Computational and Mathematical Methods in Medicine

Computational and Mathematical Methods in Medicine / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 6936175 | 8 pages |

Epigenetic Profiles Reveal That ADCYAP1 Serves as Key Molecule in Gestational Diabetes Mellitus

Academic Editor: Esmaeil Ebrahimie
Received12 Dec 2018
Revised21 Jan 2019
Accepted24 Jul 2019
Published14 Aug 2019


Gestational diabetes mellitus (GDM) refers to the condition which shows abnormal glucose metabolism that occurs during pregnancy, while normal glucose metabolism before pregnancy. In the present study, a novel analytical procedure was used to explore the key molecule of gestational diabetes mellitus. First, the weighted pathway model was carried out subsequently to eliminate the gene-overlapping effects among pathways. Second, we assessed the enriched pathways by a combination of Fisher’s t-test and the Mann–Whitney U test. We carried out the functional principal component analysis by estimating F values of genes to identify the hub genes in the enriched pathways. Results showed that a total of 4 differential pathways were enriched. The key pathway was considered as the insulin secretion pathway. F values of each gene in the key pathway were calculated. Three hub molecules were identified as hub differentially methylated genes, namely, CAMK2B, ADCYAP1, and KCNN2. In addition, by further comparing the gene expression data in a validation cohort, one key molecule was obtained, ADCYAP1. Therefore, ADCYAP1 may serve as a potential target for the treatment of GDM.

1. Introduction

Gestational diabetes mellitus (GDM) is the most common medical complication of pregnancy, characterized by glucose intolerance which did not occur before pregnancy but becomes clinically apparent in the late stage of fetation [1]. Glucose metabolism in most GDM patients returns to normal after delivery, but there is an increased chance of developing type 2 diabetes, metabolic syndrome, and cerebrovascular disease in the future [2]. High blood sugar in the womb environment is also very unfavorable to the fetus, increasing the risk of macrosomia and premature delivery and abortion rates, fetal growth restriction (FGR), conferring a predisposition for obesity, neonatal respiratory distress syndrome, cardiovascular complications, and neonatal hypoglycemia [1]. The complication caused by GDM is more complicated and harmful to both mothers and their child.

In view of the serious consequences of GDM, more and more researchers are committed to the study of the dynamic changes in pathogenesis and molecular mechanism of GDM. The occurrence of GDM is related to the formation of insulin resistance in patients. It has been reported that the placenta secretes large amounts of anti-insulin hormones during pregnancy [3]. Inflammatory factors such as C-reactive protein, tumor necrosis factor, serum proteins, interleukin, and other chronic inflammatory reactions were involved in the occurrence of GDM [4, 5]. Analysis of gene methylation data could help us to reveal the relationship between methylation and disease [6]. Epigenome analyses showed that the occurrence of GDM is closely related to changes in gene methylation and expression, especially in the human leukocyte antigens [1]. Several recent reports have described that gene methylation and its expression profile are altered in GDM [79]. Research studies showed that distinctive high-level expression changes of genes are associated with their lower promoter methylation prior to the onset of GDM [8]. Although associations between GDM and changes to the epigenomic and genomic profiles for genes have been studied, the molecular landscape changes for pathway level during the GDM are still substantially unknown. It has been elucidated recently that activation of mammalian target of rapamycin (mTOR) pathway upregulated functions of insulin secretion and pancreatic β-cells proliferation. Diverse regulation of the mTOR pathway is involved in functions of pancreatic β-cells as well as the development of the obstetric complications studied [10].

Currently, researchers use a dynamically modified pipeline, named FUNNEL-GSEA, to analyze the biological mechanisms of gene sets. This kind of model combined the functional principal component analysis and an elastic net regression model [11, 12]. Different from the classic regression analysis, which is a predictive modeling technique that studies the relationship between dependent and independent variables, those techniques were often used for both predictive analysis and causal inference among variables [13]. There are two advances in the methods currently available. First, designed comparisons or regression analyses were not only applied to the comparison between control and experimental groups but also effectively exploited individual information in the group of transcriptomic measurements. Therefore, in this model, one could assess the differences in gene changes among different individuals or time points. Second, it overcomes the problem that overlapping genes, which refers to genes that exist in multiple pathways, play multiple roles in hypothesis testing, where the weight coefficients are overestimated [14].

In our research, we first applied the preweight pathway model to find the enriched signaling pathways of GDM using DNA methylation profiles. Second, we used functional principal component analysis (FPCA) to identify hub genes in the significantly changed pathways. Finally, the gene expression of key molecular pathways in GDM was further tested in an independent cohort.

2. Methods

2.1. Data Recruitment and Preprocess

DNA methylation data for GDM were deposited in the Gene Expression Omnibus database at GSE70453. A total of 82 sample data were divided into two groups, namely, cases with gestational diabetes and controls without gestational diabetes. The GDM cases contained 41 samples (GDM), and the matched pregnancies (control) contained 41 samples.

A total of 10 GDM patients’ tissues and 10 age- and body mass index-matched normal tissues for further validation were obtained from the Department of Obstetrics, First People’s Hospital of Jining between June 2016 and June 2017. The decidua basalis placental sampling methods were used as Binder et al. [1]. The umbilical cord was immediately frozen in liquid nitrogen and stored at −80°C after delivery. This study was reviewed and approved by the Ethics Committee of First People’s Hospital of Jining (Shandong, China).

2.2. Screening for Differentially Methylated Genes

Microarray data contain 473,864 CpG of methylation sites. CpG sites were eliminated when they met the following three types of probes: (1) distance from CpG to single-nucleotide polymorphism (SNP) is less than or equal to 2; (2) minimum equipotential frequency (MAF) less than 0.05; and (3) cross-hybridized probes and probes on sex chromosomes. A total of 426,693 CpGs were kept for further study. In this paper, β values represented percentage methylation changes ranging between 0 and 1. Mean β values of GDM and normal population were calculated, respectively. We took the mean value of all the related CpGs as the methylation level of a gene since differentially methylated probes may functionally implicate more distal genes. Differentially methylated CpGs were identified at the threshold of and then kept for further study.

2.3. Estimating the Weights of Genes in Each Pathway

Genes that present in multiple pathways case the “overlapping problems” which were often overestimated in the enrichment analysis [13]. We basically used the FUNNEL-GSEA model with some modifications to deal with our methylation data [15, 16]. We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway as our database [17]. The weights of the overlapping genes can be obtained bywhere ki is all the pathways which contain gene k. β is the vector of gene coefficients, which is set as 1 here. Therefore, the weights of the overlapped genes would be estimated. The model above would decompose an overlapping gene between gene sets and eliminate the effects of overlapping genes [14].

2.4. Assessment of Significant Enriched Pathways

Pathway analysis was used to find out significant pathways of the GDM. In this study, Fisher’s exact test and the Mann–Whitney U (MWU) test were carried out to select enriched pathways. The Mann–Whitney U (MWU) test is a rank-based nonparametric test that usually is used in a competitive gene set enrichment analysis. The MWU test utilized the gene weight value to test whether the weight of this gene is significantly greater than other genes of the differential pathway (background genes) [18]. Combined with Fisher’s exact test, the final value was calculated aswhere is the final value of a pathway i, while and represent the values calculated from the MWU test and Fisher’s exact test, respectively. The list of differential methylated genes was assessed as gene list, while the whole genome was set as the background. Pathways with and gene count >1 were extracted and were considered as enriched pathways.

2.5. Estimating the F-Statistics of Genes in the Enriched Pathways Using the FPCA Model

In this model, each gene gets an F value [14]. The mean methylation of each gene is subtracted, and FPCA is adopted across all the centered methylation values. Each gene methylation value is calculated according to the following functions:

In the above formula, is the FPC score which could quantify how much can be explained by . represents the temporal sample average expression, and represents the lth eigenfunction.

We net use functional F-statistics to summarize the gene pattern information for each gene.where is the residual sum of squares of null hypotheses, represents the residual sum of squares of alternative hypotheses, could be considered as a “signal-to-noise” ratio, and revealed the importance of genes [19]. Genes with higher F value indicate higher importance.

2.6. Identification of Gene Expression of Hub Genes Using Fresh-Frozen Umbilical Cord Tissue by Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR)

Total RNA was extracted from cells or tissues using TRIzol (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA). cDNA synthesis was performed at 37°C for 15 min and then 85°C for 5 sec using reverse transcriptase (Applied Biosystems; Thermo Fisher Scientific, Inc.) following the manufacturer protocol. qPCR was conducted with the ABI 7500 system (Applied Biosystems; Thermo Fisher Scientific, Inc.) using SYBR-Green (Takara Biotechnology Co., Ltd., Jinan, China). PCR was performed for 25 cycles of 10 sec at 98°C, 10 sec at 55°C, and 20 sec at 72°C. The primer sequences used were as follows: CAMK2B: forward, 5′-TACGAGGATATTGGCAAGGG-3′ and reverse, 5′-GCT TCT GGT GAT AGT GTG C-3′; ADCYAP1: forward, 5′-ATC CTT AAC GAG GCC TAC C-3′ and reverse, 5′-CAT TTG TTT CCG GTA GCG G-3′; KCNN2: forward, 5′-CCA GGA ACT GTA CTC TTG GT-3′ and reverse, 5′-ATCATGGTACCTTTCACAAGC-3′; GAPDH: forward, 5′-ACA CCC ACT CCT CCA CCT TT-3′ and reverse, 5′-TTA CTC CTT GGA GGC CAT GT-3′. mRNA expression levels were normalized using GAPDH. Fold changes were counted using the 2-ΔΔCt method.

3. Results

3.1. Identification of Differentially Methylated Genes

With the threshold of , a total of 2310 differentially methylated CpGs (covering 1520 genes) were obtained. Among the 2310 methylated CpGs, 851 of the CpGs were down-methylated and 1459 of the CpGs were up-methylated in the GDM group. Figure 1(a) shows the volcanic map of differentially methylated CpGs. According to the threshold, 2310 differentially methylated CpGs initially extracted were subjected to further filtering to obtain the high differentially methylated CpGs. CpGs meeting were retained, resulting in 87 differentially methylated CpGs covered 87 genes. The top 10 differentially methylated CpGs are shown in Figure 1(b).

3.2. Screening for Significantly Enriched Pathways Using a Preweighted Pathway Database

Given a gene associated with multiple gene sets, we assume that the overlapping genes are activated by all gene sets to which they belong. Estimated weights were assigned as 1/n, where n is the number of gene sets that this gene is associated with. Pathway enrichment analysis of GDM was conducted on the basis of the KEGG pathway database. A total of 286 pathways covered 6893 genes were obtained. Figure 2(a) shows the distribution of weights of all pathway genes, while Figure 2(b) shows the distribution of sum weights of all pathways and Figure 2(c) shows the weights of genes in the 4 enriched pathways. Based on the preweighted pathway database, 4 differential pathways were yielded. Table 1 shows the differential signaling pathways in ascending order based on the final value.

Pathway nameFisher’s exact testMWU testFinal valueCountTotal

hsa04740: olfactory transduction0.00140.1260832530.001311408
hsa05215: prostate cancer0.00430.1641162730.00261989
hsa04911: insulin secretion0.00080.0943566840.00911686
hsa05031: amphetamine addiction0.00010.497765610.00921568

Count: number of genes in a pathway. Total: total number of genes in a pathway.

After Fisher’s exact test and the Mann–Whitney U (MWU) test, 4 members of the pathway are shown in Table 1: olfactory transduction, prostate cancer, insulin secretion, and amphetamine addiction. These 4 signaling pathways may play important roles in the occurrence of GDM. The insulin secretion pathway was considered as the most important pathway and kept for further identification of key molecules in this pathway since it has been widely approved to be associated with GDM [3, 20, 21]. The insulin secretion pathway here contained 16 differentially methylated genes. Figure 3 shows the heatmap of DNA methylation level of genes in the insulin secretion pathway.

3.3. FPCA Analysis of Expression Profile for Hub Genes in the Enriched Pathways

The FPCA model was used to identify hub genes in the enriched pathways. FPCA could effectively utilize the time series information and overcome the traditional control design deficiencies [14]. Each gene got an F value. Higher F value indicated a higher activation in their pathways. Figure 4(a) shows the distribution of the F value of all pathway genes. Figure 4(b) shows the F value of all genes in the insulin secretion pathway. Genes and their F values were listed in descending order. The top 3 genes (CAMK2B, ADCYAP1, and KCNN2) with high F values in the insulin secretion pathway were selected for further validation.

3.4. Validation of the Gene Expression of Hub Genes

To investigate the relationship between the methylation and the gene expression of hub genes in GDM, the additional cohort was used to identify the expressions of hub genes in GDM. The expression profiles of GDM and normal control groups of our cohort were used. By assessing the RNA expression data of hub genes, significant changes between GDM and normal control groups were found in the gene expression of ADCYAP1. The expression levels of CAMK2B, ADCYAP1, and KCNN2 between GDM and normal control groups are shown in Figure 5.

4. Discussion

GDM refers to a varying degree of impaired glucose tolerance occurring for the first time during pregnancy, excluding patients who were with diabetes previous to gestation but were first diagnosed during pregnancy [1]. In China, the incidence of GDM is about 5%–7%, and there is an increasing trend [22]. According to the results of traditional research methods, the etiology of GDM is closely related to insulin resistance. In recent years, with the development of molecular genetics, molecular immunology, and bioinformatics, more and more studies have shown that many factors such as life style, β-cell dysfunction, inflammatory factors, and adipokines are involved in the development of GDM [2325].

Epigenetics refers to heritable changes in gene function that occurs under the condition of not changing the DNA sequence, including DNA methylation, genomic imprinting, maternal effects, gene silencing, and RNA editing [26]. As one of the important epigenetic phenomena, DNA methylation plays an important regulatory role in the gene expression. DNA methylation is closely related to the occurrence and development of many diseases, such as type 2 diabetes [27], autoimmune diseases, and various cancers [28, 29].

In this study, GDM pathogenesis was analyzed using bioinformatics, including KEGG enrichment method, functional principal component analysis (FPCA), elastic net regression, and the Mann–Whitney U test. According to this new analytical procedure, four signaling pathways for olfactory transduction, prostate cancer, insulin secretion, and amphetamine addiction were found out. There were some genes involved in the enriched pathways which were related to GDM. Herein, one differentially methylated key molecule was identified: adenylate cyclase activating polypeptide 1 (ADCYAP1). ADCYAP1 gene encodes a pituitary adenylate cyclase activating polypeptide (PACAP). PACAP is a secreted proprotein with the ability to activate adenylyl cyclase, which is a membrane-bound enzyme that converts ATP to cAMP. ADCY3 is one of the adenylate cyclases that participates in the insulin secretion pathway and also identified as a key molecule/gene by our analysis. Those results provide a novel insight into GDM diagnosis and therapy. Numerous studies demonstrate that PACAP and adenylyl cyclase have a potential role in islet physiology and as a basis for development of islet-promoting therapy in diabetes [3032]. Adenylyl cyclase is an effector in the G protein-coupled system [33], and its enzymatic activity is under the control of several hormones, including insulin [34, 35]. PACAP and adenylyl cyclase were capable of influencing pancreatic islet function by stimulating pancreatic beta cells to secrete insulin and glucagon [36]. For the clinical treatment of type 2 diabetes, PACAP and adenylyl cyclase were also thought to be effective due to stimulation of insulin secretion [37] and increased proliferation and differentiation of β cells [38]. In addition, PACAP is also a neurotransmitter and a member of the vasoactive intestinal peptide/secretin/glucagon peptide superfamily. PACAP shows highly potent neuroprotective and general cytoprotective effects [39]. PACAP is also protective in diabetes-induced pathologies, like retinopathy and nephropathy [4042]. Consistent with those studies, our results showed that several signal transduction pathways, such as olfactory transduction pathway, were enriched as well.

There are limitations present in our study. Unfortunately, the methylation data along with the expression data for samples were unprovided. Further study will be directly tested for the most significant CpGs that impact upon each hub gene since multiple CpGs could impact the same gene.

5. Conclusions

Based on the analytical results of the present study, there was significant ADCYAP1 methylation and gene expression differences between GDM and normal control groups. GDM was associated with insulin resistance and insulin-signaling system may require ADCYAP1 participation [43]. We speculate that ADCYAP1 may be related to the GDM, and more experimental data were needed to support our prediction.

Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


  1. A. M. Binder, J. Larocca, C. Lesseur, C. J. Marsit, and K. B. Michels, “Epigenome-wide and transcriptome-wide analyses reveal gestational diabetes is associated with alterations in the human leukocyte antigen complex,” Clinical Epigenetics, vol. 7, no. 1, pp. 1–12, 2015. View at: Publisher Site | Google Scholar
  2. A. Vaag, C. Brøns, L. Gillberg et al., “Genetic, nongenetic and epigenetic risk determinants in developmental programming of type 2 diabetes,” Acta Obstetricia et Gynecologica Scandinavica, vol. 93, no. 11, pp. 1099–1108, 2014. View at: Publisher Site | Google Scholar
  3. E. P. Gunderson, M. M. Hedderson, V. Chiang et al., “Lactation intensity and postpartum maternal glucose tolerance and insulin resistance in women with recent GDM: the SWIFT cohort,” Diabetes Care, vol. 35, no. 1, pp. 50–56, 2012. View at: Publisher Site | Google Scholar
  4. A. Syngelaki, G. H. A. Visser, K. Krithinakis, A. Wright, and K. H. Nicolaides, “First trimester screening for gestational diabetes mellitus by maternal factors and markers of inflammation,” Metabolism, vol. 65, no. 3, pp. 131–137, 2016. View at: Publisher Site | Google Scholar
  5. Y. Yang, J. Yang, G. Zhong, and W. Xu, “Potential risk factor of pre-eclampsia among healthy Chinese women: a retrospective case control study,” Biomedical Research, vol. 28, no. 3, pp. 1183–1188, 2017. View at: Google Scholar
  6. H. Lee and M. Shin, “Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data,” Biodata Mining, vol. 10, no. 1, p. 3, 2017. View at: Publisher Site | Google Scholar
  7. L. Bouchard, S. Thibault, S.-P. Guay et al., “Leptin gene epigenetic adaptation to impaired glucose metabolism during pregnancy,” Diabetes Care, vol. 33, no. 11, pp. 2436–2441, 2010. View at: Publisher Site | Google Scholar
  8. J. Alexander, A. M. Teague, J. Chen et al., “Offspring sex impacts DNA methylation and gene expression in placentae from women with diabetes during pregnancy,” PLoS One, vol. 13, no. 2, Article ID e0190698, 2018. View at: Publisher Site | Google Scholar
  9. P. Wu, W. E. Farrell, K. E. Haworth et al., “Maternal genome-wide DNA methylation profiling in gestational diabetes shows distinctive disease-associated changes relative to matched healthy pregnancies,” Epigenetics, vol. 13, no. 2, pp. 122–128, 2018. View at: Publisher Site | Google Scholar
  10. K. Xu, D. Bian, L. Hao et al., “MicroRNA-503 contribute to pancreatic beta cell dysfunction by targeting the mTOR pathway in gestational diabetes mellitus,” EXCLI Journal, vol. 16, pp. 1177–1187, 2017. View at: Publisher Site | Google Scholar
  11. F. Al-Shahrour, P. Minguez, J. Tarraga et al., “BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments,” Nucleic Acids Research, vol. 34, pp. W472–W476, 2006. View at: Publisher Site | Google Scholar
  12. D. Tabas-Madrid, R. Nogales-Cadenas, and A. Pascual-Montano, “GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics,” Nucleic Acids Research, vol. 40, no. W1, pp. W478–W483, 2012. View at: Publisher Site | Google Scholar
  13. C. J. Lu, J. Y. Wu, and T. S. Lee, “Application of independent component analysis preprocessing and support vector regression in time series prediction,” in Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, pp. 468–471, IEEE, Sanya, China, April 2009. View at: Publisher Site | Google Scholar
  14. Y. Zhang, D. J. Topham, J. Thakar, and X. Qiu, “FUNNEL-GSEA: functional elastic-net regression in time-course gene set enrichment analysis,” Bioinformatics, vol. 33, no. 13, pp. 1944–1952, 2017. View at: Publisher Site | Google Scholar
  15. J. O. Ogutu, T. Schulz-Streeck, and H.-P. Piepho, “Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions,” BMC Proceedings, vol. 6, no. S2, p. S10, 2012. View at: Publisher Site | Google Scholar
  16. X. Li and Y. Fang, “Bioinformatics identification of potential genes and pathways in preeclampsia based on functional gene set enrichment analyses,” Experimental and Therapeutic Medicine, vol. 18, no. 3, pp. 1837–1844, 2019. View at: Publisher Site | Google Scholar
  17. J. Zhu and X. Yao, “Use of DNA methylation for cancer detection: promises and challenges,” The International Journal of Biochemistry & Cell Biology, vol. 41, no. 1, pp. 147–154, 2009. View at: Publisher Site | Google Scholar
  18. C.-A. Brandsma, M. van den Berge, D. S. Postma et al., “A large lung gene expression study identifying fibulin-5 as a novel player in tissue repair in COPD,” Thorax, vol. 70, no. 1, pp. 21–32, 2015. View at: Publisher Site | Google Scholar
  19. S. Wu and H. Wu, “More powerful significant testing for time course gene expression data using functional principal component analysis approaches,” BMC Bioinformatics, vol. 14, no. 1, p. 6, 2013. View at: Publisher Site | Google Scholar
  20. B. Liu, Y. Xu, C. Voss et al., “Altered protein expression in gestational diabetes mellitus placentas provides insight into insulin resistance and coagulation/fibrinolysis pathways,” PLoS One, vol. 7, no. 9, Article ID e44701, 2012. View at: Publisher Site | Google Scholar
  21. V. Pisprasert, K. H. Ingram, M. F. Lopez-Davila, A. J. Munoz, and W. T. Garvey, “Limitations in the use of indices using glucose and insulin levels to predict insulin sensitivity: impact of race and gender and superiority of the indices derived from oral glucose tolerance test in African Americans,” Diabetes Care, vol. 36, no. 4, pp. 845–853, 2013. View at: Publisher Site | Google Scholar
  22. M. Anand, M. Anand, and D. S. Mahajan, “To study the incidence of gestational diabetes mellitus and risk factors associated with GDM,” International Journal of Advances in Medicine, vol. 4, no. 1, p. 112, 2017. View at: Publisher Site | Google Scholar
  23. P. Molęda, A. Fronczyk, K. Safranow, and L. Majkowska, “Adipokines and β-cell dysfunction in normoglycemic women with previous gestational diabetes mellitus,” Polish Archives of Internal Medicine, vol. 125, no. 9, pp. 641–648, 2015. View at: Publisher Site | Google Scholar
  24. S. K. Abell, B. D. Courten, J. A. Boyle, and H. Teede, “Inflammatory and other biomarkers: role in pathophysiology and prediction of gestational diabetes mellitus,” International Journal of Molecular Sciences, vol. 16, no. 12, pp. 13442–13473, 2015. View at: Publisher Site | Google Scholar
  25. M. R. Al-Badri, M. S. Zantout, and S. T. Azar, “The role of adipokines in gestational diabetes mellitus,” Therapeutic Advances in Endocrinology & Metabolism, vol. 6, no. 3, pp. 103–108, 2015. View at: Publisher Site | Google Scholar
  26. A. P. Feinberg and M. D. Fallin, “Epigenetics at the crossroads of genes and the environment,” JAMA, vol. 314, no. 11, pp. 1129-1130, 2015. View at: Publisher Site | Google Scholar
  27. H. R. Elliott, H. A. Shihab, G. A. Lockett et al., “Role of DNA methylation in type 2 diabetes etiology: using genotype as a causal anchor,” Diabetes, vol. 66, no. 6, pp. 1713–1722, 2017. View at: Publisher Site | Google Scholar
  28. M. J. Ziller, H. Gu, F. Müller et al., “Charting a dynamic DNA methylation landscape of the human genome,” Nature, vol. 500, no. 7463, pp. 477–481, 2013. View at: Publisher Site | Google Scholar
  29. B. Sun, L. Hu, Z.-Y. Luo, X.-P. Chen, H.-H. Zhou, and W. Zhang, “DNA methylation perspectives in the pathogenesis of autoimmune diseases,” Clinical Immunology, vol. 164, pp. 21–27, 2016. View at: Publisher Site | Google Scholar
  30. M. Basille, B. J. Gonzalez, L. Desrues, M. Demas, A. Fournier, and H. Vaudry, “Pituitary adenylate cyclase-activating polypeptide (PACAP) stimulates adenylyl cyclase and phospholipase C activity in rat cerebellar neuroblasts,” Journal of Neurochemistry, vol. 65, no. 3, pp. 1318–1324, 2002. View at: Publisher Site | Google Scholar
  31. M. Solymar, I. Ivic, M. Balasko et al., “Pituitary adenylate cyclase-activating polypeptide ameliorates vascular dysfunction induced by hyperglycaemia,” Diabetes and Vascular Disease Research, vol. 15, no. 4, pp. 277–285, 2018. View at: Publisher Site | Google Scholar
  32. B. Ahrén, “Role of pituitary adenylate cyclase-activating polypeptide in the pancreatic endocrine system,” Annals of the New York Academy of Sciences, vol. 1144, no. 1, pp. 28–35, 2008. View at: Publisher Site | Google Scholar
  33. J. Shengunther, C. M. Wang, G. M. Poage et al., “Molecular pap smear: HPV genotype and DNA methylation of ADCY8, CDH8, and ZNF582 as an integrated biomarker for high-grade cervical cytology,” Clinical Epigenetics, vol. 8, no. 1, p. 96, 2016. View at: Publisher Site | Google Scholar
  34. D. Vaudry, B. J. Gonzalez, M. Basille, L. Yon, A. Fournier, and H. Vaudry, “Pituitary adenylate cyclase-activating polypeptide and its receptors: from structure to functions,” Pharmacological Reviews, vol. 52, no. 2, pp. 269–324, 2000. View at: Google Scholar
  35. L. A. Kuznetsova, S. A. Plesneva, T. S. Sharova, M. N. Pertseva, and A. O. Shpakov, “Regulation of adenylyl cyclase signaling system by insulin, biogenic amines and glucagon at their separate and combined action in muscle membranes of mollusc Anodonta cygnea,” Journal of Evolutionary Biochemistry and Physiology, vol. 49, no. 2, pp. 145–152, 2013. View at: Publisher Site | Google Scholar
  36. K. Filipsson, F. Sundler, J. Hannibal, and B. Ahrén, “PACAP and PACAP receptors in insulin producing tissues: localization and effects,” Regulatory Peptides, vol. 74, no. 2-3, pp. 167–175, 1998. View at: Publisher Site | Google Scholar
  37. M. S. Winzell and B. Ahrén, “Role of VIP and PACAP in islet function,” Peptides, vol. 28, no. 9, pp. 1805–1813, 2007. View at: Publisher Site | Google Scholar
  38. K. Yamamoto, H. Hashimoto, S. Tomimoto et al., “Overexpression of PACAP in transgenic mouse pancreatic beta-cells enhances insulin secretion and ameliorates streptozotocin-induced diabetes,” Diabetes, vol. 52, no. 5, pp. 1155–1162, 2003. View at: Publisher Site | Google Scholar
  39. D. Reglodi, T. Atlasz, A. Jungling et al., “Alternative routes of administration of the neuroprotective pituitary adenylate cyclase activating polypeptide,” Current Pharmaceutical Design, vol. 24, no. 33, pp. 3892–3904, 2018. View at: Publisher Site | Google Scholar
  40. K. Szabadfi, A. Szabo, P. Kiss et al., “PACAP promotes neuron survival in early experimental diabetic retinopathy,” Neurochemistry International, vol. 64, pp. 84–91, 2014. View at: Publisher Site | Google Scholar
  41. E. Banki, P. Degrell, P. Kiss et al., “Effect of PACAP treatment on kidney morphology and cytokine expression in rat diabetic nephropathy,” Peptides, vol. 42, pp. 125–130, 2013. View at: Publisher Site | Google Scholar
  42. A. G. D’Amico, G. Maugeri, D. M. Rasà et al., “Modulation of IL-1β and VEGF expression in rat diabetic retinopathy after PACAP administration,” Peptides, vol. 97, pp. 64–69, 2017. View at: Publisher Site | Google Scholar
  43. Y. Li, C. Hadden, P. Singh et al., “GDM-associated insulin deficiency hinders the dissociation of SERT from ERp44 and down-regulates placental 5-HT uptake,” Proceedings of the National Academy of Sciences, vol. 111, no. 52, pp. E5697–E5705, 2014. View at: Publisher Site | Google Scholar

Copyright © 2019 Xue Li 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.

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