Previous studies on HSCs have attributed their acti-vation to the regulation of many signal transduction pathways, including transforming growth factor-b TGF-b ⁄ Smad, platelet-derived g
Trang 1cell activation status identify signaling pathways
Can-Jie Guo1,*, Qin Pan1,*, Tao Cheng2, Bo Jiang3, Guang-Yu Chen1 and Ding-Guo Li1
1 Digestive Disease Laboratory and Department of Gastroenterology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, China
2 Department of Orthopaedics, The Sixth Affiliated People’s Hospital, School of Medicine, Shanghai Jiaotong University, China
3 Business School, Central South University, Changsha, China
Introduction
Liver fibrosis is the excessive accumulation of
extracel-lular matrix that occurs in most types of chronic liver
diseases Hepatic stellate cells (HSCs), the major
mesen-chymal cells in liver, are widely accepted as playing a
critically important role in liver fibrosis [1] In the
qui-escent 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 acti-vation to the regulation of many signal transduction pathways, including transforming growth factor-b (TGF-b) ⁄ Smad, platelet-derived growth factor,
mito-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
*These authors contributed equally to this
work
(Received 9 April 2009, revised 9 July 2009,
accepted 14 July 2009)
doi:10.1111/j.1742-4658.2009.07213.x
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 (miR-NAs) To better understand the critical pathways of HSC activation, we performed comprehensive comparative bioinformatics analysis of micro-arrays 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, mitochon-drial integrity, based on highly upregulated Bcl-2 and downregulated cas-pase-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.
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
Trang 2chondrial 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
identi-fied as a novel mechanism that regulates protein
expression at the translational level [9] The
differen-tially 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
incom-plete binding to target sequences within the 3¢-UTR,
and generally do not affect mRNA levels [9] Thus,
high-throughput gene expression analysis by
micro-array 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
Ency-clopedia 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
higher-order functional meanings and utilities of the cell or
the organism from its genome information Integration
of current knowledge on molecular interaction
net-works, such as signaling pathways and complexes
(PATHWAY database), features the reference
knowl-edge base (http://www.genome.ad.jp/kegg/) [12] We
therefore performed a global analysis of
miRNA-regu-lated signaling pathways and remiRNA-regu-lated genes on the basis
of miRNA expression profile and bioinformatic
inter-pretation The predicted signaling pathways, some of
which had not been previously described in the
activa-tion 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
differ-entially expressed during HSC activation A volcano
plot provided further information about the
signifi-cance 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 upregu-lated GOs and 24 downreguupregu-lated 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 out-lining 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 prolifera-tion, differentiaprolifera-tion, 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 func-tion via signaling pathway.
CCl4administration induced liver fibrosis in rats Histopathological analysis revealed little fibrosis in the liver of normal rats On the contrary, fatty degenera-tion, 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, some-times 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 expres-sion was virtually undetectable in quiescent HSCs However, dramatic increases in Bcl-2 level occurred in HSCs after their activation (P < 0.05) The opposite
Trang 3was seen for caspase-9, another member of the
mito-chondrial 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 increas-ingly 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).
Fig 2 The volcano plot shows the
upregu-lated and downreguupregu-lated miRNAs in
acti-vated HSCs The horizontal axis represents
the fold change between quiescent and
acti-vated HSCs The vertical axis represents the
P-value of the t-test for the differences
between samples
A B G H
C D I J
E F K L
Fig 1 Characterization of HSCs isolated from rat liver (· 400) After isolation, the cells were cultured for 2 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)
Trang 4MicroRNAs, 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 apop-tosis, by regulating signal transduction pathways [13] With the use of in vitro cell activation and miRNA
Table 1 MicroRNA targets significant GO in HSCs
Upregulated GOs by GO analysisa
GO:0045817 Positive regulation of transcription
from RNA polymerase II promoter
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
Downregulated GOs by GO analysisb
a
GOs targeted by upregulated miRNA.bGOs targeted by downregulated miRNA All of these GOs show increased enrichment, P-values and FDRs
Trang 5microarray 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
acti-vated HSCs, including myofibroblastic
transdifferentia-tion, active proliferatransdifferentia-tion, 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 junc-tion and focal adhension,), oncogenic (pancreatic can-cer, prostate cancan-cer, colorectal cancan-cer, 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].
Fig 3 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 7 (degree 7) In contrast, rno-miR-16 are the highest degree in under-expression miRNAs
Trang 6TGF-b signaling, the key pathway in fibrogenesis, has
been found to be essential for myofibroblastic
transdif-ferentiation of HSCs [18,19] Signaling from the VEGF
pathway also stimulates proliferation and type I
colla-gen synthesis in activated HSCs subjected to hypoxia
treatment [20–22] The central role of the Wnt
signal-ing 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
trans-duction, signal transtrans-duction, transmembrane receptor
protein tyrosine kinase signaling pathway, regulation
of small GTPase-mediated signal transduction,
G-pro-tein-coupled receptor protein signaling pathway, and
signal transduction), cell growth (cell proliferation,
positive regulation of cell proliferation, cell cycle,
posi-tive regulation of mitosis, negaposi-tive 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 func-tional 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 out-lined 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 down-regulated caspase-9 were identified in activated HSCs
in vitro and fibrotic liver in vivo, using immunofluores-cence assay, quantitative RT-PCR, and western blot
A
B
Fig 4 Pathway analysis based on miRNA-targeted 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
Trang 7Table 2 Regulation of target gene significant pathways by miRNA GnRH, gonadotropin-releasing hormone; PPAR, peroxisome proliferator-activated receptor
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
Trang 8analysis Acting as an antiapoptotic member, Bcl-2
preserves mitochondrial integrity and potentially
blocks the release of some soluble prodeath
inter-membrane 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.
Table 2 Continued
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-b 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
a
Pathways targeted by upregulated miRNA.bPathways targeted by downregulated miRNA All of these pathways show increased enrich-ment, P-values, FDRs, and predicted targeted genes
Trang 9In 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
HSCs were isolated from Sprague–Dawley rats (350–400 g; Shanghai Laboratory Animal Center of the Chinese Acad-emy of Sciences) by perfusion with collagenase and pron-ase, 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
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://micr-orna.sanger.ac.uk/) The top 25% miRNA targets that had been assigned the highest numbers of miRNA interac-tion sites were collected, and subjected to GO term analy-sis GO analysis was applied in order to organize genes into hierarchical categories and uncover the miR–gene reg-ulatory network on the basis of biological process and molecular function; the network of miRNA–mRNA inter-action, representing the critical miRNAs and their targets, was established according to the miRNA degree Mean-while, 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 calcu-lated to correct the P-value Within a KO, the enrichment (Re) was given by
A
B
C
D
Fig 5 HE and VG staining of liver tissue (· 200) HE staining of
normal and CCl4-treated liver tissue is shown in (A) and (B),
respec-tively VG staining of normal and CCl4-treated liver tissue is shown
in (C) and (D), respectively
Trang 10Re¼ ðnf= Þ=ðNf=NÞ
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
differen-tial 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
Labora-tory 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 mLÆkg)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.
Histological examination
Liver tissues were fixed in 40 gÆL)1 solutions of formalde-hyde 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 [28] Histopathology was inter-preted 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
Triton X-100 (Sigma, St Louis, MO, USA) Following blocking in 10% preimmune goat serum for 2 h, cells were
A
C
B
D
Fig 6 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 2 and day 14, respectively (C) and (D) show the expres-sion of caspase-9 on day 2 and day 14, respectively Positive cells per microscopic field: *statistically significant differences
P < 0.05 versus control group