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Journal of Diabetes Research
Volume 2013 (2013), Article ID 970435, 9 pages
http://dx.doi.org/10.1155/2013/970435
Research Article

Identifying Candidate Genes for Type 2 Diabetes Mellitus and Obesity through Gene Expression Profiling in Multiple Tissues or Cells

1School of Bioscience and Bioengineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, South China University of Technology, Guangzhou 510006, China
2Chinese PLA General Hospital, Beijing 100853, China
3Guangdong Laboratory Animals Monitoring Institute, Guangzhou 510555, China

Received 16 July 2013; Revised 30 September 2013; Accepted 25 October 2013

Academic Editor: Mark A. Yorek

Copyright © 2013 Junhui Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Type 2 Diabetes Mellitus (T2DM) and obesity have become increasingly prevalent in recent years. Recent studies have focused on identifying causal variations or candidate genes for obesity and T2DM via analysis of expression quantitative trait loci (eQTL) within a single tissue. T2DM and obesity are affected by comprehensive sets of genes in multiple tissues. In the current study, gene expression levels in multiple human tissues from GEO datasets were analyzed, and 21 candidate genes displaying high percentages of differential expression were filtered out. Specifically, DENND1B, LYN, MRPL30, POC1B, PRKCB, RP4-655J12.3, HIBADH, and TMBIM4 were identified from the T2DM-control study, and BCAT1, BMP2K, CSRNP2, MYNN, NCKAP5L, SAP30BP, SLC35B4, SP1, BAP1, GRB14, HSP90AB1, ITGA5, and TOMM5 were identified from the obesity-control study. The majority of these genes are known to be involved in T2DM and obesity. Therefore, analysis of gene expression in various tissues using GEO datasets may be an effective and feasible method to determine novel or causal genes associated with T2DM and obesity.

1. Introduction

T2DM, a complex endocrine and metabolic disorder, has become more prevalent in recent years, with significant adverse effects on human health. T2DM is characterized by insulin resistance (IR) and deficient -cell function [1]. Interactions between multiple genetic and environmental factors are proposed to contribute to pathogenesis of the disease [1, 2]. Association of obesity with T2DM has been reported, both within and among different populations [3]. Earlier research has shown that obesity and its duration are major risk factors for T2DM, and IR pathological state generally exists in obesity [4, 5].

In recent years, numerous susceptibility loci have been identified through genome-wide association studies (GWAS) and meta-analyses on T2DM and obesity, and nearby candidate genes are proposed to be directly involved in the diseases [6, 7]. However, the underlying mechanisms by which these susceptibility loci affect and cause T2DM or obesity are currently unclear. Known SNPs associated with disease typically account for only a small fraction of overall disease [8, 9]. Gene expression patterns play a key role in determining pathogenesis and candidate genes of T2DM and obesity. A large-scale computable model has been created to analyze the molecular actions and effects of insulin on muscle gene expression [10]. Based on GWAS results, investigators integrated expression quantitative trait loci (eQTL) with coexpression networks to establish novel genes and networks relevant to the disease. Sixty-two candidate genes were identified through integrating 32 SNPs associated with T2DM and nearby gene expression from blood samples of 1008 morbidly obese patients. Many of the highly ranked genes are known to be involved in the regulation and metabolism of insulin, glucose, and lipids [11].

Different gene expression patterns exist in various tissues of organisms, and complex metabolic diseases, such as T2DM and obesity, are affected by comprehensive gene expression in multiple tissues. Analysis of gene expression in six tissues of mice from obesity-induced diabetes-resistant and diabetes-susceptible strains before and after the onset of diabetes led to the identification of 105 coexpression gene modules [12]. In the present study, gene expression profiles of human skeletal muscle, adipose tissue, islet, liver, blood and arterial tissue (or skeletal muscle, omental adipose tissue, cumulus cells, liver, blood, and subcutaneous abdominal adipose tissue) from GEO datasets were analyzed to identify the candidate genes for T2DM and obesity. Furthermore, candidate genes 1 Mb upstream and downstream (±1 Mb) of susceptibility SNPs for human T2DM and obesity were screened. Our analysis of gene expression in various tissues using GEO datasets provides a valuable method to determine novel candidate genes for T2DM and obesity.

2. Materials and Methods

The overall experimental design is shown in Figure 1.

970435.fig.001
Figure 1: Overall experimental design.
2.1. GEO Dataset Selection and Statistical Analysis

Human GEO datasets for T2DM or obesity were downloaded from the Gene Expression Omnibus (GEO) database of NCBI (http://www.ncbi.nlm.nih.gov/gds/). In total, 23 datasets (14 for T2DM and 9 for obesity) were selected and downloaded. Some datasets were separated into several groups according to sample phenotype. Overall, 21 groups for T2DM and 14 for obesity were obtained. Samples of disease and control were included in the case and control subgroups, respectively (details of samples for each group are provided in Table in Supplementary Material available online at http://dx.doi.org/10.1155/2013/970435). Three or more samples were included in each case or control subgroup for every microarray experiment. CEL files of samples were submitted to RMAExpress, Version 1.0.4, to yield normalized log2 expression values for each probe in individual groups with default parameters [13]. Analysis of variance (ANOVA) for normalized log2 expression values of two independent samples in each group was performed with the test. The -test for equal or unequal variances was used, depending on the -value of the tests.

Gene annotation files were downloaded from Ensembl (http://asia.ensembl.org/biomart/martview/45e0798c53bbd97ed0cf3d61142da3df) depending on the platform (GPL) of each group. Probes were matched with unique genes through gene annotation files. Probes corresponding to more than one gene were excluded. Probes or genes with significant differential expression were defined as -value ≤ 0.05. We calculated the differential expression ( ) percentage of each gene in all 21 T2DM and 14 obesity groups. For a gene with several probes, values ≤ 0.05 were selected to represent significance.

2.2. Statistical Analysis of Differential Expression Percentages of Genes

Genes were ranked based on differential expression in the T2DM and obesity groups. Genes with the highest percentage of differential expression were identified as candidates (≥50% for T2DM and ≥60% for obesity). Ranked genes are presented in Supplementary Materials (Table S2).

2.3. Screening of Genes within ±1 Mb of Susceptibility SNPs for T2DM and Obesity

In total, 54 and 95 SNPs associated with T2DM and obesity, respectively, were selected ( , detailed information in Tables S3 and S4). The coordinate of each SNP in the chromosome was searched in the NCBI database (http://www.ncbi.nlm.nih.gov/SNP/). Consensus CDS (CCDS) files for human data were downloaded (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/), and genes within ±1 Mb of SNPs were filtered out. Overall, 445 and 917 genes within 2 Mb of SNPs were associated with T2DM and obesity, respectively. The genes were reordered based on differential expression percentages with the above method, and those with the highest percentages were selected as candidates for T2DM (>40%) and obesity (>50%). Detailed information on all ranked genes in close proximity to SNPs is provided in Supplementary Materials (Table S5).

2.4. GO (Gene Ontology) and Pathway Analysis of Candidate Genes

Enrichment analysis of GO and pathways of all candidate genes was performed using Capital Bio Molecule Annotation System 3 (http://bioinfo.capitalbio.com/mas3/).

3. Results

3.1. Candidate Genes for T2DM and Obesity

In total, expression patterns of 23,810 genes were analyzed in the T2DM-control study. All genes were ranked based on the differential expression percentage. The average percentage of all genes was ~11%. Six highly ranked genes (DENND1B, LYN, MRPL30, POC1B, PRKCB, and RP4-655J12.3) were identified as candidates for T2DM (Table 1).

tab1
Table 1: Highly ranked genes in T2DM-control study.

Since less groups were available for the obesity-control study, genes with fewer than 10 values were excluded in order to obtain better statistical results. Expression of 14,367 genes was analyzed using the above method. The average percentage of all genes was ~17.5%. Eight genes (BCAT1, BMP2 K, CSRNP2, MYNN, NCKAP5L, SAP30BP, SLC35B4, and SP1) were isolated as candidates for obesity (Table 2).

tab2
Table 2: Highly ranked genes in the obesity-control study.
3.2. Candidate Genes within ±1 Mb of SNPs Conferring Susceptibility to T2DM and Obesity

In total, 445 genes in close proximity to T2DM SNPs were reordered based on their differential expression percentages. In particular, two highly ranked genes, HIBADH and TMBIM4, within ±1 Mb of rs864745, rs849134, and rs1531343 SNPs were filtered out (Table 1).

Using the same method, seven highly ranked genes (BAP1, GRB14, HSP90AB1, ITGA5, NCKAP5L, SP1, and TOMM5) within ±1 Mb of obesity SNPs were identified (Table 2).

Gene symbols and the corresponding full names of all candidate genes are supplied in Tables 1 and 2.

3.3. GO and Pathway Analysis of Candidate Genes

Results of GO and pathway analyses revealed that PRKCB is mainly associated with T2DM, and PRKCB and GRB14 are involved in insulin signaling within the gene pathway network (Figure 2). Further analysis of the correlation pathways of genes disclosed that PRKCB, SP1, GRB14, LYN, and ITGA5 are correlated with each other (Figure 3).

970435.fig.002
Figure 2: Enrichment analysis of GO and pathways for all genes. Yellow represents pathway directly or indirectly related to T2DM or obesity.
970435.fig.003
Figure 3: Correlation pathways of candidate genes. Red line, BioCarta; green line, GenMAPP; blue line, KEGG; number: counts of correlation pathways of two genes.

4. Discussion

Complex metabolic diseases are often caused by alterations in gene expression or metabolic pathways in various tissues. Here, we analyzed differences in gene expression levels in various human tissues from GEO datasets in T2DM- or obesity-control experiments with the -test. The values were adjusted using the Bonferroni or FDR method [20] to allow for multiple testing. We introduced strict criteria with FDR ≤ 0.05. However, with these criteria, no genes were filtered out in most groups (16 of 21 groups, Table S6), while the percentage of genes with -test values ≤ 0.05 was lower than 10% in most groups (15 of 21 groups, Table S6). Therefore, the -test statistic was ultimately applied for the present study. In total, we filtered out 21 candidate genes (8 for T2DM and 13 for obesity). The list of up- and downregulated candidate genes is provided in Supplementary Material (Table S7). Similarly, an eGWAS was performed across 130 independent experiments in human, rat, and mouse to identify additional genes implicated in the molecular pathogenesis of T2DM [21]. Interestingly, the same genes were not identified among the different studies. These discrepancies may be attributed to the use of various species, statistical methods, and tissues by different groups.

Analysis of the correlation pathways of the identified genes revealed that PRKCB, SP1, GRB14, LYN, and ITGA5 are correlated with each other (Figures 2 and 3). The proteins interact directly within cells or indirectly among different tissues in the etiological process of T2DM or obesity. PRKCB mediates Ca2+ and DAG-evoked insulin secretion processes in Langerhans’ cells [22], functions downstream of insulin-receptor substrate 1 (IRS1) in muscle cells, and participates in the regulation of glucose transport in adipocytes by negatively modulating insulin-stimulated translocation of the glucose transporter, SLC2A4/GLUT4 [23, 24]. Under high glucose conditions in pancreatic beta cells, PRKCB may be involved in the inhibition of insulin gene transcription [25]. In the present study, we observed PRKCB upregulation in skeletal muscle, islets, adipose tissue, and blood and downregulation in liver of T2DM individuals (Table S7). These findings suggest that PRKCB may be involved in IR and deficient -cell function in vivo. GRB14 binds directly to IR and regulates insulin-induced IR tyrosine phosphorylation [19]. GRB14-deficient mice display enhanced insulin signaling via IRS1 and AKT activation in liver and skeletal muscle, despite lower circulating insulin levels [18]. An earlier study showed increased GRB14 expression in adipose tissues of both ob/ob mice and Goto-Kakizaki (GK) rats, but no changes in liver [26]. In our experiments, GRB14 expression was similarly increased in subcutaneous adipose tissue of obese humans, while a decrease was observed in liver (Table S7). In addition, GRB14 is located within ±1 Mb of obese SNP rs10195252, and the rs10195252 T-allele is associated with increased GRB14 subcutaneous adipose tissue mRNA expression [27]. However, LYN is implicated in the insulin signaling pathway via phosphorylation of IRS1 and PI3 K in liver and adipose tissues [14]. The insulin secretagogue, glimepiride, activates LYN in adipocytes [28]. This indirect LYN activation may modulate glycemic control activity of glimepiride in the extrapancreatic environment [28, 29]. In the present study, LYN expression was increased in adipose tissue, skeletal muscle, and blood of T2DM individuals, while a decrease was observed in islets and liver (Table ). Moreover, LYN is a highly ranked gene with the highest differential expression percentage in the T2DM-control study (61.1%) and may therefore be a valuable candidate gene for future T2DM research. ITGA5 additionally promotes PI3 K and AKT phosphorylation [30]. ITGA5 expression was shown to be upregulated in adipose tissue of New Zealand obese (NZO) mice (high fat diet versus standard diet) [31]. We observed increased expression of ITGA5 in human subcutaneous adipose tissue (Table ). Moreover, ITGA5 is located within ±1 Mb of the obesity SNP, rs1443512. SP1 is a zinc finger transcription factor that binds to GC-rich motifs and may be involved in insulin-mediated glucose uptake through positively regulating Glut4 expression in adipose tissue, skeletal muscle, and heart [32, 33]. SP1 was downregulated in adipose tissue, while increased expression was observed in blood. Pathway analysis revealed the involvement of SP1 in oxidative stress and adipogenesis (Figure 2). SP1 not only is located within ±1 Mb of obesity SNP rs1443512 (similar to ITGA5), but also has the highest differential expression percentage (63.6%). Therefore, further studies are necessary to determine whether rs1443512 is related to ITGA5 or SP1 expression.

Differential expression of HIBADH ((+) ) was reported in liver mitochondria during development of Goto-Kakizaki (GK) rats [15]. We observed no changes in HIBADH expression in liver, while a decrease was evident in skeletal muscle and blood of humans with T2DM. In addition, HIBADH is located within ±1 Mb of T2DM SNPs, rs864745, and rs849134. The association of HIBADH with T2DM requires further evaluation.An earlier study reported higher BCAT1 expression in subcutaneous adipose tissue of females in the insulin-resistant than insulin-sensitive group [34]. Interestingly, higher BCAT1 expression was observed in subcutaneous adipose tissue of obese humans in this study. We additionally recorded an increase in blood and decrease in omental adipose tissue (Table ). BCAT1 has been identified as the optimal marker for weight regain [35]. Moreover, the rs2242400 polymorphism in BCAT1 appears to be associated with T2DM in more than one population [36]. SLC35B4 has been identified as a potential regulator of obesity and insulin resistance in mouse models. Both in vivo and in vitro studies in mice disclosed that decreased SLC35B4 expression is associated with a decrease in gluconeogenesis [17]. An increase in SLC35B4 expression was observed in subcutaneous adipose tissue of obese humans in our study (Table ). Interestingly, a SNP in the human SLC35B4 gene (rs1619682) is associated with waist circumference [16]. HSP90AB1 mRNA is reported to be upregulated in 3T3-L1 cells 6 h after stimulation of adipogenesis [37]. Moreover, HSP90AB1 is located near the obesity SNP, rs6905288. Expression levels of MYNN are negatively correlated to BW (body weight) in adipose tissues of F2 mice (C57BL/6J × TALLYHO/JngJ) [38]. Consistently, our data showed that MYNN expression is downregulated in subcutaneous adipose of obese humans (Table S7). Furthermore, SAP30BP may be involved in body mass index (BMI) in adipose tissue (Pearson correlation (−0.51)) [39]. A decrease in SAP30BP expression was detected in subcutaneous adipocytes of obese human subjects in the present study (Table S7).

The rest of the candidates, C2orf15, DENND1B, MRPL30, POC1B, RP4-655J12.3, TMBIM4, BMP2 K, CSRNP2, NCKAP5L, TOMM5, and BAP1, may be novel genes related to T2DM or obesity. TMBIM4 is located within ±1 Mb of the SNP rs1531343 conferring susceptibility to T2DM, while NCKAP5L, TOMM5, and BAP1 are mapped within ±1 Mb of SNPs conferring susceptibility to obesity. TMBIM4 encodes transmembrane BAX inhibitor motif containing 4, which inhibits apoptosis induced by intrinsic and extrinsic stimuli and modulates both capacitative Ca2+ entry and inositol 1,4,5-trisphosphate (IP3)-mediated Ca2+ release [40]. In our study, TMBIM4 was mainly upregulated in skeletal muscle, while downregulation was observed in liver (Table S7). The NCKAP5L gene encoding Nck-associated protein 5-like displayed upregulation in adipose tissue but was downregulated in blood (Table S7). TOMM5 encodes the mitochondrial import receptor subunit TOM5 homolog. TOMM5 was mainly involved in four GO terms (GO:0008565, protein transporter activity; GO:0015031, protein transport; GO:0005739, mitochondrion; GO:0005742, mitochondrial outer membrane). BAP1 (ubiquitin carboxyl-terminal hydrolase) localizes at the nucleus and contains three domains (a ubiquitin carboxyl-terminal hydrolase (UCH) with an N-terminal catalytic domain, a unique linker region, and a C-terminal domain). The UCH domain conveys deubiquitinase activity to BAP1 [41]. In flies and humans, the Polycomb repressive deubiquitinase (PR-DUB) complex is formed through interactions of BAP1 and ASXL1 [42]. DENND1B may promote the exchange of GDP with GTP and play a role in clathrin-mediated endocytosis [43]. The product of MRPL30 is a constituent of mitochondrial ribosomes. POC1B is involved in the early steps of centriole duplication and the later steps of centriole length control [44, 45]. The CSRNP2 protein binds to the consensus sequence, 5′-AGAGTG-3′, and has a transcriptional activator. However, C2orf15 and RP4-655J12.3 have been rarely reported in databases or publications to date. Associations of all new candidate genes identified in the present study with obesity or T2DM require verification in future analyses.

5. Conclusions

LYN, a gene reported to be involved in the insulin pathway, was highly ranked with the highest differential expression percentage in the T2DM-control study (61.1%) and may therefore be a valuable candidate gene for future T2DM research. NCKAP5L with the highest differential expression percentage (63.6%) was located within ±1 Mb of the obesity susceptibility SNP, rs7132908, and was thus identified as the most likely novel candidate gene for obesity. We conclude that analysis of gene expression in various tissues via GEO datasets is an effective and feasible method to identify novel or causal genes associated with T2DM and obesity.

Conflict of Interests

The authors declare that they have no conflict of interests.

Acknowledgments

This study was funded by the National Science and Technology Major Project of Key Drug Innovation and Development (2011ZX09307-303-03), the National Natural Science Foundation of China (31340045), the Science and Technology Planning Project of Guangdong Province, China (2012B060300006), the Fundamental Research Funds for the Central Universities (2012zz0091), and the BGI-SCUT Innovation Fund Project (SW20130802).

References

  1. M. Stumvoll, B. J. Goldstein, and T. W. Van Haeften, “Type 2 diabetes: principles of pathogenesis and therapy,” The Lancet, vol. 365, no. 9467, pp. 1333–1346, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. G. V. Z. Dedoussis, A. C. Kaliora, and D. B. Panagiotakos, “Genes, diet and type 2 diabetes mellitus: a review,” Review of Diabetic Studies, vol. 4, no. 1, pp. 13–24, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. K. M. West and J. M. Kalbfleisch, “Influence of nutritional factors on prevalence of diabetes,” Diabetes, vol. 20, no. 2, pp. 99–108, 1971. View at Google Scholar · View at Scopus
  4. S. M. Haffner, M. P. Stern, B. D. Mitchell, H. P. Hazuda, and J. K. Patterson, “Incidence of type II diabetes in Mexican Americans predicted by fasting insulin and glucose levels, obesity, and body-fat distribution,” Diabetes, vol. 39, no. 3, pp. 283–288, 1990. View at Google Scholar · View at Scopus
  5. S. Lillioja, D. M. Mott, M. Spraul et al., “Insulin resistance and insulin secretory dysfunction as precursors of non- insulin-dependent diabetes mellitus: prospective studies of Pima Indians,” The New England Journal of Medicine, vol. 329, no. 27, pp. 1988–1992, 1993. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Fall and E. Ingelsson, “Genome-wide association studies of obesity and metabolic syndrome,” Molecular and Cellular Endocrinology, 2012. View at Publisher · View at Google Scholar
  7. B. F. Voight, L. J. Scott, V. Steinthorsdottir et al., “Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis,” Nature Genetics, vol. 42, pp. 579–589, 2010. View at Google Scholar
  8. J. Dupuis, C. Langenberg, I. Prokopenko et al., “New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk,” Nature Genetics, vol. 42, pp. 105–116, 2010. View at Google Scholar
  9. J. P. Pandey, “Genomewide association studies and assessment of risk of disease,” The New England Journal of Medicine, vol. 363, no. 21, pp. 2076–2077, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Pollard Jr., A. J. Butte, S. Hoberman, M. Joshi, J. Levy, and J. Pappo, “A computational model to define the molecular causes of type 2 diabetes mellitus,” Diabetes Technology and Therapeutics, vol. 7, no. 2, pp. 323–336, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. H. P. Kang, X. Yang, R. Chen et al., “Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes,” Diabetologia, vol. 55, pp. 2205–2213, 2012. View at Google Scholar
  12. M. P. Keller, Y. Choi, P. Wang et al., “A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility,” Genome Research, vol. 18, no. 5, pp. 706–716, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. B. M. Bolstad, R. A. Irizarry, M. Åstrand, and T. P. Speed, “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias,” Bioinformatics, vol. 19, no. 2, pp. 185–193, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. G. Müller, S. Wied, and W. Frick, “Cross talk of pp125(FAK) and pp59(Lyn) non-receptor tyrosine kinases to insulin-mimetic signaling in adipocytes,” Molecular and Cellular Biology, vol. 20, no. 13, pp. 4708–4723, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. W.-J. Deng, S. Nie, J. Dai, J.-R. Wu, and R. Zeng, “Proteome, phosphoproteome, and hydroxyproteome of liver mitochondria in diabetic rats at early pathogenic stages,” Molecular and Cellular Proteomics, vol. 9, no. 1, pp. 100–116, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. C. S. Fox, N. Heard-Costa, L. A. Cupples, J. Dupuis, R. S. Vasan, and L. D. Atwood, “Genome-wide association to body mass index and waist circumference: the Framingham Heart Study 100K project,” BMC Medical Genetics, vol. 8, supplement 1, article S18, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. S. N. Yazbek, D. A. Buchner, J. M. Geisinger et al., “Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis,” Genome Research, vol. 21, no. 7, pp. 1065–1073, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. G. J. Cooney, R. J. Lyons, A. J. Crew et al., “Improved glucose homeostasis and enhanced insulin signalling in Grb14-deficient mice,” The EMBO Journal, vol. 23, no. 3, pp. 582–593, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. L. J. Holt, R. J. Lyons, A. S. Ryan et al., “Dual ablation of Grb10 and Grb14 in mice reveals their combined role in regulation of insulin signaling and glucose homeostasis,” Molecular Endocrinology, vol. 23, no. 9, pp. 1406–1414, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Pawitan, S. Michiels, S. Koscielny, A. Gusnanto, and A. Ploner, “False discovery rate, sensitivity and sample size for microarray studies,” Bioinformatics, vol. 21, no. 13, pp. 3017–3024, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Kodama, M. Horikoshi, K. Toda et al., “Expression-based genome-wide association study links the receptor CD44 in adipose tissue with type 2 diabetes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, pp. 7049–7054, 2012. View at Google Scholar
  22. H. Zhang, M. Nagasawa, S. Yamada, H. Mogami, Y. Suzuki, and I. Kojima, “Bimodal role of conventional protein kinase C in insulin secretion from rat pancreatic β cells,” Journal of Physiology, vol. 561, no. 1, pp. 133–147, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. M. L. Standaert, L. Galloway, P. Karnam, G. Bandyopadhyay, J. Moscat, and R. V. Farese, “Protein kinase C-ζ as a downstream effector of phosphatidylinositol 3- kinase during insulin stimulation in rat adipocytes. Potential role in glucose transport,” Journal of Biological Chemistry, vol. 272, no. 48, pp. 30075–30082, 1997. View at Publisher · View at Google Scholar · View at Scopus
  24. D. C. Wright, C. A. Fick, J. B. Olesen, and B. W. Craig, “Evidence for the involvement of a phospholipase C—protein kinase C signaling pathway in insulin stimulated glucose transport in skeletal muscle,” Life Sciences, vol. 73, no. 1, pp. 61–71, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. C. M. Taniguchi, B. Emanuelli, and C. R. Kahn, “Critical nodes in signalling pathways: insights into insulin action,” Nature Reviews Molecular Cell Biology, vol. 7, no. 2, pp. 85–96, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Cariou, N. Capitaine, V. Le Marcis et al., “Increased adipose tissue expression of Grb14 in several models of insulin resistance,” The FASEB Journal, vol. 18, no. 9, pp. 965–967, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. D. Schleinitz, N. Kloting, C. M. Lindgren et al., “Fat depot-specific mRNA expression of novel loci associated with waist-hip ratio,” International Journal of Obesity, 2013. View at Publisher · View at Google Scholar
  28. G. Müller, “The molecular mechanism of the insulin-mimetic/sensitizing activity of the antidiabetic sulfonylurea drug Amaryl,” Molecular Medicine, vol. 6, no. 11, pp. 907–933, 2000. View at Google Scholar · View at Scopus
  29. G. Müller, A. Schulz, S. Wied, and W. Frick, “Regulation of lipid raft proteins by glimepiride- and insulin-induced glycosylphosphatidylinositol-specific phospholipase C in rat adipocytes,” Biochemical Pharmacology, vol. 69, no. 5, pp. 761–780, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Urtasun, A. Lopategi, J. George et al., “Osteopontin, an oxidant stress sensitive cytokine, up-regulates collagen-I via integrin αVβ3 engagement and PI3K/pAkt/NFκB signaling,” Hepatology, vol. 55, no. 2, pp. 594–608, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Balwierz, A. Polus, U. Razny et al., “Angiogenesis in the New Zealand obese mouse model fed with high fat diet,” Lipids in Health and Disease, vol. 8, article 13, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. B. B. Kahn, “Glucose transport: pivotal step in insulin action,” Diabetes, vol. 45, no. 11, pp. 1644–1654, 1996. View at Google Scholar · View at Scopus
  33. J. Rüegg, W. Cai, M. Karimi et al., “Epigenetic regulation of glucose transporter 4 by estrogen receptor β,” Molecular Endocrinology, vol. 25, no. 12, pp. 2017–2028, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. J. Soronen, P.-P. P.-P. Laurila, J. Naukkarinen et al., “Adipose tissue gene expression analysis reveals changes in inflammatory, mitochondrial respiratory and lipid metabolic pathways in obese insulin-resistant subjects,” BMC Medical Genomics, vol. 5, article 9, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. N. Viguerie, E. Montastier, J. J. Maoret et al., “Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status, and cis genetic regulation,” PLOS Genetics, vol. 8, Article ID e1002959, 2012. View at Google Scholar
  36. E. Rampersaud, C. M. Damcott, M. Fu et al., “Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the old order amish: evidence for replication from diabetes-related quantitative traits and from independent populations,” Diabetes, vol. 56, no. 12, pp. 3053–3062, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. C. Fromm-Dornieden, S. von der Heyde, O. Lytovchenko et al., “Novel polysome messages and changes in translational activity appear after induction of adipogenesis in 3T3-L1 cells,” BMC Molecular Biology, p. 13, article 9, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. T. P. Stewart, H. Y. Kim, A. M. Saxton, and J. H. Kim, “Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice,” BMC Genomics, vol. 11, no. 1, article 713, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Naukkarinen, I. Surakka, K. H. Pietiläinen et al., “Use of genome-wide expression data to mine the “Gray Zone” of GWA studies leads to novel candidate obesity genes,” PLoS Genetics, vol. 6, no. 6, Article ID e1000976, 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. C. Gubser, D. Bergamaschi, M. Hollinshead, X. Lu, F. J. M. van Kuppeveld, and G. L. Smith, “A new inhibitor of apoptosis from vaccinia virus and eukaryotes,” PLoS Pathogens, vol. 3, no. 2, p. e17, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. D. E. Jensen, M. Proctor, S. T. Marquis et al., “BAP1: a novel ubiquitin hydrolase which binds to the BRCA1 RING finger and enhances BRCA1-mediated cell growth suppression,” Oncogene, vol. 16, no. 9, pp. 1097–1112, 1998. View at Google Scholar · View at Scopus
  42. H. Abbaszadegan, K. Von Sivers, and U. Jonsson, “Late displacement of Colles' fractures,” International Orthopaedics, vol. 12, no. 3, pp. 197–199, 1988. View at Google Scholar · View at Scopus
  43. S.-I. Yoshimura, A. Gerondopoulos, A. Linford, D. J. Rigden, and F. A. Barr, “Family-wide characterization of the DENN domain Rab GDP-GTP exchange factors,” Journal of Cell Biology, vol. 191, no. 2, pp. 367–381, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. L. C. Keller, S. Geimer, E. Romijn, J. Yates, I. Zamora, and W. F. Marshall, “Molecular architecture of the centriole proteome: the conserved WD40 domain protein POC1 is required for centriole duplication and length control,” Molecular Biology of the Cell, vol. 20, no. 4, pp. 1150–1166, 2009. View at Publisher · View at Google Scholar · View at Scopus
  45. C. G. Pearson, D. P. S. Osborn, T. H. Giddings Jr., P. L. Beales, and M. Winey, “Basal body stability and ciliogenesis requires the conserved component Poc1,” Journal of Cell Biology, vol. 187, no. 6, pp. 905–920, 2009. View at Publisher · View at Google Scholar · View at Scopus