions to inhibit or delay the development of fibrosis among NAFLD patients.
Key upstream regulators or transcription factors related to the functional changes of HSCs are potential treatment targets for liver fibrosis of NASH patients. In this study, we tried to uncover those key regulators or transcription factor analyses for determining the functional change of aHSCs in NASH by two bioinformatic methods. Several key upstream regulators related to the functional changes of HSCs in NASH were identified such as SDC1, GRP, SDC4, and MUC1. SDC1 encodes heparan sulfate proteoglycan syndecan-1, which has been reported to be positively associated with liver fibrosis in patients with chronic liver diseases (49, 50). Knockdown of syndecan-1 could reduce the proliferation of keloid fibroblasts and the production of ECM (51). Another study by Parimon et al. found that syndecan-1 was a pro-fibrotic signal and could promote lung fibrosis by reprogramming the phenotypes of alveolar type II cells via augmenting TGF-β and Wnt signaling (52). The findings above suggest that syndecan-1 encoded by SDC1 may be a key regulator involving in liver fibrosis and a potential treatment target. In addition, JunB was found to be one of the most critical transcription factors in NASH-associated aHSC and could regulate the expressions of many signature genes in NASHaHSCsignature (Supplementary Table 3). JunB belongs to the JUN transcription factor family, including JunD and c-Jun, and can bind with Fos family and other transcription factors to form AP-1 dimer, which is a key transcription factor regulating cell survival and death pathways (53). It is also involved in various cellular processes such as proliferation, differentiation, apoptosis, transformation, cell migration, inflammation, and wound healing (54). Increasingly, JunB has also been found to play an important role in fibrogenesis, and JunB activates the TGF-β pathway and promotes COL1A2 deposition (55, 56, 57). In this study, we found that the expression of JunB in primary HSCs was significantly increased after TGF-β stimulation. Therefore, JunB may be involved in the progression of liver fibrosis in NASH patients. The roles of other predicted upstream regulators or transcription factors in liver fibrosis of NASH patients are largely unclear. Further studies are recommended to explore the roles of those predicted upstream regulators or transcription factors in NASH and determine whether they are potential treatment targets against liver fibrosis in NASH.
Compared to previous literature, our study added novel insights into the fibrosis of NAFLD. We found that the transcriptomic signatures had better performance in assessing the severity of fibrosis in NAFLD patients compared to single gene. Moreover, the findings from a validation study suggested that changes in these HSCs-related signatures were able to show a progressive increase with the progression of liver fibrosis as well as NASH progression in NAFLD patients, suggesting the potential role of those transcriptional signatures in the evaluation and risk stratification for liver fibrosis in NAFLD.
There are some limitations in our study. First, the sample size of scRNA-seq studies is usually small because of the high cost of scRNA-seq, and it is the same with our study. The findings from this scRNA-seq study of small sample size can be validated by scRNA-seq studies with more samples. Secondly, for the potential upstream regulators, no causality could be confirmed by bioinformatic outcomes, and further experiments need to be performed to provide empirical evidence validating their involvement. Finally, the clinical significance of those transcriptional signatures in the diagnosis or management of NAFLD need to be explored with further prospective cohort studies or randomized controlled trials.
In summary, this study defined the NASH-associated transcriptomic changes of activated HSCs and developed a useful transcriptomic signature with the potential of representing the degree of HSCs activation and fibrosis severity in NASH. This study also identified extracellular matrix-related pathways and immune-related pathways as possible key players in the activation of HSCs during the development of NASH.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/EC-22-0502.
Declaration of interest
The authors declare that they have no conflict of interest.
Funding
This study was supported by the National Natural Science Foundation of China (No. 81870606) and the Natural Science Foundation of Fujian Province (2019J01569; 2021J011344).
Ethics approval
The study was approved by the Ethics Committee of the First Affiliated Hospital of Xiamen University (Ethics Number: 2021J011344).
Authors contribution statement
Xuejun Li and Weiwei He conceived of the paper; Weiwei He and Caoxin Huang wrote the original draft; Xiulin Shi, Menghua Wu and Han Li generated the figures; Qiuhong Liu, Xiaofang Zhang and Yan Zhao reviewed and edited the paper. All the authors agreed to the published version of the manuscript.
Acknowledgement
The authors would like to thank all the researchers who have shared their data in GEO database.
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