Transcriptome analysis of hepatic fibrosis induced by common bile duct ligation

doi:10.21203/rs.3.rs-2308649/v1

Cholestatic is an important factor causing liver fibrosis with a clinical incidence rate of about 10.26%. Although the progress of cholestatic hepatic fibrosis can be delayed by clearing the etiology and improving bile toxicity, how to treat antifibrotic is still an important and difficult problem in clinical treatment. In this study, we utilized Illumina sequencing technology to deeply mine the changes of mRNA expression during BDL-induced hepatic fibrosis. A total of 239,727,428 clean reads and 3224 Differentially expressed genes (DEGs) were obtained with 2852 genes were upregulated and 372 genes were downregulated. GO enrichment revealed that DEGs were mostly associated with biological processes, among which cellular process is the most enriched with 2253 genes up-regulated and 270 genes down-regulated. KEGG annotated 340 pathways in all, of which pathways in cancer and PI3K-Akt signaling pathway were most enriched. Six significantly DEGs were verified by RT-qPCR, and the results were consistent with the transcriptome sequencing outcome. These DEGs and pathways may play a crucial role in the occurrence and development of liver fibrosis, and may be potential therapeutic targets of liver fibrosis. Further investigations are needed to clarify its specific mechanism.

Biological sciences/Biochemistry

Biological sciences/Molecular biology

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Pathogenesis

Bile duct ligation

Cholestasis

Hepatic fibrosis

Transcriptome analysis

mRNA

Hepatic fibrosis is a common pathological process caused by a variety of chronic liver diseases, such as viral, non-alcoholic, alcoholic, cholestatic, which can develop into cirrhosis, liver failure and liver cancer, and eventually requires liver transplantation, thus overburdening the healthcare systems¹. Although liver fibrosis is reversible, there is no antifibrotic therapy to date², clarifying its specific pathogenesis has great significance for clinical treatment.

The main feature of liver fibrosis is the deposition of the extracellular matrix (ECM) and scar formation ^{3 4}. The activation of hepatic stellate cells (HSCs) is the central link in the development of liver fibrosis due to they are the main source of ECM ⁵. When stimulated by a variety of external factors⁶, the quiescent HSCs activate, and subsequently transform into myofibroblasts⁷, leading to accumulation of ECM and the formation of liver fibrosis⁸.

In the past few decades, some animal models of liver fibrosis, for example hepatotoxicity, the common bile duct ligation (BDL), alcoholism, immune-mediated and the interventional introduction of target gene have been established ⁹. Compared with other animal models, the hepatic fibrosis model induced by BDL not only has the morphological changes close to human cholestatic liver fibrosis ¹⁰, but also has the advantages of simple respective protocol, short cycle, high repeatability and stability, non-toxic and difficult spontaneous reversal. Therefore, the experimental model is well accepted and used by researchers aiming to understand the pathogenesis of hepatic inflammation and fibrosis.

Cholestasis, whose total incidence in patients with chronic liver disease is about 10.26%, is an important factor causing liver fibrosis. Cholestasis liver injury leads to cell death, followed by inflammation and fibrosis¹¹, which cause strong fibrotic reaction 21–28 days after operation, originating from the periportal region ^{12 13}. It induces intrahepatic bile duct dilatation and proliferation of biliary epithelial cell, finally resulting in the excessive deposition of ECM. In our past experiment, we have frequently used this protocol to study the changes of some cytokines in the process of cholestatic hepatic fibrosis, aiming to test the change of special molecular and to explore new antifibrotic drugs^{14 15}.

Although the progress of cholestatic hepatic fibrosis can be delayed by clearing the etiology and improving bile toxicity, how to treat antifibrotic is still an important and difficult problem in clinical treatment. There are many studies on cholestatic liver fibrosis, however, the mechanisms different cytokines and signal pathways leading to disease progression are still not clear. In this study, we use RNA sequencing technology to elucidate the molecular basis in the process of common bile duct induced liver fibrosis, so as to provide the potential to target therapy for the clinical liver fibrosis.

2.1 Mouse model establishment of hepatic fibrosis induced by BDL

To verify the successful establishment of cholestatic hepatic fibrosis model, we carried out general view of liver and H&E staining. As shown in Fig. 1A, compared with control group, the color of liver tissue in BDL was yellow, the texture became hard while the edge became blunt. At the same time, the gallbladder silted bile, gradually increased. HE staining of the liver tissue from BDL showed the lesions were mainly concentrated around the interlobular bile ducts, manifested as infiltration of inflammatory cells, increase in centrilobular necrosis, proliferation and dilation of small bile ducts, enlargement of lumen, and formation of collagen fibers (Fig. 1B).

2.2 Transcriptome sequencing and de novo assembly

To further investigate the molecular mechanism of cholestatic hepatic fibrosis, we performed RNA sequencing to detect the difference of transcriptome with or without BDL in triplicate. As shown as the detail information in Table 2, a total of 35.93 Gb clean reads, 240,546,386 raw reads and 239,727,428 clean reads were obtained, of which 120,222,122 were from control group and 119,505,306 were from BDL group. The percentage of Q20 and Q30 base was more than 97% and 82% respectively. Then, these clean reads were mapped to ribosomes and reference genomes version Ensembl_release100 from Mus_musculus for quantitative measurement of gene expression. The results in Table 3 showed that the unmapped rate with ribosomes reached more than 99%, while the mapped rate with reference genome ranged from 87.57–89.53%, implying that genes in six samples are all highly expressed. According to the comparison results of total_mapped reads that can be located on the genome, we calculated the distribution position of reads in the reference genome, the results showed that more than 88% of the clean reads can be compared to the exon region (Fig. 2A). After obtaining the FPKM value, we showed the difference of gene expression distribution between control and BDL group through the expression distribution map (Fig. 2B, C).

2.3 Identification of DEGs in BDL-induced hepatic fibrosis

To identify DEGs with adjusted p ༜ 0.05, DESeq R package was adopted. A total of 3224 DEGs were obtained from control and BDL group, Among which, 2852 genes were upregulated in BDL group as well as 372 genes were downregulated (Fig. 3A-C). Twenty significant genes were selected, ten up-regulated genes of which are shown in Table 4, as well as ten down-regulated genes of which are shown in Table 5.

2.4 Enrichment analysis of DEGs in BDL-induced hepatic fibrosis

To illustrate the functional changes of gene expression of BDL-induced hepatic fibrosis, a GO enrichment analysis of 3224 DEGs from three libraries was carried out. We summed up a total of 58 enriched GO terms (p ༜ 0.05), 28 categories in biological processes, 17 categories in cellular component, 13 categories in molecular function. In biological process category, the significant enrichment included cellular process, biological regulation, regulation of biological process, response to stimulus, metabolic process. Among these, cellular process is the most enriched with 2253 genes up-regulated and 270 genes down-regulated (Fig,4A).

To further investigate the possible signaling pathways that DEGs may participate in, KEGG pathway enrichment analysis was conducted. A total of 340 pathways were annotated and the top 20 pathways were mainly concentrated in 5 categories, including metabolism, environmental information processing, cellular processes, organismal systems, human diseases (Fig. 4B). KEGG analysis a variety of molecular pathways, such as signal transduction-associated pathways, including MAPK signaling pathway (ko04010), PI3K-Akt signaling pathway (ko04151), Ras signaling pathway (ko04014), TNF signaling pathway (ko04668), TGF-beta signaling pathway (ko04350). Infectious diseases-associated pathways, including Leishmaniasis (ko05140), Amoebiasis (ko05146), Tuberculosis (ko 05152). Cancers-associated pathways, including pathways in cancer (ko05200), proteoglycans in cancer (ko05205), chemical carcinogenesis (ko05204). Figure 4C showed that in the top 20 pathways, both up-regulated and down-regulated genes were enriched in pathways in cancer and PI3K-Akt signaling pathway.

2.5 The qRT-PCR validation of DEGs

To verify the transcriptome sequencing results and further clarify the significantly different gene expression levels during BDL-induced hepatic fibrosis, six DEGs were selected for qRT-PCR from KEGG data, including bile secretion (Slco1a1), chemical carcinogenesis (Ugt2b1), complement and coagulation cascades (Serpina1e), metabolic pathway (Gpx3), amoebiasis (Serpinb6b), proteoglycans in cancer (Cd63). Among the six genes, three were up-regulated and the other three were down-regulated. The results of qRT-PCR were consistent with the transcriptome sequencing outcome (Fig. 5), which suggested the reliability and accuracy of RNA-seq data.

Cholestasis is one of the most important causes of liver fibrosis. Due to the lack of effective treatment so far, it can eventually develop into end-stage liver disease with the need for liver transplantation. Therefore, the study of reliable biomarkers for predicting cholestatic hepatic fibrosis has great clinical significance. There is increasing evidence that alterations in transcriptome are associated with human disease, ranging from cancer to autoimmune diseases to cardiovascular pathology^16–18. Herein we performed RNA high-throughput sequencing to explore the variations of transcriptome of hepatic fibrosis induced by BDL.

In this study, the hepatic fibrosis model we used was induced by BDL. Consistent with our previous study¹⁴, the appearance of many pathological features including the enlarged gallbladder, bile duct dilatation and collagen fiber formation indicated that BDL-induced hepatic fibrosis model was established successfully, which makes preparations for the next step of sequencing. Through researching differential gene expression, we look forward to discovering probable gene targets of BDL-induced hepatic fibrosis, and further described underlying signaling pathways, which may provide a new idea for the clinical treatment of cholestatic liver fibrosis.

Through transcriptome information analysis, a total of 239,727,428 clean reads were obtained. After data accusation and comparative analysis, we obtained the FPKM values of 22299 genes for subsequent analysis, and finally got 3224 DEGs. These results showed that loads of genes were significantly altered in hepatic fibrosis treated with BDL, suggesting an important role in BDL-induced hepatic fibrosis. According to the results of differential genes, we verified three significantly up-regulated and three significantly down-regulated genes, which including Gpx3, Serpinb6b, Cd63, Slco1a1, Ugt2b1, Serpina1e.

Glutathione peroxidase 3 (Gpx3) is an antioxidant enzyme, the only exocrine form of glutathione peroxidase family, which acts in detoxification of hydrogen peroxide to shields cells and enzymes from oxidative damage and regulates redox¹⁹. Besides, as a tumor suppressor gene, Gpx3 is involved in the occurrence of a variety of cancer diseases²⁰, including ovarian cancer, breast cancer²¹, thyroid cancer²²and lung cancer²³. Furthermore, Gpx3 is also a tumor suppressor gene in HCC. Qi et al reported that GPx3 overexpression obviously suppressed proliferation and invasiveness of HCC cells, possessing a potential value in the prognosis and treatment of liver cancer patients ^24,25. Similarly, the increased expression of Gpx3 in cholestatic liver fibrosis may inhibit the development of liver fibrosis to liver cancer. Granzymes (Gzms) is an exogenous serine protease, derived from cytoplasmic granules released by cytotoxic lymphocyte (CLTs) and natural killer cells (NK). Together with perforin, Gzms enters into the target cells, thus causing DNA degradation in cells. When lymphocytes combine with the damaged cells, perforin and Gzms are released. Perforating protein enters the target cells and forms pores in the plasma membrane to allow the Gzms into the cytosol of target cells where they carry out their effector functions ²⁶. Serpinb6b is a specific Granzyme A (GzmA) inhibitor. Garzon et al informed that serpinb6b increased the survival rate of abdominal sepsis model mice by reducing the inflammatory reaction of serum and peritoneal lavage fluid ²⁷. In our study, serpinb6b is significantly up-regulated, which may play a role in inhibiting biliary inflammation. Another significantly upregulated gene we found was CD63. CD63, also known as lysosomal associated membrane protein 3 (LAMP3), is the first identified member of the tetraspanin family. CD63 is a cell surface protein, which is related to cell activation, adhesion, variation and tumor invasion, metastasis and other life processes²⁸. CD63 is considered to be a tumor suppressor, and is expressed in endosomes and lysosomes. In addition, CD63 plays a significant role in immunological processes. Studies showed that CD63 enhanced the activation of CD4 + T cells in MHC-II molecule mediated antigen presentation²⁹. At the same time, CD63 plays an important role in mast cell degranulation and allergic reaction mediated by specific IgE. Knockout of CD63 results in a significant decrease in mast cell degranulation, thereby reducing the acute allergic reaction in the body³⁰. CD63 is a surface marker of extracellular vesicles (EVs), and a report found that heparinase-1 resulted in the imbalance of extracellular matrix (ECM) by increasing CD63 synthesis and exosome secretion, thus favoring HCV infection³¹. High-fat diet induced upregulation of EVs phosphatidylcholine through inhibiting the expression of genes essential for activation of the insulin signaling pathway such as PI3K and Akt, contributing to insulin resistance ³². The increase of CD63 in BDL-induced hepatic fibrosis indicates that CD63 may be involved in the regulation in the form of EVs.

Slco1a1, also known as solute carrier organic anion transporter family member 1a1, is responsible for transporting bile acids into hepatocytes. In this study, we identified Slco1a1 is the most down-regulated mRNA, and negatively correlated with serum cholic acid level, which is consistent with the report that in alcohol-associated liver diseases ³³. UDP-Glucuronosyltransferase (Ugt) is a membrane protein that binds to the endoplasmic reticulum, which plays a catalytic role in transferring glucuronic acid from UDP-glucuronic acid to other hydrophobic molecules. In this catalytic process, the water solubility of receptor molecules is greatly improved, thus promoting the elimination of these molecules, including steroid, thyroid hormones, bile acids, retinoids and fatty acids ³⁴. Ugt2b1 is a phase II drug metabolizing enzyme, which plays a major role in the metabolism of xenobiotic and endogenous compounds. It is reported that the expression of ugt2b1 gene is significantly increased in the liver, and different nonsteroidal anti-inflammatory drugs (NSAIDs) downregulated its expression ³⁵, which is consistent with the article that downregulation of ugt2b1 by metallic nanoparticles (NPs) is associated with inflammation and IL-6 gene induction in the liver ³⁶. In this study, we found the decrease of ugt2b1 in the mouse model of cholestatic liver fibrosis, indicating that the inflammatory reaction caused by toxic bile leads to the downregulation of ugt2b1, ultimately affecting the metabolism of bile acids. Serpins is the most widely distributed and largest superfamily of protease inhibitors, which is highly expressed in the liver ³⁷. There is a report shows that serpina1e has inhibitory effects on hepatic gluconeogenesis ³⁸. Here, we found that BDL treatment decreased serpina1e expression, which suggested that serpina1e may be involved in glucose metabolism in cholestatic hepatic fibrosis.

In summary, this report showed that bile duct ligation successfully induced cholestatic hepatic fibrosis. A total of 3224 DEGs were identified with 2852 genes were upregulated and 372 genes were downregulated. Most upregulated DEGs were enriched in peroxisome, Granzymes and immune molecules, indicating there is a series complex immune response in cholestatic hepatic fibrosis. Besides, genes related to metabolism were mostly downregulated, implying cholestatic hepatic fibrosis may exhibit a severe metabolic disorder. In conclusion, this work provides some possible mRNA changes in liver fibrosis. However, further investigation in vitro and in vivo will be needed to confirm these mechanisms, aiming to provide a new perspective for the pathogenesis and drug treatment of clinical cholestatic hepatic fibrosis.

4.1 Animals and common bile duct ligation

The male C57BL/6 mice were purchased from Animal Center of Shanxi Medical University and housed in pathogen-free facilities. The animal experiment was approved by the Institutional Animal Care and Use Committee of The First Hospital of Shanxi Medical University. The liver fibrosis models were induced by BDL with mice anesthetized with isoflurane inhalation. According to our previous experimental experience, cholestatic fibrosis was induced after approximately 4 weeks, and some developed early liver cirrhosis later. Therefore, liver tissues were obtained 4 weeks after operation. The animal study was approved by the Animal Care and Use Committee of Shanxi Medical University in agreement with accepted international standards. All experiments were approved and conducted in accordance with the relevant guidelines and approvals set by Shanxi Medical University and followed the recommendations in the ARRIVE guidelines.

4.2 Library building and RNA sequencing

Three samples in each group (control group: C1, C2, C3; experimental group: B1, B2, B3) were randomly selected. Liver tissues were taken out according to the manufacturer’s protocol and sent to Gene Denovo Biotechnology Co,Ltd (Guangzhou, China) for RNA extraction, RNA quality inspection, library preparation and quality inspection, and finally for RNA sequencing and analysis. The raw sequencing data reported in this study was archived in Gene Expression Omnibus (GEO) with the accession number GSE218407.

4.3 Data quality control and analysis

In order to ensure the data quality, the raw data should be filtered to reduce the analysis interference brought by invalid data. Clean reads were gained by removing adapter sequences, filtering low-quality reads (the base number of Q ≤ 20 accounts for more than 50% of the whole read ), removing reads containing over 10% poly-N and reads all containing A base. Base quality analysis was performed on the filtered clean data. The genome version we refered to was Mus_musculus.Ensembl_release100. According to the comparison results of HISAT2, we reconstructed the transcript by stringtie and calculated the expression of all genes in each sample by RESM, and finally obtain the FPKM value of the gene. Based on the expression size results of each sample, we analyzed and calculated the Pearson correlation coefficient between samples through PCA to understand the repeatability between samples.

4.4 Differentially expressed genes (DEGs) analysis and functional enrichment

Differential expression analysis was performed using the DESeq R package. We screened genes with FDR ༜ 0.05 and | log2fc | > 1 as significantly different genes. GO enrichment analysis and KEGG pathway analysis of DEGS were employed by the GOseq R packages and KOBAS software. A hypergeometric p ༜ 0.05 was considered to be significantly enriched.

4.5 Verification of gene using quantitative real-time polymerase chain reaction (qRT-PCR)

mRNA was extracted from liver tissues using TRIzol solution (Takara, shiga, Japan) per the manufacturer’s instructions, and followed by the concentration and purity of mRNA were measured. cDNA Reverse Transcription Kit (QIAGEN) and miScript SYBR Green PCR Kit (QIAGEN) were used for reverse transcription and quantitative real-time PCR of mRNA. GAPDH was identified to calculate the expression levels of mRNA. The required primers were designed and synthesized by Sangon Biotech (Shanghai, China), and the gene sequences are provided in Table 1.

4.6 Statistical Analysis

SPSS 23.0 statistical software was used to analyze calculations. All data were represented as the mean ± standard deviation (SD). For the normally distributed data, one-way analysis of variance (ANOVA) and Tukey’s test were applied to compare the multiple groups’ differences while Mann–Whitney U test was used to evaluate the non-normally distributed data. p-value ༜ 0.05 showed a statistically significant difference.

Data availability statement

The datasets generated and analysed during the current study are available in Gene Expression Omnibus (GEO) with the accession number GSE218407.

Competing Interests Statement

The authors declare that they have no conflict of interest.

Authors contribution

J-J R designed and performed the research, analyzed the data, and wrote the manuscript. C-H Z performed the research and analyzed the data. T-T L and Y-Y K collected the literature. T-J H supervised the research and revised the manuscript. All authors reviewed and approved the final manuscript.

Funding

This study was supported by Fundamental Research Program of Shanxi Province (20210302123246), Health Research Project of Shanxi Province in 2020 (2020079), and Scientific Research Talent Funding Program of the First Hospital of Shanxi Medical University—Doctoral Start-up Fund (17).

Kisseleva, T. & Brenner, D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nature reviews. Gastroenterology & hepatology 18, 151–166, doi:10.1038/s41575-020-00372-7 (2021).
Parola, M. & Pinzani, M. Liver fibrosis: Pathophysiology, pathogenetic targets and clinical issues. Molecular aspects of medicine 65, 37–55, doi:10.1016/j.mam.2018.09.002 (2019).
Tacke, F. & Trautwein, C. Mechanisms of liver fibrosis resolution. J Hepatol 63, 1038–1039, doi:10.1016/j.jhep.2015.03.039 (2015).
Pinzani, M. Pathophysiology of Liver Fibrosis. Dig Dis 33, 492–497, doi:10.1159/000374096 (2015).
Higashi, T., Friedman, S. L. & Hoshida, Y. Hepatic stellate cells as key target in liver fibrosis. Adv Drug Deliv Rev 121, 27–42, doi:10.1016/j.addr.2017.05.007 (2017).
Friedman, S. L. Hepatic stellate cells: protean, multifunctional, and enigmatic cells of the liver. Physiol Rev 88, 125–172, doi:10.1152/physrev.00013.2007 (2008).
Lemoinne, S., Cadoret, A., El Mourabit, H., Thabut, D. & Housset, C. Origins and functions of liver myofibroblasts. Biochim Biophys Acta 1832, 948–954, doi:10.1016/j.bbadis.2013.02.019 (2013).
Tsuchida, T. & Friedman, S. L. Mechanisms of hepatic stellate cell activation. Nat Rev Gastroenterol Hepatol 14, 397–411, doi:10.1038/nrgastro.2017.38 (2017).
Weiler-Normann, C., Herkel, J. & Lohse, A. W. Mouse models of liver fibrosis. Z Gastroenterol 45, 43–50, doi:10.1055/s-2006-927387 (2007).
Kountouras, J., Billing, B. H. & Scheuer, P. J. Prolonged bile duct obstruction: a new experimental model for cirrhosis in the rat. Br J Exp Pathol 65, 305–311 (1984).
Malhi, H., Guicciardi, M. E. & Gores, G. J. Hepatocyte death: a clear and present danger. Physiol Rev 90, 1165–1194, doi:10.1152/physrev.00061.2009 (2010).
Scholten, D. et al. Genetic labeling does not detect epithelial-to-mesenchymal transition of cholangiocytes in liver fibrosis in mice. Gastroenterology 139, 987–998, doi:10.1053/j.gastro.2010.05.005 (2010).
Tag, C. G. et al. Bile duct ligation in mice: induction of inflammatory liver injury and fibrosis by obstructive cholestasis. J Vis Exp, doi:10.3791/52438 (2015).
Huang, T. J. et al. IGFBPrP1 accelerates autophagy and activation of hepatic stellate cells via mutual regulation between H19 and PI3K/AKT/mTOR pathway. Biomed Pharmacother 116, 109034, doi:10.1016/j.biopha.2019.109034 (2019).
Kong, Y. et al. The lncRNA NEAT1/miR-29b/Atg9a axis regulates IGFBPrP1-induced autophagy and activation of mouse hepatic stellate cells. Life Sci 237, 116902, doi:10.1016/j.lfs.2019.116902 (2019).
Li, C. Q. et al. Transcriptome Analysis of Liver Cancer Cell Huh-7 Treated With Metformin. Front Pharmacol 13, 822023, doi:10.3389/fphar.2022.822023 (2022).
Liao, F. et al. Whole Transcriptome Sequencing Identified CircRNA Profiles and the Related Networks in Schizophrenia. Journal of molecular neuroscience: MN, doi:10.1007/s12031-022-02013-x (2022).
Ohki, D. et al. Transcriptome of sessile serrated adenoma/polyps is associated with MSI-high colorectal cancer and decreased expression of CDX2. Cancer medicine, doi:10.1002/cam4.4810 (2022).
Yang, B. et al. Madecassic Acid protects against hypoxia-induced oxidative stress in retinal microvascular endothelial cells via ROS-mediated endoplasmic reticulum stress. Biomed Pharmacother 84, 845–852, doi:10.1016/j.biopha.2016.10.015 (2016).
Chang, C., Worley, B. L., Phaëton, R. & Hempel, N. Extracellular Glutathione Peroxidase GPx3 and Its Role in Cancer. Cancers (Basel) 12, doi:10.3390/cancers12082197 (2020).
Lou, W., Ding, B., Wang, S. & Fu, P. Overexpression of GPX3, a potential biomarker for diagnosis and prognosis of breast cancer, inhibits progression of breast cancer cells in vitro. Cancer Cell Int 20, 378, doi:10.1186/s12935-020-01466-7 (2020).
Zhao, H. et al. Silencing GPX3 Expression Promotes Tumor Metastasis in Human Thyroid Cancer. Curr Protein Pept Sci 16, 316–321, doi:10.2174/138920371604150429154840 (2015).
Liu, X. et al. Circ_0078767 Inhibits the Progression of Non-Small-Cell Lung Cancer by Regulating the GPX3 Expression by Adsorbing miR-665. Int J Genomics 2022, 6361256, doi:10.1155/2022/6361256 (2022).
Qi, X. et al. Clinical significance and therapeutic value of glutathione peroxidase 3 (GPx3) in hepatocellular carcinoma. Oncotarget 5, 11103–11120, doi:10.18632/oncotarget.2549 (2014).
Qi, X. et al. The Clinical Significance and Potential Therapeutic Role of GPx3 in Tumor Recurrence after Liver Transplantation. Theranostics 6, 1934–1946, doi:10.7150/thno.16023 (2016).
Wensink, A. C., Hack, C. E. & Bovenschen, N. Granzymes regulate proinflammatory cytokine responses. J Immunol 194, 491–497, doi:10.4049/jimmunol.1401214 (2015).
Garzon-Tituana, M. et al. Granzyme A inhibition reduces inflammation and increases survival during abdominal sepsis. Theranostics 11, 3781–3795, doi:10.7150/thno.49288 (2021).
Liyanage, D. S. et al. Expression profiling, immune functions, and molecular characteristics of the tetraspanin molecule CD63 from Amphiprion clarkii. Dev Comp Immunol 123, 104168, doi:10.1016/j.dci.2021.104168 (2021).
Petersen, S. et al. The role of tetraspanin CD63 in antigen presentation via MHC class II. European journal of immunology 41, 2556–2561, doi:10.1002/eji.201141438 (2011).
Kraft, S. et al. The tetraspanin CD63 is required for efficient IgE-mediated mast cell degranulation and anaphylaxis. Journal of immunology (Baltimore, Md.: 1950) 191, 2871–2878, doi:10.4049/jimmunol.1202323 (2013).
Gallard, C. et al. Heparanase-1 is upregulated by hepatitis C virus and favors its replication. Journal of hepatology, doi:10.1016/j.jhep.2022.01.008 (2022).
Kumar, A. et al. High-fat diet-induced upregulation of exosomal phosphatidylcholine contributes to insulin resistance. Nat Commun 12, 213, doi:10.1038/s41467-020-20500-w (2021).
Jiang, L. et al. Transcriptomic Profiling Identifies Novel Hepatic and Intestinal Genes Following Chronic Plus Binge Ethanol Feeding in Mice. Digestive diseases and sciences 65, 3592–3604, doi:10.1007/s10620-020-06461-6 (2020).
Turgeon, D., Carrier, J. S., Lévesque, E., Hum, D. W. & Bélanger, A. Relative enzymatic activity, protein stability, and tissue distribution of human steroid-metabolizing UGT2B subfamily members. Endocrinology 142, 778–787, doi:10.1210/endo.142.2.7958 (2001).
Jarrar, Y., Jarrar, Q., Abu-Shalhoob, M., Abed, A. & Sha'ban, E. Relative Expression of Mouse Udp-glucuronosyl Transferase 2b1 Gene in the Livers, Kidneys, and Hearts: The Influence of Nonsteroidal Anti-inflammatory Drug Treatment. Current drug metabolism 20, 918–923, doi:10.2174/1389200220666191115103310 (2019).
Jarrar, Y. et al. The influence of five metallic nanoparticles on the expression of major drug-metabolizing enzyme genes with correlation of inflammation in mouse livers. Environmental toxicology and pharmacology 80, 103449, doi:10.1016/j.etap.2020.103449 (2020).
Farshchian, M. et al. Serpin peptidase inhibitor clade A member 1 (SerpinA1) is a novel biomarker for progression of cutaneous squamous cell carcinoma. The American journal of pathology 179, 1110–1119, doi:10.1016/j.ajpath.2011.05.012 (2011).
Yuan, Y. et al. α-Ketoglutaric acid ameliorates hyperglycemia in diabetes by inhibiting hepatic gluconeogenesis via serpina1e signaling. Science advances 8, eabn2879, doi:10.1126/sciadv.abn2879 (2022).

Table 1. Primers sequences for qRT-PCR.

Gene	Forward primer (5'-3')	Reverse primer (5'-3')
SLco1a1	GTGCATACCTAGCCAAATCACT	CCAGGCCCATAACCACACA
Ugt2b1	TTGATGTCGTTCTAGCAGATGC	TCAAAAAGCAACACCTGCAAC
Serpina1e	TAGGGAGCAAGGGTGACACTC	ACTGTCTGGTCTGTTGAGGGT
Gpx3	CCTTTTAAGCAGTATGCAGGCA	CAAGCCAAATGGCCCAAGTT
Serpinb6b	TCTCACAGAAACGAACAAGACTG	TGAACCCGAAGATAGCAACTCT
Cd63	AGAGACCAGGTGAAGTCAGAG	AGTCTGTGTAGTTAGAAGCTCCA
GAPDH	AGTCTGTGTAGTTAGAAGCTCCA	TGGTCCAGGGTTTCTTACTCC

Table 2. Summary of data statistics by Illumina sequencing.

Sample	Raw reads	Clean reads (%)	Clean Bases	Q20 (%)	Q30 (%)	GC (%)
B1	40,486,466	40,340,090 (99.64%)	6.05G	97.39	92.22	50.06
B2	39,286,030	39,167,874 (99.70%)	5.87G	97.54	92.62	50.18
B3	40,165,864	39,997,342 (99.58%)	5.99G	97.43	92.32	50.20
C1	39,952,808	39,794,622 (99.60%)	5.96G	97.41	92.32	49.11
C2	38,658,548	38,550,596 (99.72%)	5.78G	97.56	92.64	48.93
C3	41,996,670	41,876,904 (99.71%)	6.28G	97.45	92.41	49.57

Table 3. Summary of de novo assembly results of the transcriptome.

Sample	Clean reads	Ribosome comparison Unmapped (%)	Reference genome comparison
Sample	Clean reads	Ribosome comparison Unmapped (%)	Unique_Mapped (%)	Multiple_Mapped (%)
B1	40,340,090	99.70	90.18	7.36
B2	39,167,874	99.64	90.86	7.20
B3	39,997,342	99.81	90.34	7.12
C1	39,794,622	99.79	81.40	13.33
C2	38,550,596	99.77	77.87	15.54
C3	41,876,904	99.73	85.38	10.39

Table 4. Ten significantly upregulated DEGs in BDL-induced hepatic fibrosis.

Gene_ID	Gene name	Gene description	log2(FC)	Adjusted P-value
ENSMUSG00000018339	Gpx3	glutathione peroxidase 3	3.94731	5.22E-82
ENSMUSG00000042842	Serpinb6b	serine (or cysteine) peptidase inhibitor, clade B, member 6b	5.098677	2.17E-79
ENSMUSG00000025351	CD63	CD63 antigen	4.058687	3.36E-78
ENSMUSG00000005397	Nid1	nidogen 1	3.858074	6.62E-74
ENSMUSG00000060147	Serpinb6a	serine (or cysteine) peptidase inhibitor, clade B, member 6a	3.262257	5.54E-72
ENSMUSG00000031167	Rbm3	RNA binding motif (RNP1, RRM) protein 3	3.421352	1.11E-71
ENSMUSG00000027712	Anxa5	annexin A5	2.770413	1.47E-67
ENSMUSG00000019997	Ccn2	cellular communication network factor 2	4.129902	4.00E-63
ENSMUSG00000018623	Mmp7	matrix metallopeptidase 7	6.8818	9.03E-60
ENSMUSG00000015568	Lpl	lipoprotein lipase	3.629115	2.68E-59

Table 5. Ten significantly downregulated DEGs in BDL-induced hepatic fibrosis.

Gene_ID	Gene name	Gene description	log2(FC)	Adjusted P-value
ENSMUSG00000041698	Slco1a1	solute carrier organic anion transporter family, member 1a1	-8.73982	1.40E-216
ENSMUSG00000062181	Ces3b	carboxylesterase 3b	-4.12759	9.40E-164
ENSMUSG00000035836	Ugt2b1	UDP glucuronosyltransferase 2 family, polypeptide B1	-3.25907	5.80E-153
ENSMUSG00000072849	Serpina1e	serine (or cysteine) peptidase inhibitor, clade A, member 1E	-6.95354	1.47E-145
ENSMUSG00000031767	Nudt7	nudix (nucleoside diphosphate linked moiety X)-type motif 7	-4.17962	4.15E-143
ENSMUSG00000025270	Alas2	aminolevulinic acid synthase 2	-2.59758	3.44E-134
ENSMUSG00000031725	Ces1f	carboxylesterase 1F	-4.12573	2.03E-118
ENSMUSG00000024694	Keg1	kidney expressed gene 1	-4.10621	1.98E-116
ENSMUSG00000027559	Car3	carbonic anhydrase 3	-5.98986	8.93E-111
ENSMUSG00000072664	Ugt3a1	UDP glycosyltransferases 3 family, polypeptide A1	-2.84208	9.74E-104

No competing interests reported.

Transcriptome analysis of hepatic fibrosis induced by common bile duct ligation

Status:

Version 1

Abstract

Figures

1. Introduction

2. Results

2.1 Mouse model establishment of hepatic fibrosis induced by BDL

2.2 Transcriptome sequencing and de novo assembly

2.3 Identification of DEGs in BDL-induced hepatic fibrosis

2.4 Enrichment analysis of DEGs in BDL-induced hepatic fibrosis

2.5 The qRT-PCR validation of DEGs

3. Discussion

4. Materials And Methods

4.1 Animals and common bile duct ligation

4.2 Library building and RNA sequencing

4.3 Data quality control and analysis

4.4 Differentially expressed genes (DEGs) analysis and functional enrichment

4.5 Verification of gene using quantitative real-time polymerase chain reaction (qRT-PCR)

4.6 Statistical Analysis

Declarations

References

Tables

Additional Declarations

Status:

Version 1