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Journal of Diabetes Research
Volume 2016 (2016), Article ID 7684038, 9 pages
Research Article

Bioinformatic Evaluation of Transcriptional Regulation of WNT Pathway Genes with reference to Diabetic Nephropathy

1Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, UK
2Conway Institute, University College Dublin, Dublin 4, Ireland

Received 28 November 2014; Revised 18 May 2015; Accepted 24 May 2015

Academic Editor: Georgia Fousteri

Copyright © 2016 Gareth J. McKay 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.


Objective. WNT/β-catenin pathway members have been implicated in interstitial fibrosis and glomerular sclerosis disease processes characteristic of diabetic nephropathy (DN), processes partly controlled by transcription factors (TFs) that bind to gene promoter regions attenuating regulation. We sought to identify predicted cis-acting transcription factor binding sites (TFBSs) overrepresented within WNT pathway members. Methods. We assessed 62 TFBS motif frequencies from the JASPAR databases in 65 WNT pathway genes. values were estimated on the hypergeometric distribution for each TF. Gene expression profiles of enriched motifs were examined in DN-related datasets to assess clinical significance. Results. Transcription factor AP-2 alpha (TFAP2A), myeloid zinc finger 1 (MZF1), and specificity protein 1 (SP1) were significantly enriched within WNT pathway genes ( values < 6.83 × 10−29, 1.34 × 10−11, and 3.01 × 10−6, resp.). MZF1 expression was significantly increased in DN in a whole kidney dataset (fold change = 1.16; 16% increase; ). TFAP2A expression was decreased in an independent dataset (fold change = −1.02; ). No differential expression of SP1 was detected. Conclusions. Three TFBS profiles are significantly enriched within WNT pathway genes highlighting the potential of in silico analyses for identification of pathway regulators. Modification of TF binding may possibly limit DN progression, offering potential therapeutic benefit.

1. Introduction

Diabetic nephropathy (DN) is a microvascular complication of diabetes and the most frequent cause of end-stage renal disease (ESRD) in western populations [1]. Approximately one-third of those with prolonged duration of diabetes will develop DN regardless of their glycemic control [2]. The earliest phase of DN is characterized by kidney hypertrophy and an increased glomerular filtration rate (GFR) with later progression resulting in pathological changes in the kidney including expansion of mesangial matrix, glomerular sclerosis, and interstitial fibrosis. Clinical features of DN include persistent proteinuria, hypertension, and progressive decline in GFR. ESRD secondary to DN necessitates costly renal replacement therapies, such as dialysis and renal transplantation. However, a subset of individuals with poorly controlled type 1 diabetes (T1D) do not develop DN [3]. A strong predisposition to DN exists with an increased estimated sibling risk (2.3-fold) supporting an underlying genetic susceptibility to this diabetic complication [4]. In addition, evidence in support of variation in ethnic genetic DN susceptibility has been previously reported [5].

Renal interstitial fibrosis and glomerular sclerosis are characteristic hallmarks of DN and several studies have implicated members of the WNT/β-catenin pathways in these disease processes [69]. The WNT pathways can be separated into canonical β-catenin dependent and noncanonical β-catenin independent pathways (Figure 1). Canonical WNT signalling is integral to many developmental processes and associated variants have been identified in multiple WNT pathway members with respect to many complex diseases such as familial adenomatous polyposis coli, colorectal and hepatocellular cancers, type 2 diabetes, and schizophrenia [10]. Noncanonical WNT signalling remains less well characterized, partly as a consequence of further subdivisions into the WNT/Ca2+ and the WNT planar cell polarity pathways. The WNT/β-catenin pathways have been reported to alter cytoskeletal reorganization and activation of the JNK and MAPK signalling [11, 12], directly affecting mesangial cell motility and adherence resulting in blunting of mesangial cell reaction to dynamic mechanical forces (a key mesangial cell function).

Figure 1: The canonical Wnt signalling pathway implicated in diabetic nephropathy. When a WNT ligand binds to the Frizzled (FZD) and the LRP5/6 coreceptors, the Dvl protein is recruited and inhibits the binding of β-catenin, leading to an increase in cytoplasmic β-catenin levels and subsequent activation of downstream signalling targets (a). In the absence of any WNT ligand (b), β-catenin is sequestered by the Axin complex containing such proteins as GSK3-β, APC, CK1, βTrCP, and axin itself. β-catenin is then phosphorylated and ubiquitinated and enters the proteasomal degradation pathway and leading to subsequent repression of downstream signalling targets.

In vitro epithelial-to-mesenchymal transition (EMT) is induced by TGF-β1 [13], an integrin-linked kinase which promotes renal fibrosis. Both the canonical WNT pathway and TGF-β1 require activation of β-catenin implicating both β-catenin and the WNT pathway in the regulation of EMT [14]. In addition, the β-catenin phosphorylating protein, GSK3-β, is responsible for subsequent proteasomal degradation and has been reported to inhibit transition to a mesenchymal phenotype in human embryonic stem cells [15]. Differential gene expression profiles for several WNT ligands, FZD receptors, and β-catenin have been identified in the unilateral ureteral obstructed (UUO) mouse model of renal injury [6]. Independently, the WNT signalling antagonist, Dickkopf-1 (DKK1), was reported to promote hyperglycemia-induced matrix expansion in rat mesangial cells [7].

Differential gene expression of many developmental and pathological processes is partly controlled by transcription factors (TFs), proteins that bind to the promoter regions of genes affecting their transcription to mRNA [16]. Transcriptional regulation is modulated primarily by upstream elements in the DNA sequence. RNA polymerase II is directed to the transcription start site (TSS) by a series of “general transcription factors” (GTFs) necessary for in vitro transcription [17] which assemble approximately 25 to 30 base pairs (bp) upstream of the TSS and typically contain motifs such as the TATA box [18].

While the GTFs interact with a mediator forming a large complex to initiate transcription in vivo [19], additional influences are exerted by upstream elements located both proximally and distally from the TSS. Enhancer and repressor elements can initiate, amplify, reduce, or inhibit transcription of a given gene and various TFs bind to these regulatory elements [20]. The TFs are proteins or protein complexes that contain DNA-binding and activation domains which recognize specific sequence motifs and act on some target in the transcriptional machinery or the surrounding chromatin structure in order to modulate transcription [21]. A TF can recognize different sequence elements across many genes providing a mechanism for the coordinated expression of multiple genes or pathways in parallel by a single element.

Previously, we have assessed common genetic variants within key WNT pathway genes for association with DN [22, 23] and there is evidence that many WNT pathway genes are differentially regulated in the pathogenesis of DN [69]. In this study we sought to identify cis-acting regulatory elements in groups of coregulated genes by searching for an overrepresentation of known TF binding motifs within the promoters of WNT pathway genes and compare these to a background set of sequences, typically other gene promoters within the genome [2426]. We used TF binding site (TFBSs) data from the JASPAR database [27] on 65 known WNT pathway genes to identify common transcriptional regulatory mechanisms associated with the WNT pathway.

Although current therapeutic options have been shown to reduce proteinuria and retard DN progression, recent studies highlight that, despite improved care, the higher risks of cardiovascular disease, ESRD, and mortality associated with DN persist [28]. As such, identification of genetic factors that may influence susceptibility to and development of DN can help identify novel pathophysiologic mechanisms as potential therapeutic targets to improve the adverse clinical outcomes that currently exist in diabetic patients. Despite several genome wide association studies to investigate common genetic variants and more recent identification of rarer variants though whole exome sequencing, the genetic architecture of DN remains poorly understood [3, 29, 30]. Given the low level of genetic variation associated with DN, we hypothesized if overrepresentation of TFBS motifs in WNT pathway gene members might influence genetic risk and offer future potential therapeutic target pathways.

2. Methods

Research ethics approval was obtained from the South and West Multicentre Research Ethics Committee (MREC/98/6/71) and Queens University Belfast Research Ethics Committee.

2.1. Identification of WNT Pathway Genes

We used the KEGG database which is a repository that stores pathway based information and “molecular circuit” maps to identify 65 targets for investigation [31].

2.2. Definition of Promoter Sequences and Identification of TFBSs

The JASPAR database (2010; catalogues 68 human TF position frequency matrices, although six were incomplete for the purpose of reconstructing position weight matrices using the programming language R. Upstream DNA sequence information for 65 WNT pathway genes was interrogated for recognized binding sites relating to the 62 annotated TFs documented in the JASPAR database amenable to analysis. Frequency correlations for observed TF motifs identified 5,000 bp upstream of WNT pathway genes were compared to ~28,000 gene sequences from genome build hg19/GCRh37.3 ( values for each TF were estimated on their hypergeometric distribution which describes the probability of successes in draws from a finite population without replacement. This distribution was used to model the probability of finding a TFBS more frequently in a chosen set of gene promoter sequences than in a set of random gene promoters alone [24].

2.3. Clinical Evaluation of Enriched Transcription Factor Expression Profiles

The clinical significance of gene expression profiles of enriched motifs was examined from several DN-related datasets. Nephromine combines a rapidly growing compendium of publicly available human renal gene expression profiles with a sophisticated analysis engine and an application for data mining and visualization of gene expression data ( The Schmid diabetes dataset (hereafter referred to as whole kidney) is characterized within Nephromine and comprises expression data from cDNA microarrays of whole kidney biopsies from healthy living donors (), cadaveric donors (), minimal change disease patients (), and DN donors () [32]. The Woroniecka datasets in Nephromine comprise nine diabetic kidney disease (DKD) microdissected glomeruli analysed against thirteen control glomeruli and ten DKD tubulointerstitium and vascular compartments (hereafter referred to as tubulointerstitium) evaluated against twelve control tubulointerstitium samples [33]. Additional data examining the effect of the proinsulin C-peptide on the profibrotic actions of TGF-β1 [34] was obtained from the Gene Expression Omnibus (GEO) database. This data was generated from HK-2 cells (immortalised human proximal renal tubular cells [35]) for three control samples and three TGF-β1 (2 ng/mL) samples treated for 48 hours (hereafter referred to as HK-2 + TGF-β) and analysed using the gcrma R/bioconductor package for data normalization (, with changes in expression estimated using the limma R/bioconductor package for the computation of fold change (, test statistics, and values. Expression levels, fold change, and significance were assessed for each TF.

3. Results

3.1. Motif Enrichment Analysis

Motif enrichment analysis () was completed on the promoter regions of 65 WNT pathway genes and ~28,000 NCBI documented genes as comparative controls, focusing on 5,000 bp regions upstream from the TSS. The total number of binding sites for each TF/gene was also calculated with only those motifs where the confidence score exceeded 95% included (Figure 2). A Bonferroni correction for multiple testing established a significance threshold level of (). The TFBS motifs transcription factor AP-2 alpha (TFAP2A), myeloid zinc finger 1 (MZF1), and specificity protein 1 (SP1) were identified as significantly enriched within the WNT pathway dataset compared to the background gene set with values estimated at 6.83 × 10−29, 1.34 × 10−11, and 3.01 × 10−6, respectively and are represented as probability sequence motifs (Figure 3).

Figure 2: Total count for transcription factor binding site sequence motifs per WNT pathway gene. Red, green, and blue represent the counts for TFAP2A, MZF1, and SP1, respectively. Only those motifs identified with a confidence score greater than 95% were included.
Figure 3: Enrichment analysis of JASPAR transcription factor binding sites (TFBSs). Putative TFBSs were evaluated in both the WNT pathway gene set and across the entire genome for enrichment using the hypergeometric distribution with Bonferroni correction for multiple testing (the red line indicates ). TFAP2A, MZF1, and SP1 were significantly enriched across WNT pathway genes (, 1.34 × 10−11, and 3.01 × 10−6, resp.). TFBS sequence motifs are illustrated with the height of each base indicative of the probability of the presence of the corresponding base at the designated position.

Clinical evaluation of enriched transcription factor expression profiles showed significant increased gene expression of MZF1 in DN in the whole kidney dataset with a fold change of 1.16 (16% increase, ; Figure 4). TFAP2A gene expression was decreased in the HK-2 + TGF-β dataset with a fold change of −1.02 (; Figure 4). SP1 did not show any differential expression in the datasets examined.

Figure 4: Expression of enriched transcription factors from four independent datasets. Log2 expression values evaluated in Nephromine ( from whole kidney (WK [32]) biopsies, glomerulus (Glom [33]) biopsies, tubulointerstitium (Tubulo [33]) biopsies, and from HK-2 cells treated with TGF-β for 48 hours (TGF-β [34]). MZF1 gene expression was significantly increased in diabetic nephropathy (DN) whole kidney tissue compared to non-DN control (fold change = 1.16; ). TFAP2A gene expression was decreased in HK-2 cells treated with TGF-β compared to untreated HK-2 cells (−1.02-fold change; ).
3.2. Pair-Wise Correlation of Motifs

The total number of binding sites for each TF/gene was calculated (Figure 2) and Spearman’s rank correlation test performed to estimate the correlation between the number of TFBSs identified and each TF examined (Figure 5). TFAP2A was more strongly correlated with MZF1 (; ) and less so with SP1 (; ). MZF1 was also significantly correlated with SP1 (; ).

Figure 5: Correlation coefficients calculated between transcription factor binding sites (TBFSs) to estimate potential interaction and regulatory control of Wnt pathway genes. The number of binding sites identified for each motif was compared using Spearman’s rank correlation. TFAP2A and MZF1 (; ); TFAP2A and SP1 (; ); MZF1 and SP1 (; ).

4. Discussion

The in silico approach adopted in this study to assess transcription factor binding motif enrichment has predicted three TFBSs to be significantly enriched within the WNT pathway genes examined. These transcription factors have been reported previously in relation to cancer biology and other cellular processes involved in the pathogenesis of DN, such as regulating epithelial-to-mesenchymal transition, TGF-β signalling, and fibrogenesis. A common regulatory mechanism underpinning these processes with respect to DN may offer a promising potential therapeutic target. Hyperglycemia has been shown to downregulate WNT signalling resulting in increased TGF-β and fibronectin expression in glomerular mesangial cells. Induced upregulation of WNT4, WNT5a, and stabilization of cytosolic β-catenin have been reported to minimize the damaging effects of TGF-β1 induced fibronectin expression, in a manner similar to that observed through pharmacological inhibition of GSK3-β [8].

TFAP2A is a TF involved in the regulation of multiple developmental processes, such as neural crest formation and kidney development [36]. The activator protein-2 (TFAP2) family of transcription factors includes five closely related proteins TFAP2A-E [37]. Reduced expression levels of TFAP2A have been associated with increased metastatic capability in breast cancer [38] with poor prognosis reported in gastric adenocarcinoma patients [39]. Reduction of TFAP2A in extravillous trophoblasts reduces EGF-dependent invasion, as well as levels of MMP-2 and urokinase plasminogen activator, proteins involved in extracellular matrix degradation [40]. Polymorphic variants within TFAP2A have also been shown to interact directly with APC and β-catenin preventing β-catenin from associating with TCF4 and thus blocking transcription of WNT-responsive genes in colorectal cancer cells [41].

Myeloid zinc finger 1 (MZF1, also known as ZNF42) is a two-domain TF, with each domain containing four and nine zinc finger arrangements recognizing separate but similar sequences [42]. The motif found to be enriched in our study corresponds with the first four zinc finger domain. MZF1 plays a key role in embryonic stem cell hematopoietic differentiation, yet its canonical function involves regulation of genes associated with growth, differentiation, and apoptosis of cells during myeloid lineage [43]. Much like TFAP2A, MZF1 has been reported in relation to multiple cancers. In colorectal and cervical cancer cells overexpressed MZF1 was shown to induce migration and invasion. MZF1 has also been implicated in increased expression of PKCα in hepatocellular carcinoma with reported reduction in invasion, migration, and proliferation in these cells with MZF1 siRNA [44].

SP1 is reported to regulate many processes including expression of genes modulating angiogenesis, apoptosis, cell growth, differentiation, and immune response [45]. The functionality of SP1 is cell specific leading to different or even opposing roles depending on the cellular context. Treatment with TGF-β has been shown to reduce SP1 expression in human articulated chondrocyte cells but increases SP1 expression in skin cells [46, 47]. SP1 also leads to downregulation of TGF-βRI and TGF-βRII following treatment with TGF-β [47]. Fibroblasts treated with TGF-β have increased levels of SP1 and subsequently type 1 collagen synthesis. Subsequent blockade of SP1 induction leads to a reduced collagen response [46]. Zhang and colleagues [48] identified four SP1 binding sites in the putative promoter region of the adiponectin gene ADIPOQ (adipocyte C1q and collagen domain containing) providing evidence of reduced promoter transcriptional activity as a result of genetic variation. Regulation of genes, such as GAPDH, insulin-like growth factor, calmodulin, and PAI1 by insulin, has also shown to be, at least in part, mediated by SP1 [49].

A major limitation of this motif enrichment analysis is reflected in the relatively small number of human TFBS motifs represented in the JASPAR database () which is limited to investigating the effects of cis-acting elements only to the exclusion of trans-acting factors. The human genome encodes numerous transcription factors, many of which remain unidentified and may potentially modulate genes involved in the WNT pathway directly or indirectly. Gene regulation at the transcriptional level is multifaceted with multiple epigenetic mechanisms such as DNA methylation and histone modification involved, further compounding the level of complexity. Nevertheless, the motif enrichment analysis of the 65 WNT genes in this study identified three motifs that were represented significantly more frequently among WNT pathway genes than across the genome and, as such, are likely to represent major regulatory mechanisms that govern the expression, activation, and functions of the WNT pathways.

TFAP2A and MZF1 have both been implicated in the regulation of genes that control tumour invasiveness and metastases and the pathological process of EMT is known to underpin many cancer types with evidence supporting its role in metastatic cancer cells [50]. Given the role of EMT in renal fibrosis and the putative role of the WNT pathways in the aetiology of DN, our data suggests a role for these transcription factors in the pathogenesis of DN. In addition, SP1’s role in the regulation of TGF-β signalling and collagen production suggests an influence on the disease processes involved in DN. Improved transcriptional control mechanisms may offer potential therapeutic targets for the treatment of disorders such as DN, which may result as a consequence of aberrant WNT pathway mechanisms.

5. Conclusions

Our findings highlight the merit of utilizing in silico analyses for the prediction of TFBSs and key regulators of WNT pathway genes, particularly when considered in conjunction with gene expression data. Insights into the pathological processes and molecular mechanisms which contribute to the progression of DN have important therapeutic implications. Modifications of TF binding to promoter regions of genes involved in these processes have been shown to reduce the rate of DN progression in several models of diabetes [51]. Refinement of targeted therapeutic strategies to modify transcriptional control of disease processes will become possible through clearer delineation of their role.


DN:Diabetic nephropathy
ESRD:End-stage renal disease
TF:Transcription factor
TFBS:Transcription factor binding site
TSS:Transcription start site
GFR:Glomerular filtration rate
GTFs:General transcription factors
mRNA:Messenger ribonucleic acid
bp:Base pairs
HK2:Human proximal renal tubular cells
GEO:Gene Expression Omnibus
EMT:Epithelial-to-mesenchymal transition
ng/mL:Nanograms per millilitre.

Conflict of Interests

The authors declare that they have no conflict of interests.

Authors’ Contribution

David H. Kavanagh, Gareth J. McKay, John K. Crean, and Alexander P. Maxwell designed the analysis and developed the project. David H. Kavanagh performed the analysis. Gareth J. McKay and David H. Kavanagh wrote the paper. Alexander P. Maxwell, John K. Crean, and David H. Kavanagh critically read and contributed to the paper. All authors read and approved the final paper. Gareth J. McKay and David H. Kavanagh contributed equally to this work.


David H. Kavanagh was supported by a Northern Ireland Kidney Research Fund (NIKRF) PhD studentship. The authors thank the NIKRF and Northern Ireland Research and Development Office for supporting this work.


  1. J. Gilg, C. Castledine, and D. Fogarty, “Chapter 1 UK RRT incidence in 2010: national and centre-specific analyses,” Nephron Clinical Practice, vol. 120, supplement 1, pp. c1–c27, 2012. View at Google Scholar
  2. C. J. Hill, C. R. Cardwell, C. C. Patterson et al., “Chronic kidney disease and diabetes in the national health service: a cross-sectional survey of the U.K. national diabetes audit,” Diabetic Medicine, vol. 31, no. 4, pp. 448–454, 2014. View at Publisher · View at Google Scholar
  3. N. Sandholm, R. M. Salem, A. J. McKnight et al., “New susceptibility loci associated with kidney disease in type 1 diabetes,” PLoS Genetics, vol. 8, no. 9, Article ID e1002921, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. V. Harjutsalo, S. Katoh, C. Sarti, N. Tajima, and J. Tuomilehto, “Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes,” Diabetes, vol. 53, no. 9, pp. 2449–2454, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. B. I. Freedman, C. D. Langefeld, L. Lu et al., “Differential effects of MYH9 and APOL1 risk variants on FRMD3 association with diabetic ESRD in African Americans,” PLoS Genetics, vol. 7, no. 6, Article ID e1002150, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. W. He, C. Dai, Y. Li, G. Zeng, S. P. Monga, and Y. Liu, “Wnt/β-catenin signaling promotes renal interstitial fibrosis,” Journal of the American Society of Nephrology, vol. 20, no. 4, pp. 765–776, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. C.-L. Lin, J.-Y. Wang, J.-Y. Ko, Y.-T. Huang, Y.-H. Kuo, and F.-S. Wang, “Dickkopf-1 promotes hyperglycemia-induced accumulation of mesangial matrix and renal dysfunction,” Journal of the American Society of Nephrology, vol. 21, no. 1, pp. 124–135, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Ho, P.-H. Lee, Y.-C. Hsu, F.-S. Wang, Y.-T. Huang, and C.-L. Lin, “Sustained Wnt/β-catenin signaling rescues high glucose induction of transforming growth factor-β1-mediated renal fibrosis,” American Journal of the Medical Sciences, vol. 344, no. 5, pp. 374–382, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. N. Faherty, H. O'Donovan, D. Kavanagh et al., “TGFβ and CCN2/CTGF mediate actin related gene expression by differential E2F1/CREB activation,” BMC Genomics, vol. 14, article 525, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Steinberg, S. de Jong, O. A. Andreassen et al., “Common variants at VRK2 and TCF4 conferring risk of schizophrenia,” Human Molecular Genetics, vol. 20, no. 20, Article ID ddr325, pp. 4076–4081, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. M. T. Veeman, J. D. Axelrod, and R. T. Moon, “A second canon: functions and mechanisms of β-catenin-independent Wnt signaling,” Developmental Cell, vol. 5, no. 3, pp. 367–377, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Habas and I. B. Dawid, “Dishevelled and Wnt signaling: is the nucleus the final frontier?” Journal of Biology, vol. 4, no. 1, article 2, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Liu, “New insights into epithelial-mesenchymal transition in kidney fibrosis,” Journal of the American Society of Nephrology, vol. 21, no. 2, pp. 212–222, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Ivanova, M. J. Butt, and D. G. Matsell, “Mesenchymal transition in kidney collecting duct epithelial cells,” The American Journal of Physiology—Renal Physiology, vol. 294, no. 5, pp. F1238–F1248, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. U. Ullmann, C. Gilles, M. De Rycke, H. Van de Velde, K. Sermon, and I. Liebaers, “GSK-3-specific inhibitor-supplemented hESC medium prevents the epithelial-mesenchymal transition process and the up-regulation of matrix metalloproteinases in hESCs cultured in feeder-free conditions,” Molecular Human Reproduction, vol. 14, no. 3, pp. 169–179, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Levine and R. Tjian, “Transcription regulation and animal diversity,” Nature, vol. 424, no. 6945, pp. 147–151, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Orphanides, T. Lagrange, and D. Reinberg, “The general transcription factors of RNA polymerase II,” Genes and Development, vol. 10, no. 21, pp. 2657–2683, 1996. View at Publisher · View at Google Scholar · View at Scopus
  18. T. I. Lee and R. A. Young, “Transcription of eukaryotic protein-coding genes,” Annual Review of Genetics, vol. 34, pp. 77–137, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. J. W. Conaway, L. Florens, S. Sato et al., “The mammalian mediator complex,” FEBS Letters, vol. 579, no. 4, pp. 904–908, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. G. A. Maston, S. K. Evans, and M. R. Green, “Transcriptional regulatory elements in the human genome,” Annual Review of Genomics and Human Genetics, vol. 7, pp. 29–59, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Ptashne and A. Gann, “Transcriptional activation by recruitment,” Nature, vol. 386, no. 6625, pp. 569–577, 1997. View at Publisher · View at Google Scholar · View at Scopus
  22. D. H. Kavanagh, D. A. Savage, C. C. Patterson et al., “Association analysis of canonical Wnt signalling genes in diabetic nephropathy,” PLoS ONE, vol. 6, no. 8, Article ID e23904, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. D. H. Kavanagh, D. A. Savage, C. C. Patterson et al., “Haplotype association analysis of genes within the WNT signalling pathways in diabetic nephropathy,” BMC Nephrology, vol. 14, no. 1, article 126, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. R. Elkon, C. Linhart, R. Sharan, R. Shamir, and Y. Shiloh, “Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells,” Genome Research, vol. 13, no. 5, pp. 773–780, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. W. W. Wasserman and A. Sandelin, “Applied bioinformatics for the identification of regulatory elements,” Nature Reviews Genetics, vol. 5, no. 4, pp. 276–287, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. R. C. McLeay and T. L. Bailey, “Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data,” BMC Bioinformatics, vol. 11, article 165, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. J. C. Bryne, E. Valen, M.-H. E. Tang et al., “JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update,” Nucleic Acids Research, vol. 36, no. 1, pp. D102–D106, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. I. H. de Boer, T. C. Rue, Y. N. Hall, P. J. Heagerty, N. S. Weiss, and J. Himmelfarb, “Temporal trends in the prevalence of diabetic kidney disease in the United States,” The Journal of the American Medical Association, vol. 305, no. 24, pp. 2532–2539, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. M. G. Pezzolesi, G. D. Poznik, J. C. Mychaleckyj et al., “Genome-wide association scan for diabetic nephropathy susceptibility genes in type 1 diabetes,” Diabetes, vol. 58, no. 6, pp. 1403–1410, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. J. A. Bonomo, M. Guan, M. C. Ng et al., “The ras responsive transcription factor RREB1 is a novel candidate gene for type 2 diabetes associated end-stage kidney disease,” Human Molecular Genetics, vol. 23, no. 24, pp. 6441–6447, 2014. View at Publisher · View at Google Scholar
  31. M. Kanehisa, S. Goto, M. Furumichi, M. Tanabe, and M. Hirakawa, “KEGG for representation and analysis of molecular networks involving diseases and drugs,” Nucleic Acids Research, vol. 38, no. 1, pp. D355–D360, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Schmid, A. Boucherot, Y. Yasuda et al., “Modular activation of nuclear factor-κB transcriptional programs in human diabetic nephropathy,” Diabetes, vol. 55, no. 11, pp. 2993–3003, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. K. I. Woroniecka, A. S. D. Park, D. Mohtat, D. B. Thomas, J. M. Pullman, and K. Susztak, “Transcriptome analysis of human diabetic kidney disease,” Diabetes, vol. 60, no. 9, pp. 2354–2369, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. C. E. Hills, G. B. Willars, and N. J. Brunskill, “Proinsulin C-peptide antagonizes the profibrotic effects of TGF-β1 via up-regulation of retinoic acid and HGF-related signaling pathways,” Molecular Endocrinology, vol. 24, no. 4, pp. 822–831, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. M. J. Ryan, G. Johnson, J. Kirk, S. M. Fuerstenberg, R. A. Zager, and B. Torok-Storb, “HK-2: an immortalized proximal tubule epithelial cell line from normal adult human kidney,” Kidney International, vol. 45, no. 1, pp. 48–57, 1994. View at Publisher · View at Google Scholar · View at Scopus
  36. D. Eckert, S. Buhl, S. Weber, R. Jäger, and H. Schorle, “The AP-2 family of transcription factors,” Genome Biology, vol. 6, no. 13, article 246, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. F. Orso, D. Corà, B. Ubezio, P. Provero, M. Caselle, and D. Taverna, “Identification of functional TFAP2A and SP1 binding sites in new TFAP2A-modulated genes,” BMC Genomics, vol. 11, no. 1, article 355, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. J. Pellikainen, V. Kataja, K. Ropponen et al., “Reduced nuclear expression of transcription factor AP-2 associates with aggressive breast cancer,” Clinical Cancer Research, vol. 8, no. 11, pp. 3487–3495, 2002. View at Google Scholar · View at Scopus
  39. W. Wang, L. Lv, K. Pan et al., “Reduced expression of transcription factor AP-2α is associated with gastric adenocarcinoma prognosis,” PLoS ONE, vol. 6, no. 9, Article ID e24897, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. K. Biadasiewicz, S. Sonderegger, P. Haslinger et al., “Transcription factor AP-2α promotes EGF-dependent invasion of human trophoblast,” Endocrinology, vol. 152, no. 4, pp. 1458–1469, 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. Q. Li and R. H. Dashwood, “Activator protein 2alpha associates with adenomatous polyposis coli/beta-catenin and inhibits beta-catenin/T-cell factor transcriptional activity in colorectal cancer cells,” The Journal of Biological Chemistry, vol. 279, no. 44, pp. 45669–45675, 2004. View at Publisher · View at Google Scholar · View at Scopus
  42. J. F. Morris, R. Hromas, and F. J. Rauscher III, “Characterization of the DNA-binding properties of the myeloid zinc finger protein MZF1: two independent DNA-binding domains recognize two DNA consensus sequences with a common G-rich core,” Molecular and Cellular Biology, vol. 14, no. 3, pp. 1786–1795, 1994. View at Google Scholar · View at Scopus
  43. L. Bavisotto, K. Kaushansky, N. Lin, and R. Hromas, “Antisense oligonucleotides from the stage-specific myeloid zinc finger gene MZF-1 inhibit granulopoiesis in vitro,” Journal of Experimental Medicine, vol. 174, no. 5, pp. 1097–1101, 1991. View at Publisher · View at Google Scholar · View at Scopus
  44. Y.-H. Hsieh, T.-T. Wu, J.-H. Tsai, C.-Y. Huang, Y.-S. Hsieh, and J.-Y. Liu, “PKCα expression regulated by Elk-1 and MZF-1 in human HCC cells,” Biochemical and Biophysical Research Communications, vol. 339, no. 1, pp. 217–225, 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. N. Y. Tan and L. M. Khachigian, “Sp1 phosphorylation and its regulation of gene transcription,” Molecular and Cellular Biology, vol. 29, no. 10, pp. 2483–2488, 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. A. K. Ghosh, Y. Mori, E. Dowling, and J. Varga, “Trichostatin A blocks TGF-β-induced collagen gene expression in skin fibroblasts: involvement of Sp1,” Biochemical and Biophysical Research Communications, vol. 354, no. 2, pp. 420–426, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. C. Baugé, O. Cauvard, S. Leclercq, P. Galéra, and K. Boumédiene, “Modulation of transforming growth factor beta signalling pathway genes by transforming growth factor beta in human osteoarthritic chondrocytes: involvement of Sp1 in both early and late response cells to transforming growth factor beta,” Arthritis Research and Therapy, vol. 13, no. 1, article R23, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. D. Zhang, J. Ma, K. Brismar, S. Efendic, and H. F. Gu, “A single nucleotide polymorphism alters the sequence of SP1 binding site in the adiponectin promoter region and is associated with diabetic nephropathy among type 1 diabetic patients in the Genetics of Kidneys in Diabetes Study,” Journal of Diabetes and its Complications, vol. 23, no. 4, pp. 265–272, 2009. View at Publisher · View at Google Scholar · View at Scopus
  49. S. L.-A. Samson and N. C. W. Wong, “Role of Sp1 in insulin regulation of gene expression,” Journal of Molecular Endocrinology, vol. 29, no. 3, pp. 265–279, 2002. View at Publisher · View at Google Scholar · View at Scopus
  50. J. Yang and R. A. Weinberg, “Epithelial-mesenchymal transition: at the crossroads of development and tumor metastasis,” Developmental Cell, vol. 14, no. 6, pp. 818–829, 2008. View at Publisher · View at Google Scholar · View at Scopus
  51. A. P. Sanchez and K. Sharma, “Transcription factors in the pathogenesis of diabetic nephropathy,” Expert Reviews in Molecular Medicine, vol. 11, article e13, 2009. View at Publisher · View at Google Scholar · View at Scopus