These authors contributed equally to this work
Changes in microRNAs associated with hepatic stellate cell activation status identify signaling pathways
Article first published online: 10 AUG 2009
© 2009 The Authors Journal compilation © 2009 FEBS
Volume 276, Issue 18, pages 5163–5176, September 2009
How to Cite
Guo, C.-J., Pan, Q., Cheng, T., Jiang, B., Chen, G.-Y. and Li, D.-G. (2009), Changes in microRNAs associated with hepatic stellate cell activation status identify signaling pathways. FEBS Journal, 276: 5163–5176. doi: 10.1111/j.1742-4658.2009.07213.x
- Issue published online: 27 AUG 2009
- Article first published online: 10 AUG 2009
- (Received 9 April 2009, revised 9 July 2009, accepted 14 July 2009)
- hepatic stellate cells;
- liver fibrosis;
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.
false discovery rate
hepatic stellate cell
Kyoto Encyclopedia of Genes and Genomes
Kyoto Encyclopedia of Genes and Genomes orthology
mitogen-activated protein kinase
smooth muscle actin
transforming growth factor-β
vascular endothelial growth factor
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 . 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 .
Previous studies on HSCs have attributed their activation to the regulation of many signal transduction pathways, including transforming growth factor-β (TGF-β)/Smad, platelet-derived growth factor, mitochondrial 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 . 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 . 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 do not affect mRNA levels . 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 . According to these techniques, KEGG acts as a bioinformatics resource for understanding higher-order 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/) . 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.
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.
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).
|Category ID||Category name||Enrichment||P-value||FDR|
|Upregulated GOs by GO analysisa|
|GO:0006813||Potassium ion transport||0.561451066||2.26084E-05||0.002214733|
|GO:0045817||Positive regulation of transcription from RNA polymerase II promoter||0.532929713||2.15258E-10||0.000938004|
|GO:0001666||Response to hypoxia||0.522209863||5.13583E-06||0.00181087|
|GO:0045449||Regulation of transcription||0.505631772||1.207E-12||0.000690475|
|GO:0008284||Positive regulation of cell proliferation||0.461241199||2.13311E-08||0.001107366|
|GO:0042981||Regulation of apoptosis||0.387927327||2.85018E-06||0.001719675|
|GO:0007242||Intracellular signaling cascade||0.382549086||0||0.000117251|
|GO:0007264||Small GTPase-mediated signal transduction||0.368618727||3.2E-14||0.000586253|
|GO:0045941||Positive regulation of transcription||0.321359916||2.97119E-08||0.001159478|
|GO:0007169||Transmembrane receptor protein tyrosine kinase signaling pathway||0.253425375||2.3708E-11||0.00078167|
|GO:0051056||Regulation of small GTPase-mediated signal transduction||0.166000486||1E-15||0.000521114|
|GO:0007186||G-protein coupled receptor protein signaling pathway||0.125511830||0||9.11949E-05|
|GO:0045840||Positive regulation of mitosis||0.111902113||1.57057E-07||0.001315812|
|Downregulated GOs by GO analysisb|
|GO:0006629||Lipid metabolic process||−0.488882277||1.19397E-06||0.001537285|
|GO:0006886||Intracellular protein transport||−0.470581871||8.61363E-06||0.001954176|
|GO:0005975||Carbohydrate metabolic process||−0.465602169||6.25054E-06||0.001849953|
|GO:0006813||Potassium ion transport||−0.460219485||1.25103E-05||0.002058399|
|GO:0008285||Negative regulation of cell proliferation||−0.458950709||1.39364E-06||0.001563341|
|GO:0006468||Protein amino acid phosphorylation||−0.457234479||0||0.000403863|
|GO:0007275||Multicellular organism development||−0.446653243||6E-15||0.000547169|
|GO:0006816||Calcium ion transport||−0.424704681||1.52674E-05||0.002097482|
|GO:0006470||Protein amino acid dephosphorylation||−0.416070023||1.79328E-05||0.002136566|
|GO:0007156||Homophilic cell adhesion||−0.403895857||1.91987E-06||0.001602424|
|GO:0006917||Induction of apoptosis||−0.399087573||6.34031E-08||0.001211589|
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 caspase-9, 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.
|Regulated by upregulated miRNAa|
|Folate-dependent one-carbon pool||0.000646||0.005||4.47172298||Atic, Ftcd, Gart, Mtfmt, Mthfd1, Shmt1, Tyms|
|Carbon fixation||0.000508||0.005||3.73708278||Aldob, Aldoc, Fbp2, Got1, Gpt1, Mdh2, Pgk1, Pklr, Pkm2|
|Biosynthesis of steroids||0.004178||0.005||3.16366796||Ebp, Idi1, Lss, Mvd, Nqo1, Nsdhl, Tm7sf2, Vkorc1|
|Glycolysis/ gluconeogenesis||0.00115||0.005||2.58366217||Adh4, Adh7, Aldh1a1, Aldh1a3, Aldob, Aldoc, Eno1, Eno2, Fbp2, G6pc, Pfkm, Pgk1, Pklr, Pkm2|
|VEGF signaling pathway||0.000504||0.005||2.35504387||Casp9, Hspb1, Kras, Map2k2, Mapk12, Mapkapk2, Mapkapk3, Pik3cb, Pla2g6, Plcg2, Ppp3cb, Ppp3r1, Ppp3r2, Prkcb1, Ptk2, Pxn, Rac1, Sphk1, Src|
|Gap junction||0.000241||0.005||2.21456757||Adrb1, Csnk1d, Drd2, Egfr, Gja9, Gna11, Gnai2, Gucy1a2, Gucy2c, Htr2c, Kras, LOC500319, Map2k2, Map2k5, Npr1, Pdgfd, Pdgfra, Prkcb1, Prkg2, Src, Tuba6, Tubb2c, Tubb5, Tubb6|
|Pyrimidine metabolism||0.00179||0.005||2.19828399||Dck, Dhodh, Ecgf1, Entpd1, Entpd6, LOC682744, Nme3, Nme7, Pnpt1, Pola1, Pold1, Pold2, Polr3d, Prim2, Rpa1, Rrm2, Tyms, Umps|
|Purine metabolism||0.000104||0.005||2.0761571||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|
|Apoptosis||0.008088||0.0075||1.90105951||Aim1, Birc3, Cad, Casp6, Casp8, Casp9, Dffb, Fadd, Il1rap, Ntrk1, Pik3cb, Ppp3cb, Ppp3r1, Ppp3r2, Tnfrsf10b, Tnfrsf1a, Tnfsf10, Tp53, Tradd|
|Neuroactive ligand–receptor interaction||7.54E-07||0.005||1.85470034||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|
|Glycan structures – biosynthesis 1||0.004678||0.005||1.82854203||Alg6, Alg8, B4galt3, Chst1, Chst3, D1bwg1363e, Extl3, Galnt10, Galnt11, Galnt13, Gcnt3, Gcs1, H2afx, Hs3st1, Hs3st2, LOC683264, LOC687718, Mgat5, Ndst1, Pomt1, Rpn2, St3gal2, St3gal3, Xylt1|
|MAPK signaling pathway||8.86E-05||0.005||1.74129305||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|
|Regulation of actin cytoskeleton||0.002064||0.005||1.64448087||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|
|Cytokine–cytokine receptor interaction||0.002262||0.005||1.57856573||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|
|Regulated by downregulated miRNAb|
|Apoptosis||0.0081231||0.005||4.279734875||Akt2, Akt3, Apaf1, Atm, Bcl-2, Bid, Birc2, Capn2, Csf2rb1, Faslg, Ikbkb, Il1a, Il1b, Nfkbia, Pdcd8, Pik3r2, Pik3r3, Prkar2a|
|Cell cycle||0.0025457||0.005||3.894558736||Anapc7, Atm, Ccnd1, Ccne2, Ccnh, Cdc25a, Cdc2a, Cdk7, Plk1, Skp1a, Smad2, Tgfb1, Tgfb2, Ywhag, Ywhah, Ywhaq|
|Adipocytokine signaling pathway||0.0001621||0.005||2.781827668||Acsl5, Acsl6, Adipor2, Akt2, Akt3, Cpt2, Ikbkb, Jak2, Mapk8, Mapk9, Nfkbia, Pck1, Ppargc1a, Prkaa1, Prkaa2, Ptpn11, Tnfrsf1b|
|PPAR signaling pathway||0.0001621||0.005||2.781827668||Acaa1, Acadm, Acsl5, Acsl6, Angptl4, Apoa5, Cpt2, Cyp4a14, Cyp4a22, Ehhadh, Fabp7, Me1, Pck1, Plin, Ppard, Pparg, Ubc|
|SNARE interactions in vesicular transport||0.0075777||0.005||2.781827668||Bet1, Bet1l, Epim, Gosr2, Stx17, Stx3, Sybl1, Vamp1, Vamp8|
|Pancreatic cancer||0.0001231||0.005||2.74372044||Akt2, Akt3, Ccnd1, Erbb2, Figf, Ikbkb, Jak2, Mapk8, Mapk9, Pgf, Pik3r2, Pik3r3, Smad2, Tgfa, Tgfb1, Tgfb2, Tgfbr2, Vegfa|
|mTOR signaling pathway||0.0013221||0.005||2.729340354||Akt2, Akt3, Eif4b, Figf, Ins1, LOC684368, Pgf, Pik3r2, Pik3r3, Prkaa1, Prkaa2, Rps6kb1, Vegfa|
|GnRH signaling pathway||0.0001707||0.005||2.528934244||Adcy3, Adcy4, Atf4, Calm1, Calm3, Cga, Gnaq, Itpr1, Itpr2, Jun, Map2k4, Map2k6, Mapk14, Mapk8, Mapk9, Pla2g2a, Pla2g5, Plcb1, Prkca, Prkcd|
|Wnt signaling pathway||0.0007018||0.005||2.413151712||Ccnd1, Fzd6, Jun, Mapk8, Mapk9, MGC112790, Nfatc4, Plcb1, Ppard, Ppp2r2a, Ppp2r2d, Prkca, Rock2, Skp1a, Smad2, Wif1, Wnt2b|
|Prostate cancer||0.0005175||0.005||2.402487532||Akt2, Akt3, Atf4, Bcl-2, Ccnd1, Ccne2, Creb1, Creb3l2, Creb3l3, Erbb2, Ikbkb, Ins1, Nfkbia, Pdgfc, Pik3r2, Pik3r3, Srd5a1, Srd5a2, Tgfa|
|Fc epsilon RI signaling pathway||0.0024077||0.005||2.384423716||Akt2, Akt3, Gab2, Inpp5d, Map2k4, Map2k6, Mapk14, Mapk8, Mapk9, Pik3r2, Pik3r3, Pla2g2a, Pla2g5, Prkca, Prkcd|
|Insulin signaling pathway||4.593E-05||0.005||2.347167095||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|
|Type I diabetes mellitus||0.0051475||0.005||2.290916903||Faslg, Gad1, Gad2, Hspd1, Ifng, Il1a, Il1b, Ins1, RT1-A2, RT1-CE10, RT1-CE2, RT1-CE4, RT1-Dob, RT1-Ha|
|Long-term depression||0.0037573||0.005||2.2864337||Crh, Gnai3, Gnaq, Gucy2e, Itpr1, Itpr2, Nos3, Npr2, Pla2g2a, Pla2g5, Plcb1, Ppp2r2a, Ppp2r2d, Prkca, Prkg1|
|TGF-β signaling pathway||0.0020109||0.005||2.279087728||Acvr2a, Acvr2b, Amh, Fst, Gdf7, Ifng, Inhbc, Ppp2r2a, Ppp2r2d, Rock2, Rps6kb1, Skp1a, Smad2, Smad9, Tgfb1, Tgfb2, Tgfbr2|
|Colorectal cancer||0.0036279||0.0075||2.225462135||Akt2, Akt3, Bcl-2, Ccnd1, Fzd6, Jun, Mapk8, Mapk9, Met, MGC112790, Pik3r2, Pik3r3, Smad2, Tgfb1, Tgfb2, Tgfbr2|
|Small cell lung cancer||0.004734||0.005||2.171182571||Akt2, Akt3, Apaf1, Bcl-2, Birc2, Ccnd1, Ccne2, Col4a1, Fn1, Ikbkb, Itga6, Nfkbia, Nos3, Pias4, Pik3r2, Pik3r3|
|Focal adhesion||0.000293||0.0075||1.978188564||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|
|Calcium signaling pathway||0.0004375||0.005||1.959924039||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|
|MAPK signaling pathway||0.0008528||0.005||1.74985934||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|
|Regulation of actin cytoskeleton||0.0087802||0.005||1.652570892||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|
|Cytokine–cytokine receptor interaction||0.0069367||0.005||1.609321792||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|
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 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 7 and 8). The expression of caspase-9, however, was decreased significantly in fibrotic liver when compared to that of normal controls (P < 0.05) (Figs 7 and 8).
MicroRNAs, a set of small, noncoding RNAs, 21–22 nucleotides in length, have recently been recognized to be deeply involved in various crucial cell processes, such as mitosis, differentiation, oncogenesis, and apoptosis, by regulating signal transduction pathways . With the use of in vitro cell activation and miRNA microarray hybridization , 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 , 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-β 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 α1(I) collagen and matrix metalloproteinases in rat HSCs [15–17]. TGF-β 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 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 caspase-9, 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 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 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.
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.
Isolation and culture of rat HSCs
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 . 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 α-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
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%) .
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/) . 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
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/).
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 mL·kg−1; CCl4/olive oil ratio of 2 : 3) every 3 days for 8 weeks. Control rats received only olive oil in the same way. All rats received humane care according to the Guide for the Care and Use of Laboratory Animals of the Chinese Academy of Sciences.
Liver tissues were fixed in 40 g·L−1 solutions of formaldehyde in NaCl/Pi (pH 7.4) and embedded in paraffin. Five-micrometer 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 . Histopathology was interpreted by two independent board-certified pathologists who were blind to the study.
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 2 h, cells were incubated with mouse monoclonal antibody against Bcl-2 (1 : 100; Santa Cruz, CA, USA) overnight at 4 °C. Cells were then incubated with tetramethylrhodamine isothiocyanate-conjugated donkey anti-(mouse IgG) (Sigma; 1 : 100) for 1 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 performed. Immunohistochemical examination was carried out by a researcher blind to the experimental design. The percentage of cells coexpressing Bcl-2/desmin or caspase-9/desmin was determined by counting the number of Bcl-2-positive or caspase-9-positive cells in desmin-positive cells in three different fields per slide from three slides.
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). Real-time 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′-TGAACCGGCATCTGCACAC-3′ and 5′-CGTCTTCAGAGACAGCCAGGAG-3′; caspase-9 (NM_031632, 206 bp), 5′-TGCACTTCCTCTCAAGGCAGGACC-3′ and 5′-TCCAAGGTCTCCATGTACCAGGAGC-3′; and glyceraldehyde-3-phosphate dehydrogenase (NM-002046, 450bp), 5′-ACCACAGTCCATGCCATCAC-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 5 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 .
Western blot analysis of apoptosis-related gene expression
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 4 °C, and with horseradish peroxidase-conjugated goat anti-(mouse IgG) (1 : 5000; Jackson ImmunoResearch) for 2 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.
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 comparisons. In both cases, differences with P < 0.05 were considered to be statistically significant.
This work was supported by the Foundation of Shanghai Commission of Science Technology of Research Program (O7JC14044).
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