Differential expression of microRNAs in mouse liver under aberrant energy metabolic status
2009; Elsevier BV; Volume: 50; Issue: 9 Linguagem: Inglês
10.1194/jlr.m800509-jlr200
ISSN1539-7262
AutoresShengjie Li, Xi Chen, Hongjie Zhang, Xiangying Liang, Yang Xiang, Chaohui Yu, Ke Zen, Youming Li, Chen‐Yu Zhang,
Tópico(s)Cancer-related molecular mechanisms research
ResumoDespite years of effort, exact pathogenesis of nonalcoholic fatty liver disease (NAFLD) remains obscure. To gain an insight into the regulatory roles of microRNAs (miRNAs) in aberrant energy metabolic status and pathogenesis of NAFLD, we analyzed the expression of miRNAs in livers of ob/ob mice, streptozotocin (STZ)-induced type 1 diabetic mice, and normal C57BL/6 mice by miRNA microarray. Compared with normal C57BL/6 mice, ob/ob mice showed upregulation of eight miRNAs and downregulation of four miRNAs in fatty livers. Upregulation of miR-34a and downregulation of miR-122 was found in livers of STZ-induced diabetic mice. These results demonstrate that distinct miRNAs are strongly dysregulated in NAFLD and hyperglycemia. Comparison between miRNA expressions in livers of ob/ob mice and STZ-administered mice further revealed upregulation of four miRNAs and downregulation of two miRNAs in livers of ob/ob mice, indicating that these miRNAs may represent a molecular signature of NAFLD. A distinctive miRNA expression pattern was identified in ob/ob mouse liver, and hierarchical clustering of this pattern could clearly discriminate ob/ob mice from either normal C57BL/6 mice or STZ-administered mice. These findings suggest an important role of miRNAs in hepatic energy metabolism and implicate the participation of miRNAs in the pathophysiological processes of NAFLD. Despite years of effort, exact pathogenesis of nonalcoholic fatty liver disease (NAFLD) remains obscure. To gain an insight into the regulatory roles of microRNAs (miRNAs) in aberrant energy metabolic status and pathogenesis of NAFLD, we analyzed the expression of miRNAs in livers of ob/ob mice, streptozotocin (STZ)-induced type 1 diabetic mice, and normal C57BL/6 mice by miRNA microarray. Compared with normal C57BL/6 mice, ob/ob mice showed upregulation of eight miRNAs and downregulation of four miRNAs in fatty livers. Upregulation of miR-34a and downregulation of miR-122 was found in livers of STZ-induced diabetic mice. These results demonstrate that distinct miRNAs are strongly dysregulated in NAFLD and hyperglycemia. Comparison between miRNA expressions in livers of ob/ob mice and STZ-administered mice further revealed upregulation of four miRNAs and downregulation of two miRNAs in livers of ob/ob mice, indicating that these miRNAs may represent a molecular signature of NAFLD. A distinctive miRNA expression pattern was identified in ob/ob mouse liver, and hierarchical clustering of this pattern could clearly discriminate ob/ob mice from either normal C57BL/6 mice or STZ-administered mice. These findings suggest an important role of miRNAs in hepatic energy metabolism and implicate the participation of miRNAs in the pathophysiological processes of NAFLD. Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease worldwide and is becoming a major public health concern in modern society (1Clark J.M. Brancati F.L. Diehl A.M. Nonalcoholic fatty liver disease.Gastroenterology. 2002; 122: 1649-1657Abstract Full Text Full Text PDF PubMed Scopus (762) Google Scholar, 2Browning J.D. Szczepaniak L.S. Dobbins R. Nuremberg P. Horton J.D. Cohen J.C. Grundy S.M. Hobbs H.H. 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Disorders related to metabolic syndrome, such as obesity, type 2 diabetes mellitus, and dyslipidemia, were identified as the main risk factors for the development of NAFLD (5Wanless I.R. Lentz J.S. Fatty liver hepatitis (steatohepatitis) and obesity: an autopsy study with analysis of risk factors.Hepatology. 1990; 12: 1106-1110Crossref PubMed Scopus (1061) Google Scholar, 6Bacon B.R. Farahvash M.J. Janney C.G. Neuschwander-Tetri B.A. Nonalcoholic steatohepatitis: an expanded clinical entity.Gastroenterology. 1994; 107: 1103-1109Abstract Full Text PDF PubMed Scopus (1061) Google Scholar, 7Marchesini G. Brizi M. Morselli-Labate A.M. Bianchi G. Bugianesi E. McCullough A.J. Forlani G. Melchionda N. Association of nonalcoholic fatty liver disease with insulin resistance.Am. J. Med. 1999; 107: 450-455Abstract Full Text Full Text PDF PubMed Scopus (1294) Google Scholar). Although a widely accepted two-hit hypothesis (8Day C.P. James O.F.W. Steatohepatitis: A tale of two "hits"?.Gastroenterology. 1998; 114: 842-845Abstract Full Text Full Text PDF PubMed Scopus (3439) Google Scholar) may partially explain the progressive liver damage by nonalcoholic steatosis and steatohepatitis, much of the pathogenesis of NAFLD remains undiscovered and requires further study. Recently, a new class of RNA regulatory genes known as microRNAs (miRNAs) has been found to introduce a whole new layer of gene regulation in eukaryotes. miRNAs are endogenous noncoding RNAs of 19–24 nucleotides in length that play an important role in the negative regulation of gene expression by base-pairing to complementary sites on the target mRNAs, causing a block of translation or degradation of the target mRNA (9Bartel D.P. MicroRNAs: genomics, biogenesis, mechanism, and function.Cell. 2004; 116: 281-297Abstract Full Text Full Text PDF PubMed Scopus (29238) Google Scholar). In addition to the fundamental roles in diverse biological and pathological processes, including developmental timing, apoptosis, proliferation, differentiation, organ development, carcinogenesis, and immune response (10Ambros V. MicroRNA pathways in flies and worms: growth, death, fat, stress, and timing.Cell. 2003; 113: 673-676Abstract Full Text Full Text PDF PubMed Scopus (1086) Google Scholar, 11Esquela-Kerscher A. Slack F.J. Oncomirs - microRNAs with a role in cancer.Nat. Rev. Cancer. 2006; 6: 259-269Crossref PubMed Scopus (6161) Google Scholar, 12Hoefig K.P. Heissmeyer V. MicroRNAs grow up in the immune system.Curr. Opin. Immunol. 2008; 20: 281-287Crossref PubMed Scopus (56) Google Scholar), miRNAs are also reported to play important roles in energy metabolism, both in invertebrates and vertebrate animals. The role of miRNAs in energy metabolism was first indicated by a study in the fruit fly Drosophlia melanogaster, suggesting an important role of miR-14 in energy metabolism on the whole-animal level (13Xu P. Vernooy S.Y. Guo M. Hay B.A. The Drosophila microRNA Mir-14 suppresses cell death and is required for normal fat metabolism.Curr. Biol. 2003; 13: 790-795Abstract Full Text Full Text PDF PubMed Scopus (797) Google Scholar). Another study showed an involvement of miR-278 in energy homeostasis of Drosophila (14Teleman A.A. Maitra S. Cohen S.M. Drosophila lacking microRNA miR-278 are defective in energy homeostasis.Genes Dev. 2006; 20: 417-422Crossref PubMed Scopus (189) Google Scholar). For vertebrates, miRNAs were found to regulate adipocyte differentiation (15Esau C. Kang X.L. Peralta E. Hanson E. Marcusson E.G. Ravichandran L.V. Sun Y.Q. Koo S. Perera R.J. Jain R. et al.MicroRNA-143 regulates adipocyte differentiation.J. Biol. 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Chem. 2006; 281: 26932-26942Abstract Full Text Full Text PDF PubMed Scopus (302) Google Scholar). Esau et al. (19Esau C. Davis S. Murray S.F. Yu X.X. Pandey S.K. Pear M. Watts L. Booten S.L. Graham M. McKay R. et al.miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting.Cell Metab. 2006; 3: 87-98Abstract Full Text Full Text PDF PubMed Scopus (1754) Google Scholar) applied an miR-122 antagonist intraperitoneally into mice and found that this procedure could reduce plasma cholesterol level, increase hepatic fatty acid oxidation, and decrease hepatic fatty acid and cholesterol synthesis rate. Jin et al. (20Jin X. Ye Y.F. Chen S.H. Yu C.H. Liu J. Li Y.M. MicroRNA expression pattern in different stages of nonalcoholic fatty liver disease.Dig Liver Dis. 2008; 41: 289-297Abstract Full Text Full Text PDF PubMed Scopus (77) Google Scholar) also reported that miR-122 level was over 2-fold upregulated in NAFLD rat. Together, these findings suggested a strong connection between miRNAs and energy metabolism. However, whether miRNAs play a role in the pathophysiological processes of NAFLD remains to be elucidated. To address this question, we employed miRNA microarray to identify differentially expressed miRNAs in livers of ob/ob mice [a well-established model of NAFLD with hyperglycemia (21Koteish A. Diehl A.M. Animal models of steatohepatitis.Best Pract. Res. Clin. Gastroenterol. 2002; 16: 679-690Crossref PubMed Scopus (151) Google Scholar, 22Diehl A.M. Lessons from animal models of NASH.Hepatol. Res. 2005; 33: 138-144Crossref PubMed Scopus (130) Google Scholar)] and streptozotocin (STZ)-induced type 1 diabetic mice (a model simply under high blood glucose without fatty liver) to characterize the potential roles of miRNAs in livers under NAFLD and hyperglycemia, respectively. Adult (aged 11–12 weeks) obese Lepob/ob C57BL/6 mice (six male and four female) and normal control C57BL/6 mice (aged 8–12 weeks; four male and four female) were purchased from Model Animal Research Center of Nanjing University. For STZ-induced type 1 diabetic model, 8-week-old wild-type C57BL/6 mice (eight male) received a single intraperitoneal injection of 150 mg/kg STZ dissolved in citrate buffer at pH 4.5. Mice were fasted overnight before glucose measurements. Blood samples were taken from the tail vein 3, 7, and 14 days after STZ injection, respectively. Animals were euthanized 14 days after STZ injection. The mice with fasting blood glucose ≥ 12 mmol/L were considered diabetic. All mice were housed in a temperature-controlled environment with a 12 h light/dark cycle and free access to water and a standard chow diet containing 60% carbohydrate, 13% fat, and 27% protein on a caloric basis. All animals were weighed and taken fasting blood glucose measurements and serum samples before euthanized. The liver tissues were dissected and immersed in liquid nitrogen immediately and stored at −80°C until used for subsequent analysis. Serum insulin levels of these animal models were quantified by the Rat/Mouse Insulin ELISA Kit (Linco Research, St. Charles, MO). Liver triglyceride concentration was determined using the Triglyceride Kit (Jiancheng Bioengineering Institute, Nanjing, China). All animal care and handling procedures were carried out in accordance with the Institute for Laboratory Animal Research Guide for Care and Use of Laboratory Animals and approved by the Institutional Review Board of Nanjing University. Frozen liver tissues were dissected and homogenized, and total RNA were extracted using Trizol Reagent (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions. RNA labeling and hybridization on miRNA microarray chips were conducted as previously described (23Liu C.G. Calin G.A. Meloon B. Gamliel N. Sevignani C. Ferracin M. Dumitru C.D. Shimizu M. Zupo S. Dono M. et al.An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues.Proc. Natl. Acad. Sci. USA. 2004; 101: 9740-9744Crossref PubMed Scopus (817) Google Scholar, 24Thomson J.M. Parker J. Perou C.M. Hammond S.M. A custom microarray platform for analysis of microRNA gene expression.Nat. Methods. 2004; 1: 47-53Crossref PubMed Scopus (668) Google Scholar). Briefly, 50 μg of total RNA was purified using the mirVANA miRNA isolation kit (Ambion, Austin, TX) to enrich small RNA fraction. Purified RNA was labeled with fluorescein, and hybridization was carried out on the CapitalBio Mammalian miRNA Array V 3.0 (CapitalBio, Beijing, China) containing 509 probes in triplicate, corresponding to 435 human and mouse mature miRNA genes. Liver RNA sample from each mouse was analyzed on individual chip. Finally, hybridization signals were detected, and scanner images were quantified. These quantified signal intensity values of microarray were normalized to per-chip mean values. The miRNA microarray data have been deposited into Gene Expression Omnibus (GEO accession number GSE13840) and can be prepared in a fully MIAME-compliant manner. Stem-loop quantitative RT-PCR (qRT-PCR) assays to quantify the mature miRNAs were performed as previously described (25Chen C.F. Ridzon D.A. Broomer A.J. Zhou Z.H. Lee D.H. Nguyen J.T. Barbisin M. Xu N.L. Mahuvakar V.R. Andersen M.R. et al.Real-time quantification of microRNAs by stem-loop RT-PCR.Nucleic Acids Res. 2005; 33: e179Crossref PubMed Scopus (4001) Google Scholar, 26Tang F.C. Hajkova P. Barton S.C. Lao K.Q. Surani M.A. MicroRNA expression profiling of single whole embryonic stem cells.Nucleic Acids Res. 2006; 34: e9Crossref PubMed Scopus (303) Google Scholar) using fluorescent nucleic acid dye. The RT primers and real-time PCR primers were designed as described (25Chen C.F. Ridzon D.A. Broomer A.J. Zhou Z.H. Lee D.H. Nguyen J.T. Barbisin M. Xu N.L. Mahuvakar V.R. Andersen M.R. et al.Real-time quantification of microRNAs by stem-loop RT-PCR.Nucleic Acids Res. 2005; 33: e179Crossref PubMed Scopus (4001) Google Scholar). Briefly, 1 μg of total RNA was reverse transcribed under the following conditions: 16°C for 15 min, 42°C for 60 min, and 85°C for 5 min. The 20 μl PCR included 1 μl RT product and 1 μl EvaGreen dye (Biotium, Hayward, CA). The conditions for the PCR reaction were as follows: 95°C for 5 min followed by 40 cycles of 95°C for 15 s and 60°C for 1 min using an ABI PRISM 7300 thermal cycler. All reactions were run in triplicate. The threshold cycle (CT) is defined as the fractional cycle number at which the fluorescence passes the fixed threshold. The miRNA expression levels were normalized to U6 RNA. The relative expression was calculated using the comparative ΔΔCT method, and the values were expressed as 2−ΔΔCT (27Livak K.J. Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(T) (-Delta Delta C) method.Methods. 2001; 25: 402-408Crossref PubMed Scopus (121290) Google Scholar). Differentially expressed miRNAs were identified using the t-test procedure within significance analysis of microarrays (SAM) (28Tusher V.G. Tibshirani R. Chu G. Significance analysis of microarrays applied to the ionizing radiation response.Proc. Natl. Acad. Sci. USA. 2001; 98: 5116-5121Crossref PubMed Scopus (9723) Google Scholar) with a 5% false discovery rate (FDR) threshold. Cluster and Java TreeView were used to build the unsupervised tree. The genes and arrays were mean centered, and hierarchical trees were built using correlation metrics. Real-time qRT-PCR assays were performed in triplicate. Data were expressed as means ± SE, and the differences with P < 0.05 were considered statistically significant using a two-sided unpaired Student's t-test. The analysis of miRNA predicted targets was performed using the following three algorithms: TargetScan (29Lewis B.P. Shih I.H. Jones-Rhoades M.W. Bartel D.P. Burge C.B. Prediction of mammalian microRNA targets.Cell. 2003; 115: 787-798Abstract Full Text Full Text PDF PubMed Scopus (4189) Google Scholar) (http://www.targetscan.org/), PicTar (30Krek A. Grun D. Poy M.N. Wolf R. Rosenberg L. Epstein E.J. MacMenamin P. da Piedade I. Gunsalus K.C. Stoffel M. et al.Combinatorial microRNA target predictions.Nat. Genet. 2005; 37: 495-500Crossref PubMed Scopus (3871) Google Scholar) (http://pictar.mdc-berlin.de/), and miRanda (31John B. Enright A.J. Aravin A. Tuschl T. Sander C. Marks D.S. Human MicroRNA targets.PLoS Biol. 2004; 2: e363Crossref PubMed Scopus (2916) Google Scholar) (http://cbio.mskcc.org/cgi-bin/mirnaviewer/mirnaviewer.pl). To investigate the potential involvement of miRNAs in NAFLD and hyperglycemia, we performed a microarray-based analysis of miRNAs in livers of ten ob/ob mice and eight C57BL/6 mice (served as controls), respectively. As shown in Table 1, the body weight, blood glucose levels, serum insulin levels, and liver triglyceride concentration of the ob/ob mice were all significantly elevated compared with those of the C57BL/6 mice, suggesting the aberrant energy metabolic status of ob/ob mice. Next, total RNAs were extracted from liver tissues of ob/ob mice and C57BL/6 mice and subsequently used in miRNA microarray analysis. The discrimination of miRNA expression in livers of ob/ob mice and C57BL/6 mice was clearly revealed by a two-way (genes against samples) unsupervised hierarchical clustering analysis of the miRNA microarray data. As displayed in Fig. 1A, the sample dendrogram generated by hierarchical cluster analysis showed two major branches in columns (ob/ob mice vs. C57BL/6 mice), indicating a specific miRNA expression pattern in the fatty livers of ob/ob mice versus the normal livers of C57BL/6 mice.TABLE 1General characteristics of different groups of miceGroup of MiceC57BL/6 MiceOb/ob MiceSTZ Micen8108BW (g)22.4 ± 0.345.5 ± 1.1**17.7 ± 0.6**BGaFasting blood glucose levels and fasting serum insulin levels measured before the mice were euthanized; **P < 0.01. (mmol/l)7.2 ± 0.613.3 ± 0.9**28.8 ± 2.2**INSaFasting blood glucose levels and fasting serum insulin levels measured before the mice were euthanized; **P < 0.01. (ng/ml)0.52 ± 0.1010.40 ± 1.12**0.19 ± 0.03**Liver TG (mmol/100g)2.48 ± 0.4230.75 ± 2.84**1.84 ± 0.34Data plotted as means ± SE. n, number of mice; BW, body weight; BG, blood glucose levels; INS, serum insulin levels; liver TG, liver triglyceride concentration.a Fasting blood glucose levels and fasting serum insulin levels measured before the mice were euthanized; **P < 0.01. Open table in a new tab Data plotted as means ± SE. n, number of mice; BW, body weight; BG, blood glucose levels; INS, serum insulin levels; liver TG, liver triglyceride concentration. To further identify differentially expressed miRNAs, a statistical method termed SAM (28Tusher V.G. Tibshirani R. Chu G. Significance analysis of microarrays applied to the ionizing radiation response.Proc. Natl. Acad. Sci. USA. 2001; 98: 5116-5121Crossref PubMed Scopus (9723) Google Scholar) was employed. SAM is an algorithm that calculates a score for each gene and therefore identifies genes that are significantly associated with an outcome variable, such as the type of analyzed tissue (NAFLD vs. normal). Employing two-class unpaired analysis within SAM, the miRNA expression levels in livers of ob/ob mice were compared with those of C57BL/6 mice. In this test, an FDR (q-value) 2 or < 0.5; 2) q-value < 5%. Based on these principles, SAM analysis generated a list of 11 miRNAs that were differentially expressed in livers of ob/ob mice (Table 2). Among these miRNAs, eight miRNAs (miR-34a, miR-31, miR-103, miR-107, miR-194, miR-335-5p, miR-221, and miR-200a) were at least 2-fold upregulated, and three miRNAs (miR-29c, miR-451, and miR-21) were at least 0.5-fold downregulated in ob/ob mice liver samples. Collectively, these data demonstrate that distinct miRNAs are significantly regulated in NAFLD with hyperglycemia, implicating the potential roles of miRNAs as functional modulators in the pathophysiological processes of these diseases.TABLE 2Differentially expressed miRNAs in livers of ob/ob mice and STZ-induced diabetic mice compared with those of C57BL/6 micemiRNASAM ScoreFold Changeq-Value (%)aq-value: false discovery rate, the expected percentage of genes identified by chance.Ob/ob mice versus C57BL/6 miceUpregulatedmmu-miR-34a5.993.290.00mmu-miR-314.662.920.00mmu-miR-1034.032.420.00mmu-miR-1073.772.560.00mmu-miR-1942.442.180.00mmu-miR-335-5p2.432.610.00mmu-miR-2212.242.010.00mmu-miR-200a1.712.150.00Downregulatedmmu-miR-29c−2.760.450.00mmu-miR-451−2.610.480.00mmu-miR-21−1.900.440.00mmu-miR-122bThese miRNAs were not considered as significantly changed by SAM due to insufficient fold change but proved to be significantly changed by real-time qRT-PCR.−1.340.683.29STZ-induced diabetic mice versus C57BL/6 micemmu-miR-34a7.325.630.00Downregulatedmmu-miR-122bThese miRNAs were not considered as significantly changed by SAM due to insufficient fold change but proved to be significantly changed by real-time qRT-PCR.−0.750.850.00a q-value: false discovery rate, the expected percentage of genes identified by chance.b These miRNAs were not considered as significantly changed by SAM due to insufficient fold change but proved to be significantly changed by real-time qRT-PCR. Open table in a new tab By causing rapid and irreversible necrosis of pancreatic β-cells (32Junod A. Lambert A.E. Orci L. Pictet R. Gonet A.E. Renold A.E. Studies of the diabetogenic action of streptozotocin.Proc. Soc. Exp. Biol. Med. 1967; 126: 201-205Crossref PubMed Scopus (455) Google Scholar), STZ-induced type 1 diabetic mice provide us an animal model of high blood glucose but without fatty liver. This diabetic mouse model was employed here to study the relationship between hepatic miRNA expression and hyperglycemia. Within 7 days of STZ injection, fasting blood glucose levels of the mice were all above 12 mmol/L and stayed high in the following week, while the body weight of these mice decreased gradually (data not shown). On the 14th day after STZ injection, the mean fasting blood glucose level of STZ-administered mice was almost 4-fold higher than that of control C57BL/6 mice. By contrast, the average body weight of STZ-administered mice was significantly decreased compared with that of C57BL/6 mice (Table 1). At this stage, mice were killed and liver tissues were dissected for RNA extraction and miRNA microarray detection subsequently. SAM analysis of miRNA microarray data (with a 2-fold change threshold and 5% FDR) showed that only miR-34a was upregulated in liver samples of STZ-induced diabetic mice compared with samples of C57BL/6 mice (Table 2). Unsupervised hierarchical clustering of miRNA expression from liver samples of STZ-induced diabetic mice and C57BL/6 mice is depicted in Fig. 1B. Sample dendrogram in columns showed no clear separation between STZ samples and C57BL/6 samples, indicating that general miRNA expression in mice livers under 2-week hyperglycemia was not significantly altered, except for miR-34a. To find out the miRNAs that may be only involved in the pathophysiological processes of NAFLD, we next compared the miRNA expression pattern in livers of ob/ob mice with that of STZ-induced diabetic mice. Here ob/ob mice served as an animal model under both NAFLD and hyperglycemia conditions, while STZ-induced diabetic mice served as a model under a simple status of hyperglycemia. As shown in Fig. 2, unsupervised hierarchical clustering of liver miRNA expression of ob/ob mice and STZ-induced diabetic mice generated a sample dendrogram with two major branches in columns, clearly discriminating these two groups of samples. This result suggests a unique miRNA expression pattern in nonalcoholic fatty livers, excluding the influence of hyperglycemia on the hepatic miRNA expression. Under 5% FDR and 2-fold change threshold, SAM algorithm identified four upregulated miRNAs (miR-103, miR-31, miR-107, and miR-126-3p) and two downregulated miRNAs (miR-100 and miR-29c) in ob/ob samples compared with STZ samples (Table 3), indicating that these miRNAs may represent a molecular signature of NAFLD.TABLE 3Differentially expressed miRNAs in livers of ob/ob mice compared with those of STZ-induced diabetic micemiRNASAM ScoreFold Changeq-Value (%)aq-value: false discovery rate, the expected percentage of genes identified by chance.Upregulatedmmu-miR-1035.622.960.00mmu-miR-314.422.940.00mmu-miR-1074.112.670.00mmu-miR-126-3p2.562.020.00Downregulatedmmu-miR-100−3.340.450.00mmu-miR-29c−2.570.430.00a q-value: false discovery rate, the expected percentage of genes identified by chance. Open table in a new tab In this study, both male and female mice (four male and four female normal C57BL/6 mice; six male and four female ob/ob mice; and eight male STZ-induced diabetic mice) were used. To further analyze differentially expressed liver miRNAs in these animal models of different gender, we compared the liver miRNA expression in different animal models of male and female, respectively. As shown in supplementary Tables I and II, SAM analysis of miRNA microarray data (with a 2-fold change threshold and 5% FDR) listed 18 dysregulated miRNAs in livers of male ob/ob mice versus male normal C57BL/6 mice, 23 dysregulated miRNAs in livers of female ob/ob mice versus female C57BL/6 mice, four dysregulated miRNAs in livers of male STZ-induced diabetic mice versus male C57BL/6 mice, and 11 dysregulated miRNAs in livers of male ob/ob mice versus male STZ-induced diabetic mice. By unsupervised hierarchical clustering of miRNA expression in different liver samples segregated by gender, clear separation of ob/ob mouse samples from normal C57BL/6 mouse samples and of ob/ob mouse samples from STZ-induced diabetic mouse samples can been identified (see supplementary and II), which is similar to the results of mixed gender samples. These results suggest that although gender does affect the expressions of several miRNAs, the distinct miRNA expression patterns among these mouse models are not influenced by gender. To validate the accuracy of the miRNA microarray data, we carried out stem-loop qRT-PCR assay (25Chen C.F. Ridzon D.A. Broomer A.J. Zhou Z.H. Lee D.H. Nguyen J.T. Barbisin M. Xu N.L. Mahuvakar V.R. Andersen M.R. et al.Real-time quantification of microRNAs by stem-loop RT-PCR.Nucleic Acids Res. 2005; 33: e179Crossref PubMed Scopus (4001) Google Scholar, 26Tang F.C. Hajkova P. Barton S.C. Lao K.Q. Surani M.A. MicroRNA expression profiling of single whole embryonic stem cells.Nucleic Acids Res. 2006; 34: e9Crossref PubMed Scopus (303) Google Scholar) with the same RNA preparations used in microarray analysis. As shown in Fig. 3, differentially expressed miRNAs identified by SAM algorithm based on the microarray results were analyzed using qRT-PCR assay. Results showed that the directions of the changes in miRNA expression were concordant between the two platforms. Since miR-122, a liver-specific miRNA, makes up 70% of all miRNAs in liver (33Lagos-Quintana M. Rauhut R. Yalcin A. Meyer J. Lendeckel W. Tuschl T. Identification of tissue-specific microRNAs from mouse.Curr. Biol. 2002; 12: 735-739Abstract Full Text Full Text PDF PubMed Scopus (2734) Google Scholar, 34Chang J. Nicolas E. Marks D. Sander C. Lerro A. Buendia M.A. Xu C. Mason W.S. Moloshok T. Bort R. et al.miR-122, a mammalian liver-specific microRNA, is processed from hcr mRNA and may downregulate the high affinity cationic amino acid transporter CAT-1.RNA Biol. 2004; 1: 106-113Crossref PubMed Scopus (686) Google Scholar) and is reported to regulate hepatic lipid metabolism (19Esau C. Davis S. Murray S.F. Yu X.X. Pandey S.K. Pear M. Watts L. Booten S.L. Graham M. McKay R. et al.miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting.Cell Metab. 2006; 3: 87-98Abstract Full Text Full Text PDF PubMed Scopus (1754) Google Scholar, 35Krutzfeldt J. Rajewsky N. Braich R. Rajeev K.G. Tuschl T. Manoharan M. Stoffel M. Silencing of microRNAs in vivo with 'antagomirs'.Nature. 2005; 438: 685-689Crossref PubMed Scopus (3340) Google Scholar, 36Fabani M.M. Gait M.J. miR-122 targeting with LNA/2′-O-methyl oligonucleotide mixmers, peptide nucleic acids (PNA), and PNA-peptide conjugates.RNA. 2008; 14: 336-346Crossref PubMed Scopus (224) Google Scholar), we analyzed miR-122 variation using SAM algorithm with 2-fold change as a threshold and also quantified it using real-time qPCR. As shown in Fig. 3, miR-122 was significantly reduced in livers of both ob/ob mice and STZ-induced diabetic mice compared with C57BL/6 mice, suggesting the involvement of miR-122 in hepatic energy metabolism. Lack of reliable and specific methods for biological target validation hampers the full understanding of the mechanisms by which miRNAs execute their functions. Only a few miRNAs have so far been assigned target mRNAs, and the conventional methodologies are still labor intensive. Three computer-aided algorithms, including TargetScan (29Lewis B.P. Shih I.H. Jones-Rhoades M.W. Bartel D.P. Burge C.B. Prediction of mammalian microRNA targets.Cell. 2003; 115: 787-798Abstract Full Text Full Text PDF PubMed Scopus (4189) Google Scholar), miRanda (31John B
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