- Research article
- Open Access
- Open Peer Review
MicroRNA-125a is over-expressed in insulin target tissues in a spontaneous rat model of Type 2 Diabetes
- Blanca M Herrera†1, 2,
- Helen E Lockstone†1,
- Jennifer M Taylor1,
- Quin F Wills3,
- Pamela J Kaisaki1,
- Amy Barrett2,
- Carme Camps1,
- Christina Fernandez1,
- Jiannis Ragoussis1,
- Dominique Gauguier1, 4,
- Mark I McCarthy1, 2 and
- Cecilia M Lindgren1, 2Email author
© Herrera et al; licensee BioMed Central Ltd. 2009
- Received: 2 October 2008
- Accepted: 18 August 2009
- Published: 18 August 2009
MicroRNAs (miRNAs) are non-coding RNA molecules involved in post-transcriptional control of gene expression of a wide number of genes, including those involved in glucose homeostasis. Type 2 diabetes (T2D) is characterized by hyperglycaemia and defects in insulin secretion and action at target tissues. We sought to establish differences in global miRNA expression in two insulin-target tissues from inbred rats of spontaneously diabetic and normoglycaemic strains.
We used a miRNA microarray platform to measure global miRNA expression in two insulin-target tissues: liver and adipose tissue from inbred rats of spontaneously diabetic (Goto-Kakizaki [GK]) and normoglycaemic (Brown-Norway [BN]) strains which are extensively used in genetic studies of T2D. MiRNA data were integrated with gene expression data from the same rats to investigate how differentially expressed miRNAs affect the expression of predicted target gene transcripts.
The expression of 170 miRNAs was measured in liver and adipose tissue of GK and BN rats. Based on a p-value for differential expression between GK and BN, the most significant change in expression was observed for miR-125a in liver (FC = 5.61, P = 0.001, P adjusted = 0.10); this overexpression was validated using quantitative RT-PCR (FC = 13.15, P = 0.0005). MiR-125a also showed over-expression in the GK vs. BN analysis within adipose tissue (FC = 1.97, P = 0.078, P adjusted = 0.99), as did the previously reported miR-29a (FC = 1.51, P = 0.05, P adjusted = 0.99). In-silico tools assessing the biological role of predicted miR-125a target genes suggest an over-representation of genes involved in the MAPK signaling pathway. Gene expression analysis identified 1308 genes with significantly different expression between GK and BN rats (P adjusted < 0.05): 233 in liver and 1075 in adipose tissue. Pathways related to glucose and lipid metabolism were significantly over-represented among these genes. Enrichment analysis suggested that differentially expressed genes in GK compared to BN included more predicted miR-125a target genes than would be expected by chance in adipose tissue (FDR = 0.006 for up-regulated genes; FDR = 0.036 for down-regulated genes) but not in liver (FDR = 0.074 for up-regulated genes; FDR = 0.248 for down-regulated genes).
MiR-125a is over-expressed in liver in hyperglycaemic GK rats relative to normoglycaemic BN rats, and our array data also suggest miR-125a is over-expressed in adipose tissue. We demonstrate the use of in-silico tools to provide the basis for further investigation of the potential role of miR-125a in T2D. In particular, the enrichment of predicted miR-125a target genes among differentially expressed genes has identified likely target genes and indicates that integrating global miRNA and mRNA expression data may give further insights into miRNA-mediated regulation of gene expression.
- Adipose Tissue
- False Discovery Rate
- miRNA Expression
- Insulin Target Tissue
- Global miRNA Expression
MicroRNAs (miRNAs) are short (~22 nucleotides) non-coding RNA molecules that regulate gene expression at a post-transcriptional level through sequence alignment mechanisms. MiRNA molecules bind to the 3' untranslated region (UTR) of their target mRNAs and can cause either mRNA degradation or translational repression, resulting in reduced protein expression  or translational activation depending on cell cycle stage . Degradation of mRNA seems to be favoured if the binding occurs with perfect sequence complementarity and is widely observed in plant miRNAs [3, 4]. A variety of studies have demonstrated that regulation at the mRNA level also occurs for animal miRNAs [5, 6]. Microarray-based experiments have shown that overexpression of specific miRNAs in human cells down-regulates many transcripts predicted to bind the miRNA molecule [6–8]. Conversely, silencing of endogenous miR-122 in mice caused the preferential up-regulation of transcripts containing miR-122 binding sites .
MiRNA expression levels are thought to contribute to tissue-specific gene expression patterns  and computational approaches to integrating miRNA and gene expression data have provided insights into miRNA-mRNA interactions [11, 12]. A single miRNA molecule can affect the expression of many target genes and therefore miRNAs are thought to be involved in the regulation of a wide variety of normal biological processes .
Type 2 diabetes (T2D) is characterized by hyperglycaemia that arises via combined defects in insulin secretion (beta-cell dysfunction) and insulin action (in target tissues like adipose tissue, liver and skeletal muscle). Specific miRNAs involved in various aspects of glucose and lipid metabolism have been identified in recent years [14, 15]. In particular, using murine models, miR-9 and miR-375 are reported to be involved in regulation of insulin secretion [16, 17], while miR-124a2 has recently been implicated in pancreatic beta-cell development and function . Despite the growing evidence that miRNAs may be important in T2D, only one study of global miRNA expression in insulin target tissues has been published to date. He et al. (2007)  profiled miRNA expression in skeletal muscle in the Goto-Kakizaki (GK) rat, a well-characterized model of T2D, and compared it to normal Wistar rats; miR-29 paralogs were found to be over-expressed in the GK rat. The widely-studied GK rat has several features that make it a good model for T2D as hyperglycaemia appears as early as 2–4 weeks of age, and it is relatively lean . In the present study, we aim to identify differential liver and adipose tissue miRNA expression in rats of the GK strain and a genetically distant normoglycaemic strain (Brown-Norway (BN)). The GK strain is extensively used to study genetic determinants of T2D phenotypes , while the BN strain is widely used for comparison with GK and most congenic strains with a BN background. Additionally, we assessed mRNA expression levels in the same tissues from the same rats to investigate the downstream effects of altered miRNA expression on target gene expression; the approach is illustrated in additional File 1 [Additional file 1].
The GK rat is a lean and spontaneously type 2 diabetic animal, while the BN rat serves as a normoglycaemic control. Four-month old rats representative of the GK and BN colonies have been phenotyped previously. An intra-peritoneal glucose tolerance test (IPGTT)  was carried out after a 16–18 h fasting period, and plasma glucose concentrations were significantly higher in GK compared to BN rats [see Additional file 2]. The current experiments were performed using seven-month-old male rats: four from the GK strain and four from the BN strain, obtained from the Oxford colony (GK/Ox and BN/Ox), and maintained in accordance with national and institutional guidelines. Rats were fed standard laboratory chow pellets (B&K Universal, Hull, UK) and kept on 12 h light/dark cycles. Liver and adipose tissue samples were obtained from the rats, snap-frozen in liquid nitrogen and stored at -80°C. Total RNA was extracted from homogenized tissue samples using TRI-Reagent (Sigma, UK) according to the manufacturer's instructions for use in miRNA and mRNA experiments (see below). Animal procedures were approved by the ethical review panel of the University of Oxford and UK Home Office licences.
Ambion miRNA arrays
MiRNAs were isolated and purified using mirVana miRNA isolation kit (Ambion). Poly-A tailing was added to 400 ng of each small RNA fraction using Ncode miRNA labelling system (Invitrogen), which also adds the fluorescent labels Alexa Fluor® 3 and Alexa Fluor® 5. The synthetic sequence control-1 (Ambion) was spiked in to each BN and GK sample in a ratio of 1:10, just before starting the labelling. For each tissue, eight independent samples (four GK and four BN) were combined in four two-colour hybridisations, with one GK and one BN sample hybridized to each array. Within each tissue, half the arrays had GK samples labelled with Alexa Fluor® 5 and the other half had BN samples labelled with Alexa Fluor® 5.
Samples were hybridised to Ambion mirVana™ miRNA Probe Set 1564v1 arrays for 4 h at 62°C in dark conditions. The array contains 384 unique probes with 16 replicates of each probe. Each probe is 42 to 46 nucleotides (nt) long, of which an 18 to 24 nt segment targets a specific miRNA. MiRNA sequences are highly conserved between species, and the Ambion array includes probes for human, rat and mouse miRNAs. A total of 170 probes on the array are able to detect rat miRNAs and these were included in subsequent analyses. After hybridization, arrays were washed and scanned using the GenePix 4000B scanner (Axon Instruments [Molecular Devices]). GenePix Pro 4.0 software (Axon Instruments [Molecular Devices]), was used to obtain raw probe intensities.
Statistical analysis of Ambion miRNA data
Raw data were imported into the R statistical package  and processed using the 'marray' package from Bioconductor . Raw intensities were adjusted by subtracting the background intensity from each probe, and the data were then summarised by taking the median intensity across the 16 replicates of each probe. Data were normalized using the loess regression-based method within arrays to correct for any intensity-dependent dye bias . A between-array normalisation was not required as all arrays showed comparable M-ratio distributions. Due to expected tissue differences in miRNA expression, differential expression between GK and BN rats was assessed in adipose tissue and liver samples separately. The experimental design (see above) generated M-ratios (log-ratio of red and green intensities) representing the log ratio of expression in the two strains. The M-ratios could therefore be used to investigate strain differences, the main objective of this study. We used the limma package from BioConductor  to fit a linear model (including a dye effect) to the M-ratio data from the 4 arrays corresponding to each tissue. This generated a list of miRNA probes ranked by p-value for the evidence for differential expression between the GK and BN rats. Raw p-values were adjusted using the method of Benjamini and Hochberg  to control the false discovery rate (FDR). Differential expression between liver and adipose tissue could also be investigated using the A-ratios, which represent the average intensity (log scale) of the GK and BN samples hybridised to each array. Assuming strain differences are small in comparison to tissue differences, the fold change (liver vs. adipose tissue) can be estimated as the difference in mean A-ratio between the 4 liver and 4 adipose tissue arrays.
Illumina mRNA arrays
Gene expression profiling of liver and adipose tissue samples from the same rats investigated in the miRNA experiment was performed using Sentrix® BeadChip RatRef-12 v1 Whole-Genome Gene Expression Arrays (Illumina Inc., San Diego, California, USA), which contain 22,523 oligonucleotides probes (replicated an average of 30 times). Double-stranded cDNA and purified biotin-labelled cRNA were synthesised from 300 ng high quality total RNA using the Illumina® TotalPrep RNA Amplification Kit (Ambion Inc., Austin, Texas, USA). cRNA concentrations were determined using a NanoDrop spectrophotometer, and cRNA quality and integrity were assessed using an Agilent 2100 Bioanalyser (Agilent Technologies, Waldbronn, Germany). Hybridisations onto Sentrix® BeadChip RatRef-12 v1 arrays were carried out using 750 ng of each biotinylated cRNA in a 58°C hybridisation oven for 18 hours. Following washing and staining with Streptavidin-Cy3, the BeadChip Arrays were scanned on the Illumina® BeadArray Reader (Illumina Inc., San Diego, USA). The quality of the resulting data was checked using the Illumina® BeadStudio Application software
Statistical analysis of Illumina mRNA data
Raw intensity data were imported into the R statistical package  for further processing. First, an array-specific background measure, based on the average signal from negative control probes, was subtracted from all probes on the array. Data were then transformed and normalized using the vsn2 BioConductor package . Hierarchical clustering showed samples clustered primarily according to tissue, with GK and BN samples forming two sub-clusters within the adipose tissue cluster. Differential expression between the GK and BN strains was assessed in liver and adipose tissue samples separately using the limma package from BioConductor . Raw p-values were adjusted to control the false discovery rate (FDR) using the method of Benjamini and Hochberg ; adjusted p-values below 0.05 were considered significant.
The entire datasets (for miRNA and mRNA) described here are available from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/projects/geo/) through series accession number GSE17060.
Quantitative real-time PCR validation methods
Quantitative real-time PCR was carried out on the remainder of RNA available from liver tissues using ABI's TaqMan microRNA assays (Applied Biosystems (ABI), Warrington, Cheshire, UK). Briefly, reverse transcription (RT) was carried out in a total reaction volume of 15 μl containing 5 μl of total RNA (10 ng/ul), 3 μl of reverse transcription primer, 1.50 μl of 10× RT buffer, 1.00 μl MultiScribe Reverse Transcriptase (50 U/μl), 0.15 μl of 100 mM dNTPs (with dTTP), 0.19 μl of RNase Inhibitor, 20 U/μl and 4.16 μl of nuclease-free water. Reactions were incubated as per manufacturer's recommendation. PCRs were performed in triplicate, the 20-μL PCR reaction contained: 1.33 μL RT product, 10 μL 2× PCR Master Mix, 1-μL microRNA primer (ABI, UK) and 7.67 μl of nuclease-free water. The reactions were incubated at 95°C for 10 min, followed by 40 cycles of 95°C for 15 s, 60°C for 35 s. The highly conserved snoRNA and 4.5S RNA(H) (rat) were used as normalizing endogenous controls. Fold changes (FC) in expression were calculated using the 2-(ΔΔCt) method . Stocks of adipose tissue were depleted when used in the miRNA and mRNA studies, which did not allow for validation using RT-PCR.
In-silicoprediction of miRNA target genes
In-silico prediction of miRNA target genes was carried out using three different prediction algorithms: TargetScan v4.1 , PicTar  and miRanda . The first two algorithms predict binding sites preferentially based on the 5' end of the 22 nt miRNA sequence, while miRanda predictions are more sensitive to the sequence at the 3' end. Each prediction algorithm generally identifies hundreds of potential target genes for any single miRNA and there is relatively little overlap between different algorithms. However, TargetScan and PicTar show a higher degree of overlap than either does with miRanda [Additional file 3], as might be expected based on the approaches implemented by the different algorithms. These predicted target lists may also contain a significant proportion of false-positives i.e. the sequences align, but no functional miRNA-mRNA interaction occurs in vivo . With little experimental data, it is difficult to determine which algorithm performs best; thus, all analyses were performed with each list separately as well as with the subset of target genes predicted by at least two of the three algorithms. The latter list may be more likely to include a higher proportion of true target genes than any one list alone, but will be biased towards TargetScan/PicTar predictions.
Biological pathways defined by KEGG  that are enriched among the predicted target genes of miR-125a and the lists of differentially expressed mRNAs found in liver and adipose tissue were identified using the GENECODIS software . This profiling tool uses the hypergeometric distribution to determine whether individual pathways or combinations of pathways are significantly over-represented among the genes of interest. P-values computed for each pathway were adjusted using the method of Benjamini and Hochberg  to control the false discovery rate (FDR) and adjusted p-values < 0.05 were considered significant.
Enrichment of miR-125a target genes among differentially expressed genes
Overlap between predicted miR-125a target genes and genes significantly altered in GK compared to BN rats in adipose tissue and in liver.
Predicted miR-125a target genes
Number genes tested
Liver up-regulated (95 genes)
Liver down-regulated (138 genes)
Adipose up-regulated (477 genes)
Adipose down-regulated (598 genes)
Differences in miRNA expression between GK and BN rats
MiRNA microarray analysis reveals differential expression between hyperglycaemic and normoglycaemic rats
(GK vs. BN)
Differences in gene (mRNA) expression between GK and BN rats
Functional profiling of differentially expressed genes in GK compared to BN rats in adipose tissue (n = 1075) and liver (n = 233) using GENECODIS.
Number of genes
Prostaglandin and leukotriene metabolism
Focal adhesion | ECM-receptor interaction | Hematopoietic cell lineage | Regulation of actin cytoskeleton
ECM-receptor interaction | Hematopoietic cell lineage
Focal adhesion | ECM-receptor interaction
Arginine and proline metabolism
Glycolysis/Gluconeogenesis | Fructose and mannose metabolism
Glycolysis/Gluconeogenesis | Pentose phosphate pathway | Fructose and mannose metabolism
Hematopoietic cell lineage
Cell Communication | Focal adhesion | ECM-receptor interaction
Fructose and mannose metabolism
Porphyrin and chlorophyll metabolism
Metabolism of xenobiotics by cytochrome P450
Cytokine-cytokine receptor interaction | Focal adhesion
Valine, leucine and isoleucine degradation
Cholera – Infection
Fructose and mannose metabolism | Galactose metabolism
Urea cycle and metabolism of amino groups
Glycine, serine and threonine metabolism
Arginine and proline metabolism
Alanine and aspartate metabolism | Arginine and proline metabolism
Alanine and aspartate metabolism
Urea cycle and metabolism of amino groups | Arginine and proline metabolism
Prostaglandin and leukotriene metabolism
Valine, leucine and isoleucine degradation | Butanoate metabolism
Fatty acid metabolism
Predicted targets for miR-125a and in-silicofunctional profiling
The miRNA target gene prediction algorithms TargetScan 4.1 , PicTar  and miRanda  generated target gene lists for miR-125a comprising 165, 350 and 975 genes, respectively. Of these, 152 genes were predicted by more than one algorithm [Additional file 3] and only 17 are predicted by all algorithms. The target gene lists were analysed using the GENECODIS tool to investigate the biological pathways (KEGG) that may be affected by overexpression of miR-125a in liver and, to a lesser extent, adipose tissue. When the set of 152 miR-125a target genes was tested, mitogen-activated protein kinase (MAPK) signaling was the only pathway to show significant over-representation (P adjusted = 3 × 10-5). The MAPK signaling pathway was also significant when each predicted target list was analysed separately, and was the top-ranking pathway for the lists generated by TargetScan and PicTar [Additional file 6]. Other significant pathways of note included insulin signaling (P adjusted = 0.03, PicTar gene list) and, with the miRanda list, glycerolipid metabolism (P adjusted = 0.02) and calcium signaling (P adjusted = 0.05).
Enrichment of miR-125a target genes among differentially expressed genes
Given the evidence for miRNA regulation at the mRNA level in animals [6, 9, 10], integration of miRNA-mRNA expression data was also performed. Since miR-125a was the top-ranking miRNA in liver, and fifth-ranked in adipose tissue, we hypothesized that target genes of miR-125a would be differentially expressed between GK and BN rats in both tissues. Specifically, overexpression of miR-125a in these tissues in the GK rat would be expected to down-regulate miR-125a target genes [1, 6, 39]. Target genes predicted by all algorithms (n = 17), plus the set of 152 genes predicted by more than one algorithm, were tested for enrichment among the 598 genes significantly down-regulated in GK compared to BN rats in adipose tissue and among the 138 down-regulated genes in liver (Table 1). Greater overlap than expected by chance (see Methods) was observed in adipose tissue (overlap = 22, FDR = 0.04) but not in liver (overlap = 5, FDR = 0.25) for the set of 536 target genes predicted by the miRanda algorithm and present on the Illumina RatRef 12 array. Three miR-125a target genes (Slc35c2, Umps and Ptges2) were significantly down-regulated in GK rats in both tissues. Contrary to expectation, a significant enrichment was also observed when the up-regulated genes were tested (overlap = 21, FDR = 0.006 in adipose tissue; overlap = 5, FDR = 0.07 in liver). For both up- and down-regulated genes, there was more overlap in adipose tissue compared to liver, even though miR-125a showed a higher fold induction in liver.
Neither of the target gene lists generated by PicTar and TargetScan, nor the combined list, showed significant overlap with differentially expressed genes (Table 1). This was because the overlap between differentially expressed genes and miR-125a targets occurred largely for genes uniquely predicted by miRanda. Furthermore, the PicTar and TargetScan lists are more similar to each other than to the miRanda list, and so the combined list is biased toward the genes predicted by these algorithms [Additional file 3]. It should be noted that it is not the greater number of targets predicted by miRanda that produces the significant result, as the size of the target gene list is accounted for in the analysis.
The role of altered miRNA expression in a variety of diseases is increasingly being recognized but the precise nature and downstream effects of such changes is largely unknown. Previous studies have shown that miRNAs regulate various physiological events relevant to T2D pathophysiology, such as insulin secretion, insulin responsiveness and energy homeostasis. Here, we have characterized differential miRNA and mRNA expression in insulin sensitive tissues of rats of a model of T2D (GK rat) and in normoglycaemic BN controls, a strain combination extensively used to study the genetic determinants of T2D phenotypes in F2 hybrids and congenic strains . In the miRNA data, the most striking finding was the over-expression of miR-125a in both liver and adipose tissue (with nearly 6-fold and 2-fold higher expression, respectively) in GK compared to BN rats. This finding was validated by RT-PCR in liver (FC = 13.15, p = 0.0005). The higher sensitivity and dynamic range of the RT-PCR technique is the most likely reason why the fold change is higher than detected by microarray.
MiR-125a and its close homolog miR-125b differ by a single nucleotide , and thus share many predicted target genes. Experimentally validated target genes of both miR-125a and miR-125b in humans include ERBB2 and ERBB3  and LIN28 . Both miR-125a and miR-125b have been reported to be down-regulated in ovarian  and breast cancers , with potential roles in cell proliferation and differentiation. To further investigate the potential role of miR-125a in T2D, we assessed the functional roles of predicted miR-125a target genes. This analysis suggested that increased miR-125a levels may particularly affect genes involved in the MAPK signalling pathway. MAPK has been implicated in T2D [28, 30] and plays a critical role in insulin signalling , and may contribute to insulin resistance . Further functional experiments will help elucidate the role of MAPK signalling in T2D and to what extent increased miR-125a expression may affect this pathway.
We integrated our miRNA results with gene expression data from the same animals and found an enrichment of miR-125a target genes predicted by the miRanda algorithm  among genes down-regulated in GK rats in adipose tissue. Of particular interest, a proposed functional candidate gene for T2D, Ptges2, is a predicted miR-125a target gene and was significantly down-regulated in both tissues. Two other miR-125a target genes of interest in the context of T2D and obesity are Ppap2c and Sult1a1, both of which were significantly down-regulated in adipose tissue from the GK rat. In addition; single nucleotide polymorphisms (SNPs) in the SULT1A1 region have previously been associated with increased BMI in humans . Contrary to expectation, it was surprising to find an even stronger enrichment among the up-regulated genes. One possibility is that these are not direct target genes themselves, but represent changes further downstream of the miRNA regulation. No enrichment was observed with the target gene lists predicted by the other two algorithms, which may be due to differences in the target-gene prediction methods implemented or to the effects of long-term exposure to hyperglycaemia further affecting the control of gene expression. These results show that the relationship between miRNAs and gene expression is not a simple one.
Though the enrichment of miR-125a target genes predicted by miRanda is an interesting finding, it raises a number of questions, including why both down and up-regulated genes should show significant enrichment of miR-125a target genes, and why stronger enrichment was observed for the tissue with lower fold induction of miR-125a. The difficulty of predicting genuine target genes combined with other influences on gene expression could provide some explanation. Furthermore, the current data do not demonstrate whether the expression of any of the genes is dysregulated as a direct consequence of the increased miR-125a levels. One approach to identify genes likely to be regulated by miR-125a is to find those that show a strong negative correlation between their expression and miR-125a levels. Unfortunately, the two-colour experimental design used for the miRNA study (generating GK v BN expression ratios) combined with the small sample size precluded such an analysis here. Integrating miRNA and mRNA data has the potential to give further insights into miRNA-mediated regulation of gene expression but is limited to detecting events in which the target mRNA is degraded. Still, in-silico approaches will likely be useful for prioritizing target genes for functional validation.
Our results suggest that increased miR-125a expression may be a characteristic feature of hyperglycaemic GK rats. However, as this study was carried out in rats with long-established T2D phenotypes, it remains unclear whether the observed changes are causative, or reflect adaptation to prolonged hyperglycaemia. GK and BN rats are also genetically different strains, and it is therefore possible that strain differences unrelated to hyperglycaemia could contribute to the altered expression of miR-125a. Although in-silico functional profiling of gene expression data and miR-125a target genes lends some support to the idea that miR-125a over-expression is linked to the hyperglycaemic phenotype, further evidence from other model systems or functional studies is certainly desirable.
Among the other miRNAs showing robust differences between GK and BN rats was a 1.5 fold up-regulation of miR-29a in adipose tissue. This is exactly comparable with the only previous study of miRNAs in T2D, which identified a 1.5 fold up-regulation of miR-29a, together with the paralogs 29b and 29c, in skeletal muscle from GK and Wistar rats (normoglycaemic controls). Northern blot analysis confirmed the up-regulation of all three miR-29 paralogs in muscle, adipose tissue and liver. This study further showed that high glucose and insulin (which indicate insulin resistance) up-regulate the expression of miR-29a/b in adipocyte cell lines. They also suggest the over-expression of miR-29a/b inhibits insulin-induced glucose import in the same adipocyte cell lines. Taken together, these data support the involvement of miR-29a in insulin resistance and the insulin-signaling pathway.
We have shown that miR-125a expression is increased in two insulin target tissues in a rat model of T2D. Gene expression analysis in the same animals revealed a distinct profile characterizing the hyperglycaemic GK rat, including altered expression of several predicted miR-125 target genes. In-silico functional profiling of predicted miR-125a targets and differentially expressed genes indicated that lipid metabolism pathways and MAPK signaling may be dysregulated in the hyperglycaemic state. Knowledge of miRNA differential expression between GK and BN rats provides important information which may contribute to the understanding of processes which underlie the T2D phenotype in GK rats through investigations in crosses and congenic models derived in this strain combination [48–50]. Further studies at the protein level and translation across species, especially to humans, will be important to elucidate the potential role of miR-125a in T2D pathophysiology.
Funding for this study was provided by Diabetes UK (grant code BDA: RD06/0003287) the Throne-Holst Foundation and Nuffield Department of Medicine. DG holds a Wellcome Senior Fellowship in Basic Biomedical Science (057733). C.M.L. is a Wellcome Trust Research Career Development Fellow (086596).
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Vasudevan S, Tong Y, Steitz JA: Switching from repression to activation: microRNAs can up-regulate translation. Science. 2007, 318 (5858): 1931-1934. 10.1126/science.1149460.View ArticlePubMedGoogle Scholar
- Hutvagner G, Zamore PD: A microRNA in a multiple-turnover RNAi enzyme complex. Science. 2002, 297 (5589): 2056-2060. 10.1126/science.1073827.View ArticlePubMedGoogle Scholar
- Dugas DV, Bartel B: MicroRNA regulation of gene expression in plants. Curr Opin Plant Biol. 2004, 7 (5): 512-520. 10.1016/j.pbi.2004.07.011.View ArticlePubMedGoogle Scholar
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science. 2001, 294 (5543): 853-858. 10.1126/science.1064921.View ArticlePubMedGoogle Scholar
- Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005, 433 (7027): 769-773. 10.1038/nature03315.View ArticlePubMedGoogle Scholar
- Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N: Widespread changes in protein synthesis induced by microRNAs. Nature. 2008, 455 (7209): 58-63. 10.1038/nature07228.View ArticlePubMedGoogle Scholar
- Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP: The impact of microRNAs on protein output. Nature. 2008, 455 (7209): 64-71. 10.1038/nature07242.View ArticlePubMedPubMed CentralGoogle Scholar
- Krutzfeldt J, Rajewsky N, Braich R, Rajeev KG, Tuschl T, Manoharan M, Stoffel M: Silencing of microRNAs in vivo with 'antagomirs'. Nature. 2005, 438 (7068): 685-689. 10.1038/nature04303.View ArticlePubMedGoogle Scholar
- Sood P, Krek A, Zavolan M, Macino G, Rajewsky N: Cell-type-specific signatures of microRNAs on target mRNA expression. Proc Natl Acad Sci USA. 2006, 103 (8): 2746-2751. 10.1073/pnas.0511045103.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang X: Systematic identification of microRNA functions by combining target prediction and expression profiling. Nucleic Acids Res. 2006, 34 (5): 1646-1652. 10.1093/nar/gkl068.View ArticlePubMedPubMed CentralGoogle Scholar
- Cheng C, Li LM: Inferring microRNA activities by combining gene expression with microRNA target prediction. PLoS ONE. 2008, 3 (4): e1989-10.1371/journal.pone.0001989.View ArticlePubMedPubMed CentralGoogle Scholar
- Ambros V: The functions of animal microRNAs. Nature. 2004, 431 (7006): 350-355. 10.1038/nature02871.View ArticlePubMedGoogle Scholar
- Poy MN, Spranger M, Stoffel M: microRNAs and the regulation of glucose and lipid metabolism. Diabetes Obes Metab. 2007, 9 (Suppl 2): 67-73. 10.1111/j.1463-1326.2007.00775.x.View ArticlePubMedGoogle Scholar
- Tang X, Tang G, Ozcan S: Role of microRNAs in diabetes. Role of microRNAs in diabetes. Biochim Biophys Acta. 2008, 1779 (11): 697-701.View ArticlePubMedPubMed CentralGoogle Scholar
- Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma X, Macdonald PE, Pfeffer S, Tuschl T, Rajewsky N, Rorsman P, Stoffel M: A pancreatic islet-specific microRNA regulates insulin secretion. Nature. 2004, 432 (7014): 226-230. 10.1038/nature03076.View ArticlePubMedGoogle Scholar
- Plaisance V, Abderrahmani A, Perret-Menoud V, Jacquemin P, Lemaigre F, Regazzi R: MicroRNA-9 controls the expression of Granuphilin/Slp4 and the secretory response of insulin-producing cells. J Biol Chem. 2006, 281 (37): 26932-26942. 10.1074/jbc.M601225200.View ArticlePubMedGoogle Scholar
- Baroukh N, Ravier MA, Loder MK, Hill EV, Bounacer A, Scharfmann R, Rutter GA, Van Obberghen E: MicroRNA-124a regulates Foxa2 expression and intracellular signaling in pancreatic beta-cell lines. J Biol Chem. 2007, 282 (27): 19575-19588. 10.1074/jbc.M611841200.View ArticlePubMedGoogle Scholar
- He A, Zhu L, Gupta N, Chang Y, Fang F: Overexpression of micro ribonucleic acid 29, highly up-regulated in diabetic rats, leads to insulin resistance in 3T3-L1 adipocytes. Mol Endocrinol. 2007, 21 (11): 2785-2794. 10.1210/me.2007-0167.View ArticlePubMedGoogle Scholar
- Picarel-Blanchot F, Berthelier C, Bailbe D, Portha B: Impaired insulin secretion and excessive hepatic glucose production are both early events in the diabetic GK rat. Am J Physiol. 1996, 271 (4 Pt 1): E755-762.PubMedGoogle Scholar
- Gauguier D: The rat as a model physiological system. 2006, London: Wiley, 3.Google Scholar
- Solberg LC, Valdar W, Gauguier D, Nunez G, Taylor A, Burnett S, Arboledas-Hita C, Hernandez-Pliego P, Davidson S, Burns P, Bhattacharya S, Hough T, Higgs D, Klenerman P, Cookson WO, Zhang Y, Deacon RM, Rawlins JN, Mott R, Flint J: A protocol for high-throughput phenotyping, suitable for quantitative trait analysis in mice. Mamm Genome. 2006, 17 (2): 129-146. 10.1007/s00335-005-0112-1.View ArticlePubMedGoogle Scholar
- R: A Language and Environment for Statistical Computing. [http://www.R-project.org]
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.View ArticlePubMedPubMed CentralGoogle Scholar
- Smyth GK, Speed T: Normalization of cDNA microarray data. Methods. 2003, 31 (4): 265-273. 10.1016/S1046-2023(03)00155-5.View ArticlePubMedGoogle Scholar
- Smyth GK: Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004, 3.Google Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995, 57: 289-300.Google Scholar
- Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M: Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002, 18 (Suppl 1): S96-104.View ArticlePubMedGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.View ArticlePubMedGoogle Scholar
- Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. Cell. 2003, 115 (7): 787-798. 10.1016/S0092-8674(03)01018-3.View ArticlePubMedGoogle Scholar
- Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N: Combinatorial microRNA target predictions. Nat Genet. 2005, 37 (5): 495-500. 10.1038/ng1536.View ArticlePubMedGoogle Scholar
- John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS: Human MicroRNA targets. PLoS Biol. 2004, 2 (11): e363-10.1371/journal.pbio.0020363.View ArticlePubMedPubMed CentralGoogle Scholar
- John B, Sander C, Marks DS: Prediction of human microRNA targets. Methods Mol Biol. 2006, 342: 101-113.PubMedGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.View ArticlePubMedPubMed CentralGoogle Scholar
- Carmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A: GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol. 2007, 8 (1): R3-10.1186/gb-2007-8-1-r3.View ArticlePubMedPubMed CentralGoogle Scholar
- Hochberg Y, Benjamini Y: More powerful procedures for multiple significance testing. Stat Med. 1990, 9 (7): 811-818. 10.1002/sim.4780090710.View ArticlePubMedGoogle Scholar
- Wilder SP, Kaisaki PJ, Argoud K, Salhan A, Ragoussis J, Bihoreau MT, Gauguier D: Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes. BMC Genomics. 2009, 10: 63-10.1186/1471-2164-10-63.View ArticlePubMedPubMed CentralGoogle Scholar
- Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP: MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007, 27 (1): 91-105. 10.1016/j.molcel.2007.06.017.View ArticlePubMedPubMed CentralGoogle Scholar
- Yekta S, Shih IH, Bartel DP: MicroRNA-directed cleavage of HOXB8 mRNA. Science. 2004, 304 (5670): 594-596. 10.1126/science.1097434.View ArticlePubMedGoogle Scholar
- Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS: MicroRNA targets in Drosophila. Genome Biol. 2003, 5 (1): R1-10.1186/gb-2003-5-1-r1.View ArticlePubMedPubMed CentralGoogle Scholar
- Scott GK, Goga A, Bhaumik D, Berger CE, Sullivan CS, Benz CC: Coordinate suppression of ERBB2 and ERBB3 by enforced expression of micro-RNA miR-125a or miR-125b. J Biol Chem. 2007, 282 (2): 1479-1486. 10.1074/jbc.M609383200.View ArticlePubMedGoogle Scholar
- Wu L, Belasco JG: Micro-RNA regulation of the mammalian lin-28 gene during neuronal differentiation of embryonal carcinoma cells. Mol Cell Biol. 2005, 25 (21): 9198-9208. 10.1128/MCB.25.21.9198-9208.2005.View ArticlePubMedPubMed CentralGoogle Scholar
- Nam EJ, Yoon H, Kim SW, Kim H, Kim YT, Kim JH, Kim JW, Kim S: MicroRNA expression profiles in serous ovarian carcinoma. Clin Cancer Res. 2008, 14 (9): 2690-2695. 10.1158/1078-0432.CCR-07-1731.View ArticlePubMedGoogle Scholar
- Mattie MD, Benz CC, Bowers J, Sensinger K, Wong L, Scott GK, Fedele V, Ginzinger D, Getts R, Haqq C: Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies. Mol Cancer. 2006, 5: 24-10.1186/1476-4598-5-24.View ArticlePubMedPubMed CentralGoogle Scholar
- Fujishiro M, Gotoh Y, Katagiri H, Sakoda H, Ogihara T, Anai M, Onishi Y, Ono H, Abe M, Shojima N, Fukushima Y, Kikuchi M, Oka Y, Asano T: Three mitogen-activated protein kinases inhibit insulin signaling by different mechanisms in 3T3-L1 adipocytes. Mol Endocrinol. 2003, 17 (3): 487-497. 10.1210/me.2002-0131.View ArticlePubMedGoogle Scholar
- Engelman JA, Berg AH, Lewis RY, Lisanti MP, Scherer PE: Tumor necrosis factor alpha-mediated insulin resistance, but not dedifferentiation, is abrogated by MEK1/2 inhibitors in 3T3-L1 adipocytes. Mol Endocrinol. 2000, 14 (10): 1557-1569. 10.1210/me.14.10.1557.PubMedGoogle Scholar
- Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, Styrkarsdottir U, Gretarsdottir S, Thorlacius S, Jonsdottir I, Jonsdottir T, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Jonsson F, Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Lauritzen T, Aben KK, Verbeek AL, Roeleveld N, Kampman E, Yanek LR, Becker LC, Tryggvadottir L, Rafnar T, Becker DM, Gulcher J, Kiemeney LA, Pedersen O, Kong A, Thorsteinsdottir U, Stefansson K: Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009, 41 (1): 18-24. 10.1038/ng.274.View ArticlePubMedGoogle Scholar
- Gauguier D, Froguel P, Parent V, Bernard C, Bihoreau MT, Portha B, James MR, Penicaud L, Lathrop M, Ktorza A: Chromosomal mapping of genetic loci associated with non-insulin dependent diabetes in the GK rat. Nat Genet. 1996, 12 (1): 38-43. 10.1038/ng0196-38.View ArticlePubMedGoogle Scholar
- Dumas ME, Wilder SP, Bihoreau MT, Barton RH, Fearnside JF, Argoud K, D'Amato L, Wallis RH, Blancher C, Keun HC, Baunsgaard D, Scott J, Sidelmann UG, Nicholson JK, Gauguier D: Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models. Nat Genet. 2007, 39 (5): 666-672. 10.1038/ng2026.View ArticlePubMedGoogle Scholar
- Wallis RH, Collins SC, Kaisaki PJ, Argoud K, Wilder SP, Wallace KJ, Ria M, Ktorza A, Rorsman P, Bihoreau MT, Gauguier D: Pathophysiological, genetic and gene expression features of a novel rodent model of the cardio-metabolic syndrome. PLoS ONE. 2008, 3 (8): e2962-10.1371/journal.pone.0002962.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/2/54/prepub
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