Introduction

Molecular Medicine Reports

1791-2997 1791-3004

D.A. Spandidos

10.3892/mmr.2020.11281

mmr-22-03-1868

Articles

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis

Yana

1 Li

Yihang

1 Li

Guang

1 Lu

Haitao

1Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Jinghong, Yunnan 666100, P.R. China 2Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China

Correspondence to: Dr Guang Li, Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, 138 Xuanwei Road, Jinghong, Yunnan 666100, P.R. China, E-mail: lhbg311@hotmail.com Professor Haitao Lu, Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, 800 Dongchuan Road, Minxing, Shanghai 200240, P.R. China, E-mail: haitao.lu@sjtu.edu.cn

092020

26062020

22 3 1868 1882 20032019 20012020

2020

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β-cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein-protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in ‘inflammatory response’ and ‘lipid metabolic process’, and 37 downregulated candidate genes were involved in ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. These candidate genes were also involved in different signaling pathways associated with ‘PI3K/Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ and ‘MAPK signaling pathway’, which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)-target gene regulatory network and a transcription factor-target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa-miR-192-5p, hsa-miR-124-5p and hsa-miR-335-5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C-endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C-C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic-based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.

type 2 diabetes differentially expressed genes functional analysis protein-protein interaction subcellular location transcription factors microRNA

Introduction

Type 2 diabetes mellitus (T2DM) has become the third main chronic non-infectious disease following tumors and cardiovascular disease, and threatens human health worldwide (1). In total, ~425 million adults are currently living with diabetes in the world, with the majority of cases being T2DM (2). The International Diabetes Federation reported that in 2045, ~629 million individuals globally will suffer from diabetes, of which ~90% will be T2DM (2). It is well known that insulin resistance and pancreatic β-cell dysfunction are major pathophysiological characteristics of T2DM. Pancreatic β-cells are needed to yield more insulin to meet mounting requirements when insulin resistance occurs (3). Previous studies of pancreatic β-cells provide a basis for improved insight into the pathogenesis and pathophysiology of T2DM, as pancreatic β-cells help in the regulation of the blood glucose level (4,5). T2DM is a complex, polygenic disease that results from the interplay of environmental and genetic factors. Candidate gene association high-throughput methods have been carried out to uncover the genetic aspects of the pathogenesis of T2DM (6–8).

In recent years, single gene research and genome-wide association studies have determined genetic susceptibility genes for the increased risk of T2DM (9–11). Previous studies of gene expression in T2DM demonstrated that decreased expression of insulin (12,13), and a reduced expression of syntaxin 1A and transcription factor 7 like 2 contributed to impaired insulin secretion (12,14,15). The downregulation of FXYD domain containing ion transport regulator 2-stimulated β-cell proliferation (13) and the upregulation of genes δ like non-canonical Notch ligand 1, diacylglycerol kinase β and zinc finger MIZ-type containing 1 were implicated in T2DM (11,16). Previous genetic studies identified several dozen genes leading to monogenic diabetes due to impaired insulin secretion (17,18). These genes play a key role in pancreatic β-cell lineage, phenotype and function. How genetic and epigenetic factors are involved in β-cell development, proliferation, differentiation and function requires further investigation. Understanding of the underlying mechanisms is vital to the development of new therapeutic methods to prevent β-cell dysfunction and failure in the development of T2DM. The identification of T2DM candidate genes has been challenging in biomedical research and the majority of the genes have yet to be discovered. The aim of the present study was to contribute to research efforts to identify the biological markers and signaling pathways associated with T2DM. These molecular mechanisms may provide insight for aspects of T2DM pathogenesis or pathophysiology.

High-throughput sequencing is becoming an important tool, extensively applied in life sciences, including in cancer detection (19–21) and for identifying global gene expression changes in T2DM (22). Knowledge of the subcellular localization of proteins provides new insight into protein function and the complex pathways that modulate biological processes on a sub-cellular level, contributing to the current understanding of the proteins that interact with each other and with other molecules in the cellular environment (23). Accordingly, subcellular proteomics, as an important step to functional proteomics, has been the focus of the prediction of subcellular protein location, which is associated with molecular cell biology, proteomics, systems biology and drug discovery (24–26); it is used to better understand complex diseases (27), such as breast cancer (28), ovarian carcinoma (29), ischemic dilated cardiomyopathy (30), esophageal squamous cell carcinoma (31) and asthma (32). It was previously demonstrated that an integrative analysis of gene expression and a protein-protein interaction (PPI) network could offer insight of the molecular mechanisms of a variety of diseases (33–35). Consequently, the present study proposed a comprehensive bioinformatics analysis of gene expression data combining protein subcellular localization information and the construction of a layered PPI network (as opposed to a traditional PPI network) to identify candidate genes. Functional enrichment analyses were performed for candidate genes. A microRNA (miRNA/miR)-target gene regulatory network and transcription factor (TF)-target gene regulatory network were also constructed to identify miRNAs and TFs, which could be involved in T2DM development. The findings of the present study may help in the discovery of potentially novel predictive and prognostic markers for T2DM, and provide insight into the underlying molecular mechanisms of T2DM.

Materials and methods <sec> <title>Data acquisition, preprocessing and differentially expressed genes (DEGs) analysis

GSE20966 (36), the gene chip datasets of β-cells acquired from cadaver pancreases of non-diabetic subjects (control group, n=10) and T2D subjects (T2D group, n=10), was assessed and obtained from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). The annotation information of GeneChip was acquired from the GPL1352 Affymetrix Human X3P Array platform (Affymetrix; Thermo Fisher Scientific, Inc.). The probes were mapped to gene names based on the GPL1352 platform and the average expression value for the probes was calculated when there was more than one gene corresponding to the same probe. In the original gene expression profiles, after normalization, MATLAB 2018a (https://www.ilovematlab.cn/forum.php?mod=home) was used to identify the DEGs by value of a fold change >1.5 and a false discovery rate_Benjamini & Hochberg (fdr_BH) <0.1. The differences in gene expression between the control and T2DM subjects were assessed using hierarchical clustering and principal component analysis (PCA).

PPI network, layering and network analysis

The PPI data were retrieved from the Human Protein Reference Database v9.0 (HPRD) (37), BioGRID v3.5 (38), IntACT v4.2 (39) and STRING v10.5 (40) databases. First, single nodes, self-loops and duplicates were removed from the PPI data. Second, the total DEGs were mapped to the PPI data. To improve the reliability, only the direct interaction proteins of these DEGs were matched. Third, the integrated PPI network was visualized and analyzed using Cytoscape v3.6.1 (41). Then, the subcellular localization information for each protein in the integrated PPI network obtained from the HPRD, the UniProt database (http://www.uniprot.org/help/uniprotkb) and the Human Protein Atlas database (http://www.proteinatlas.org/) (42) was input as a node attribute. The Cerebral plug-in in Cytoscape was applied to redistribute nodes on the basis of subcellular localization without changing their interactions (43). The layered PPI network was split into five layers: Extracellular, plasma membrane, cytoplasm, nucleus and mitochondria. Hub protein nodes that encoded DEGs in the layered network with a connectivity degree >8 were screened as candidate genes.

Functional interpretations for the candidate genes

To investigate the functions of the candidate genes, functional enrichment analysis was performed using the ClueGO and the CluePedia plug-ins (44) for Cytoscape v3.6.1 software (45). ClueGO was used to decipher functionally grouped Gene Ontology (GO) (46) and pathway annotation networks to understand their implication in three different classifications; biological process (BP), molecular function (MF) and cell component (CC), in addition to the Kyoto Encyclopedia of Genes and Genomes (KEGG) (47) signaling pathway. The relationship between the terms was calculated using κ statistics and the ClueGO network was built based on the similarity of their related genes. The CluePedia plug-in is a search tool for new markers potentially associated to pathways, and can provide a broad viewpoint of a pathway using integrated experimental and in silico data. In the present study, the enrichment analysis of gene-BP and gene-pathway was statistically validated using the Cytoscape plug-ins ClueGO and CluePedia. BPs/signaling pathways were functionally split into several groups with κ score ≥0.4 and a network was constructed, where the node represents a BP/pathway and the edge between two nodes indicates that the two BPs/pathways share common genes.

Prediction of target miRNAs and TFs for the candidate genes

Genes need to interact to react to the environment of an organism, as they cannot alone to regulate the organism. Gene expression is modulated by TFs and miRNAs at the transcriptional and post-transcriptional levels. Information on TFs, miRNAs and their corresponding target genes could provide insight into the processes of T2DM. miRNet v2.0 (http://www.mirnet.ca/) (48) was used to predict the miRNAs associated with candidate genes noted in miRTarBase v7.6 (49) and miRecords (50). The 8 most captivating groups (top 15) and a minimum of two genes for each of the groups were picked as the threshold. Then, the TFs encoded by candidate genes were used for prediction coupled with human TF information (NetworkAnalyst v3.0; http://www.networkanalyst.ca) (51) noted in Binding and Expression Target Analysis v1.0.7 (BETA) (http://cistrome.org/BETA/) (52). The miRNA-target gene regulatory network and TF-target gene regulatory network were visualized using Cytoscape.

Results <sec> <title>Screening for DEGs

Following data preprocessing, 108 DEGs were identified to be differentially expressed in 10 control subjects and 10 T2DM subjects, with 66 upregulated and 42 downregulated genes, as presented in the heat map of the cluster analysis of DEGs, according to the cut-off criteria of fold change >1.5 and fdr_BH <0.1 (Fig. 1A; Table SI). The PCA plot demonstrated that the DEGs could roughly divide the majority of T2DM subjects from the non-diabetic controls (Fig. 1B).

PPI network, layering network construction and network analysis

The identification of proteins that interact directly with proteins encoded by the 108 DEGs could help understand the molecular mechanism underlying T2DM pathophysiology. In the present study, a PPI network was built from the 108 DEGs with Cytoscape and was composed of 1,546 nodes and 1,842 edges, including 52 proteins that were encoded by upregulated genes, 36 that were encoded by downregulated genes and 1,458 nodes marking proteins that were not encoded by DEGs (Fig. 2; Table I).

Subcellular protein localization is a crucial process in numerous cells. Following synthesis, proteins are transported to distinct compartments depending on their molecular function within the cell. Certain proteins are even transported to distant sites. Protein localization data can contribute to the elucidation of protein functions. The subcellular localization information for each protein in the integrated PPI network obtained from the HPRD, the UniProt database (http://www.uniprot.org/help/uniprotkb) and the Human Protein Atlas database (http://www.proteinatlas.org/) (42), was input as a node attribute. Then, the layered network was created from the PPI network using the Cerebral plug-in (43) of Cytoscape, according to the subcellular localization information of 1,546 proteins, which was split into five layers as follows: Extracellular, plasma membrane, cytoplasm, nucleus and mitochondrion (Fig. 3).

The degree distribution of a network is a standard feature of scale-free networks. The degree distributions of the layered network closely followed the power law distribution, with an R2=0.865. This suggested that the integrated PPI network is a true cellular complex biological network and scale-free. The other topological parameters of the network are presented in Table II. The results also suggested that a small number of nodes are hubs with a number of links to nodes. A total of 83 DEGs were identified as hub genes with an interaction degree ≥8 and were selected as candidate genes (Table SII). The top 20 candidate genes are presented in Table III. Of these candidate genes, ISG15 ubiquitin like modifier (ISG15) had the highest degree (185), followed by phosphoenolpyruvate carboxykinase 1 (PCK1) (85) and neural precursor cell expressed, developmentally downregulated 9 (NEDD9) (50). The present study identified 83 candidate genes for T2DM (Table IV). The identified candidate genes may serve as biological markers for future T2DM treatment research.

Functional enrichment analysis

To clarify possible biological functions of candidate genes, and examine the relationship between the functional groups and their underlying annotations in the networks, BP enrichment analyses were performed for the 46 upregulated and 37 downregulated candidate genes using ClueGO and CluePedia. A κ score >0.4 was set as the criterion. The results are presented in Fig. 4. Specifically, for the upregulated groups, the results yielded BPs related to the activation of ‘cellular response to cytokine stimulus’, ‘chemotaxis’, ‘inflammatory response’, ‘lipid metabolic process’, ‘macromolecule catabolic process’, ‘positive regulation of biosynthetic process’, ‘neurogenesis’, ‘regulation of cell differentiation’, ‘regulation of peptidase activity’, ‘regulation of transport’, ‘response to external biotic stimulus’ and ‘response to wounding’ (Fig. 4A). For the downregulated groups, the results yielded BPs related to the activation of ‘ion transport’, ‘neuron differentiation’, ‘positive regulation of cell death’, ‘positive regulation of cell proliferation’, ‘positive regulation of cellular component biogenesis’, ‘positive regulation of signaling’, ‘regulation of cell cycle process’ and ‘regulation of ion transport’ (Fig. 4B).

To obtain an improved understanding of the functional involvement of these candidate genes, pathway-based functional enrichment analyses was performed using ClueGO and CluePedia. A κ score >0.4 was set as the criterion. These genes were involved in pathways associated with ‘amyotrophic lateral sclerosis (ALS)’ [neurofilament light (NEFL) neurofilament medium], ‘axon guidance’ [ephrin A3 (EFNA3) and plexin A1 (PLXNA1)], ‘cellular senescence’ [insulin like growth factor binding protein 3 (IGFBP3) and TRAF3 interacting protein 2 (TRAF3IP2)], ‘complement and coagulation cascades’ [serpin family A member 5 (SERPINA5) and serpin family G member 1 (SERPING1)], ‘fat digestion and absorption’ [carboxyl ester lipase (CEL) and phospholipase A2 group IB (PLA2G1B)], ‘glucagon signaling pathway’ (PCK1 and solute carrier family 2 member 2), ‘influenza A’ [serine protease 2 (PRSS2) and transmembrane serine protease 2 (TMPRSS2)], ‘MAPK signaling pathway’ [EFNA3 and Ras protein specific guanine nucleotide releasing factor 1 (RASGRF1)], ‘neuroactive ligand-receptor interaction’ [lysophosphatidic acid receptor 3 (LPAR3) and PRSS2], ‘PI3K-Akt signaling pathway’ (EFNA3, LPAR3 and PCK1), ‘pancreatic secretion’ [CEL, carboxypeptidase A2 (CPA2), chymotrypsinogen B1 (CTRB1), PLA2G1B and PRSS2], ‘pathways in cancer’ [CDC28 protein kinase regulatory subunit 2 (CKS2) and LPAR3), ‘phagosome’ [neutrophil cytosolic factor 4 (NCF4) and surfactant protein D (SFTPD)], ‘protein digestion and absorption’ (CPA2, CTRB1 and PRSS2), ‘purine metabolism’ [fragile histidine triad diadenosine triphosphatase (FHIT) and DNA primase subunit 2), ‘Rap1 signaling pathway’ (EFNA3 and LPAR3), ‘Ras signaling pathway’ (EFNA3, phospholipase A1 member A, PLA2G1B and RASGRF1), ‘small cell lung cancer’ (CKS2 and FHIT), ‘synaptic vesicle cycle’ [synaptotagmin (SYT1) and unc-13 homolog B (UNC13B)] and ‘transcriptional misregulation in cancer’ (IGFBP3, MYCN proto-oncogene, bHLH transcription factor and TMPRSS2) (Fig. 5).

miRNA-target gene regulatory network

The miRNAs for DEGs were predicted using the two microRNA prediction tools through miRNet. The miRNA-gene regulatory network was built, which included 22 upregulated target genes, 19 downregulated target genes and 12 miRNAs (Fig. 6). A total of 12 miRNAs were selected, including hsa-mir-335-5p (degree=15), hsa-mir-8485 (degree=4), hsa-mir-1277-5p (degree=5), hsa-mir-190a-3p (degree=5), hsa-mir-5011-5p (degree=5), hsa-mir-124-3p (degree=6), hsa-mir-7106-5p (degree=5), hsa-let-7a-5p (degree=5), hsa-mir-192-5p (degree=5), hsa-mir-26b-5p (degree=6), hsa-let-7b-5p (degree=5) and hsa-mir-98-5p (degree=5).

TF-target gene regulatory network

In order to understand the topology and dynamics of the transcriptional regulatory network, TFs with a P<0.05 in BETA with its target genes via network analysis were built into a TF-target gene regulatory network using Cytoscape. The network consisted of 127 edges and 66 nodes (Fig. 7). Based on the degree, the top 8 TFs were selected to be enhancers of SUZ12 polycomb repressive complex 2 subunit (SUZ12; degree=10), enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2; degree=15), BCL6 transcription repressor (BCL6; degree=9), zinc finger protein 580 (ZNF580; degree=10), Kruppel like factor 9 (KLF9; degree=8), MYC associated zinc finger protein (MAZ; degree=15), activating transcription factor 1 (ATF1; degree=12), structure specific recognition protein 1 (SSRP1; degree=10), WRN helicase interacting protein 1 (WRNIP1; degree=10), chromodomain helicase DNA binding protein 1 (CHD1; degree=10) and SMAD5 (degree=11).

Discussion

T2DM is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. A PPI network and a layered network for DEGs were constructed and it was observed that the majority of the proteins were localized in the cytoplasm, followed by the nucleus. The modules were mined from the PPI network and ISG15, PCK1, NEDD9, thymosin β 4 X-linked (TMSB4X), SYT1, IGFBP3, NEFL, tensin 2, translocase of inner mitochondrial membrane 44 (TIMM44), hyaluronan mediated motility receptor (HMMR), ankyrin repeat and SOCS box containing 9 and TRAF3IP2 were screened as the candidate genes with the highest degree of connectivity.

ISG15 has an anti-apoptotic capability on MIN6 cells (53). Upregulated ISG15 could be a potential therapeutic approach for type 1 diabetes (T1D) in pancreatic β-cells (53). PCK1 has been a candidate gene for T2DM susceptibility (54). SYT1 is a Ca²⁺ sensor that plays a central role in insulin release, which is a characteristic deterioration in the early stages of T2DM (55,57). Higher levels of IGFBP3 might raise the risk of T2DM (57,58). The TIMM44 gene could be a new target for T2DM therapy (59). The procession of vascular diseases can be delayed by targeting TRAF3IP2 during diabetes and atherosclerosis, as TRAF3IP2 reconciles high glucose-induced NF-κB and AP-1-dependent inflammatory signaling and endothelial dysfunction (60). TRAF3IP2 may play a role in the pathogenesis of T1D (61). Notably, to the best of the authors' knowledge, ribosomal protein S19 binding protein 1 (RPS19BP1) and SFTPD have not been previously reported to be dysregulated in T2DM. RPS19BP1 is a direct regulator of NAD-dependent deacetylase sirtuin-1 (SIRT1), which is a promising molecular target for the treatment of obesity. RPS19BP1 can serve as a prognostic indicator via the direct regulation of SIRT1 in obese patients with T2DM (62–64). SFTPD is an element of lung innate immunity that strengthens pathogen clearance and regulates inflammatory responses (65); its expression is decreased in obesity and in impaired glucose tolerance, both of which are related to the development of T2DM.

The functional assay for candidate genes using ClueGO and CluePedia in GO terms or KEGG pathways identified several molecular mechanisms, widely known to underlie the pathogenesis of T2DM. A number of vital processes/signaling pathways and key factors connected with the pathogenesis of T2DM were identified from the functional enrichment analyses. In the upregulated group, a number of the corresponding encoded proteins were distributed in the extracellular and cytoplasmic layers. In particular, it was identified that the majority of BPs were associated with ‘inflammatory response’, ‘cellular response to cytokine stimulus’ and ‘lipid metabolic process’. In previous years, a number of studies have suggested that T2DM may be a chronic inflammatory response modulated by inflammatory factors (66–69). Immune cell infiltration and high levels of cytokines were observed in the pancreas islets of T2DM (70,71), which caused differing degrees of impairment to pancreatic β-cell activity, resulting in β-cell failure (72,73). Lipotoxic effects lead to impaired insulin secretion and apoptosis of β-cells, which can give rise to the β-cell functional loss in the pathogenesis of T2DM (74). In the downregulated group, more corresponding encoded proteins were distributed in the plasma membrane and nucleus layers, and BPs were related to the ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. Previous studies demonstrated that glucotoxic conditions are used to elevate β-cell proliferation and neogenesis, and the inhibition of apoptosis and death lead to insulin release defects, which is typical of diabetes (75,76); β-cell death is the major cause of T2DM. According to protein subcellular localization, the composition and biological value of proteins could change; analyzing PPIs may help identify the signaling pathways. The present study identified three interactions among 83 candidate genes, including SERPINA3 and CTRB1 in the extracellular matrix, SERPING1 and thymosin b4 X-linked (TMSB4X) in the cytoplasm, and SYT1 and UNC13B in the cytoplasm, which were shared between BPs, including protein input into the cytoplasm and cell-cell signaling, which might indicate that their expression was altered by the signaling cascades of the extracellular-plasma membrane-cytoplasm or nucleus and alteration in cell development. Takahashi et al (77) identified that SERPINA3 levels were significantly increased in T2DM. The rs7202877 locus for CTRB1 and CTRB2, a known diabetes risk locus, might be able to prevent diabetes via the incretin pathway (78). SERPINA5 inhibits activated protein C (APC). APC has a potential preventative role for islet β-cell damage and diabetes (79). A previous study observed that the plasma levels of APC were notably decreased in T2DM (80). SERPINA5 may be involved in T2DM via inhibited APC expression. TMSB4X is involved in cell proliferation, migration and differentiation, and its level increased in diabetic membranes (81).

The enriched KEGG pathways of candidate genes involved ‘MAPK signaling pathway’, ‘Ras signaling pathway’, ‘PI3K-Akt signaling pathway’, ‘Rap1 signaling pathway’ and ‘purine metabolism’. Previous studies demonstrated that p38 MAPK and ERK signaling were activated to inhibit obesity and associated T2DM (82,83). The Ras/Raf/ERK signaling pathway may control β-cell proliferation, and Ras is essential for normal β-cell development and function (84). Saltiel and Kahn (75) demonstrated that any obstacles in the PI3K/Akt insulin signaling pathway result in islet β-cells insulin resistance and lead to β-cell function reduction. Previous studies demonstrated that the Rap1 pathway may yield targets for β-cell dysfunction therapy in diabetes (85–88). The pathway enrichment results for candidate genes in the present study identified the MAPK. Ras, PI3K-Akt and Rap1 signaling pathways in diabetes. RASGRF1 is mainly involved in the MAPK and Ras signaling pathways (89). Suppressing the expression of Rasgrf1 may contribute to insufficient insulin secretion (90), which due to insulin resistance, causes T2DM. In addition, EFNA3 in the extracellular matrix was enriched in the PI3K-Akt, MAPK, Rap1 and Ras signaling pathways. EFNA3 is an upstream gene of the MAPK signaling pathway and the PI3K-Akt signaling pathway (91). Therefore, it was hypothesized that a low expression of EFNA3 may regulate β-cell proliferation by activating Ras/Raf/MEK/ERK. FHIT, a protein product involved in purine metabolism that participates in the T2DM pathway, is expressed in the pancreas (92). Its single-nucleotide polymorphism (rs3845971) was related to an intensified risk of T2DM (93). FHIT increases adenosine-diphosphate in the purine metabolism pathway (94). Therefore, FHIT may induce β-cell apoptosis in the pancreas due to T2DM.

miRNA-target gene interaction networks were constructed from 12 miRNAs. HMMR, ubiquitin like 3, ornithine decarboxylase 1, muscleblind like splicing regulator 3 and procollagen C-endopeptidase enhancer 2 (PCOLCE2) were regulated by hsa-miR-192-5p. Connective tissue growth factor (CTGF), family with sequence similarity 105, member A (FAM105A), MyoD family inhibitor domain containing, soluble carrier family 22 member 3, IGFBP3, CKS2 and EF-hand domain family member D2 were regulated by hsa-miR-124-3p. Protein phosphatase 1 regulatory inhibitor subunit 1 A (PPP1R1A), C-C motif chemokine receptor 4 (CCR4), SYT1, LMBR1 domain containing 2, C-type lectin domain containing 11A, NLR family pyrin domain containing 13, SERPINA3, protein phosphatase, Mg2+/Mn2+ dependent 1E (PPM1E), GIPC PDZ domain containing family member 2, NCF4, SERPING1, SIX homebox 6 CTRB1, CEL, cell adhesion molecular L1 like (CHL1), SFTPD and PLXNA1 were regulated by hsa-miR-335-5p. It was observed that hsa-miR-192-5p, hsa-miR-124-3p and hsa-miR-335-5p appeared to regulate the majority of the candidate genes identified in T2DM in the present study. It was previously identified that the downregulation of miR-192-5p usually occurs in the more extreme stages of diabetes (95). PCOLCE2, a collagen-related gene, is significantly reduced in T2DM (96). Zhu et al (97), identified that the expression level of hsa-miR-124-3p is decreased in patients with T2DM [9 high-body mass index (BMI) and 1 low-BMI] CTGF expression, a vital adjudicator of progressive pancreatic fibrosis, is elevated in T2DM (98). FAM105A is reported to be associated with T2DM (36). miR-335-5p expression was increased by islets in a diabetic Goto-Kakizaki-rat model (99). PPP1R1A has previously been identified as a potential participant and experimentally validated in the pathogenesis of islet dysfunction in T2DM (100). The ratio of CXCR3 to CCR4 receptor expression was positively correlated with the duration of T1D (r=0.947; P=0.0004) (101). The expression of SERPINA3 was increased significantly in T2DM and can be used for the early detection of T2DM (77). PPM1E is a potential drug target for diabetic therapies (102). The CTRB1/2 locus influences the susceptibility and treatment for diabetes via the incretin pathway (78). It was previously identified that mutations for the highly polymorphic CEL gene can be a rare cause of monogenic diabetes (103). CHL1 affects insulin secretion in INS-1 cells and has been identified as being potentially involved in T2DM (104). SFTPD expression was decreased in patients with T2DM (65). Therefore, has-miR-8485, has-miR-1277-3p, has-miR-190a-3p, has-miR-5011-3p, has-let-7a-5p, has-let-7b-5p, has-miR-98-5p, has-miR-7106-5p and has-miR-26b-5p may also be involved in T2DM by potentially regulating the expression of various candidate genes, such as CTGF, PCK1, PPP1RA, PCOLCE2, FAM105A, TRAF3PI2 and neuraminidase 3.

A TF-target gene regulatory network was constructed, from which 10 TFs were identified and Smad5 was a potential target for T2DM treatment (105). The Forkhead box class O/Bcl6/cyclin D2 pathway connects nutrient and growth factor status to cell cycle control in pancreatic β-cells, and should therefore be considered for its therapeutic potential in diabetes (106). Notably, 8 of these transcription regulatory factors, SUZ12, EZH2, ZNF580, KLF9, MAZ, ATF1, SSRP1, WRNIP1, CHD1 were shown to be involved in the development of T2DM by modulating the expression of various candidate genes such as ankyrin repeat domain 23 (ANKRD23), transmembrane protein 37 (TMEM37), PPP1R1A, PCK1, CTGF, ISG1, SSRP1, WRNIP1 and CHD1, which have not been previously reported to be dysregulated in T2DM. The present study predicted that these TFs might play key roles in the occurrence and development of T2DM. This result provided preliminary evidence that a lower expression of TMEM37 could reflect a decrease in β-cell numbers in T2DM (107). ANKRD23, a diabetes-related ankyrin repeat protein, was identified as a novel gene that is upregulated in the hearts of KKA(y) mice, a T2DM and insulin-resistant animal model (108). Solimena et al (110) also identified that ANKRD23, PPP1R1A and TMEM37 were enriched in β-cells and downregulated in T2DM. TMEM37 prohibits Ca²⁺-influx and insulin secretion in β-cells (109).

The present study has some limitations. The number of samples was relatively inadequate, although the combining of multiple datasets can compensate for missing or unreliable information in any single dataset. Additionally, the present results are preliminary and descriptive. Integrative analysis of gene profiling data cannot entirely exclude false positive results. Furthermore, the present study only discussed mRNA expression and did not refer to the protein expression of the factors identified. Due to post-transcription regulatory events, protein expression levels may or may not correlate with mRNA levels. However, the alteration of protein structure, function and interaction is the underlying mechanism of a number of diseases, including diabetes (110–112). Therefore, some experiments, such as reverse transcription-quantitative PCR (113,114), western blotting (115), cross-linking immunoprecipitation (116) or functional experimental validation, are needed to validate key genes, TFs and miRNAs and relevant proteins in T2DM development. Despite these limitations, the present study still provided insight for understanding the complicated underlying molecular mechanisms of T2DM.

Overall, the bioinformatics analysis of the present study identified potential markers that may play a potential role in the occurrence, development and treatment of T2DM. A total of 83 candidate genes were selected, and ISG15, PCK1, SYT1, IGFBP3, TIMM44 and TRAF3IP2 could be the core genes of T2DM. Certain key BPs such as ‘inflammatory response’, ‘cellular responses to cytokine stimulus’, ‘lipid metabolic process’, ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’, and certain signaling pathways associated with the PI3K-Akt, MAPK, Rap1 and Ras signaling pathways were identified to be involved in T2DM. The present study also identified miRNAs, including hsa-miR-192-5p, hsa-miR-124-5p and hsa-miR-335-5p, and TFs, including Smad5 and Bcl6, that might be potential targets for the diagnosis and treatment of T2DM. In addition, has-miR-8485, has-miR-1277-3p, has-miR-190a-3p, has-miR-5011-3p, has-let-7a-5p, has-let-7b-5p, has-miR-98-5p, has-miR-7106-5p and has-miR-26b-5p, and TFs SUZ12, EZH2, ZNF580, KLF9, MAZ, ATF1, SSRP1, WRNIP1 and CHD1 have not been previously identified to be related to T2DM, to the best of the authors’ knowledge, while they and their target genes may serve as diagnostic indicators for patients with T2DM. To obtain more reliable correlation results, it is necessary to validate the predicted results with a series of verification experiments. The present study identified candidate genes for T2DM development, which might be redefined as pathogenic genes for T2DM diagnosis and therapy. The experimental results could provide insight for future genomic individualized treatment of T2DM and help to identify the underlying molecular mechanisms that lead to T2DM.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

The present study was supported by The National Natural Science Foundation of China (grant no. c010201), Startup Funding for Specialized Professorship provided by The Shanghai Jiao Tong University (grant no. WF220441502), The National Key Research and Development Program (grant no. 2017YFC1308605), The Fundamental Research Funds for The Central Universities Key Grant (grant no. CQDXWL-2014-Z002), a grant (grant no. YZYN-15-04) from The Basic Scientific Research Special of The Central Public Welfare Research Institutes of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, The Applied Basic Research Foundation of Yunnan Province of China (grant no. 2016FB143), The National Natural Science Foundation of China (grant no. 81503289) and The PUMC Youth Fund (grant no. 2017350013).

Availability of data and materials

The datasets used or analyzed during the present study are included in this article.

Authors' contributions

HL and GL designed and conceived the experiments. YLu, GL and YLi collected and analyzed the data. HL and YL wrote the manuscript, and all authors reviewed the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References 1

Zheng

Ley

Global aetiology and epidemiology of type 2 diabetes mellitus and its complications

Nat Rev Endocrinol1488982018

10.1038/nrendo.2017.151

29219149

International Diabetes

Federation

IDF Diabetes Atlas

8th

IDF

Brussels, Belgium

2017http://www.diabetesatlas.org

Ohn

Kwak

Cho

Lim

Jang

Park

Cho

10-year trajectory of β-cell function and insulin sensitivity in the development of type 2 diabetes: A community-based prospective cohort study

Lancet Diabetes Endocrinol427342016

10.1016/S2213-8587(15)00336-8

26577716

Ashcroft

Rohm

Clark

Brereton

Is type 2 diabetes a glycogen storage disease of pancreatic β cells?

Cell Metab2617232017

10.1016/j.cmet.2017.05.014

28683284

Almaça

Weitz

Rodriguez-Diaz

Pereira

Caicedo

The pericyte of the pancreatic islet regulates capillary diameter and local blood flow

Cell Metab27630644.e42018

10.1016/j.cmet.2018.02.016

29514070

Wei

Cai

Ling

Shi

Jiao

Chang

Yang

Tian

Quantitative candidate gene association studies of metabolic traits in Han Chinese type 2 diabetes patients

Genet Mol Res1415471154812015

10.4238/2015.November.30.25

26634513

Chen

Meng

Zhou

Zhuo

Ling

Zhang

Wang

Identifying candidate genes for type 2 diabetes mellitus and obesity through gene expression profiling in multiple tissues or cells

J Diabetes Res20139704352013

10.1155/2013/970435

24455749

Lynch

Adams

Branched-chain amino acids in metabolic signalling and insulin resistance

Nat Rev Endocrinol107237362014

10.1038/nrendo.2014.171

25287287

Xue

Zhu

Zhang

Kemper

Zheng

Yengo

Lloyd-Jones

Sidorenko

eQTLGen Consortium: Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Nat Commun929412018

10.1038/s41467-018-04951-w

30054458

Lawlor

Khetan

Ucar

Stitzel

Genomics of Islet (Dys)function and Type 2 Diabetes

Trends Genet332442552017

10.1016/j.tig.2017.01.010

28245910

Lee

Blom

Wang

Shim

Marcotte

Prioritizing candidate disease genes by network-based boosting of genome-wide association data

Genome Res21110911212011

10.1101/gr.118992.110

21536720

Lawlor

George

Bolisetty

Kursawe

Sun

Sivakamasundari

Kycia

Robson

Stitzel

Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

Genome Res272082222017

10.1101/gr.212720.116

27864352

Segerstolpe

Palasantza

Eliasson

Andersson

Andréasson

Sun

Picelli

Sabirsh

Clausen

Bjursell

Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes

Cell Metab245936072016

10.1016/j.cmet.2016.08.020

27667667

Zeggini

Scott

Saxena

Voight

Marchini

de Bakker

Abecasis

Almgren

Andersen

Wellcome Trust Case Control Consortium

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Nat Genet406386452008

10.1038/ng.120

18372903

Maruthur

Gribble

Bennett

Bolen

Wilson

Balakrishnan

Sahu

Bass

Kao

Clark

The pharmacogenetics of type 2 diabetes: A systematic review

Diabetes Care378768862014

10.2337/dc13-1276

24558078

van de Bunt

Manning Fox

Dai

Barrett

Grey

Bennett

Johnson

Rajotte

Gaulton

Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors

PLoS Genet11e10056942015

10.1371/journal.pgen.1005694

26624892

Bonnefond

Froguel

Rare and common genetic events in type 2 diabetes: What should biologists know?

Cell Metab213573682015

10.1016/j.cmet.2014.12.020

25640731

Ndiaye

Ortalli

Canouil

Huyvaert

Salazar-Cardozo

Lecoeur

Verbanck

Pawlowski

Boutry

Durand

Expression and functional assessment of candidate type 2 diabetes susceptibility genes identify four new genes contributing to human insulin secretion

Mol Metab64594702017

10.1016/j.molmet.2017.03.011

28580277

Pellegrino

Sciambi

Treusch

Durruthy-Durruthy

Gokhale

Jacob

Chen

Geis

Oldham

Matthews

High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics

Genome Res28134513522018

10.1101/gr.232272.117

30087104

Zhang

Xia

Zhu

Gao

Tian

Guo

Suleman

Zhang

Kohli

High-throughput screening of prostate cancer risk loci by single nucleotide polymorphisms sequencing

Nat Commun920222018

10.1038/s41467-018-04451-x

29789573

Nepal

O'Rourke

Oliveira

DVNP

Taranta

Shema

Gautam

Calderaro

Barbour

Raggi

Wennerberg

Genomic perturbations reveal distinct regulatory networks in intrahepatic cholangiocarcinoma

Hepatology689499632018

10.1002/hep.29764

29278425

Fuchsberger

Flannick

Teslovich

Mahajan

Agarwala

Gaulton

Fontanillas

Moutsianas

McCarthy

The genetic architecture of type 2 diabetes

Nature53641472016

10.1038/nature18642

27398621

Thul

Åkesson

Wiking

Mahdessian

Geladaki

Ait Blal

Alm

Asplund

Björk

Breckels

A subcellular map of the human proteome

Science3568062017

10.1126/science.aal3321

28546174

Zhu

Bilgin

Snyder

Proteomics

Annu Rev Biochem727838122003

10.1146/annurev.biochem.72.121801.161511

14527327

Larance

Lamond

Multidimensional proteomics for cell biology

Nat Rev Mol Cell Biol162692802015

10.1038/nrm3970

25857810

Stuart

Boulais

Charriere

Hennessy

Brunet

Jutras

Goyette

Rondeau

Letarte

Huang

A systems biology analysis of the Drosophila phagosome

Nature445951012007

10.1038/nature05380

17151602

Zhao

Chen

Bajaj

Eblimit

Soens

Wang

Jung

Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes

Genome Res266606692016

10.1101/gr.198911.115

26912414

Hunt

Karakas

Biernacka

Sahin

Adjapong

Hortobagyi

Bondy

Thompson

Cytoplasmic Cyclin E Predicts Recurrence in Patients with Breast Cancer

Clin Cancer Res23299130022017

10.1158/1078-0432.CCR-16-2217

27881578

Mezzanzanica

Fabbi

Bagnoli

Staurengo

Losa

Balladore

Alberti

Lusa

Ditto

Ferrini

Subcellular localization of activated leukocyte cell adhesion molecule is a molecular predictor of survival in ovarian carcinoma patients

Clin Cancer Res14172617332008

10.1158/1078-0432.CCR-07-0428

18347173

Zhu

Yang

Layered functional network analysis of gene expression in human heart failure

PLoS One4e62882009

10.1371/journal.pone.0006288

19629191

Wang

Shen

Lin

Zheng

Qiu

Zhan

Shortest path analyses in the protein-protein interaction network of NGAL (neutrophil gelatinase-associated lipocalin) overexpression in esophageal squamous cell carcinoma

Asian Pac J Cancer Prev15689969042014

10.7314/APJCP.2014.15.16.6899

25169543

Diao

Liu

Zhang

Liu

Functional network analysis with the subcellular location and gene ontology information in human allergic asthma

Genet Test Mol Biomarkers16128712922012

10.1089/gtmb.2011.0325

23057572

Lee

Zhang

Kilicarslan

Piening

Bjornson

Hallström

Groen

Ferrannini

Laakso

Snyder

Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance

Cell Metab241721842016

10.1016/j.cmet.2016.05.026

27345421

Alvarez

Shen

Giorgi

Lachmann

Ding

Califano

Functional characterization of somatic mutations in cancer using network-based inference of protein activity

Nat Genet488388472016

10.1038/ng.3593

27322546

Krell

Stebbing

Frampton

Carissimi

Harding

De Giorgio

Fulci

Macino

Colombo

Castellano

The role of TP53 in miRNA loading onto AGO2 and in remodelling the miRNA-mRNA interaction network

Lancet385(Suppl 1)S152015

10.1016/S0140-6736(15)60330-0

26312837

Marselli

Thorne

Dahiya

Sgroi

Sharma

Bonner-Weir

Marchetti

Weir

Gene expression profiles of Beta-cell enriched tissue obtained by laser capture microdissection from subjects with type 2 diabetes

PLoS One5e114992010

10.1371/journal.pone.0011499

20644627

Keshava Prasad

Goel

Kandasamy

Keerthikumar

Kumar

Mathivanan

Telikicherla

Raju

Shafreen

Venugopal

Human Protein Reference Database--2009 update

Nucleic Acids Res 37 (Database)D767D7722009

10.1093/nar/gkn892

Chatr-Aryamontri

Breitkreutz

Oughtred

Boucher

Heinicke

Chen

Stark

Breitkreutz

Kolas

O'Donnell

The BioGRID interaction database: 2015 update

Nucleic Acids Res43D1D470D4782015

10.1093/nar/gku1204

25428363

Kerrien

Aranda

Breuza

Bridge

Broackes-Carter

Chen

Duesbury

Dumousseau

Feuermann

Hinz

The IntAct molecular interaction database in 2012

Nucleic Acids Res40D1D841D8462012

10.1093/nar/gkr1088

22121220

Szklarczyk

Franceschini

Wyder

Forslund

Heller

Huerta-Cepas

Simonovic

Roth

Santos

Tsafou

STRING v10: Protein-protein interaction networks, integrated over the tree of life

Nucleic Acids Res43D1D447D4522015

10.1093/nar/gku1003

25352553

Shannon

Markiel

Ozier

Baliga

Wang

Ramage

Amin

Schwikowski

Ideker

Cytoscape: A software environment for integrated models of biomolecular interaction networks

Genome Res13249825042003

10.1101/gr.1239303

14597658

Pontén

Jirström

Uhlen

The Human Protein Atlas--a tool for pathology

J Pathol2163873932008

10.1002/path.2440

18853439

Barsky

Gardy

Hancock

Munzner

Cerebral: A Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation

Bioinformatics23104010422007

10.1093/bioinformatics/btm057

17309895

Bindea

Galon

Mlecnik

CluePedia Cytoscape plugin: Pathway insights using integrated experimental and in silico data

Bioinformatics296616632013

10.1093/bioinformatics/btt019

23325622

Bindea

Mlecnik

Hackl

Charoentong

Tosolini

Kirilovsky

Fridman

Pagès

Trajanoski

Galon

ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

Bioinformatics25109110932009

10.1093/bioinformatics/btp101

19237447

Gene Ontology

Consortium

Gene Ontology Consortium: Going forward

Nucleic Acids Res43D1D1049D10562015

10.1093/nar/gku1179

25428369

Kanehisa

Furumichi

Tanabe

Sato

Morishima

KEGG: New perspectives on genomes, pathways, diseases and drugs

Nucleic Acids Res45D1D353D3612017

10.1093/nar/gkw1092

27899662

Fan

Siklenka

Arora

Ribeiro

Kimmins

Xia

miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

Nucleic Acids Res 44 (W1)W135412016

10.1093/nar/gkw288

Chou

Chang

Shrestha

Hsu

Lin

Lee

Yang

Hong

Wei

miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database

Nucleic Acids Res44D1D239D2472016

10.1093/nar/gkv1258

26590260

Xiao

Zuo

Cai

Kang

Gao

miRecords: An integrated resource for microRNA-target interactions

Nucleic Acids Res 37 (Database)D105D1102009

10.1093/nar/gkn851

Xia

Gill

Hancock

NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data

Nat Protoc108238442015

10.1038/nprot.2015.052

25950236

Wang

Sun

Zang

Wang

Tang

Meyer

Zhang

Liu

Target analysis by integration of transcriptome and ChIP-seq data with BETA

Nat Protoc8250225152013

10.1038/nprot.2013.150

24263090

Yoshikawa

Imagawa

Nakata

Fukui

Kuroda

Miyata

Sato

Hanafusa

Matsuoka

Kaneto

Interferon stimulated gene 15 has an anti-apoptotic effect on MIN6 cells

Endocr J618838902014

10.1507/endocrj.EJ14-0219

25031023

Rees

Britten

Bellary

O'Hare

Kumar

Barnett

Kelly

The promoter polymorphism −232C/G of the PCK1 gene is associated with type 2 diabetes in a UK-resident South Asian population

BMC Med Genet10832009

10.1186/1471-2350-10-83

19725958

Gustavsson

Han

Calcium-sensing beyond neurotransmitters: Functions of synaptotagmins in neuroendocrine and endocrine secretion

Biosci Rep292452592009

10.1042/BSR20090031

19500075

Marshall

Hitman

Partridge

Clark

Shearer

Turner

Evidence that an isoform of calpain-10 is a regulator of exocytosis in pancreatic beta-cells

Mol Endocrinol192132242005

10.1210/me.2004-0064

15471947

Rajpathak

Sun

Kaplan

Muzumdar

Rohan

Gunter

Pollak

Kim

Pessin

Insulin-like growth factor axis and risk of type 2 diabetes in women

Diabetes61224822542012

10.2337/db11-1488

22554827

Drogan

Schulze

Boeing

Pischon

Insulin-like growth factor 1 and insulin-like growth factor-binding protein 3 in relation to the risk of type 2 diabetes mellitus: Results from the EPIC-Potsdam study

Am J Epidemiol1835535602016

10.1093/aje/kwv188

26880678

Wang

Katayama

Terami

Han

Nunoue

Zhang

Teshigawara

Eguchi

Nakatsuka

Murakami

Translocase of inner mitochondrial membrane 44 alters the mitochondrial fusion and fission dynamics and protects from type 2 diabetes

Metabolism646776882015

10.1016/j.metabol.2015.02.004

25749183

Venkatesan

Valente

Das

Carpenter

Yoshida

Delafontaine

Siebenlist

Chandrasekar

CIKS (Act1 or TRAF3IP2) mediates high glucose-induced endothelial dysfunction

Cell Signal253593712013

10.1016/j.cellsig.2012.10.009

23085260

Bergholdt

Brorsson

Palleja

Berchtold

Fløyel

Bang-Berthelsen

Frederiksen

Jensen

Størling

Pociot

Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression

Diabetes619549622012

10.2337/db11-1263

22344559

Kokkola

Suuronen

Molnár

Määttä

Salminen

Jarho

Lahtela-Kakkonen

AROS has a context-dependent effect on SIRT1

FEBS Lett588152315282014

10.1016/j.febslet.2014.03.020

24681097

Haigis

Sinclair

Mammalian sirtuins: Biological insights and disease relevance

Annu Rev Pathol52532952010

10.1146/annurev.pathol.4.110807.092250

20078221

Zhou

Zhang

Zhou

Gao

Yin

SIRT1 attenuates neuropathic pain by epigenetic regulation of mGluR1/5 expressions in type 2 diabetic rats

Pain1581301392017

10.1097/j.pain.0000000000000739

27749604

Ortega

Pueyo

Moreno-Navarrete

Sabater

Rodriguez-Hermosa

Ricart

Tinahones

Fernandez-Real

The lung innate immune gene surfactant protein-D is expressed in adipose tissue and linked to obesity status

Int J Obes (Lond) (2005)37153215382013https://doi.org/10.1038/ijo.2013.23

10.1038/ijo.2013.23

Crook

Type 2 diabetes mellitus: A disease of the innate immune system? An update

Diabet Med212032072004

10.1046/j.1464-5491.2003.01030.x

15008827

Yang

Wang

Han

Wang

Circulating levels of adipose tissue-derived inflammatory factors in elderly diabetes patients with carotid atherosclerosis: A retrospective study

Cardiovasc Diabetol17752018

10.1186/s12933-018-0723-y

29848323

Prattichizzo

De Nigris

Spiga

Mancuso

La Sala

Antonicelli

Testa

Procopio

Olivieri

Ceriello

Inflammageing and metaflammation: The yin and yang of type 2 diabetes

Ageing Res Rev411172018

10.1016/j.arr.2017.10.003

29081381

Naidoo

Ghai

Cell- and tissue-specific epigenetic changes associated with chronic inflammation in insulin resistance and type 2 diabetes mellitus

Scand J Immunol88e127232018

10.1111/sji.12723

30589455

Ehses

Perren

Eppler

Ribaux

Pospisilik

Maor-Cahn

Gueripel

Ellingsgaard

Schneider

Biollaz

Increased number of islet-associated macrophages in type 2 diabetes

Diabetes56235623702007

10.2337/db06-1650

17579207

Böni-Schnetzler

Thorne

Parnaud

Marselli

Ehses

Kerr-Conte

Pattou

Halban

Weir

Donath

Increased interleukin (IL)-1beta messenger ribonucleic acid expression in beta -cells of individuals with type 2 diabetes and regulation of IL-1beta in human islets by glucose and autostimulation

J Clin Endocrinol Metab93406540742008

10.1210/jc.2008-0396

18664535

Donath

Böni-Schnetzler

Ellingsgaard

Ehses

Islet inflammation impairs the pancreatic beta-cell in type 2 diabetes

Physiology (Bethesda)243253312009

19996363

Marchetti

Lupi

Del Guerra

Bugliani

D'Aleo

Occhipinti

Boggi

Marselli

Masini

Goals of treatment for type 2 diabetes: Beta-cell preservation for glycemic control

Diabetes Care32(Suppl 2)S178S1832009

10.2337/dc09-S306

19875548

Cnop

Fatty acids and glucolipotoxicity in the pathogenesis of Type 2 diabetes

Biochem Soc Trans363483522008

10.1042/BST0360348

18481955

Saltiel

Kahn

Insulin signalling and the regulation of glucose and lipid metabolism

Nature4147998062001

10.1038/414799a

11742412

Wajchenberg

beta-cell failure in diabetes and preservation by clinical treatment

Endocr Rev281872182007

10.1210/10.1210/er.2006-0038

17353295

Takahashi

Unoki-Kubota

Shimizu

Okamura

Iwata

Kajio

Yamamoto-Honda

Shiga

Yamashita

Tobe

Proteomic analysis of serum biomarkers for prediabetes using the Long-Evans Agouti rat, a spontaneous animal model of type 2 diabetes mellitus

J Diabetes Investig86616712017

10.1111/jdi.12638

28150914

Hart

Fritsche

Nijpels

van Leeuwen

Donnelly

Dekker

Alssema

Fadista

Carlotti

Gjesing

The CTRB1/2 locus affects diabetes susceptibility and treatment via the incretin pathway

Diabetes62327532812013

10.2337/db13-0227

23674605

Xue

Jackson

Activated protein C and its potential applications in prevention of islet β-cell damage and diabetes

Vitam Horm953233632014

10.1016/B978-0-12-800174-5.00013-2

24559924

Matsumoto

Yano

Gabazza

Araki

Bruno

Suematsu

Akatsuka

Katsuki

Taguchi

Adachi

Inverse correlation between activated protein C generation and carotid atherosclerosis in Type 2 diabetic patients

Diabet Med24132213282007

10.1111/j.1464-5491.2007.02289.x

17971179

Wang

Tao

Jiang

Jonas

Intraocular expression of thymosin β4 in proliferative diabetic retinopathy

Acta Ophthalmol89e396e4032011

10.1111/j.1755-3768.2011.02114.x

21332672

Sidarala

Kowluru

The regulatory roles of mitogen-activated protein kinase (MAPK) pathways in health and diabetes: Lessons learned from the pancreatic β-cell

Recent Pat Endocr Metab Immune Drug Discov1076842017

10.2174/1872214810666161020154905

27779078

Pang

Zhu

Yang

Liu

β-arrestin-2 is involved in irisin induced glucose metabolism in type 2 diabetes via p38 MAPK signaling

Exp Cell Res3601992042017

10.1016/j.yexcr.2017.09.006

28888936

Stewart

Hussain

García-Ocaña

Vasavada

Bhushan

Bernal-Mizrachi

Kulkarni

Human β-cell proliferation and intracellular signaling: Part 3

Diabetes64187218852015

10.2337/db14-1843

25999530

Fraenkel

Ketzinel-Gilad

Ariav

Pappo

Karaca

Castel

Berthault

Magnan

Cerasi

Kaiser

mTOR inhibition by rapamycin prevents beta-cell adaptation to hyperglycemia and exacerbates the metabolic state in type 2 diabetes

Diabetes579459572008

10.2337/db07-0922

18174523

Baeder

Sredy

Sehgal

Chang

Adams

Rapamycin prevents the onset of insulin-dependent diabetes mellitus (IDDM) in NOD mice

Clin Exp Immunol891741781992

10.1111/j.1365-2249.1992.tb06928.x

1638761

Zhang

Katoh

Shibasaki

Minami

Sunaga

Takahashi

Yokoi

Iwasaki

Miki

Seino

The cAMP sensor Epac2 is a direct target of antidiabetic sulfonylurea drugs

Science3256076102009

10.1126/science.1172256

19644119

Sabbatini

Chen

Ernst

Williams

Rap1 activation plays a regulatory role in pancreatic amylase secretion

J Biol Chem28323884238942008

10.1074/jbc.M800754200

18577515

Manyes

Arribas

Gomez

Calzada

Fernandez-Medarde

Santos

Transcriptional profiling reveals functional links between RasGrf1 and Pttg1 in pancreatic beta cells

BMC Genomics1510192014

10.1186/1471-2164-15-1019

25421944

Hoffmann

Spengler

Transient neonatal diabetes mellitus gene Zac1 impairs insulin secretion in mice through Rasgrf1

Mol Cell Biol32254925602012

10.1128/MCB.06637-11

22547676

Yang

Wang

Liu

Zeng

Gao

Bao

Liu

Gao

Comparative transcriptome analysis of the swimbladder reveals expression signatures in response to low oxygen stress in channel catfish, Ictalurus punctatus

Physiol Genomics506366472018

10.1152/physiolgenomics.00125.2017

29799804

Dong

Ginsberg

Erlanger

Overexpression of the A1 adenosine receptor in adipose tissue protects mice from obesity-related insulin resistance

Diabetes Obes Metab33603662001

10.1046/j.1463-1326.2001.00158.x

11703426

Rampersaud

Damcott

Shen

McArdle

Shi

Shelton

Yin

Chang

Ott

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

Diabetes56305330622007

10.2337/db07-0457

17846126

Straub

Sharp

Glucose-stimulated signaling pathways in biphasic insulin secretion

Diabetes Metab Res Rev184514632002

10.1002/dmrr.329

12469359

Wang

The expression of miR-192 and its significance in diabetic nephropathy patients with different urine albumin creatinine ratio

J Diabetes Res201667894022016

10.1155/2016/6789402

26881255

Chen

Shu

Lee

Whole-genome expression analyses of type 2 diabetes in human skin reveal altered immune function and burden of infection

Oncotarget834601346092017

10.18632/oncotarget.16118

28427244

Zhu

Yin

Mao

Role of microRNAs in the treatment of type 2 diabetes mellitus with Roux-en-Y gastric bypass

Braz J Med Biol Res50e58172017

10.1590/1414-431x20175817

28273212

Pivovarova

Fisher

Dudziak

Ilkavets

Dooley

Slominsky

Limborska

Weickert

Spranger

Fritsche

A polymorphism within the connective tissue growth factor (CTGF) gene has no effect on non-invasive markers of beta-cell area and risk of type 2 diabetes

Dis Markers312412462011

10.1155/2011/971702

22045431

Salunkhe

Ofori

Gandasi

Salo

Hansson

Andersson

Wendt

Barg

Esguerra

JLS

Eliasson

MiR-335 overexpression impairs insulin secretion through defective priming of insulin vesicles

Physiol Rep52017https://doi.org/10.14814/phy2.13493

29122960

100

Taneera

Fadista

Ahlqvist

Atac

Ottosson-Laakso

Wollheim

Groop

Identification of novel genes for glucose metabolism based upon expression pattern in human islets and effect on insulin secretion and glycemia

Hum Mol Genet24194519552015

10.1093/hmg/ddu610

25489054

101

Aso

Matsuura

Momobayashi

Inukai

Sugawara

Nakano

Yamamoto

Wakabayashi

Takebayashi

Inukai

Profound reduction in T-helper (Th) 1 lymphocytes in peripheral blood from patients with concurrent type 1 diabetes and Graves' disease

Endocr J533773852006

10.1507/endocrj.K05-136

16717396

102

Voss

Paterson

Kelsall

Martín-Granados

Hastie

Peggie

Cohen

Ppm1E is an in cellulo AMP-activated protein kinase phosphatase

Cell Signal231141242011

10.1016/j.cellsig.2010.08.010

20801214

103

Torsvik

Johansson

Johansen

Minton

Raeder

Ellard

Hattersley

Pedersen

Hansen

Mutations in the VNTR of the carboxyl-ester lipase gene (CEL) are a rare cause of monogenic diabetes

Hum Genet12755642010

10.1007/s00439-009-0740-8

19760265

104

Taneera

Lang

Sharma

Fadista

Zhou

Ahlqvist

Jonsson

Lyssenko

Vikman

Hansson

A systems genetics approach identifies genes and pathways for type 2 diabetes in human islets

Cell Metab161221342012

10.1016/j.cmet.2012.06.006

22768844

105

Anhê

Lellis-Santos

Leite

Hirabara

Boschero

Curi

Anhê

Bordin

Smad5 regulates Akt2 expression and insulin-induced glucose uptake in L6 myotubes

Mol Cell Endocrinol31930382010

10.1016/j.mce.2010.01.003

20079400

106

Glauser

Schlegel

The FoxO/Bcl-6/cyclin D2 pathway mediates metabolic and growth factor stimulation of proliferation in Min6 pancreatic beta-cells

J Recept Signal Transduct Res292932982009

10.3109/10799890903241824

19929250

107

Rechsteiner

Floros

Boehm

Marselli

Marchetti

Stoffel

Moch

Spinas

Automated assessment of β-cell area and density per islet and patient using TMEM27 and BACE2 immunofluorescence staining in human pancreatic β-cells

PLoS One9e989322014

10.1371/journal.pone.0098932

24905913

108

Ikeda

Emoto

Matsuo

Yokoyama

Molecular identification and characterization of a novel nuclear protein whose expression is up-regulated in insulin-resistant animals

J Biol Chem278351435202003

10.1074/jbc.M204563200

12456686

109

Solimena

Schulte

Marselli

Ehehalt

Richter

Kleeberg

Mziaut

Knoch

Parnis

Bugliani

Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes

Diabetologia616416572018

10.1007/s00125-017-4500-3

29185012

110

Scott

Carter

Findlay

The application of proteomics to diabetes

Diab Vasc Dis Res254602005

10.3132/dvdr.2005.009

16305059

111

Khan

Awan

Mining of protein based biomarkers for type 2 diabetes mellitus

Pak J Pharm Sci258899012012

23010011

112

Okada

List

Sankaran

Kopchick

Plasma protein biomarkers correlated with the development of diet-induced Type 2 diabetes in mice

Clin Proteomics66172010

10.1007/s12014-009-9040-5

20625478

113

Bustin

Mueller

Real-time reverse transcription PCR (qRT-PCR) and its potential use in clinical diagnosis

Clin Sci (Lond)1093653792005

10.1042/CS20050086

16171460

114

Guénin

Mauriat

Pelloux

Van Wuytswinkel

Bellini

Gutierrez

Normalization of qRT-PCR data: The necessity of adopting a systematic, experimental conditions-specific, validation of references

J Exp Bot604874932009

10.1093/jxb/ern305

19264760

115

Kim

Western Blot Techniques

Methods Mol Biol16061331392017

10.1007/978-1-4939-6990-6_9

28501998

116

Ule

Hwang

Darnell

The future of cross-linking and immunoprecipitation (CLIP)

Cold Spring Harb Perspect Biol10a0322432018

10.1101/cshperspect.a032243

30068528

Figure 1.

Hierarchical clustering and principal component analysis of DEGs between control and T2DM subjects. (A) Hierarchical clustering analysis of DEGs was performed using MATLAB software, which split samples into groups with similar models in gene expression data. Red represents upregulated genes and green represents downregulated genes. (B) Principal component analysis of control and T2DM subjects based on DEGs. Yellow dots and blue dots refer to controls and T2DM subjects, respectively. DEGs, differentially expressed genes; T2DM; type 2 diabetes mellitus.

Figure 2.

Integrated protein-protein interaction network. Different color nodes represent the proteins that were encoded by differentially expressed genes. Red nodes are proteins that encoded upregulated genes and the green nodes are proteins that encoded downregulated genes. Pink nodes are proteins that were not encoded by significant differentially expressed genes.

Figure 3.

Layered protein-protein interaction network. Red nodes are upregulated genes, green nodes are downregulated genes and pink nodes are unchanged genes.

Figure 4.

Enriched GO network groups. (A) BP-enrichment analysis using ClueGO and CluePedia for upregulated genes (red). (B) BP-enrichment analysis using ClueGO and CluePedia for downregulated genes (green). Each node is a BP. Edges are links between the nodes and the length of edge indicates the degree of relatedness of two processes. The most significant parent or child term per group is displayed in the ClueGO grouped network as a group title. The size of the nodes indicates enrichment significance of the GO terms. Node color indicates the class. Mixed colors indicate that the particular node is owned by multiple classes. GO, Gene Ontology; BP, biological process.

Figure 5.

Group of significant Kyoto Encyclopedia of Genes and Genomes pathways of differentially expressed genes. Each node is a main pathway and their relation to genes (red is upregulated and green is downregulated). The node size indicates the significance of the pathway and the edge between nodes indicates shared or common genes. Dissimilar colors of node indicate dissimilar functional groups. The most significant pathway of each group is highlighted in different colors.

Figure 6.

miRNA-target gene regulatory networks. The red diamonds indicate upregulated genes, the green diamonds indicate downregulated genes and the blue squares indicate miRNAs. miRNA/miR, microRNA.

Figure 7.

TF-target gene regulatory network. Red indicates upregulated genes, green indicates downregulated genes and blue indicates TFs. TF, transcription factor.

Table I.

Distribution of nodes.

Localization	Upregulated	Downregulated	Unchanged	Total
Extracellular	13	5	132	150
Plasma membrane	7	9	244	260
Cytoplasm	21	8	427	456
Nucleus	8	14	516	538
Mitochondrion	3	0	139	142
Total	52	36	1,458	1,546

Table II.

Topological parameters of network.

Parameters	Value
y=βx^α	y=73.313×^−1.347
R²	0.865
Correlation	0.946
Clustering coefficient	0.2
Network centralization	0.118
Network density	0.002

Table III.

Top 20 hub genes.

No.	Gene name	Degree	Gene expression
1	ISG15	185	Upregulated
2	PCK1	85	Upregulated
3	NEDD9	50	Downregulated
4	TMSB4X	44	Upregulated
5	SYT1	44	Upregulated
6	IGFBP3	41	Downregulated
7	NEFL	41	Upregulated
8	TENC1	37	Upregulated
9	TIMM44	36	Upregulated
10	HMMR	35	Upregulated
11	ASB9	34	Downregulated
12	TRAF3IP2	34	Downregulated
13	KLF5	32	Upregulated
14	SERPINA3	31	Upregulated
15	NEFM	30	Upregulated
16	PHC1	29	Downregulated
17	RASGRF1	29	Downregulated
18	SERPING1	28	Upregulated
19	MYCN	27	Downregulated
20	SERPINA5	26	Upregulated

Table IV.

Candidate gene identification for type 2 diabetes mellitus.

A, Upregulated genes, n=46

Gene name	PIMID
CCR4	PMID: 17244154, PMID: 12464673
CEL	PMID: 19760265
CKS2
CPA2
CTRB1
DKK3
DPYSL3
EFHD2
EFNA3
FHIT
FXYD3	PMID: 25058609
GAD1
GIPC2
HMMR
ISG15	PMID: 25031023
KANK4
KLF5
MDFIC
NCF4
NEFL
NEFM
NEU3	PMID: 17292733
NHLH2
PAIP2B
PCK1	PMID: 24089092, PMID: 25997216
PLA2G1B	PMID: 16567514
PLXNA1
PRIM2
PRSS2
REG1A
REG1B
RENBP
RPS19BP1
SERPINA3	PMID: 28150914
SERPINA5
SFTPD
SIX6	PMID: 23478426
SLC22A3
SMOC1	PMID: 28163738
SPINK1
SYT1
TCTEX1D1	PMID: 15144884
TENC1
TIMM44	PMID: 25749183
TMPRSS2	PMID: 25749183, PMID: 9419343
TMSB4X

B, Downregulated, n=37

Gene names	PIMID

SERPING1	PMID: 23277452
ANKRD23	PMID: 26398569
ASB9
CDK2AP2
CHD5
CHL1	PMID: 22768844
CTGF	PMID: 22045431
EDN3
ESM1	PMID: 27756187
FAM105A	PMID: 20644627
GALNT14
GLIS3	PMID: 23737756
IGFBP3	PMID: 22554827, PMID: 26880678
KCNG3
LPAR3
MBNL3
MCOLN3
MYCN
NEDD9
NXPH1
ODC1
PCOLCE2
PHC1
PIGA
PLA1A
PPM1E	PMID: 20801214
PPP1R1A	PMID: 25489054
RASGRF1
RCAN2
SLC2A2	PMID: 28052964
TMEM27	PMID: 24905913
TMEM37	PMID: 29185012
TRAF3IP2	PMID: 23085260
UBL3
UNC13B
ZNF610
ZNF697

PIMID, PubMed number.