Chromosomal mapping of pancreatic islet morphological features and regulatory hormones in the spontaneously diabetic (Type 2) Goto-Kakizaki rat

Insulin resistance and altered endocrine pancreas function are central pathophysiological features of type 2 diabetes mellitus (T2DM). The Goto-Kakizaki (GK) rat is a model of spontaneous T2DM characterised by reduced beta cell mass and genetically determined glucose intolerance and altered insulin secretion. To identify genetic determinants of endocrine pancreas histopathology, we carried out quantitative trait locus (QTL) mapping of histological phenotypes (beta cell mass -BCM and insulin-positive cell area -IPCA) and plasma concentration of hormones and growth factors in a F2 cohort derived from GK and normoglycemic Brown Norway rats. Although IPCA and BCM in the duodenal region of the pancreas were highly positively correlated (P < 10−6), and similarly in the splenic region, both measures were poorly correlated when comparing duodenal and splenic phenotypes. Strongest evidence of linkage to pancreas morphological traits was obtained between BCM and chromosome 10 (LOD 3.2). Evidence of significant linkage (LOD 4.2) to plasma corticosterone was detected in a region of chromosome 1 distal to other QTLs previously identified in the GK. Male-specific genetic effects were detected, including linkages (LOD > 4) to growth hormome (GH) on chromosome 6 and prolactin on chromosome 17. These data suggest independent genetic control of the structure and function of ontologically different regions of the endocrine pancreas. Novel QTLs for corticosterone, prolactin and GH may contribute to diabetes in the GK. The QTLs that we have identified in this, and previous genetic studies collectively underline the complex and multiple mechanisms involved in diabetes in the GK strain

Global microRNA expression profiles in insulin target tissues in a spontaneous rat model of type 2 diabetes

AIMS/HYPOTHESIS: MicroRNAs regulate a broad range of biological mechanisms. To investigate the relationship between microRNA expression and type 2 diabetes, we compared global microRNA expression in insulin target tissues from three inbred rat strains that differ in diabetes susceptibility. METHODS: Using microarrays, we measured the expression of 283 microRNAs in adipose, liver and muscle tissue from hyperglycaemic (Goto-Kakizaki), intermediate glycaemic (Wistar Kyoto) and normoglycaemic (Brown Norway) rats (n = 5 for each strain). Expression was compared across strains and validated using quantitative RT-PCR. Furthermore, microRNA expression variation in adipose tissue was investigated in 3T3-L1 adipocytes exposed to hyperglycaemic conditions. RESULTS: We found 29 significantly differentiated microRNAs (p(adjusted) &lt; 0.05): nine in adipose tissue, 18 in liver and two in muscle. Of these, five microRNAs had expression patterns that correlated with the strain-specific glycaemic phenotype. MiR-222 (p(adjusted) = 0.0005) and miR-27a (p(adjusted) = 0.006) were upregulated in adipose tissue; miR-195 (p(adjusted) = 0.006) and miR-103 (p(adjusted) = 0.04) were upregulated in liver; and miR-10b (p(adjusted) = 0.004) was downregulated in muscle. Exposure of 3T3-L1 adipocytes to increased glucose concentration upregulated the expression of miR-222 (p = 0.008), miR-27a (p = 0.02) and the previously reported miR-29a (p = 0.02). Predicted target genes of these differentially expressed microRNAs are involved in pathways relevant to type 2 diabetes. CONCLUSION: The expression patterns of miR-222, miR-27a, miR-195, miR-103 and miR-10b varied with hyperglycaemia, suggesting a role for these microRNAs in the pathophysiology of type 2 diabetes, as modelled by the Gyoto-Kakizaki rat. We observed similar patterns of expression of miR-222, miR-27a and miR-29a in adipocytes as a response to increased glucose levels, which supports our hypothesis that altered expression of microRNAs accompanies primary events related to the pathogenesis of type 2 diabetes

Functional annotations of diabetes nephropathy susceptibility loci through analysis of genome-wide renal gene expression in rat models of diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>Hyperglycaemia in diabetes mellitus (DM) alters gene expression regulation in various organs and contributes to long term vascular and renal complications. We aimed to generate novel renal genome-wide gene transcription data in rat models of diabetes in order to test the responsiveness to hyperglycaemia and renal structural changes of positional candidate genes at selected diabetic nephropathy (DN) susceptibility loci.</p> <p>Methods</p> <p>Both Affymetrix and Illumina technologies were used to identify significant quantitative changes in the abundance of over 15,000 transcripts in kidney of models of spontaneous (genetically determined) mild hyperglycaemia and insulin resistance (Goto-Kakizaki-GK) and experimentally induced severe hyperglycaemia (Wistar-Kyoto-WKY rats injected with streptozotocin [STZ]).</p> <p>Results</p> <p>Different patterns of transcription regulation in the two rat models of diabetes likely underlie the roles of genetic variants and hyperglycaemia severity. The impact of prolonged hyperglycaemia on gene expression changes was more profound in STZ-WKY rats than in GK rats and involved largely different sets of genes. These included genes already tested in genetic studies of DN and a large number of protein coding sequences of unknown function which can be considered as functional and, when they map to DN loci, positional candidates for DN. Further expression analysis of rat orthologs of human DN positional candidate genes provided functional annotations of known and novel genes that are responsive to hyperglycaemia and may contribute to renal functional and/or structural alterations.</p> <p>Conclusion</p> <p>Combining transcriptomics in animal models and comparative genomics provides important information to improve functional annotations of disease susceptibility loci in humans and experimental support for testing candidate genes in human genetics.</p

The architecture of parent-of-origin effects in mice.

The number of imprinted genes in the mammalian genome is predicted to be small, yet we show here, in a survey of 97 traits measured in outbred mice, that most phenotypes display parent-of-origin effects that are partially confounded with family structure. To address this contradiction, using reciprocal F1 crosses, we investigated the effects of knocking out two nonimprinted candidate genes, Man1a2 and H2-ab1, that reside at nonimprinted loci but that show parent-of-origin effects. We show that expression of multiple genes becomes dysregulated in a sex-, tissue-, and parent-of-origin-dependent manner. We provide evidence that nonimprinted genes can generate parent-of-origin effects by interaction with imprinted loci and deduce that the importance of the number of imprinted genes is secondary to their interactions. We propose that this gene network effect may account for some of the missing heritability seen when comparing sibling-based to population-based studies of the phenotypic effects of genetic variants

Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes.

BACKGROUND: Microarray technologies are widely used to quantify the abundance of transcripts corresponding to thousands of genes. To maximise the robustness of transcriptome results, we have tested the performance and reproducibility of rat and mouse gene expression data obtained with Affymetrix, Illumina and Operon platforms. RESULTS: We present a thorough analysis of the degree of reproducibility provided by analysing the transcriptomic profile of the same animals of several experimental groups under different popular microarray technologies in different tissues. Concordant results from inter- and intra-platform comparisons were maximised by testing many popular computational methods for generating fold changes and significances and by only considering oligonucleotides giving high expression levels. The choice of Affymetrix signal extraction technique was shown to have the greatest effect on the concordance across platforms. In both species, when choosing optimal methods, the agreement between data generated on the Affymetrix and Illumina was excellent; this was verified using qRT-PCR on a selection of genes present on all platforms. CONCLUSION: This study provides an extensive assessment of analytical methods best suited for processing data from different microarray technologies and can assist integration of technologically different gene expression datasets in biological systems