Time-course RNA sequencing reveals high similarity in mRNAome between hepatic stellate cells activated by agalactosyl IgG and TGF-β1

Time-course RNA sequencing reveals high similarity in mRNAome between hepatic stellate cells activated by agalactosyl IgG and TGF-β1 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Time-course RNA sequencing reveals high similarity in mRNAome between hepatic stellate cells activated by agalactosyl IgG and TGF-β1 Cheng-Hsun Ho, Chieh Kao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5082024/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Nov, 2024 Read the published version in Functional & Integrative Genomics → Version 1 posted 11 You are reading this latest preprint version Abstract Previous studies have demonstrated the clinical relevance of aberrant serum immunoglobulin G (IgG) N -glycomic profiles in liver fibrosis and the pathogenic effects of agalactosyl IgG on activating hepatic stellate cells (HSCs). However, the dynamics of gene expression changes during HSC activation by agalactosyl IgG remain poorly understood. We performed RNA sequencing to analyze the mRNAome of human LX-2 HSCs at multiple time points after treatment with agalactosyl IgG and then compared these results with those obtained after normal IgG and transforming growth factor (TGF)-β1 treatments. Gene expression changes were significantly pronounced on day 5 and subsided by day 11 after HSC activation. A high degree of similarity in gene expression patterns between HSCs treated with agalactosyl IgG and TGF-β1 was observed, of which 1796 and 1785 differentially expressed genes (DEGs) were identified, respectively. Disease ontology analyses revealed that 114 and 115 DEGs in activated HSCs following agalactosyl IgG and TGF-β1 treatments, respectively, were linked to liver cirrhosis, hepatitis, fatty liver disease, hepatitis B, and alcoholic hepatitis, with CCL5 and FAS being the most commonly affected genes. DEGs associated with liver fibrosis or aforementioned liver diseases involved in gene annotation, physiological functions, and signaling pathways regarding secretion of cytokines and chemokines, expression of fibrosis-related growth factors and their receptors, modification of extracellular matrices, and regulation of cell viability in activated HSCs. In conclusion, this study characterized the dynamics of mRNAome and gene networks and identified the liver fibrosis-related DEGs during HSC activation by agalactosyl IgG and TGF-β1. hepatic stellate cells liver fibrosis IgG glycosylation RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatic stellate cells (HSCs), located in the perisinusoidal area between endothelial cells and hepatocytes, are the primary cell type contributing to liver fibrosis (Moreira 2007 ). In the absence of liver injury, HSCs remain quiescent and store retinoids and vitamin A metabolites in their cytoplasmic lipid droplets (Knook et al. 1982 ; Hendriks et al. 1985 ; Blaner et al. 2009 ). HSCs also perform many immunomodulatory functions; for instance, they express fragment crystallizable gamma receptors (FcγRs) (Shen et al. 2005 ), express various clusters of differentiation markers (Vinas et al. 2003 ), present antigens (Vinas et al. 2003 ), stimulate T-lymphocyte proliferation (Vinas et al. 2003 ), and engulf apoptotic bodies (Canbay et al. 2003 ). Following liver injury, HSCs differentiate into myofibroblast-like cells that proliferate, generate collagen-rich extracellular matrices, and produce extracellular matrix remodeling enzymes (Eng and Friedman 2000 ; Friedman 2008 ). In addition, HSCs respond to inflammatory stimuli, such as transforming growth factor (TGF)-β and oxidative stress, by secreting cytokines and chemokines. These secreted molecules support autocrine and paracrine signaling both within HSCs and to other cell types in liver tissues (Purps et al. 2007 ; Kamm and McCommis 2022 ). An increase in serum agalactosyl immunoglobulin G (IgG) is a serological indicator for liver fibrosis and cirrhosis (Callewaert et al. 2004 ; Klein et al. 2010 ). Our previous studies have demonstrated that the level of serum agalactosyl IgG was correlated with the concentration of serum TGF-β1 and the severity of liver fibrosis in patients with chronic hepatitis B (Ho et al. 2015 ; Ho et al. 2024 ). Furthermore, we determined that agalactosyl IgG mainly activates HSCs through FcγRIIIa expressed on HSCs (Ho et al. 2024 ). Treatment with agalactosyl IgG induces morphological changes in HSCs, enhances their migration and invasion abilities, and promotes collagen secretion and fibrogenesis-related protein expression (Ho et al. 2024 ). A vicious cycle involving agalactosyl IgG, HSC-FcγR, and TGF-β1 may occur during HSC activation and subsequent liver fibrogenesis. However, the molecular basis of HSC activation caused by agalactosyl IgG or TGF-β1 remains largely unknown. As information regarding the changes in gene expression profiles during HSC activation may lead to a better understanding of the pathoetiology of liver fibrogenesis and is crucial for defining therapeutic targets, we performed a time-course transcriptomic study to analyze the dynamics of mRNAome and gene regulatory networks of HSCs activated by agalactosyl IgG and TGF-β1. Materials and methods Cell culture A human immortalized HSC line, namely LX2, was purchased from Merck Millipore (Darmstadt, Germany) and cultured in Dulbecco’s Modified Eagle Medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 2% ultra-low-IgG fetal bovine serum (Thermo Fisher Scientific) to minimize the nonspecific crosslinking of bovine IgGs to HSC-FcγRs (Ho et al. 2024 ), 100 U/mL of penicillin, and 100 µg/mL of streptomycin at 37°C with 5% CO2. Oleic acid (100 µM) (Merck Millipore) was used to maintain HSCs in a quiescent state for 7 days prior to treatment. Generation and analysis of agalactosyl IgG Normal human serum IgG proteins were purchased from Merck Millipore and treated with α2–3,6,8 neuraminidase (New England Biolabs, Ipswich, MA, USA) at 37°C for 48 hr. This process was followed by an additional 48-h treatment with β1–4 galactosidase S (New England Biolabs) at 37°C. The liquid chromatography-tandem mass spectrometry-based IgG-Fc glycan analysis has been described previously (Ho et al. 2024 ). RNA sequencing LX2 cells were treated with 5 µg/µl of normal IgG, 5 µg/µl of normal IgG plus 2 ng/mL of human TGF-β1 recombinant protein (Thermo Fisher Scientific), or 5 µg/µl of agalactosyl IgG for 2, 5, 8, 11, and 14 days. Total RNA was extracted from the LX2 cells at each of these time points by using REzol C&T (Protech Technology, Taipei City, Taiwan). The quantity of purified RNAs was determined using the NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific) and the quality of these RNAs was examined using the RNA 6000 Nano kit (Agilent Technology, Santa Clara, CA, USA) and the 2100 Bioanalyzer system (Agilent Technology). The mRNA library for each sample using 500 ng of total RNA was prepared using the SureSelect XT HS2 mRNA Library Prep Kit (Agilent Technology) and AMPure XP beads (Beckman Coulter, Brea, CA, USA). RNA sequencing was performed using the NovaSeq X Plus Sequencing Systems (Illumina, San Diego, CA, USA). Sequenced FASTQ reads were generated using the Welgene Biotech (Taipei, Taiwan) pipeline based on Illumina's basecalling program bcl2fastq2 conversion Software v2.20. Bioinformatic and statistical analyses The reads were trimmed using Trimmomatic v0.36 to remove adapters and leading (below quality 20) and trailing (below quality 13) low quality or N bases (Bolger et al. 2014 ). The reads below 30 bases in length were dropped. The trimmed reads were then aligned to human genome assembly GRCh38 using the HISAT2 (v2.2.1) program (Kim et al. 2019 ). Transcript abundance was normalized to transcript per million (TPM) by using StringTie v2.1.7 (Pertea et al. 2015 ). The minimal detection threshold of TPM was set to 0.1 (Gu et al. 2023 ). Differential gene expression analysis was performed using DEseq v1.39.0 (Anders and Huber 2010 ), an R/Bioconductor package that incorporates genome bias detection and correction through the Welgene Biotech pipeline. Differentially expressed genes (DEGs) were filtered using thresholds of log2(fold change) > 1.0 or < -1.0 to identify significant genes with changes in the expression level with an optimal signal-to-noise ratio, with significance set at p < 0.05. Functional enrichment analyses for DEGs were performed using clusterProfiler v4.7.1 (Yu et al. 2012 ). DEGs were assessed for their associations with disease ontology, gene ontology in three aspects: molecular function, cellular component, and biological process, and signaling pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Statistical analyses were conducted using SPSS Statistics v18.0 for Windows. Pearson’s correlation coefficient ( r ) was employed to evaluate linear relationships between parameters, with significance set at p < 0.05. Venn diagrams were generated using InteractiVenn (Heberle et al. 2015 ). Results Time-course mRNAome in HSCs Based on our previous results, agalactosyl IgG treatment caused changes in many HSC properties approximately on day 7. Accordingly, RNA sequencing for LX2 cells treated with agalactosyl IgG or TGF-β1 was performed on days 2, 5, 8, 11, and 14 to observe the process of mRNAone change. The quality of RNA sequencing is presented in Table S1 . The sequence reads were mapped to 60671 genes, of which 19966 were characterized as protein-coding genes, 15259 as pseudogenes, and 25446 as other gene types. The mRNAome analysis revealed that the numbers of upregulated and downregulated DEGs markedly increased from day 5 to day 8 and then stabilized from day 8 to day 14 in LX2 cells treated with agalactosyl IgG compared with those treated with normal IgG (Fig. 1 A and Table S2 ). A similar trend was observed in LX2 cells treated with TGF-β1. We compared gene expression between LX2 cells treated with agalactosyl IgG and TGF-β1 and identified that 92, 98, 805, 841, and 752 DEGs were commonly detected in both groups on days 2, 5, 8, 11, and 14, respectively (Fig. 1 B). The expression levels of these DEGs were strongly correlated following both treatments (Fig. 1 B). Regarding gene expression trends, 92, 95, 196, 165, and 193 DEGs were specifically detected at day 2, 5, 8, 11, and 14, respectively, following agalactosyl IgG treatment, whereas 102, 114, 222, 207, and 139 DEGs were specifically detected at these time points, respectively, following TGF-β1 treatment (Fig. 1 C). Moreover, the genes MIR3142HG , AC008878.1 , NPHP3-ACAD11 , and AC242842.3 were detected consistently from day 2 to day 14 after agalactosyl IgG treatment, and the gene AC018521.1 was detected throughout the same period after TGF-β1 treatment. DEGs associated with liver diseases and fibrosis Because liver fibrosis is not documented in disease ontology databases, we assessed the relevance of DEGs in different liver diseases. After treatments with agalactosyl IgG and TGF-β1, 114 and 105 DEGs, respectively, were identified as being involved in liver cirrhosis, hepatitis, fatty liver disease, hepatitis B, and alcoholic hepatitis. Among these DEGs, 20 after agalactosyl IgG treatment and 11 after TGF-β1 treatment were uniquely associated with liver diseases in LX2 cells. The number of altered genes increased significantly after 8 days of treatment (Fig. 2 A). The expression of 17, 17, and 10 genes associated only with liver cirrhosis, hepatitis, and fatty liver disease, respectively, changed after both treatments (Fig. 2 B and Table S3 ). In activated LX2 cells, CYP27B1 was the only gene associated with hepatitis B, and no gene was associated with alcoholic hepatitis alone. Moreover, 29 DEGs were associated with three or more liver diseases; all of them were liver fibrosis-related gene markers. CCL5 and FAS in LX2 cells were associated with all five of the studied liver diseases after agalactosyl IgG and TGF-β1 treatments. Furthermore, we analyzed the association of 15 additional liver fibrosis-related genes with the aforementioned liver diseases and identified the involvement of FAP in hepatitis and FAP, MMP1 , and SMAD7 in liver cirrhosis (Fig. 3 and Table S3 ). Next, we analyzed the expression dynamics of 44 aforementioned fibrosis-related DEGs (29 commonly detected in liver diseases and 15 additional ones). The levels of CCL2 , CCL5 , COL6A1 , COL8A1 , FAP , FAS , FGF2 , FGF7 , ICAM1 , IL1B , IL6 , MMP1 , MTHFR , PDGFRA , PPARGC1A , PTGS2 , SREBF1 , TGFBR3 , TIMP3 , and TNFSF10 increased primarily from day 5 or day 8 after agalactosyl IgG or TGF-β1 treatment, whereas those of BIRC5 , CDH2 , COL1A1 , ELFN2 , FGFR3 , FN1 , HMOX1 , and SMAD7 decreased after treatment (Fig. 3 ). Molecular function analyses of DEGs Overall, the DEGs in LX2 cells after agalactosyl IgG and TGF-β1 treatments were involved in 58 and 52 molecular functions, respectively; 43 of these genes were detected in both groups (Figure S1 and Table S4 ). After agalactosyl IgG treatment, the fibrosis-related DEGs were involved in different molecular functions: 9 DEGs in cytokine and chemokine activity, 14 in matrix and membrane factor binding, 13 in growth factors and receptors, 1 in cytoskeleton organization, 23 in kinase and protease activity, and 0 in nucleic acid processing (Fig. 4 ). Similarly, TGF-β1 treatment led to 9, 12, 14, 1, 14, and 1 DEGs involved in these functions, respectively (Fig. 4 ). Cellular component analyses of DEGs Total DEGs in LX2 cells after treatments with agalactosyl IgG and TGF-β1 were involved in 49 and 45 cellular components, respectively; 37 of these components were affected by both treatments (Figure S2 and Table S5 ). After agalactosyl IgG treatment, 2, 0, 1, 7, 12, and 11 fibrosis-related DEGs were related to chromosome, nucleus, kinase, cytoplasm, membrane, and secretion, respectively, whereas 2, 0, 1, 6, 10, and 10 fibrosis-related DEGs by TGF-β1 treatment were related to these components, respectively (Fig. 5 A). Biological process analyses of DEGs The total DEGs in LX2 cells following treatments with agalactosyl IgG and TGF-β1 were involved in 969 and 1103 biological processes, respectively; 834 of these processes were detected in both groups (Table S6 ). The fibrosis-related DEGs in LX2 cells by agalactosyl IgG and TGF-β1 treatments were involved in 926 different biological processes: 37.0% regarding cellular responses to physiological changes, 24.9% regarding immune modulation, 21.5% regarding viability and differentiation, 11.7% regarding nucleus and chromosome, 4.0% regarding extracellular organization, and 0.9% regarding cytoskeleton (Fig. 5 B and Table S6 ). Similarly, TGF-β1 treatment led to the fibrosis-related DEGs involved in 869 biological processes: 40.7%, 25.3%, 19.8%, 9.0%, 5.1%, and 0.1% regarding these categories, respectively. A total of 711 biological processes were detected in both groups. Signaling pathway analyses of DEGs According to KEGG database mapping, LX2 cells exhibited the involvement of total DEGs in 60 and 64 events after agalactosyl IgG and TGF-β1 treatments, respectively; 56 of these events were detected in both groups (Table S7 ). Regarding signaling pathways, 13 such pathways were found to be associated with the activation of LX2 cells. Gene network analyses revealed that these pathways involved 23 and 20 fibrosis-related DEGs in LX2 cells after agalactosyl IgG and TGF-β1 treatments, respectively (Fig. 6 ); in both treatments, 6 of these DEGs were associated with nucleotide-binding oligomerization domain-like receptor signaling pathway, 6 with advanced glycation endproducts signaling pathway, 8 with interleukin (IL)-17 signaling pathway, and 10 with tumor necrosis factor (TNF) signaling pathway (Fig. 6 ). Discussion An increase in the abnormal glycosylation of serum IgG is a common symptom of chronic liver damage. However, whether IgG glycosylation initiates and promotes liver fibrosis remains unclear. HSCs are key responders to liver injury in the hepatic microenvironment and are the primary matrix-producing cells contributing to liver fibrogenesis (Moreira 2007 ). Accordingly, we investigated the pathogenic effects of aberrant IgG glycosylation on HSC activation and reported that agalactosyl IgG primarily activates HSCs through FcγRIIIa expressed on these cells (Ho et al. 2024 ). Treatment with agalactosyl IgG induced a transition of HSCs to a fibroblast-like morphology, increased their migration and invasion capabilities, and upregulated fibrogenic markers. Furthermore, we identified a positive feedback loop involving agalactosyl IgG, TGF-β1, and HSC-FcγRIIIa. These findings indicate a causal relationship between aberrant serum IgG-Fc glycosylation, particularly agalactosylation, and liver fibrosis (Ho et al. 2024 ). However, the gene profiles and their changes during HSC activation by agalactosyl IgG are poorly understood. In this study, we investigated the dynamics of the mRNAome and gene regulatory networks in human LX2 HSCs activated by agalactosyl IgG. In addition, we assessed the correlation between these changes and those in HSCs activated by TGF-β1. The interaction between IgG-Fc and activating FcγRs triggers the phosphorylation of the immunoreceptor tyrosine-based activation motif located either in the cytoplasm portion of FcγRs or in the associated FcR common γ-chain, and subsequently activates Syk kinase (Takai 2002 ). Once activated, Syk kinase can initiate a variety of signaling pathways, including PI3K-Akt, bruton tyrosine kinase-Rac-Rho, phosphatidylinositol (3,4,5)-trisphosphate-phospholipase Cγ-protein kinase C, and Sos-Ras (Nimmerjahn and Ravetch 2008 ). On the other side, TGF-β1 not only mediates signaling through the suppressor of mothers against decapentaplegic family proteins but also activates pathways such as PI3K-Akt, Rac-Rho, and Sos-Ras (Akhurst and Hata 2012 ). Our time-course analyses revealed substantial gene changes in HSCs by day 8 following treatment with either agalactosyl IgG of TGF-β1. The high correspondence between the gene expression patterns in HSCs treated with agalactosyl IgG and TGF-β1 is expected because both factors regulate common signaling pathways and downstream genes. This phenomenon is further proved by a close relationship between the two gene sets with respect to molecular function, KEGG, and disease annotations. This relationship explains the absence of the additive or synergistic effects of agalactosyl IgG and TGF-β1 on the induction of some fibroblast markers and characteristics in HSCs (Ho et al. 2024 ). Activated HSCs secrete a wide range of proinflammatory cytokines and chemokines, including C-C motif chemokine ligand (CCL) 2, CCL5, IL-1β, IL-6, and express adhesion molecules, such as intercellular adhesion molecule (ICAM) 1, to help recruit immune cells to the liver (Pinzani and Marra 2001 ; Uemura and Gandhi 2001 ; Carter and Friedman 2022 ). Our findings regarding the enhanced expression levels of CCL2 , CCL5 , IL1B , IL6 , and ICAM1 in activated HSCs by both agalactosyl IgG and TGF-β1 coincide with those of previous reports. In addition, we observe an increase in the expression of FAS (encodes FAS receptor) and TNFSF10 (encodes TNF-related apoptosis-inducing ligand) and a decrease in the expression of HMOX1 (encodes heme oxygenase 1). Both the FAS receptor and the TNF-related apoptosis-inducing ligand can induce apoptosis in HSCs (Singh et al. 2017 ; Tan et al. 2021 ). By contrast, heme oxygenase 1 inhibits HSC apoptosis and ameliorates liver fibrosis (Luo et al. 2022 ). Nevertheless, we found that the expression levels of PPARGC1A and PTGS2 , which tend to prevent apoptosis in HSCs, were increased after agalactosyl IgG and TGF-β1 treatments. The product of PPARGC1A , namely peroxisome proliferator-activated receptor γ coactivator-1α, has been demonstrated to inhibit HSC activation and alleviate liver fibrosis (Jing-Jing Yang 2022; Zhang et al. 2023 ). In previous studies, the Ppargc1a mRNA level has not significantly changed in mouse HSCs activated by fucoxanthin but has been reduced in activated rat HSCs (Smith-Cortinez et al. 2020 ; Bae et al. 2022 ). Prostaglandin-endoperoxide synthase 2, also known as COX-2, is encoded by PTGS2 . In a model of transgenic mice constitutively expressing human COX-2 in hepatocytes, COX-2 restricted HSC activation and attenuated liver fibrosis (Frances et al. 2015 ; Brea et al. 2018 ). Moreover, the knockout of PTGS2 promoted HSC apoptosis (Zhou et al. 2024 ). These findings suggest the homeostasis in the activation process of HSCs and the initial stage of liver fibrogenesis. Furthermore, the inconsistency in results regarding gene functions in HSCs may be attributed to differences in the species origin of HSCs or the varied treatments used for their activation. Methylenetetrahydrofolate reductase, encoded by MTHFR , is a crucial enzyme for folate metabolism. Expression of MTHFR did not change in activated mouse HSCs; however, polymorphisms in this gene are associated with liver steatosis and fibrosis in cases of chronic hepatitis C (Adinolfi et al. 2005 ; Yanjie Gao 2023). Accordingly, the role of MTHFR in HSC activation warrants further investigation. In addition to modulating immune responses and cell apoptosis, HSCs express multiple growth factors and receptors, including platelet-derived growth factors (PDGFs) and their receptors, and secrete various types of collagens in the perpetuation phase of liver fibrosis (Ikeda et al. 1999 ; Pinzani and Marra 2001 ). Moreover, activated HSCs produce diverse MMPs and tissue inhibitors of metalloproteinases (TIMPs), which are crucial for the development of liver fibrosis (Iredale et al. 1998 ). Therefore, we explored the time-course changes in the expression of fibrosis-related gene markers. The mRNA levels of FAP (encodes fibroblast activation protein-α), FGF7 (encodes fibroblast growth factor 2), PDGFRA , MMP1 , MMP3 , MMP9 , and TIMP3 in HSCs increased consistently from day 5 to day 11, and then fluctuated on day 14. Collectively, these findings indicate that the effects of agalactosyl IgG and GF-β1 on HSC activation and their fibrogenic features may begin after day 2 and persist for more than one week. Gene ontology and signaling analyses revealed that these DEGs were associated with cellular components involving cytoplasm, membrane, and secretion; molecular functions involving kinase and protease activity, matrix and membrane factor binding, growth factors and receptors, and cytokine and chemokine activity; signaling pathways involving immune modulation and cell growth. These findings indicate that agalactosyl IgG and TGF-β1 affect HSCs in multiple aspects, particularly secretion of cytokines and chemokines, expression of fibrosis-related growth factors and their receptors, modification of extracellular matrices, and regulation of cell viability in the first few days after HSC activation; these effects may cause subsequent changes in HSC physiology, leading to enhanced fibrogenic activities. Nevertheless, the mechanisms underlying HSC activation and liver fibrosis in vivo may be pluralistic and merit further study. Conclusion Our works elucidated mRNA profiles, gene networks, and the expression dynamics of fibrosis-related DEGs during HSC activation. Furthermore, the involvement of fibrosis-related DEGs in different biomedical ontologies that may contribute to liver fibrogenesis was revealed. Although the validation on liver tissues was not performed due to the activated and heterogeneous HSCs obtained from patients with liver fibrosis or cirrhosis, this study advances our understanding of the gene regulation model during HSC activation and potentially benefits the exploration of new antifibrotic therapeutic targets and development of effective treatment strategies. Abbreviations CCL C-C motif chemokine ligand DEG differentially expressed gene FcγR fragment crystallizable gamma receptor HSC hepatic stellate cell IgG immunoglobulin G IL interleukin KEGG Kyoto Encyclopedia of Genes and Genomes MMP matrix metalloproteinase MTHFR Methylenetetrahydrofolate reductase PDGF platelet-derived growth factor PI3K phosphoinositide 3-kinase TGF transforming growth factor TIMP tissue inhibitors of metalloproteinase TNF tumor necrosis factor TPM transcript per million. Declarations Conflict of interest statements: The authors have no relevant financial or non-financial interests to disclose. Funding: This work was supported by the National Science and Technology Council, Taiwan (grant NSTC 111-2314-B-214-003-MY3). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and writing the manuscript. Data Availability: Sequencing reads are available from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/sra/PRJNA1126014). Informed consent: not applicable Large Language Models: no AI tools have been used in this study and manuscript Acknowledgments: We thank Welgene Biotech Co., Ltd. for the RNA sequencing and Wallace Academic Editing for English editing. Author Contributions: C.H.H was responsible for the study design, data interpretation, and manuscript writing. C.K. assisted in data analysis. Previous presentation or publication: This manuscript has not been submitted, presented in any meetings, or accepted for publication elsewhere. References Adinolfi LE, Ingrosso D, CesaroG et al (2005) Hyperhomocysteinemia and the MTHFR C677T polymorphism promote steatosis and fibrosis in chronic hepatitis C patients. Hepatology 41:995–1003. https://doi.org/10.1002/hep.20664 Akhurst RJ, Hata A (2012) Targeting the TGFbeta signalling pathway in disease. 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Sci Rep 7:5514. https://doi.org/10.1038/s41598-017-05845-5 Smith-Cortinez N, van Eunen K, Heegsma J et al (2020) Simultaneous Induction of Glycolysis and Oxidative Phosphorylation during Activation of Hepatic Stellate Cells Reveals Novel Mitochondrial Targets to Treat Liver Fibrosis. Cells 9:2456. https://doi.org/10.3390/cells9112456 Takai T (2002) Roles of Fc receptors in autoimmunity. Nat Rev Immunol 2:580–592. https://doi.org/10.1038/nri856 Tan S, Liu X, Chen L et al (2021) Fas/FasL mediates NF-kappaBp65/PUMA-modulated hepatocytes apoptosis via autophagy to drive liver fibrosis. Cell Death Dis 12:474. https://doi.org/10.1038/s41419-021-03749-x Uemura T, Gandhi CR (2001) Inhibition of DNA synthesis in cultured hepatocytes by endotoxin-conditioned medium of activated stellate cells is transforming growth factor-beta and nitric oxide-independent. Br J Pharmacol 133:1125–1133. https://doi.org/10.1038/sj.bjp.0704151 Vinas O, Bataller R, Pau Sancho-Bru P et al (2003) Human hepatic stellate cells show features of antigen-presenting cells and stimulate lymphocyte proliferation. Hepatology 38:919–929. https://doi.org/10.1053/jhep.2003.50392 Gao Y, Zheng B, Xu S et al (2023) Mitochondrial folate metabolism-mediated α-linolenic acid exhaustion masks liver fibrosis resolution. J Biol Chem 299:104909. https://doi.org/10.1016/j.jbc.2023.104909 Yu G, Wang LG, Han Y et al (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287. https://doi.org/10.1089/omi.2011.0118 Zhang Y, Zhang L, Zhao Y et al (2023) PGC-1alpha inhibits M2 macrophage polarization and alleviates liver fibrosis following hepatic ischemia reperfusion injury. Cell Death Discov 9:337. https://doi.org/10.1038/s41420-023-01636-2 Zhou H, Wang Y, Zhu G et al (2024) CRISPR/Cas9 knockout of Ptgs2 promotes apoptosis of hepatic stellate cells. Clin Res Hepatol Gastroenterol 48:102345. https://doi.org/10.1016/j.clinre.2024.102345 Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx FigureS1.pdf FigureS2.pdf Supplementaryfigurelegends.docx Cite Share Download PDF Status: Published Journal Publication published 16 Nov, 2024 Read the published version in Functional & Integrative Genomics → Version 1 posted Editorial decision: Revision requested 31 Oct, 2024 Reviews received at journal 31 Oct, 2024 Reviews received at journal 31 Oct, 2024 Reviews received at journal 25 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviewers agreed at journal 23 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers invited by journal 01 Oct, 2024 Editor assigned by journal 17 Sep, 2024 Submission checks completed at journal 17 Sep, 2024 First submitted to journal 13 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5082024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372752835,"identity":"b1f594b2-a26d-48e9-b8e1-9ebff8501673","order_by":0,"name":"Cheng-Hsun Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACCSBmbAAzGB8AKR4+UrQwG4C0sJGihQ3EZiCohX9287GHX3fUJW6Xbj5W+TXHToaNgfnhoxv4LLlzLN1Y9szhxJ1zjqXdlt2WDHQYm7FxDh4tBhI5ZtKSbQcSN9zIMbstuY0ZqIWHTRq/lvxvQC11YC3FktvqidGSwyb5sY0ZrIXx47bDhLVI3Egzk2ZsO2y84UZasjTjtuM8bMwE/MI/I/mZ5M+2OtkNN5IPfvy5rdqen7354WN8WkCAmQeFwUxAOQgw/kBnjIJRMApGwShABgBnGUe5YbaVsQAAAABJRU5ErkJggg==","orcid":"","institution":"I-Shou University","correspondingAuthor":true,"prefix":"","firstName":"Cheng-Hsun","middleName":"","lastName":"Ho","suffix":""},{"id":372752836,"identity":"b6c96771-931b-4374-aa0e-eb2ed73e7693","order_by":1,"name":"Chieh Kao","email":"","orcid":"","institution":"I-Shou University","correspondingAuthor":false,"prefix":"","firstName":"Chieh","middleName":"","lastName":"Kao","suffix":""}],"badges":[],"createdAt":"2024-09-13 07:59:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5082024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5082024/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10142-024-01502-z","type":"published","date":"2024-11-16T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68745445,"identity":"9647270f-24bb-4886-90da-83c93f37fc02","added_by":"auto","created_at":"2024-11-11 15:09:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":443739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression.\u003c/strong\u003e(A) Volcano plots, (B) correlations and (C) Venn diagrams illustrating differential gene expression in LX2 cells at multiple time points of agalactosyl IgG (5 μg/μl) or normal IgG (5 μg/μl) plus TGF-β1 (2 ng/mL) treatment. In (A), the red dots represent upregulated genes, the blue dots represent downregulated genes, and the gray dots indicate no difference.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/6fad59d854d183255cf3b5c4.jpg"},{"id":68745446,"identity":"4fbe397e-c181-481d-b6e1-5f7579e516d4","added_by":"auto","created_at":"2024-11-11 15:09:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":412845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisease ontology.\u003c/strong\u003e (A) A heatmap depicting the differentially expressed genes involved in various liver diseases in LX2 cells treated with agalactosyl IgG (5 μg/μl) or normal IgG (5 μg/μl) plus TGF-β1 (2 ng/mL). (B) A Venn diagram showing differentially expressed genes involved in various liver diseases. Genes detected only by agalactosyl IgG and TGF-β1 treatments are shown in blue and red, respectively. TGF, transforming growth factor.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/d3ba3aa2981568a9ad30220e.jpg"},{"id":68745839,"identity":"b10ed48b-45ba-4c6a-86b4-8178bde33b0a","added_by":"auto","created_at":"2024-11-11 15:17:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":654476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression dynamics. \u003c/strong\u003eLine charts showing relative levels of gene expression in LX2 cells at multiple time points of agalactosyl IgG (5 μg/μl) or normal IgG (5 μg/μl) plus TGF-β1 (2 ng/mL) treatment. TGF, transforming growth factor. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/734c90521754552f01e1848d.jpg"},{"id":68745841,"identity":"7035ce2d-4743-4cfd-b933-c7411ca92b7c","added_by":"auto","created_at":"2024-11-11 15:17:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":590730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular function annotation.\u003c/strong\u003e A heatmap illustrating liver fibrosis-related DEGs involved in various molecular functions in LX2 cells treated with agalactosyl IgG (5 μg/μl) or normal IgG (5 μg/μl) plus TGF-β1 (2 ng/mL). A, agalactosyl IgG treatment; DEGs, differentially expressed genes; GO, gene ontology; T, normal IgG plus TGF-β1 treatment; TGF, transforming growth factor.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/cbd583081888687c8b55282d.jpg"},{"id":68745838,"identity":"3cf8953b-8149-4925-ba4b-49bb3cd150cb","added_by":"auto","created_at":"2024-11-11 15:17:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":538458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCellular component and annotation.\u003c/strong\u003e (A) A heatmap illustrating various cellular components and (B) a pie chart illustrating the distribution of various biological processes involve liver fibrosis-related DEGs in LX2 cells treated with agalactosyl IgG (5 μg/μl) or normal IgG (5 μg/μl) plus TGF-β1 (2 ng/mL). A, agalactosyl IgG treatment; DEGs, differentially expressed genes; GO, gene ontology; T, normal IgG plus TGF-β1 treatment; TGF, transforming growth factor.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/b7ccc4f897014838fe1ef3e5.jpg"},{"id":68745447,"identity":"70841e8f-c125-47bd-87cb-01d354f3efa4","added_by":"auto","created_at":"2024-11-11 15:09:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":446420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG annotation.\u003c/strong\u003e Interactome plots illustrating the association of liver disease- or fibrosis-related DEGs with various signaling pathways in LX2 cells treated with agalactosyl IgG (5 μg/μl) or normal IgG (5 μg/μl) plus TGF-β1 (2 ng/mL). DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; TGF, transforming growth factor.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/3b8da3d072917ce34450aaf8.jpg"},{"id":69285687,"identity":"3aa6f1fb-8c06-4711-80ad-765bc9cabab7","added_by":"auto","created_at":"2024-11-18 19:27:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3644707,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/f3b5f406-df19-4912-80ba-29180c9ab1db.pdf"},{"id":68746605,"identity":"a2b6bf94-5da9-45a0-ba96-1c1d591b79fb","added_by":"auto","created_at":"2024-11-11 15:25:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20662,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/d818e489f62e0b43130b9c5f.docx"},{"id":68746604,"identity":"1e8228e8-edc9-4a5e-b9e1-9cd1aa82d366","added_by":"auto","created_at":"2024-11-11 15:25:13","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":173464,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/44d4a70d79a7ae7e4e90d922.xlsx"},{"id":68744720,"identity":"956e0b0c-1b42-4831-8c14-f7d88cee1d73","added_by":"auto","created_at":"2024-11-11 15:01:13","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19517,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/0ed8dc7a7bbaaaed7fa22eed.xlsx"},{"id":68744722,"identity":"c8c51ac0-9244-4b58-be32-d7638ef676e6","added_by":"auto","created_at":"2024-11-11 15:01:13","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19540,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/17775a231b9bc7f700abb061.xlsx"},{"id":68744728,"identity":"76d23799-f538-47c2-be44-3190b21a6139","added_by":"auto","created_at":"2024-11-11 15:01:13","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18750,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/91f833e406f5fc2b0193c555.xlsx"},{"id":68745455,"identity":"48bc82fe-1b38-4bea-88ea-b2107f42ac12","added_by":"auto","created_at":"2024-11-11 15:09:14","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":178099,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/cb9d1f897841390d1ab43ddc.xlsx"},{"id":68744724,"identity":"d0edd728-a66a-4231-b7e2-471c30ba7feb","added_by":"auto","created_at":"2024-11-11 15:01:13","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":21604,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/aea96a92fca965331a928460.xlsx"},{"id":68745452,"identity":"da8eb745-cdb7-446a-a210-bdbd80a4fcf3","added_by":"auto","created_at":"2024-11-11 15:09:14","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2721280,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/09f7bf051c726e94754141e0.pdf"},{"id":68744731,"identity":"4f83ce92-25cd-4f7c-89f0-1e0265f63380","added_by":"auto","created_at":"2024-11-11 15:01:14","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":2635163,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/57a2a56a2e2ebefd2cbf60bd.pdf"},{"id":68745453,"identity":"7904eed6-03f5-4406-b864-6d015a24644c","added_by":"auto","created_at":"2024-11-11 15:09:14","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":14383,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigurelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5082024/v1/24e12244eb471b6773c7780e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-course RNA sequencing reveals high similarity in mRNAome between hepatic stellate cells activated by agalactosyl IgG and TGF-β1","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatic stellate cells (HSCs), located in the perisinusoidal area between endothelial cells and hepatocytes, are the primary cell type contributing to liver fibrosis (Moreira \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the absence of liver injury, HSCs remain quiescent and store retinoids and vitamin A metabolites in their cytoplasmic lipid droplets (Knook et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Hendriks et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Blaner et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). HSCs also perform many immunomodulatory functions; for instance, they express fragment crystallizable gamma receptors (FcγRs) (Shen et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), express various clusters of differentiation markers (Vinas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), present antigens (Vinas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), stimulate T-lymphocyte proliferation (Vinas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and engulf apoptotic bodies (Canbay et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Following liver injury, HSCs differentiate into myofibroblast-like cells that proliferate, generate collagen-rich extracellular matrices, and produce extracellular matrix remodeling enzymes (Eng and Friedman \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Friedman \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In addition, HSCs respond to inflammatory stimuli, such as transforming growth factor (TGF)-β and oxidative stress, by secreting cytokines and chemokines. These secreted molecules support autocrine and paracrine signaling both within HSCs and to other cell types in liver tissues (Purps et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kamm and McCommis \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn increase in serum agalactosyl immunoglobulin G (IgG) is a serological indicator for liver fibrosis and cirrhosis (Callewaert et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Klein et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Our previous studies have demonstrated that the level of serum agalactosyl IgG was correlated with the concentration of serum TGF-β1 and the severity of liver fibrosis in patients with chronic hepatitis B (Ho et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, we determined that agalactosyl IgG mainly activates HSCs through FcγRIIIa expressed on HSCs (Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Treatment with agalactosyl IgG induces morphological changes in HSCs, enhances their migration and invasion abilities, and promotes collagen secretion and fibrogenesis-related protein expression (Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A vicious cycle involving agalactosyl IgG, HSC-FcγR, and TGF-β1 may occur during HSC activation and subsequent liver fibrogenesis. However, the molecular basis of HSC activation caused by agalactosyl IgG or TGF-β1 remains largely unknown. As information regarding the changes in gene expression profiles during HSC activation may lead to a better understanding of the pathoetiology of liver fibrogenesis and is crucial for defining therapeutic targets, we performed a time-course transcriptomic study to analyze the dynamics of mRNAome and gene regulatory networks of HSCs activated by agalactosyl IgG and TGF-β1.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eA human immortalized HSC line, namely LX2, was purchased from Merck Millipore (Darmstadt, Germany) and cultured in Dulbecco\u0026rsquo;s Modified Eagle Medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 2% ultra-low-IgG fetal bovine serum (Thermo Fisher Scientific) to minimize the nonspecific crosslinking of bovine IgGs to HSC-FcγRs (Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), 100 U/mL of penicillin, and 100 \u0026micro;g/mL of streptomycin at 37\u0026deg;C with 5% CO2. Oleic acid (100 \u0026micro;M) (Merck Millipore) was used to maintain HSCs in a quiescent state for 7 days prior to treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGeneration and analysis of agalactosyl IgG\u003c/h2\u003e \u003cp\u003eNormal human serum IgG proteins were purchased from Merck Millipore and treated with α2\u0026ndash;3,6,8 neuraminidase (New England Biolabs, Ipswich, MA, USA) at 37\u0026deg;C for 48 hr. This process was followed by an additional 48-h treatment with β1\u0026ndash;4 galactosidase S (New England Biolabs) at 37\u0026deg;C. The liquid chromatography-tandem mass spectrometry-based IgG-Fc glycan analysis has been described previously (Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing\u003c/h2\u003e \u003cp\u003eLX2 cells were treated with 5 \u0026micro;g/\u0026micro;l of normal IgG, 5 \u0026micro;g/\u0026micro;l of normal IgG plus 2 ng/mL of human TGF-β1 recombinant protein (Thermo Fisher Scientific), or 5 \u0026micro;g/\u0026micro;l of agalactosyl IgG for 2, 5, 8, 11, and 14 days. Total RNA was extracted from the LX2 cells at each of these time points by using REzol C\u0026amp;T (Protech Technology, Taipei City, Taiwan). The quantity of purified RNAs was determined using the NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific) and the quality of these RNAs was examined using the RNA 6000 Nano kit (Agilent Technology, Santa Clara, CA, USA) and the 2100 Bioanalyzer system (Agilent Technology). The mRNA library for each sample using 500 ng of total RNA was prepared using the SureSelect XT HS2 mRNA Library Prep Kit (Agilent Technology) and AMPure XP beads (Beckman Coulter, Brea, CA, USA). RNA sequencing was performed using the NovaSeq X Plus Sequencing Systems (Illumina, San Diego, CA, USA). Sequenced FASTQ reads were generated using the Welgene Biotech (Taipei, Taiwan) pipeline based on Illumina's basecalling program bcl2fastq2 conversion Software v2.20.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic and statistical analyses\u003c/h2\u003e \u003cp\u003eThe reads were trimmed using Trimmomatic v0.36 to remove adapters and leading (below quality 20) and trailing (below quality 13) low quality or N bases (Bolger et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The reads below 30 bases in length were dropped. The trimmed reads were then aligned to human genome assembly GRCh38 using the HISAT2 (v2.2.1) program (Kim et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Transcript abundance was normalized to transcript per million (TPM) by using StringTie v2.1.7 (Pertea et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The minimal detection threshold of TPM was set to 0.1 (Gu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Differential gene expression analysis was performed using DEseq v1.39.0 (Anders and Huber \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), an R/Bioconductor package that incorporates genome bias detection and correction through the Welgene Biotech pipeline. Differentially expressed genes (DEGs) were filtered using thresholds of log2(fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1.0 or \u0026lt; -1.0 to identify significant genes with changes in the expression level with an optimal signal-to-noise ratio, with significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Functional enrichment analyses for DEGs were performed using clusterProfiler v4.7.1 (Yu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). DEGs were assessed for their associations with disease ontology, gene ontology in three aspects: molecular function, cellular component, and biological process, and signaling pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Statistical analyses were conducted using SPSS Statistics v18.0 for Windows. Pearson\u0026rsquo;s correlation coefficient (\u003cem\u003er\u003c/em\u003e) was employed to evaluate linear relationships between parameters, with significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Venn diagrams were generated using InteractiVenn (Heberle et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTime-course mRNAome in HSCs\u003c/h2\u003e \u003cp\u003eBased on our previous results, agalactosyl IgG treatment caused changes in many HSC properties approximately on day 7. Accordingly, RNA sequencing for LX2 cells treated with agalactosyl IgG or TGF-β1 was performed on days 2, 5, 8, 11, and 14 to observe the process of mRNAone change. The quality of RNA sequencing is presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The sequence reads were mapped to 60671 genes, of which 19966 were characterized as protein-coding genes, 15259 as pseudogenes, and 25446 as other gene types. The mRNAome analysis revealed that the numbers of upregulated and downregulated DEGs markedly increased from day 5 to day 8 and then stabilized from day 8 to day 14 in LX2 cells treated with agalactosyl IgG compared with those treated with normal IgG (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). A similar trend was observed in LX2 cells treated with TGF-β1. We compared gene expression between LX2 cells treated with agalactosyl IgG and TGF-β1 and identified that 92, 98, 805, 841, and 752 DEGs were commonly detected in both groups on days 2, 5, 8, 11, and 14, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The expression levels of these DEGs were strongly correlated following both treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Regarding gene expression trends, 92, 95, 196, 165, and 193 DEGs were specifically detected at day 2, 5, 8, 11, and 14, respectively, following agalactosyl IgG treatment, whereas 102, 114, 222, 207, and 139 DEGs were specifically detected at these time points, respectively, following TGF-β1 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Moreover, the genes \u003cem\u003eMIR3142HG\u003c/em\u003e, \u003cem\u003eAC008878.1\u003c/em\u003e, \u003cem\u003eNPHP3-ACAD11\u003c/em\u003e, and \u003cem\u003eAC242842.3\u003c/em\u003e were detected consistently from day 2 to day 14 after agalactosyl IgG treatment, and the gene \u003cem\u003eAC018521.1\u003c/em\u003e was detected throughout the same period after TGF-β1 treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDEGs associated with liver diseases and fibrosis\u003c/h2\u003e \u003cp\u003eBecause liver fibrosis is not documented in disease ontology databases, we assessed the relevance of DEGs in different liver diseases. After treatments with agalactosyl IgG and TGF-β1, 114 and 105 DEGs, respectively, were identified as being involved in liver cirrhosis, hepatitis, fatty liver disease, hepatitis B, and alcoholic hepatitis. Among these DEGs, 20 after agalactosyl IgG treatment and 11 after TGF-β1 treatment were uniquely associated with liver diseases in LX2 cells. The number of altered genes increased significantly after 8 days of treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The expression of 17, 17, and 10 genes associated only with liver cirrhosis, hepatitis, and fatty liver disease, respectively, changed after both treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). In activated LX2 cells, \u003cem\u003eCYP27B1\u003c/em\u003e was the only gene associated with hepatitis B, and no gene was associated with alcoholic hepatitis alone. Moreover, 29 DEGs were associated with three or more liver diseases; all of them were liver fibrosis-related gene markers. \u003cem\u003eCCL5\u003c/em\u003e and \u003cem\u003eFAS\u003c/em\u003e in LX2 cells were associated with all five of the studied liver diseases after agalactosyl IgG and TGF-β1 treatments. Furthermore, we analyzed the association of 15 additional liver fibrosis-related genes with the aforementioned liver diseases and identified the involvement of \u003cem\u003eFAP\u003c/em\u003e in hepatitis and \u003cem\u003eFAP, MMP1\u003c/em\u003e, and \u003cem\u003eSMAD7\u003c/em\u003e in liver cirrhosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we analyzed the expression dynamics of 44 aforementioned fibrosis-related DEGs (29 commonly detected in liver diseases and 15 additional ones). The levels of \u003cem\u003eCCL2\u003c/em\u003e, \u003cem\u003eCCL5\u003c/em\u003e, \u003cem\u003eCOL6A1\u003c/em\u003e, \u003cem\u003eCOL8A1\u003c/em\u003e, \u003cem\u003eFAP\u003c/em\u003e, \u003cem\u003eFAS\u003c/em\u003e, \u003cem\u003eFGF2\u003c/em\u003e, \u003cem\u003eFGF7\u003c/em\u003e, \u003cem\u003eICAM1\u003c/em\u003e, \u003cem\u003eIL1B\u003c/em\u003e, \u003cem\u003eIL6\u003c/em\u003e, \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMTHFR\u003c/em\u003e, \u003cem\u003ePDGFRA\u003c/em\u003e, \u003cem\u003ePPARGC1A\u003c/em\u003e, \u003cem\u003ePTGS2\u003c/em\u003e, \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eTGFBR3\u003c/em\u003e, \u003cem\u003eTIMP3\u003c/em\u003e, and \u003cem\u003eTNFSF10\u003c/em\u003e increased primarily from day 5 or day 8 after agalactosyl IgG or TGF-β1 treatment, whereas those of \u003cem\u003eBIRC5\u003c/em\u003e, \u003cem\u003eCDH2\u003c/em\u003e, \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eELFN2\u003c/em\u003e, \u003cem\u003eFGFR3\u003c/em\u003e, \u003cem\u003eFN1\u003c/em\u003e, \u003cem\u003eHMOX1\u003c/em\u003e, and \u003cem\u003eSMAD7\u003c/em\u003e decreased after treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMolecular function analyses of DEGs\u003c/h2\u003e \u003cp\u003eOverall, the DEGs in LX2 cells after agalactosyl IgG and TGF-β1 treatments were involved in 58 and 52 molecular functions, respectively; 43 of these genes were detected in both groups (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). After agalactosyl IgG treatment, the fibrosis-related DEGs were involved in different molecular functions: 9 DEGs in cytokine and chemokine activity, 14 in matrix and membrane factor binding, 13 in growth factors and receptors, 1 in cytoskeleton organization, 23 in kinase and protease activity, and 0 in nucleic acid processing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, TGF-β1 treatment led to 9, 12, 14, 1, 14, and 1 DEGs involved in these functions, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCellular component analyses of DEGs\u003c/h2\u003e \u003cp\u003eTotal DEGs in LX2 cells after treatments with agalactosyl IgG and TGF-β1 were involved in 49 and 45 cellular components, respectively; 37 of these components were affected by both treatments (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). After agalactosyl IgG treatment, 2, 0, 1, 7, 12, and 11 fibrosis-related DEGs were related to chromosome, nucleus, kinase, cytoplasm, membrane, and secretion, respectively, whereas 2, 0, 1, 6, 10, and 10 fibrosis-related DEGs by TGF-β1 treatment were related to these components, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBiological process analyses of DEGs\u003c/h2\u003e \u003cp\u003eThe total DEGs in LX2 cells following treatments with agalactosyl IgG and TGF-β1 were involved in 969 and 1103 biological processes, respectively; 834 of these processes were detected in both groups (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). The fibrosis-related DEGs in LX2 cells by agalactosyl IgG and TGF-β1 treatments were involved in 926 different biological processes: 37.0% regarding cellular responses to physiological changes, 24.9% regarding immune modulation, 21.5% regarding viability and differentiation, 11.7% regarding nucleus and chromosome, 4.0% regarding extracellular organization, and 0.9% regarding cytoskeleton (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Similarly, TGF-β1 treatment led to the fibrosis-related DEGs involved in 869 biological processes: 40.7%, 25.3%, 19.8%, 9.0%, 5.1%, and 0.1% regarding these categories, respectively. A total of 711 biological processes were detected in both groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSignaling pathway analyses of DEGs\u003c/h2\u003e \u003cp\u003eAccording to KEGG database mapping, LX2 cells exhibited the involvement of total DEGs in 60 and 64 events after agalactosyl IgG and TGF-β1 treatments, respectively; 56 of these events were detected in both groups (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Regarding signaling pathways, 13 such pathways were found to be associated with the activation of LX2 cells. Gene network analyses revealed that these pathways involved 23 and 20 fibrosis-related DEGs in LX2 cells after agalactosyl IgG and TGF-β1 treatments, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e); in both treatments, 6 of these DEGs were associated with nucleotide-binding oligomerization domain-like receptor signaling pathway, 6 with advanced glycation endproducts signaling pathway, 8 with interleukin (IL)-17 signaling pathway, and 10 with tumor necrosis factor (TNF) signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAn increase in the abnormal glycosylation of serum IgG is a common symptom of chronic liver damage. However, whether IgG glycosylation initiates and promotes liver fibrosis remains unclear. HSCs are key responders to liver injury in the hepatic microenvironment and are the primary matrix-producing cells contributing to liver fibrogenesis (Moreira \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Accordingly, we investigated the pathogenic effects of aberrant IgG glycosylation on HSC activation and reported that agalactosyl IgG primarily activates HSCs through FcγRIIIa expressed on these cells (Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Treatment with agalactosyl IgG induced a transition of HSCs to a fibroblast-like morphology, increased their migration and invasion capabilities, and upregulated fibrogenic markers. Furthermore, we identified a positive feedback loop involving agalactosyl IgG, TGF-β1, and HSC-FcγRIIIa. These findings indicate a causal relationship between aberrant serum IgG-Fc glycosylation, particularly agalactosylation, and liver fibrosis (Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the gene profiles and their changes during HSC activation by agalactosyl IgG are poorly understood. In this study, we investigated the dynamics of the mRNAome and gene regulatory networks in human LX2 HSCs activated by agalactosyl IgG. In addition, we assessed the correlation between these changes and those in HSCs activated by TGF-β1.\u003c/p\u003e \u003cp\u003eThe interaction between IgG-Fc and activating FcγRs triggers the phosphorylation of the immunoreceptor tyrosine-based activation motif located either in the cytoplasm portion of FcγRs or in the associated FcR common γ-chain, and subsequently activates Syk kinase (Takai \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Once activated, Syk kinase can initiate a variety of signaling pathways, including PI3K-Akt, bruton tyrosine kinase-Rac-Rho, phosphatidylinositol (3,4,5)-trisphosphate-phospholipase Cγ-protein kinase C, and Sos-Ras (Nimmerjahn and Ravetch \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). On the other side, TGF-β1 not only mediates signaling through the suppressor of mothers against decapentaplegic family proteins but also activates pathways such as PI3K-Akt, Rac-Rho, and Sos-Ras (Akhurst and Hata \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Our time-course analyses revealed substantial gene changes in HSCs by day 8 following treatment with either agalactosyl IgG of TGF-β1. The high correspondence between the gene expression patterns in HSCs treated with agalactosyl IgG and TGF-β1 is expected because both factors regulate common signaling pathways and downstream genes. This phenomenon is further proved by a close relationship between the two gene sets with respect to molecular function, KEGG, and disease annotations. This relationship explains the absence of the additive or synergistic effects of agalactosyl IgG and TGF-β1 on the induction of some fibroblast markers and characteristics in HSCs (Ho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eActivated HSCs secrete a wide range of proinflammatory cytokines and chemokines, including C-C motif chemokine ligand (CCL) 2, CCL5, IL-1β, IL-6, and express adhesion molecules, such as intercellular adhesion molecule (ICAM) 1, to help recruit immune cells to the liver (Pinzani and Marra \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Uemura and Gandhi \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Carter and Friedman \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our findings regarding the enhanced expression levels of \u003cem\u003eCCL2\u003c/em\u003e, \u003cem\u003eCCL5\u003c/em\u003e, \u003cem\u003eIL1B\u003c/em\u003e, \u003cem\u003eIL6\u003c/em\u003e, and \u003cem\u003eICAM1\u003c/em\u003e in activated HSCs by both agalactosyl IgG and TGF-β1 coincide with those of previous reports. In addition, we observe an increase in the expression of \u003cem\u003eFAS\u003c/em\u003e (encodes FAS receptor) and \u003cem\u003eTNFSF10\u003c/em\u003e (encodes TNF-related apoptosis-inducing ligand) and a decrease in the expression of \u003cem\u003eHMOX1\u003c/em\u003e (encodes heme oxygenase 1). Both the FAS receptor and the TNF-related apoptosis-inducing ligand can induce apoptosis in HSCs (Singh et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By contrast, heme oxygenase 1 inhibits HSC apoptosis and ameliorates liver fibrosis (Luo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, we found that the expression levels of \u003cem\u003ePPARGC1A\u003c/em\u003e and \u003cem\u003ePTGS2\u003c/em\u003e, which tend to prevent apoptosis in HSCs, were increased after agalactosyl IgG and TGF-β1 treatments. The product of \u003cem\u003ePPARGC1A\u003c/em\u003e, namely peroxisome proliferator-activated receptor γ coactivator-1α, has been demonstrated to inhibit HSC activation and alleviate liver fibrosis (Jing-Jing Yang 2022; Zhang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In previous studies, the \u003cem\u003ePpargc1a\u003c/em\u003e mRNA level has not significantly changed in mouse HSCs activated by fucoxanthin but has been reduced in activated rat HSCs (Smith-Cortinez et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bae et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Prostaglandin-endoperoxide synthase 2, also known as COX-2, is encoded by \u003cem\u003ePTGS2\u003c/em\u003e. In a model of transgenic mice constitutively expressing human COX-2 in hepatocytes, COX-2 restricted HSC activation and attenuated liver fibrosis (Frances et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Brea et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, the knockout of \u003cem\u003ePTGS2\u003c/em\u003e promoted HSC apoptosis (Zhou et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings suggest the homeostasis in the activation process of HSCs and the initial stage of liver fibrogenesis. Furthermore, the inconsistency in results regarding gene functions in HSCs may be attributed to differences in the species origin of HSCs or the varied treatments used for their activation. Methylenetetrahydrofolate reductase, encoded by \u003cem\u003eMTHFR\u003c/em\u003e, is a crucial enzyme for folate metabolism. Expression of \u003cem\u003eMTHFR\u003c/em\u003e did not change in activated mouse HSCs; however, polymorphisms in this gene are associated with liver steatosis and fibrosis in cases of chronic hepatitis C (Adinolfi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Yanjie Gao 2023). Accordingly, the role of \u003cem\u003eMTHFR\u003c/em\u003e in HSC activation warrants further investigation.\u003c/p\u003e \u003cp\u003eIn addition to modulating immune responses and cell apoptosis, HSCs express multiple growth factors and receptors, including platelet-derived growth factors (PDGFs) and their receptors, and secrete various types of collagens in the perpetuation phase of liver fibrosis (Ikeda et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Pinzani and Marra \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Moreover, activated HSCs produce diverse MMPs and tissue inhibitors of metalloproteinases (TIMPs), which are crucial for the development of liver fibrosis (Iredale et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Therefore, we explored the time-course changes in the expression of fibrosis-related gene markers. The mRNA levels of \u003cem\u003eFAP\u003c/em\u003e (encodes fibroblast activation protein-α), \u003cem\u003eFGF7\u003c/em\u003e (encodes fibroblast growth factor 2), \u003cem\u003ePDGFRA\u003c/em\u003e, \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMMP3\u003c/em\u003e, \u003cem\u003eMMP9\u003c/em\u003e, and \u003cem\u003eTIMP3\u003c/em\u003e in HSCs increased consistently from day 5 to day 11, and then fluctuated on day 14. Collectively, these findings indicate that the effects of agalactosyl IgG and GF-β1 on HSC activation and their fibrogenic features may begin after day 2 and persist for more than one week. Gene ontology and signaling analyses revealed that these DEGs were associated with cellular components involving cytoplasm, membrane, and secretion; molecular functions involving kinase and protease activity, matrix and membrane factor binding, growth factors and receptors, and cytokine and chemokine activity; signaling pathways involving immune modulation and cell growth. These findings indicate that agalactosyl IgG and TGF-β1 affect HSCs in multiple aspects, particularly secretion of cytokines and chemokines, expression of fibrosis-related growth factors and their receptors, modification of extracellular matrices, and regulation of cell viability in the first few days after HSC activation; these effects may cause subsequent changes in HSC physiology, leading to enhanced fibrogenic activities. Nevertheless, the mechanisms underlying HSC activation and liver fibrosis \u003cem\u003ein vivo\u003c/em\u003e may be pluralistic and merit further study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur works elucidated mRNA profiles, gene networks, and the expression dynamics of fibrosis-related DEGs during HSC activation. Furthermore, the involvement of fibrosis-related DEGs in different biomedical ontologies that may contribute to liver fibrogenesis was revealed. Although the validation on liver tissues was not performed due to the activated and heterogeneous HSCs obtained from patients with liver fibrosis or cirrhosis, this study advances our understanding of the gene regulation model during HSC activation and potentially benefits the exploration of new antifibrotic therapeutic targets and development of effective treatment strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-C motif chemokine ligand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFcγR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efragment crystallizable gamma receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatic stellate cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIgG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunoglobulin G\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ematrix metalloproteinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTHFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethylenetetrahydrofolate reductase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDGF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplatelet-derived growth factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI3K\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ephosphoinositide 3-kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etransforming growth factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etissue inhibitors of metalloproteinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrosis factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etranscript per million.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statements:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Science and Technology Council, Taiwan (grant NSTC 111-2314-B-214-003-MY3). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e Sequencing reads are available from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/sra/PRJNA1126014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLarge Language Models:\u0026nbsp;\u003c/strong\u003eno AI tools have been used in this study and manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe thank Welgene Biotech Co., Ltd. for the RNA sequencing and Wallace Academic Editing for English editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e C.H.H was responsible for the study design, data interpretation, and manuscript writing. C.K. assisted in data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevious presentation or publication:\u003c/strong\u003e This manuscript has not been submitted, presented in any meetings, or accepted for publication elsewhere.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdinolfi LE, Ingrosso D, CesaroG et al (2005) Hyperhomocysteinemia and the MTHFR C677T polymorphism promote steatosis and fibrosis in chronic hepatitis C patients. Hepatology 41:995\u0026ndash;1003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hep.20664\u003c/span\u003e\u003cspan address=\"10.1002/hep.20664\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhurst RJ, Hata A (2012) Targeting the TGFbeta signalling pathway in disease. 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Clin Res Hepatol Gastroenterol 48:102345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clinre.2024.102345\u003c/span\u003e\u003cspan address=\"10.1016/j.clinre.2024.102345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"hepatic stellate cells, liver fibrosis, IgG, glycosylation, RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-5082024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5082024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious studies have demonstrated the clinical relevance of aberrant serum immunoglobulin G (IgG) \u003cem\u003eN\u003c/em\u003e-glycomic profiles in liver fibrosis and the pathogenic effects of agalactosyl IgG on activating hepatic stellate cells (HSCs). However, the dynamics of gene expression changes during HSC activation by agalactosyl IgG remain poorly understood. We performed RNA sequencing to analyze the mRNAome of human LX-2 HSCs at multiple time points after treatment with agalactosyl IgG and then compared these results with those obtained after normal IgG and transforming growth factor (TGF)-β1 treatments. Gene expression changes were significantly pronounced on day 5 and subsided by day 11 after HSC activation. A high degree of similarity in gene expression patterns between HSCs treated with agalactosyl IgG and TGF-β1 was observed, of which 1796 and 1785 differentially expressed genes (DEGs) were identified, respectively. Disease ontology analyses revealed that 114 and 115 DEGs in activated HSCs following agalactosyl IgG and TGF-β1 treatments, respectively, were linked to liver cirrhosis, hepatitis, fatty liver disease, hepatitis B, and alcoholic hepatitis, with \u003cem\u003eCCL5\u003c/em\u003e and \u003cem\u003eFAS\u003c/em\u003e being the most commonly affected genes. DEGs associated with liver fibrosis or aforementioned liver diseases involved in gene annotation, physiological functions, and signaling pathways regarding secretion of cytokines and chemokines, expression of fibrosis-related growth factors and their receptors, modification of extracellular matrices, and regulation of cell viability in activated HSCs. In conclusion, this study characterized the dynamics of mRNAome and gene networks and identified the liver fibrosis-related DEGs during HSC activation by agalactosyl IgG and TGF-β1.\u003c/p\u003e","manuscriptTitle":"Time-course RNA sequencing reveals high similarity in mRNAome between hepatic stellate cells activated by agalactosyl IgG and TGF-β1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 15:01:08","doi":"10.21203/rs.3.rs-5082024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-31T20:50:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-31T12:44:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-31T11:15:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-25T19:14:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148738858047929335758724998644277130139","date":"2024-10-25T15:15:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208531081781459407683555595910634683226","date":"2024-10-23T19:01:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190893692987428443138560193370504686300","date":"2024-10-18T16:51:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-01T20:22:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-17T08:21:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-17T08:18:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Functional \u0026 Integrative Genomics","date":"2024-09-13T07:58:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"771ba088-ec7e-4681-8ad4-956d9ec22e35","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T19:22:39+00:00","versionOfRecord":{"articleIdentity":"rs-5082024","link":"https://doi.org/10.1007/s10142-024-01502-z","journal":{"identity":"functional-and-integrative-genomics","isVorOnly":false,"title":"Functional \u0026 Integrative Genomics"},"publishedOn":"2024-11-16 15:57:46","publishedOnDateReadable":"November 16th, 2024"},"versionCreatedAt":"2024-11-11 15:01:08","video":"","vorDoi":"10.1007/s10142-024-01502-z","vorDoiUrl":"https://doi.org/10.1007/s10142-024-01502-z","workflowStages":[]},"version":"v1","identity":"rs-5082024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5082024","identity":"rs-5082024","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}