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Methods
Ethics Statement
This study was approved by the review board of the first affiliated hospital, school of medicine, Zhejiang University. All animal studies were conducted according to the regulations and guidelines for the use and care of experimental animals of department of gastroenterology, the first affiliated hospital, school of medicine, Zhejiang University. The protocol on human study was approved by the institutional review board at Zhejiang University and conducted in accordance with the declaration of Helsinki.
Experimental Animal Model, Serology and Pathology Markers
Sprague-Dawley (SD) rats (12 week old, 160–170 g) were purchased from the Medical Science Institution of Zhejiang Province (Hangzhou, China) and randomly divided into two groups: ASH group (alcoholic steatohepatitis, n = 12) and Control group (n = 12). All rats received basic food and water ad libitum and were maintained on a 12/12-hour light/dark cycle. Rats in ASH group were gavaged with Chinese distillate spirit (concentration of 56% alcohol and 17.86 ml/kg once per day) while those in control group were given saline, as detailed in our previous work. At the end of 16 weeks, rats were sacrificed by femoral exsanguination and serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG) and hepatic TG were routinely tested. Hepatic index (liver wet weight/body weight) was calculated as indicated in the brackets.
Haematoxylin–eosin (H–e) stained liver sections were observed and estimated for hepatic steatosis and inflammation by Olympus microscope, according to the histological activation index (HAI). We have also described the existence of steatosis and steatohepatitis according to the guideline from Chinese liver association. In detail, steatosis is divided into four degrees (F0–4): F0, hepatic lipid accumulation <5%; F1, between 5 and 30%; F2, between 31 and 50%; F3, between 51 and 5%; F4, over 75%. Steatohepatitis is divided into four degrees (G0–4): G0, no inflammation; G1, a few balloon-shaped hepatocytes in zone 3 and sporadic-isolated spotty acinar necrosis and peri-central vein inflammation; G2, apparent balloon- shaped hepatocytes in zone 3, more spotty acinar necrosis, Mallory bodies and mild to moderate inflammation of the portal area; G3, extensive balloon-shaped hepatocytes in zone 3, pronounced spotty acinar necrosis, Mallory bodies and moderate inflammation of portal area or periportal inflammation, or both; G4, confluent necrosis or bridging necrosis, or both.
RNA Extraction, miRNA Microarray and Real-time Quantitative Reverse Transcription-polymerase Chain Reaction
Total RNA of liver and serum were harvested using TRIzol (Invitrogen) and miRNeasy mini kit (QIAGEN) according to manufacturer's instructions. After having passed RNA quantity measurement using the NanoDrop 1000, samples were labelled using the miRCURY™ Hy3™/Hy5™ Power labelling kit and hybridized on the miRCURY™ LNA Array (v.16.0). Following washing steps the slides were scanned using the Axon GenePix 4000B microarray scanner. Scanned images were then imported into GenePix Pro 6.0 software (Axon, Sunnyvale, CA, USA) for grid alignment and data extraction. Replicated miRNAs were averaged and miRNAs that intensities>50 in all samples were chosen for calculating normalization factor. Expressed data were normalized using the Median normalization. After normalization, differentially expressed miRNAs were identified through Volcano Plot filtering. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was carried out to verify significantly changed miRNAs revealed by microarray by using stem-loop antisense primer mix and AMV transcriptase (TaKaRa, Dalian, China). All primers were purchased from RiboBio (Guangzhou, China) based on miRNA sequences released by the Sanger Institute. The relative amount of each miRNA to U6 RNA was calculated using equation 2−ΔCT, where ΔCT = CTmiRNA−CTU6.
Bioinformatics Analysis of miRNAs With Significantly Differential Expression
Raw −ΔCT from PCR results were normalized, mean-centred, log2-transformed and sequentially analysed by algorithm of significance analysis of microarrays (SAM, http://www-stat.stanford.edu/~tibs/SAM/) and prediction analysis for microarrays (PAM, http://www-stat.stanford.edu/~tibs/PAM/). SAM calculates a score for each gene as the change in expression relative to the standard deviation of all measurements. In SAM test, a false discovery rate (FDR) less than 5% was selected and miRNAs over two-fold expression change were considered significantly altered. Moreover, miRNA signature was determined by PAM, a statistical technique identifying a subgroup of genes that best characterizes samples. The division of samples was done by randomly splitting data into training and test sets. Finally, to visualize numerical changes through graphical representation of the raw −ΔCT, hierarchical clustering was used to generate both miRNA (from SAM results) and sample (from S and SH) trees based on the algorithm of average linkage and Euclidian distance.
Computational Analysis of miRNA Downstream Targets' Functions and Pathways
Different computational methods were applied to maximally analyse gene function and pathway targeted by differentially expressed miRNAs. Firstly, an open access platform for miRNA-target prediction (http://www.microrna.org/microrna/home.do) and Target scan (http://www.targetscan.org/) were jointly performed to generate miRNA targeted genes. Secondly, databases of GO and KEGG pathway were searched for functional annotation and pathway analysis by Fisher's exact test and chi-squared test, where FDR was calculated to correct the P-value as previously described. We computed P-values for the GOs and KEGG pathways of all dysregulated miRNAs targeted genes, where the smaller the FDR, the smaller the error in judging the P-value. Finally, the MiRNA-Gene-Network was established according to the interactions of miRNA and genes, where the circle and triangle, respectively, represent gene and miRNA whereas their relationship was represented by one edge. The centre of the network was represented by degree that means the contribution of one miRNA to the genes around or vice versa. The key miRNA and gene in the network always have the biggest degrees.
Preliminary miRNAs Verification in Humans
Three ASH patients were randomly selected from our outpatient clinics according to the Chinese guideline of ASH. However, liver ultrasound was used instead of liver biopsy to evaluate steatosis for safety concerns. Serum TG and Chol levels were also collected for steatosis evaluation. In addition, serum ALT and AST were routinely used for inflammation assessment. Three other healthy volunteers were used as control. Five top dysregulated-SAM-released miRNAs were investigated by qRT-PCR as previously described in the Methods section.
Statistics
Each experiment was performed in triplicate and data were expressed as means ± SE Student's t-test for unpaired two groups and one way anova for more than three groups were executed by SPSS 17.0 (Chicago, IL, USA). The differences were considered statistically significant at P < 0.05.
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