Changes in microRNAs associated with hepatic stellate cell activation status identify signaling pathways Can-Jie Guo1,*, Qin Pan1,*, Tao Cheng2, Bo Jiang3, Guang-Yu Chen1 and Ding-Guo Li1 Digestive Disease Laboratory and Department of Gastroenterology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, China Department of Orthopaedics, The Sixth Affiliated People’s Hospital, School of Medicine, Shanghai Jiaotong University, China Business School, Central South University, Changsha, China Keywords bioinformatics; hepatic stellate cells; liver fibrosis; microarray; pathway Correspondence D G Li, Department of Gastroenterology, Xinhua Hospital, No 1665 Kongjiang Road, Shanghai 200092, China Fax: +86 21 62712237 Tel: +86 21 62712237 E-mail: lidingguo13612@yahoo.com.cn Q Pan, Department of Gastroenterology, Xinhua Hospital, No 1665 Kongjiang Road, Shanghai 200092, China Fax: +86 21 62712237 Tel: +86 21 62712237 E-mail: pan_qin@yeah.net Activation of hepatic stellate cells (HSCs), which is regulated by multiple signal transduction pathways, is the key event in liver fibrosis Moreover, members of these pathways are important targets for microRNAs (miRNAs) To better understand the critical pathways of HSC activation, we performed comprehensive comparative bioinformatics analysis of microarrays of quiescent and activated HSCs Changes in miRNAs associated with HSC activation status revealed that 13 pathways were upregulated and 22 pathways were downregulated by miRNA Furthermore, mitochondrial integrity, based on highly upregulated Bcl-2 and downregulated caspase-9, was confirmed in HSCs and fibrotic livers by immnofluorescence assay, quantitative RT-PCR, and western blot analysis These findings provide in vitro and in vivo evidence that the mitochondrial pathway of apoptosis plays a significant role in the progression of liver fibrogenesis via HSC activation *These authors contributed equally to this work (Received April 2009, revised July 2009, accepted 14 July 2009) doi:10.1111/j.1742-4658.2009.07213.x Introduction Liver fibrosis is the excessive accumulation of extracellular matrix that occurs in most types of chronic liver diseases Hepatic stellate cells (HSCs), the major mesenchymal cells in liver, are widely accepted as playing a critically important role in liver fibrosis [1] In the quiescent state, HSCs are lipid-storing cells located in the perisinusoidal endothelium In contrast, they undergo myofibroblastic transdifferentiation, also known as activation, when stimulated by fibrogenic stimuli, which reflects the critical step of liver fibrogenesis [2] Previous studies on HSCs have attributed their activation to the regulation of many signal transduction pathways, including transforming growth factor-b (TGF-b) ⁄ Smad, platelet-derived growth factor, mito- Abbreviations FDR, false discovery rate; GO, gene ontology; HE, hematoxylin ⁄ eosin; HSC, hepatic stellate cell; KEGG, Kyoto Encyclopedia of Genes and Genomes; KO, Kyoto Encyclopedia of Genes and Genomes orthology; MAPK, mitogen-activated protein kinase; miRNA, microRNA; SMA, smooth muscle actin; TGF-b, transforming growth factor-b; VEGF, vascular endothelial growth factor; VG, Van Gieson FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS 5163 Signaling pathways regulated in HSCs by miRNAs C.-J Guo et al chondrial pathway of apoptosis, mitogen-activated protein kinase (MAPK), Wnt, and vascular endothelial growth factor (VEGF) [3–8] Furthermore, microRNA (miRNA)-mediated RNA interference has been identified as a novel mechanism that regulates protein expression at the translational level [9] The differentially expressed miRNAs and their inhibitory effect on gene expression, especially those relevant to signal transduction, add a new level to our knowledge about the regulatory mechanisms of HSC activation [10] However, the miRNAs often negatively modulate gene expression at the post-transcriptional level by incomplete binding to target sequences within the 3¢-UTR, and generally not affect mRNA levels [9] Thus, high-throughput gene expression analysis by microarray is not suitable for exploring the target signaling pathways of miRNA during activation Fortunately, significant progress in data mining has provided a wide range of bioinformatics analysis options, such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology (KO), to aid researchers in the interpretation of their data [11] According to these techniques, KEGG acts as a bioinformatics resource for understanding higherorder functional meanings and utilities of the cell or the organism from its genome information Integration of current knowledge on molecular interaction networks, such as signaling pathways and complexes (PATHWAY database), features the reference knowledge base (http://www.genome.ad.jp/kegg/) [12] We therefore performed a global analysis of miRNA-regulated signaling pathways and related genes on the basis of miRNA expression profile and bioinformatic interpretation The predicted signaling pathways, some of which had not been previously described in the activation of HSCs, were further selected and validated within both activated HSCs and fibrotic liver of rats Results Bioinformatics interpretation revealed the GOs and signaling pathways regulated by miRNAs The purity of quiescent (> 95%) and activated (> 95%) HSCs was confirmed immunofluorescently, using desmin (Fig 1) Microarray hybridization preliminarily identified 21 miRNAs as being differentially expressed during HSC activation A volcano plot provided further information about the significance and magnitude of expressive alteration of selected miRNAs (Fig 2), which was helpful in judging the most significant candidates for follow-up studies 5164 david gene annotation was used to interpret the biological effect of miRNAs filtered by volcano plot According to the results of data mining, 19 upregulated GOs and 24 downregulated GOs were classified on the basis of the top 25% miRNA targets (Table 1) Additionally, miRNA–mRNA network analysis integrated these miRNAs and GOs by outlining the interactions of miRNA and GO-related genes (Fig 3) Another functional analysis of miRNAs by KEGG revealed that 13 signal transduction pathways were upregulated and 22 were downregulated (Fig 4) Many of these signaling pathways, such as VEGF, MAPK, and biosynthesis of steroids, have been shown to participate in the activation of HSCs (Table 2) A wide variety of cellular processes, including cell proliferation, differentiation, and stress responses, also featured the functions of significant signaling pathways (Table 2) However, some other signaling pathways have never been reported to play a role in resting or activated HSCs, e.g folate-dependent one-carbon pool and carbon fixation Among all these differentially regulated signaling pathways, apoptosis appeared to be the most enriched one A similar phenomenon was observed in GO analysis In detail, Bcl-2 and caspase9, the critical members of the mitochondrial apoptosis pathway (http://www.genome.ad.jp/kegg/pathway.html), served as the significant targets of miR15b/16, and miR-138, respectively This represents novel evidence for the modulatory effect of miRNAs on HSC function via signaling pathway CCl4 administration induced liver fibrosis in rats Histopathological analysis revealed little fibrosis in the liver of normal rats On the contrary, fatty degeneration, necrosis and infiltration of inflammatory cells were obvious in the fibrosis model group Moreover, there was nodular fibrosis with extensive collagen deposition and well-delineated fibrosis septa, which were continuous and extended in each section, sometimes even bridging portal regions (Fig 5) In contrast to the normal rats (stage 0), the Ishak staging for the fibrosis model group reached 5.2 ± 1.2 Members of apoptosis pathways were differentially expressed during HSC activation and liver fibrosis As evaluated by immnofluorescence assay, Bcl-2 expression was virtually undetectable in quiescent HSCs However, dramatic increases in Bcl-2 level occurred in HSCs after their activation (P < 0.05) The opposite FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS C.-J Guo et al Signaling pathways regulated in HSCs by miRNAs A B G H C D I J E F K L Fig Characterization of HSCs isolated from rat liver (· 400) After isolation, the cells were cultured for days (quiescent HSCs) or for 14 days (activated HSCs) (A) Immunofluorescence analysis of desmin expression in quiescent HSCs (C) Desmin expression in activated HSCs (E, K) Negative controls without primary antibody were performed in activated HSCs (G, I) Negative controls with antibody against a-SMA (1 : 100; Santa Cruz, USA) were performed in activated HSCs Hoechst 33258 nuclear staining for all conditions is shown in (B), (D), (F), (H), (J), and (L) Fig The volcano plot shows the upregulated and downregulated miRNAs in activated HSCs The horizontal axis represents the fold change between quiescent and activated HSCs The vertical axis represents the P-value of the t-test for the differences between samples was seen for caspase-9, another member of the mitochondrial apoptosis pathway (Fig 6) Its level was reduced by a statistically significant amount throughout HSC activation (P < 0.05) These findings were confirmed in CCl4-induced experimental hepatic fibrosis Bcl-2, which can rarely be detected in the normal liver, was expressed increasingly in intrahepatic HSCs after CCl4 injury (P < 0.05) (Figs and 8) The expression of caspase9, however, was decreased significantly in fibrotic liver when compared to that of normal controls (P < 0.05) (Figs and 8) FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS 5165 Signaling pathways regulated in HSCs by miRNAs C.-J Guo et al Table MicroRNA targets significant GO in HSCs Category ID Category name Enrichment Upregulated GOs by GO analysisa GO:0006813 Potassium ion transport GO:0045817 Positive regulation of transcription from RNA polymerase II promoter GO:0008283 Cell proliferation GO:0001666 Response to hypoxia GO:0045449 Regulation of transcription GO:0008284 Positive regulation of cell proliferation GO:0042981 Regulation of apoptosis GO:0007242 Intracellular signaling cascade GO:0006350 Transcription GO:0007049 Cell cycle GO:0007264 Small GTPase-mediated signal transduction GO:0007165 Signal transduction GO:0008152 Metabolic process GO:0006916 Antiapoptosis GO:0045941 Positive regulation of transcription GO:0007169 Transmembrane receptor protein tyrosine kinase signaling pathway GO:0051056 Regulation of small GTPase-mediated signal transduction GO:0007186 G-protein coupled receptor protein signaling pathway GO:0045840 Positive regulation of mitosis Downregulated GOs by GO analysisb GO:0006915 Apoptosis GO:0006629 Lipid metabolic process GO:0006281 DNA repair GO:0006886 Intracellular protein transport GO:0005975 Carbohydrate metabolic process GO:0006457 Protein folding GO:0006813 Potassium ion transport GO:0008285 Negative regulation of cell proliferation GO:0006468 Protein amino acid phosphorylation GO:0007275 Multicellular organism development GO:0015031 Protein transport GO:0006816 Calcium ion transport GO:0030154 Cell differentiation GO:0006512 Ubiquitin cycle GO:0006118 Electron transport GO:0006810 Transport? GO:0006470 Protein amino acid dephosphorylation GO:0007156 Homophilic cell adhesion GO:0006350 Transcription GO:0007165 Signal transduction GO:0007283 Spermatogenesis GO:0006917 Induction of apoptosis GO:0007155 Cell adhesion GO:0006955 Immune response a GOs targeted by upregulated miRNA and FDRs b FDR 0.561451066 0.532929713 2.26084E-05 2.15258E-10 0.002214733 0.000938004 0.522209863 0.522209863 0.505631772 0.461241199 0.387927327 0.382549086 0.379418103 0.369898653 0.368618727 0.366806928 0.352776154 0.340043632 0.321359916 0.253425375 7.55331E-06 5.13583E-06 1.207E-12 2.13311E-08 2.85018E-06 0 1E-15 3.2E-14 0 1.65703E-08 2.97119E-08 2.3708E-11 0.001902064 0.00181087 0.000690475 0.001107366 0.001719675 0.000117251 0.000260557 0.000508086 0.000586253 0.000247529 0.000143306 0.001081311 0.001159478 0.00078167 0.166000486 1E-15 0.000521114 0.125511830 9.11949E-05 0.111902113 1.57057E-07 0.001315812 )0.508836656 )0.488882277 )0.474776596 )0.470581871 )0.465602169 )0.465602169 )0.460219485 )0.458950709 )0.457234479 )0.446653243 )0.446252468 )0.424704681 )0.421677436 )0.420795265 )0.420278064 )0.417881171 )0.416070023 )0.403895857 )0.403764381 )0.400692483 )0.400090306 )0.399087573 )0.380947229 )0.307297431 6.68083E-10 1.19397E-06 4.28892E-06 8.61363E-06 6.25054E-06 3.93087E-08 1.25103E-05 1.39364E-06 6E-15 1.7828E-11 1.52674E-05 4E-15 2.9346E-08 3.4327E-11 1.79328E-05 1.91987E-06 0 1.48658E-07 6.34031E-08 0 0.000951032 0.001537285 0.001784814 0.001954176 0.001849953 0.001172505 0.002058399 0.001563341 0.000403863 0.000547169 0.000742587 0.002097482 0.000534141 0.001133422 0.000807726 0.000495058 0.002136566 0.001602424 0.000469002 0.000455974 0.001302784 0.001211589 0.000325696 0.000364779 GOs targeted by downregulated miRNA All of these GOs show increased enrichment, P-values Discussion MicroRNAs, a set of small, noncoding RNAs, 21–22 nucleotides in length, have recently been recognized to 5166 P-value be deeply involved in various crucial cell processes, such as mitosis, differentiation, oncogenesis, and apoptosis, by regulating signal transduction pathways [13] With the use of in vitro cell activation and miRNA FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS C.-J Guo et al Signaling pathways regulated in HSCs by miRNAs Fig GO network Blue box nodes represent miRNA, and red cycle nodes represent mRNA Edges show the inhibitory effect of microRNA on mRNA Upregulated and downregulated microRNA have separate, specific targets The upper subgraph shows under-expression microRNA–mRNA network and the lower subgraph is the overexpression microRNA–mRNA network Four overexpressed miRNAs (miR-140, miR-207, miR-325-5p and miR-874) showed the most target mRNAs of (degree 7) In contrast, rno-miR-16 are the highest degree in under-expression miRNAs microarray hybridization [10], many differentially expressed miRNAs, 12 upregulated ones (miR-874, miR-29C*, miR-501, miR-349, miR-325-5p, miR-328, miR-138, miR-143, miR-207, miR-872, miR-140, and miR-193) and nine downregulated ones (miR-341, miR-20b-3p, miR-15b, miR-16, miR-375, miR-122, miR-146a, miR-92b, and miR-126), were also identified in rat HSCs during activation Taking into account the aberrant phenotypes that are closely related to activated HSCs, including myofibroblastic transdifferentiation, active proliferation, and apoptosis resistance [14], an indispensable role of miRNAs was hypothesized throughout their activation on the basis of signaling pathway alternation In order to gain insights into the function of miRNAs, GO term and KEGG pathway annotation were applied to their target gene pool As a result, KEGG annotation showed that important proliferative (cell cycle, VEGF, MAPK, and Wnt), survival (TGF-b and mTOR), apoptotic (apoptosis), adhesive (gap junction and focal adhension,), oncogenic (pancreatic cancer, prostate cancer, colorectal cancer, and small cell lung cancer) and metabolic (biosynthesis of steroids, glycolysis ⁄ gluconeogenesis, pyrimidine metabolism, purine metabolism, glycan structure biosynthesis, adipocytokine signaling pathway, insulin signaling pathway) signaling pathways were abundant among the significantly enriched ones Most of them have already been reported to take part in HSC activation and even hepatic fibrogenesis For example, MAPK mediates mitosis and the synthesis of a1(I) collagen and matrix metalloproteinases in rat HSCs [15–17] FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS 5167 Signaling pathways regulated in HSCs by miRNAs C.-J Guo et al A B Fig Pathway analysis based on miRNAtargeted genes (A) and (B) show significant pathways targeted by upregulated and downregulated miRNA, respectively The vertical axis is the pathway category, and the horizontal axis is the enrichment of pathways TGF-b signaling, the key pathway in fibrogenesis, has been found to be essential for myofibroblastic transdifferentiation of HSCs [18,19] Signaling from the VEGF pathway also stimulates proliferation and type I collagen synthesis in activated HSCs subjected to hypoxia treatment [20–22] The central role of the Wnt signaling pathway in HSC activation and survival has recently been discovered [5,23,24] The GOs related to signal transduction (intracellular signaling cascade, small GTPase-mediated signal transduction, signal transduction, transmembrane receptor protein tyrosine kinase signaling pathway, regulation of small GTPase-mediated signal transduction, G-protein-coupled receptor protein signaling pathway, and signal transduction), cell growth (cell proliferation, positive regulation of cell proliferation, cell cycle, positive regulation of mitosis, negative regulation of cell proliferation, and multicellular organism development), apoptosis (antiapoptosis, apoptosis, and induction of 5168 apoptosis) and metabolism (lipid metabolic processes and carbohydrate metabolic process) represented up to 33% of the significantly enriched GO terms, which was in accordance with the KEGG analysis This functional identity revealed by different bioinformatic interpretation confirmed that miRNAs have regulatory effects on HSC activation by affecting signaling pathways Enrichment ranking of both signaling pathways and GOs indicated apoptosis to be the most enriched The miRNA–mRNA interaction network analysis further integrated the bioinformatic findings, and then outlined the major targets of miRNAs Bcl-2 and caspase9, both of which had the highest ratio and enrichment in the apoptosis-related pathway, were noted In line with the in silico analysis, upregulated Bcl-2 and downregulated caspase-9 were identified in activated HSCs in vitro and fibrotic liver in vivo, using immunofluorescence assay, quantitative RT-PCR, and western blot FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS C.-J Guo et al Signaling pathways regulated in HSCs by miRNAs Table Regulation of target gene significant pathways by miRNA GnRH, gonadotropin-releasing hormone; PPAR, peroxisome proliferatoractivated receptor Pathway P-value FDR Enrichment Target genes 0.005 4.47172298 Atic, Ftcd, Gart, Mtfmt, Mthfd1, Shmt1, Tyms 0.005 0.005 3.73708278 3.16366796 Aldob, Aldoc, Fbp2, Got1, Gpt1, Mdh2, Pgk1, Pklr, Pkm2 Ebp, Idi1, Lss, Mvd, Nqo1, Nsdhl, Tm7sf2, Vkorc1 0.005 2.58366217 0.005 2.35504387 0.005 2.21456757 Adh4, Adh7, Aldh1a1, Aldh1a3, Aldob, Aldoc, Eno1, Eno2, Fbp2, G6pc, Pfkm, Pgk1, Pklr, Pkm2 Casp9, Hspb1, Kras, Map2k2, Mapk12, Mapkapk2, Mapkapk3, Pik3cb, Pla2g6, Plcg2, Ppp3cb, Ppp3r1, Ppp3r2, Prkcb1, Ptk2, Pxn, Rac1, Sphk1, Src Adrb1, Csnk1d, Drd2, Egfr, Gja9, Gna11, Gnai2, Gucy1a2, Gucy2c, Htr2c, Kras, LOC500319, Map2k2, Map2k5, Npr1, Pdgfd, Pdgfra, Prkcb1, Prkg2, Src, Tuba6, Tubb2c, Tubb5, Tubb6 Dck, Dhodh, Ecgf1, Entpd1, Entpd6, LOC682744, Nme3, Nme7, Pnpt1, Pola1, Pold1, Pold2, Polr3d, Prim2, Rpa1, Rrm2, Tyms, Umps Allc, Atic, Dck, Ecgf1, Entpd1, Entpd2, Entpd6, Fnta, Gart, Gucy1a2, Gucy2c, LOC682744, Nme3, Nme7, Npr1, Pde10a, Pde1c, Pde2a, Pde3a, Pde3b, Pde4a, Pklr, Pkm2, Pnpt1, Pola1, Pold2, Polr3d, Prim2, Rpa1, Rrm2 Aim1, Birc3, Cad, Casp6, Casp8, Casp9, Dffb, Fadd, Il1rap, Ntrk1, Pik3cb, Ppp3cb, Ppp3r1, Ppp3r2, Tnfrsf10b, Tnfrsf1a, Tnfsf10, Tp53, Tradd Adcyap1r1, Adra1a, Adra1b, Adra2c, Adrb1, Agtrl1, Avpr2, Chrm3, Crhr1, Drd2, Edn2, Ednra, F2rl1, Fshb, Gabbr1, Gabrb1, Gal, Galr3, Gcg, Ghsr, Gip, Gnrhr, Gpr35, Gria2, Gria4, Grid1, Grik1, Grik3, Grin2a, Grin2d, Grm7, Hcrtr2, Htr1b, Htr2c, Kiss1r, Lep, Lgr8, Lhcgr, Ltb4r, Ltb4r2, Mtnr1a, Nbpwr1, Nmur1, Npffr1, Npffr2, Oprd1, Oxt, P2rx1, P2rx5, P2rx7, P2ry13, P2ry2, Ppyr1, Prl, Prlhr, Ptger2, Pthr1, Sct, Sstr2, Sstr3, Taar5, Tac2, Tac4, Trhr2, Trpv1, Tshr Alg6, Alg8, B4galt3, Chst1, Chst3, D1bwg1363e, Extl3, Galnt10, Galnt11, Galnt13, Gcnt3, Gcs1, H2afx, Hs3st1, Hs3st2, LOC683264, LOC687718, Mgat5, Ndst1, Pomt1, Rpn2, St3gal2, St3gal3, Xylt1 Aim1, Arrb2, Cacna1b, Cacna1e, Cacna1h, Cacna2d1, Cacna2d2, Cacng8, Ddit3, Dusp7, Egfr, Fgf1, Fgf11, Fgf17, Fgf22, Fgf5, Fgfr2, Hspb1, Jund, Kras, Map2k2, Map2k5, Map2k7, Map3k1, Map3k10, Map3k12, Mapk12, Mapk4, Mapk8ip, Mapk8ip3, Mapkapk2, Mapkapk3, Mapt, MGC116327, Mras, Myc, Ntrk1, Pdgfra, Pla2g6, Ppm1b, Ppp3cb, Ppp3r1, Ppp3r2, Ppp5c, Prkcb1, Ptk7, Rac1, Rap1b, Stmn1, Tgfbr1, Tnfrsf1a, Tp53 Arhgef1, Arhgef6, Arhgef7, Arpc1b, Arpc5, Bcar1, Cfl1, Chrm3, Egfr, Fgf1, Fgf11, Fgf17, Fgf22, Fgf5, Fgfr2, Gsn, Itgad, Itgam, Itgb1, Itgb6, Itgb7, Kras, Map2k2, Mras, Myh10, Mylk2, Pak4, Pdgfd, Pdgfra, Pik3cb, Pip5k1a, Ppp1ca, Ptk2, Pxn, Rac1, Scin, Ssh3, Tiam1, Was Blr1, Bmpr2, Ccl17, Ccl21b, Ccl24, Ccl4, Ccl6, Ccr1, Ccr3, Ccr5, Cd40lg, Cxcl1, Cxcl14, Cxcl5, Cxcl7, Cxcl9, Cxcr6, Egfr, Flt1, Flt3, Flt4, Gnrhr, Hgf, Il10, Il11ra1, Il13ra1, Il17b, Il1rap, Il2, Il23a, Il2rg, Il5ra, Il7, Lep, LOC679119, LOC688065, Osm, Pdgfd, Pdgfra, Prl, Tgfbr1, Tnfrsf10b, Tnfrsf1a, Tnfrsf8, Tnfsf10, Tpte2 a Regulated by upregulated miRNA Folate-dependent 0.000646 one-carbon pool Carbon fixation 0.000508 Biosynthesis of 0.004178 steroids Glycolysis ⁄ 0.00115 gluconeogenesis VEGF signaling 0.000504 pathway Gap junction 0.000241 Pyrimidine metabolism Purine metabolism 0.00179 0.005 2.19828399 0.000104 0.005 2.0761571 Apoptosis 0.008088 0.0075 1.90105951 Neuroactive ligand–receptor interaction 7.54E-07 0.005 1.85470034 Glycan structures – biosynthesis 0.004678 0.005 1.82854203 MAPK signaling pathway 8.86E-05 0.005 1.74129305 Regulation of actin cytoskeleton 0.002064 0.005 1.64448087 Cytokine–cytokine receptor interaction 0.002262 0.005 1.57856573 Regulated by downregulated miRNAb Apoptosis 0.0081231 0.005 4.279734875 Cell cycle 0.0025457 0.005 3.894558736 Adipocytokine signaling pathway 0.0001621 0.005 2.781827668 Akt2, Akt3, Apaf1, Atm, Bcl-2, Bid, Birc2, Capn2, Csf2rb1, Faslg, Ikbkb, Il1a, Il1b, Nfkbia, Pdcd8, Pik3r2, Pik3r3, Prkar2a Anapc7, Atm, Ccnd1, Ccne2, Ccnh, Cdc25a, Cdc2a, Cdk7, Plk1, Skp1a, Smad2, Tgfb1, Tgfb2, Ywhag, Ywhah, Ywhaq Acsl5, Acsl6, Adipor2, Akt2, Akt3, Cpt2, Ikbkb, Jak2, Mapk8, Mapk9, Nfkbia, Pck1, Ppargc1a, Prkaa1, Prkaa2, Ptpn11, Tnfrsf1b FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS 5169 Signaling pathways regulated in HSCs by miRNAs C.-J Guo et al Table Continued Pathway P-value FDR Enrichment Target genes PPAR signaling pathway SNARE interactions in vesicular transport Pancreatic cancer 0.0001621 0.005 2.781827668 0.0075777 0.005 2.781827668 Acaa1, Acadm, Acsl5, Acsl6, Angptl4, Apoa5, Cpt2, Cyp4a14, Cyp4a22, Ehhadh, Fabp7, Me1, Pck1, Plin, Ppard, Pparg, Ubc Bet1, Bet1l, Epim, Gosr2, Stx17, Stx3, Sybl1, Vamp1, Vamp8 0.0001231 0.005 2.74372044 mTOR signaling pathway GnRH signaling pathway Wnt signaling pathway Prostate cancer 0.0013221 0.005 2.729340354 0.0001707 0.005 2.528934244 0.0007018 0.005 2.413151712 0.0005175 0.005 2.402487532 Fc epsilon RI signaling pathway Insulin signaling pathway 0.0024077 0.005 2.384423716 4.593E-05 0.005 2.347167095 Type I diabetes mellitus Long-term depression TGF-b signaling pathway Colorectal cancer 0.0051475 0.005 2.290916903 0.0037573 0.005 2.2864337 0.0020109 0.005 2.279087728 0.0036279 0.0075 2.225462135 Small cell lung cancer Focal adhesion 0.004734 0.005 2.171182571 0.000293 0.0075 1.978188564 Calcium signaling pathway 0.0004375 0.005 1.959924039 MAPK signaling pathway 0.0008528 0.005 1.74985934 Regulation of actin cytoskeleton 0.0087802 0.005 1.652570892 Cytokine–cytokine receptor interaction 0.0069367 0.005 1.609321792 Akt2, Akt3, Ccnd1, Erbb2, Figf, Ikbkb, Jak2, Mapk8, Mapk9, Pgf, Pik3r2, Pik3r3, Smad2, Tgfa, Tgfb1, Tgfb2, Tgfbr2, Vegfa Akt2, Akt3, Eif4b, Figf, Ins1, LOC684368, Pgf, Pik3r2, Pik3r3, Prkaa1, Prkaa2, Rps6kb1, Vegfa Adcy3, Adcy4, Atf4, Calm1, Calm3, Cga, Gnaq, Itpr1, Itpr2, Jun, Map2k4, Map2k6, Mapk14, Mapk8, Mapk9, Pla2g2a, Pla2g5, Plcb1, Prkca, Prkcd Ccnd1, Fzd6, Jun, Mapk8, Mapk9, MGC112790, Nfatc4, Plcb1, Ppard, Ppp2r2a, Ppp2r2d, Prkca, Rock2, Skp1a, Smad2, Wif1, Wnt2b Akt2, Akt3, Atf4, Bcl-2, Ccnd1, Ccne2, Creb1, Creb3l2, Creb3l3, Erbb2, Ikbkb, Ins1, Nfkbia, Pdgfc, Pik3r2, Pik3r3, Srd5a1, Srd5a2, Tgfa Akt2, Akt3, Gab2, Inpp5d, Map2k4, Map2k6, Mapk14, Mapk8, Mapk9, Pik3r2, Pik3r3, Pla2g2a, Pla2g5, Prkca, Prkcd Akt2, Akt3, Calm1, Calm3, Exoc7, Fbp1, Ikbkb, Inpp5d, Ins1, Insr, LOC361377, LOC684368, Mapk8, Mapk9, MGC112775, Pck1, Phka1, Pik3r2, Pik3r3, Ppargc1a, Ppp1cc, Ppp1r3b, Prkaa1, Prkaa2, Prkar2a, Pygm, Rps6kb1 Faslg, Gad1, Gad2, Hspd1, Ifng, Il1a, Il1b, Ins1, RT1-A2, RT1-CE10, RT1-CE2, RT1-CE4, RT1-Dob, RT1-Ha Crh, Gnai3, Gnaq, Gucy2e, Itpr1, Itpr2, Nos3, Npr2, Pla2g2a, Pla2g5, Plcb1, Ppp2r2a, Ppp2r2d, Prkca, Prkg1 Acvr2a, Acvr2b, Amh, Fst, Gdf7, Ifng, Inhbc, Ppp2r2a, Ppp2r2d, Rock2, Rps6kb1, Skp1a, Smad2, Smad9, Tgfb1, Tgfb2, Tgfbr2 Akt2, Akt3, Bcl-2, Ccnd1, Fzd6, Jun, Mapk8, Mapk9, Met, MGC112790, Pik3r2, Pik3r3, Smad2, Tgfb1, Tgfb2, Tgfbr2 Akt2, Akt3, Apaf1, Bcl-2, Birc2, Ccnd1, Ccne2, Col4a1, Fn1, Ikbkb, Itga6, Nfkbia, Nos3, Pias4, Pik3r2, Pik3r3 Akt2, Akt3, Arhgap5, Bcl-2, Birc2, Capn2, Ccnd1, Col4a1, Col5a2, Erbb2, Figf, Fn1, Ibsp, Itga6, Itgb4, Jun, Mapk8, Mapk9, Met, Myl2, Pak1, Pdgfc, Pgf, Pik3r2, Pik3r3, Ppp1cc, Ppp1cc, Ppp1r12a, Prkca, Rock2, Sgpp1, Vegfa, Vwf Adcy3, Adcy4, Adra1d, Adrb2, Atp2a2, Atp2b4, Bdkrb1, Calm1, Calm3, Chrm2, Chrm5, Chrna7, Erbb2, F2r, Gna14, Gnaq, Grin1, Grpr, Hrh1, Htr2b, Htr5a, Itpr1, Itpr2, LOC361377, Nos3, Oxtr, Phka1, Plcb1, Pln, Prkca, Slc25a5 Akt2, Akt3, Atf4, Cacnb1, Cacnb2, Cacnb4, Cacng5, Daxx, Dusp5, Faslg, Fgf2, Fgf6, Fgfr3, Hspa2, Ikbkb, Il1a, Il1b, JIK, Jun, Map2k1ip1, Map2k4, Map2k6, Map4k3, Mapk14, Mapk6, Mapk8, Mapk9, MGC112775, Nfatc4, Ntf5, Pak1, Pla2g2a, Pla2g5, Prkca, Ptpn5, Rasa1, Tgfb1, Tgfb2, Tgfbr2 Abi2, Bdkrb1, Chrm2, Chrm4, Chrm5, Enah, F2r, Fgf2, Fgf6, Fgfr3, Fn1, Ins1, Itga6, Itgb4, Limk2, LOC683685, LOC684227, Myh9, Myl2, Nckap1, Pak1, Pdgfc, Pik3r2, Pik3r3, Pip5k1c, Pip5k2a, Ppp1cc, Ppp1r12a, Rock2, Slc9a1 Acvr2a, Acvr2b, Amh, Ccl2, Ccl7, Clcf1, Csf1r, Csf2rb1, Csf3, Cxcl11, Cxcr3, Faslg, Figf, Gdf7, Ifng, Il1a, Il1b, Il24, Il2ra, Il4ra, Inhbc, Kit, Lif, LOC681692, Ltb, Met, Pdgfc, Pgf, Tgfb1, Tgfb2, Tgfbr2, Tnfrsf1b, Tnfrsf5, Tpo, Vegfa a Pathways targeted by upregulated miRNA b Pathways targeted by downregulated miRNA All of these pathways show increased enrichment, P-values, FDRs, and predicted targeted genes analysis Acting as an antiapoptotic member, Bcl-2 preserves mitochondrial integrity and potentially blocks the release of some soluble prodeath intermembrane proteins Therefore, caspase-9-dependent 5170 apoptosis is inhibited These results may provide more evidence for the reliability of bioinformatics analysis, and be helpful in shedding light on the mechanisms underlying HSC activation FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS C.-J Guo et al A Signaling pathways regulated in HSCs by miRNAs In conclusion, most of the signaling pathways involved in HSC activation may be regulated by miRNAs Among these, the mitochondrial pathway of apoptosis is likely to take the critical place during activation by miRNA-targeted Bcl-2 and caspase-9 Experimental procedures Isolation and culture of rat HSCs B C HSCs were isolated from Sprague–Dawley rats (350–400 g; Shanghai Laboratory Animal Center of the Chinese Academy of Sciences) by perfusion with collagenase and pronase, followed by centrifugation (1500 g, 17 min) over a Nycodenz gradient [25] They were then cultured in DMEM supplemented with 10% fetal bovine serum The quiescent and activated HSCs were then harvested on the second and the 14th day, respectively Immunofluorescence staining for desmin was performed using antibody against desmin Cells were counterstained with fluorescein isothiocyanate-conjugated rabbit anti-(goat IgG) (1 : 100; Molecular Probes, Eugene, OR, USA) Nuclei were labeled with Hoechst 33258 (Roche, Germany) Negative controls were performed both with antibody against a-smooth muscle actin (SMA) (1 : 100; Santa Cruz, CA, USA) and without primary antibody GO terms and KEGG pathway annotation based on miRNA expression profile D Fig HE and VG staining of liver tissue (· 200) HE staining of normal and CCl4-treated liver tissue is shown in (A) and (B), respectively VG staining of normal and CCl4-treated liver tissue is shown in (C) and (D), respectively Total RNA of HSCs was extracted and hybridized to the miRCURY LNA array, version 8.0 (Exiqon, Denmark) We selected the miRNAs measured as present in at least the smallest class in the dataset (25%) [26] Thereafter, we pooled the reported and predicted targets of filtered miRNAs from the Sanger database (http://microrna.sanger.ac.uk/) The top 25% miRNA targets that had been assigned the highest numbers of miRNA interaction sites were collected, and subjected to GO term analysis GO analysis was applied in order to organize genes into hierarchical categories and uncover the miR–gene regulatory network on the basis of biological process and molecular function; the network of miRNA–mRNA interaction, representing the critical miRNAs and their targets, was established according to the miRNA degree Meanwhile, the top 25% miRNA targets were collected, and subjected to KEGG pathway annotation using the david gene annotation tool (http://david.abcc.ncifcrf.gov/) [27] In detail, a two-sided Fisher’s exact test and chi-square test were used to classify the enrichment (Re) of pathway category, and the false discovery rate (FDR) was calculated to correct the P-value Within a KO, the enrichment (Re) was given by FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS 5171 Signaling pathways regulated in HSCs by miRNAs A B C C.-J Guo et al D Fig Immunofluorescence staining of quiescent and activated HSCs for Bcl-2 and caspase-9 (· 400) (A) and (B) show the expression of Bcl-2 on day and day 14, respectively (C) and (D) show the expression of caspase-9 on day and day 14, respectively Positive cells per microscopic field: *statistically significant differences P < 0.05 versus control group humane care according to the Guide for the Care and Use of Laboratory Animals of the Chinese Academy of Sciences Re ẳ nf =nị=Nf =Nị where nf and n represent the number of target genes and total genes, respectively, in the particular KO, and Nf and N represent the number of genes among the entire differential miRNA-corresponding target genes and the total number of genes on the pathway, respectively We chose only pathways that had a P-value of < 0.01 and an FDR of < 0.01 The regulator pathway annotation was also performed on the basis of scoring and visualization of the pathways collected in the KEGG database (http://www genome.jp/kegg/) Histological examination Liver tissues were fixed in 40 gỈL)1 solutions of formaldehyde in NaCl ⁄ Pi (pH 7.4) and embedded in paraffin Fivemicrometer thick section slides were prepared All of the sections were stained with hematoxylin ⁄ eosin (HE) and standard Van Gieson (VG) stain, which was used to detect collagen fibers Fibrosis was graded according to the Ishak modified staging system [28] Histopathology was interpreted by two independent board-certified pathologists who were blind to the study Animal model of liver fibrosis Thirty Sprague–Dawley rats (250–400 g; Shanghai Laboratory Animal Center of Chinese Academy of Sciences) were divided into three groups (normal, control, and fibrosis model; n = 10 in each group) Fibrosis model rats were injected subcutaneously with 40% CCl4 (3 mLỈkg)1; CCl4 ⁄ olive oil ratio of : 3) every days for weeks Control rats received only olive oil in the same way All rats received 5172 Immunofluorescence staining of HSCs The expression of Bcl-2 and caspase-9 in quiescent (2 days) and in culture-activated (14 days) HSCs was evaluated by immunocytochemistery The adherent HSCs were fixed with 4% paraformaldehyde and permeabilized with 0.1% Triton X-100 (Sigma, St Louis, MO, USA) Following blocking in 10% preimmune goat serum for h, cells were FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS C.-J Guo et al Signaling pathways regulated in HSCs by miRNAs Fig Double immunofluorescence staining of Bcl-2, caspase-9 and desmin in liver tissue (· 200) Double staining of desmin (green), Bcl-2, caspase-9 (red) and Hoechst in nuclei (blue) was in the liver tissue The arrowheads indicate the expression of Bcl-2 and caspase-9 in HSCs showing red ⁄ green double-stained cytoplasm and blue-stained nuclei incubated with mouse monoclonal antibody against Bcl-2 (1 : 100; Santa Cruz, CA, USA) overnight at °C Cells were then incubated with tetramethylrhodamine isothiocyanate-conjugated donkey anti-(mouse IgG) (Sigma; : 100) for h Tetramethylrhodamine isothiocyanate fluorescence were visualized using a fluorescence microscope The positive cells of three randomly selected areas per slide from three slides was used to calculate the expression of Bcl-2 and caspase-9 in HSCs Double immunostaining on cryosections of rat liver Double immunostaining on cryosections of rat liver were performed as previously described [29,30] Liver tissue from five rats per group were blocked with 0.3% H2O2 in methanol for endogenous peroxidase activity Double staining experiments on rat livers for desmin in combination with Bcl-2 or caspase-9 were per- FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS 5173 Signaling pathways regulated in HSCs by miRNAs C.-J Guo et al Bcl-2-positive or caspase-9-positive cells in desminpositive cells in three different fields per slide from three slides A Quantitative RT-PCR analysis of apoptosis-related gene expression HSCs from three rats per group were isolated from rat livers by perfusion of collagenase and pronase, followed by centrifugation (1500 g, 17 min) over a Nycodenz gradient, as described above The extracted total RNA of HSCs from three rats per group was reverse transcribed into cDNA using an ExScript RT reagent Kit (Takara, Kusatsu, Japan) Realtime PCR was performed using the SYBR Premix ExTaq (Takara) on a LightCycler (Roche Diagnostics GmbH, Penzberg, Germany) The sense and antisense primers used in this study are as follows: Bcl-2 (NM-016993, 116 bp), 5¢-TGA ACCGGCATCTGCACAC-3¢ and 5¢-CGTCTTCAGAGACA GCCAGGAG-3¢; caspase-9 (NM_031632, 206 bp), 5¢-TGC ACTTCCTCTCAAGGCAGGACC-3¢ and 5¢-TCCAAGGTCT CCATGTACCAGGAGC-3¢; and glyceraldehyde-3-phosphate dehydrogenase (NM-002046, 450bp), 5¢-ACCACAGTCCATG CCATCAC-3¢ and 5¢-TCCACCACCCTGTTGCTGTA-3¢ PCR was performed as follows: 95 °C for 10 s, followed by 40 cycles of denaturation at 95 °C for s and annealing ⁄ extension at 60 °C for 20 s Each sample was run in triplicate Independent experiments were repeated twice Glyceraldehyde-3-phosphate dehydrogenase was used as endogenous control Relative gene expression levels were calculated by the 2[-Delta Delta C(T)] method [19] B C Western blot analysis of apoptosis-related gene expression Fig Expression of Bcl-2 and caspase-9 in intrahepatic HSCs after CCl4 injury (A) Double immunofluorescence staining assay; the positive rate is determined by comparing the number of red ⁄ green double-stained cells with the number of desmin-positive cells (B) The mRNA levels of Bcl-2 and caspase-9 in intrahepatic HSCs after CCl4 injury, detected by quantitative real-time PCR (C) The protein levels of Bcl-2 and caspase-9 in intrahepatic HSCs after CCl4 injury, analyzed by western blotting A statistically significant difference between control and liver fibrosis is indicated by P < 0.01 *Statistically significant differences formed Immunohistochemical examination was carried out by a researcher blind to the experimental design The percentage of cells coexpressing Bcl-2 ⁄ desmin or caspase9 ⁄ desmin was determined by counting the number of 5174 Total proteins were prepared by standard procedures and quantified by the bicinchoninic acid method (Pierce, Rockford, IL, USA) Thirty micrograms of protein per sample was loaded onto a 10% SDS ⁄ PAGE gel After electrophoresis, the protein was transferred onto a poly(vinylidene difluoride) membrane (Millipore, Billerica, MA, USA) by electroelution The membrane was incubated with antibody against Bcl-2 or caspase-9 (1 : 500; Santa Cruz, CA, USA) overnight at °C, and with horseradish peroxidase-conjugated goat anti-(mouse IgG) (1 : 5000; Jackson ImmunoResearch) for h at room temperature After washing, the membrane was processed using SuperSignal West Pico chemiluminescent substrate (Pierce), and antibody against actin (Santa Cruz, CA, USA) (1 : 500) as an internal standard Statistical analysis All of the results are expressed as mean ±standard deviation Statistical analysis was performed with Student’s t-test for comparison of two groups, and with anova for multiple FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS C.-J Guo et al Signaling pathways regulated in HSCs by miRNAs comparisons In both cases, differences with P < 0.05 were considered to be statistically significant 12 Acknowledgements This work was supported by the Foundation of Shanghai Commission of Science Technology of Research Program (O7JC14044) 13 14 15 References Brenner DA, Waterboer T, Choi SK, Lindquist JN, Stefanovic B, Burchardt E, Yamauchi M, Gillan A & Rippe RA (2000) New aspects of hepatic fibrosis J Hepatol 32, 32–38 Friedman SL (2004) Mechanisms of disease: mechanisms of hepatic fibrosis and therapeutic implications Nat Clin Pract Gastroenterol Hepatol 1, 98–105 Gressner AM & Weiskirchen R (2006) Modern pathogenetic concepts of liver fibrosis suggest stellate cells and TGF-beta as major players and therapeutic targets J Cell Mol Med 10, 76–99 Godichaud S, Si-Tayeb K, Auge N, Desmouliere A, Balabaud C, Payrastre B, Negre-Salvayre A & Rosenbaum J (2006) The grape-derived polyphenol resveratrol differentially affects epidermal and platelet-derived growth factor signaling in human liver myofibroblasts Int J Biochem Cell Biol 38, 629–637 Jiang F, Parsons CJ & Stefanovic B (2006) Gene expression profile of quiescent and activated rat hepatic stellate cells implicates Wnt signaling pathway in activation J Hepatol 45, 401–409 Kanematsu M, Semelka RC, Osada S & Amaoka N (2005) Magnetic resonance imaging and expression of vascular endothelial growth factor in hepatocellular nodules in cirrhosis and hepatocellular carcinomas Top Magn Reson Imaging 16, 67–75 Langer DA, Das A, Semela D, Kang-Decker N, Hendrickson H, Bronk SF, Katusic ZS, Gores GJ & Shah VH (2008) Nitric oxide promotes caspase-independent hepatic stellate cell apoptosis through the generation of reactive oxygen species J Hepatol 47, 1983–1993 Gabele E, Brenner DA & Rippe RA (2003) Liver broă sis: signals leading to the amplication of the fibrogenic hepatic stellate cell Front Biosci 8, 69–77 Chitwood DH & Timmermans MC (2007) Target mimics modulate miRNAs Nat Genet 39, 935–936 10 Guo CJ, Pan Q, Li DG, Sun H & Liu BW (2009) miR-15b and miR-16 are implicated in activation of the rat hepatic stellate cell: an essential role for apoptosis J Hepatol 50, 766–778 11 Mao X, Cai T, Olyarchuk JG & Wei L (2005) Automated genome annotation and pathway identification 16 17 18 19 20 21 22 using the KEGG Orthology (KO) as a controlled vocabulary Bioinformatics 21, 3787–3793 Kanehisa M, Goto S, Kawashima S, Okuno Y & Hattori M (2004) The KEGG resource for deciphering the genome Nucleic Acids Res 11, 277–280 Ambros V (2004) The functions of animal microRNAs Nature 431, 350–355 Iredale JP (2001) Hepatic stellate cell behavior during resolution of liver injury Semin Liver Dis 21, 427–436 Popov Y, Patsenker E, Bauer M, Niedobitek E, Schulze-Krebs A & Schuppan D (2006) Halofuginone induces matrix metalloproteinases in rat hepatic stellate cells via activation of p38 and NFkappaB J Biol Chem 281, 15090–15098 Tsukada S, Westwick JK, Ikejima K, Sato N & Rippe RA (2005) SMAD and p38 MAPK signaling pathways independently regulate alpha1(I) collagen gene expression in unstimulated and transforming growth factor-beta-stimulated hepatic stellate cells J Biol Chem 280, 10055–10064 Saxena NK, Titus MA, Ding X, Floyd J, Srinivasan S, Sitaraman SV & Anania FA (2004) Leptin as a novel profibrogenic cytokine in hepatic stellate cells: mitogenesis and inhibition of apoptosis mediated by extracellular regulated kinase (Erk) and Akt phosphorylation FASEB J 18, 1612–1614 Dooley S, Hamzavi J, Ciuclan L, Godoy P, Ilkavets I, Ehnert S, Ueberham E, Gebhardt R, Kanzler S, Geier A et al (2008) Hepatocyte-specific Smad7 expression attenuates TGF-beta-mediated fibrogenesis and protects against liver damage Phosphorylation Gastroenterology 135, 642–659 Marquez-Aguirre A, Sandoval-Rodriguez A, GonzalezCuevas J, Bueno-Topete M, Navarro-Partida J, ArellanoOlivera I, Lucano-Landeros S & Armendariz-Borunda J (2009) Adenoviral delivery of dominant-negative transforming growth factor beta type II receptor up-regulates transcriptional repressor SKI-like oncogene, decreases matrix metalloproteinase in hepatic stellate cell and prevents liver fibrosis in rats J Gene Med 11, 207–219 ` Novo E, Cannito S, Zamara E, Valfre di Bonzo L, Caligiuri A, Cravanzola C, Compagnone A, Colombatto S, Marra F, Pinzani M et al (2007) Proangiogenic cytokines as hypoxia-dependent factors stimulating migration of human hepatic stellate cells Am J Pathol 170, 1942–1953 Liu Y, Lui EL, Friedman SL, Li L, Ye T, Chen Y, Poon RT, Wo J, Kok TW & Fan ST (2009) PTK787 ⁄ ZK22258 attenuates stellate cell activation and hepatic fibrosis in vivo by inhibiting VEGF signaling Lab Invest 89, 209–221 Ankoma-Sey V, Wang Y & Dai Z (2000) Hypoxic stimulation of vascular endothelial growth factor expression in activated rat hepatic stellate cells Hepatology 31, 141–148 FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS 5175 Signaling pathways regulated in HSCs by miRNAs C.-J Guo et al 23 Cheng JH, She H, Han YP, Wang J, Xiong S, Asahina K & Tsukamoto H (2008) Wnt antagonism inhibits hepatic stellate cell activation and liver fibrosis Am J Physiol Gastrointest Liver Physiol 294, G39–G49 24 Myung SJ, Yoon JH, Gwak GY, Kim W, Lee JH, Kim KM, Shin CS, Jang JJ, Lee SH, Lee SM et al (2007) Wnt signaling enhances the activation and survival of human hepatic stellate cells FEBS Lett 581, 2954–2958 25 Friedman SL & Roll FJ (1987) Isolation and culture of hepatic lipocytes, Kupffer cells, and sinusoidal endothelial cells by density gradient centrifugation with Stractan Anal Biochem 161, 207–218 26 Liu CG, Calin GA, Meloon B, Gamliel N, Sevignani C, Ferracin M, Dumitru CD, Shimizu M, Zupo S, Dono M et al (2004) An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues Proc Natl Acad Sci USA 101, 9740–9744 27 Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Clifford Lane H & Lempicki RA (2003) DAVID: 5176 28 29 30 31 database for annotation, visualization, and integrated discovery Genome Biol 4, Ishak K, Baptista A, Bianchi L, Callea F, De Groote J, Gudat F, Denk H, Desmet V, Korb G, MacSween RNM et al (1995) Histological grading and staging of chronic hepatitis J Hepatol 22, 696–699 Cassiman D, van Pelt J, De Vos R, Van Lommel F, Desmet V, Yap SH & Roskams T (1999) Synaptophysin: a novel marker for human and rat hepatic stellate cells Am J Pathol 155, 1831–1839 Cassiman D, Denef C, Desmet V & Roskams T (2001) Human and rat hepatic stellate cells express neurotrophins and neurotrophin receptors Hepatology 33, 148– 158 Livak KJ & Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T))method Methods 25, 402– 408 FEBS Journal 276 (2009) 5163–5176 ª 2009 The Authors Journal compilation ª 2009 FEBS ... HSC activation (P < 0.05) These findings were confirmed in CCl4-induced experimental hepatic fibrosis Bcl-2, which can rarely be detected in the normal liver, was expressed increasingly in intrahepatic... C.-J Guo et al Signaling pathways regulated in HSCs by miRNAs Fig Double immunofluorescence staining of Bcl-2, caspase-9 and desmin in liver tissue (· 200) Double staining of desmin (green), Bcl-2,... caspase-9 in intrahepatic HSCs after CCl4 injury (A) Double immunofluorescence staining assay; the positive rate is determined by comparing the number of red ⁄ green double-stained cells with the