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Identification of hub genes related to the progression of type 1 diabetes by computational analysis

BMC Endocrine Disorders volume 21, Article number: 61 (2021) Cite this article

Abstract

Background

Type 1 diabetes (T1D) is a serious threat to childhood life and has fairly complicated pathogenesis. Profound attempts have been made to enlighten the pathogenesis, but the molecular mechanisms of T1D are still not well known.

Methods

To identify the candidate genes in the progression of T1D, expression profiling by high throughput sequencing dataset GSE123658 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and gene ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI), modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were constructed and analyzed using HIPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, validation of hub genes was conducted by using ROC (Receiver operating characteristic) curve and RT-PCR analysis. A molecular docking study was performed.

Results

A total of 284 DEGs were identified, consisting of 142 up regulated genes and 142 down regulated genes. The gene ontology (GO) and pathways of the DEGs include cell-cell signaling, vesicle fusion, plasma membrane, signaling receptor activity, lipid binding, signaling by GPCR and innate immune system. Four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell-cell signaling, cytokine signaling in immune system, signaling by GPCR and innate immune system. ROC curve and RT-PCR analysis showed that EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN might be involved in the advancement of T1D. Molecular docking studies showed high docking score.

Conclusions

DEGs and hub genes identified in the present investigation help us understand the molecular mechanisms underlying the advancement of T1D, and provide candidate targets for diagnosis and treatment of T1D.

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Introduction

Type 1 diabetes (T1D) (insulin-dependent) is a core challenge for endocrine research around the world [1]. Approximately 5 to 10% of the childhood population is affected with T1D worldwide [2]. T1D affects the eyes, kidneys, heart, peripheral and autonomic nervous systems [3]. Pancreatic cells, particularly β-cells, play a key role in the occurrence and progression of T1D [4]. Treatment for T1D includes targeting β-cells and β-cells regeneration [5]. However, T1D is a complex disease and its biology remains poorly understood [6].

There are several important risk factors for T1D, such as genetic and environmental factors [7, 8]. Previous studies identified aspects of the molecular mechanism of T1D advancement. T1D has been genetically associated with genes and signaling pathways, CTLA-4 [9], SUMO4 [10], CYP27B1 [11], PD-1 [12], KIAA0350 [13], tumor necrosis factor alpha signaling pathways [14], NLRP3 and NLRP1 inflammasomes signaling pathways [15], HIF-1/VEGF signaling pathway [16], l-arginine/NO pathway [17], and CaMKII/NF-κB/TGF-β1 and PPAR-γ signaling pathway [18]. Next-generation sequencing (NGS) has drastically increased the understanding mechanism of T1D, and analyses of these data can provide insight into effective diagnostic and therapeutic T1D treatments [19]. Thus, identifying key molecular biomarkers is essential for early diagnosis, prevention, and treatment of T1D.

It worth a lot of money and time to identify disease related molecular biomarkers by experiment alone. With the wide application of expression profiling by high throughput sequencing data, there were huge genomics data deposited in public databases [20]. The progression of computational tools gives us an alternative method to diagnose novel molecular biomarkers.

In this investigation, we employed the bioinformatics approach to discover the differentially expressed genes between T1D patients and healthy donors. Original expression profiling by high throughput sequencing dataset GSE123658 was downloaded. 39 T1D patients’ samples and 43 healthy donors’ samples were analyzed in our investigation. Commonly altered DEGs were isolated from integrated data. Additionally, GO/ REACTOME pathway analysis, construction of protein–protein interaction network, modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were performed to analyze these data. Four hub genes (EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN) were identified. ROC (receiver operating characteristic) curve and RT-PCR analysis were used to verify clinically relevant hub genes. The aim of this investigation was to gain a better understanding of the underlying molecular mechanisms and to discover molecular biomarkers for T1D.

Material and methods

Data resources

Expression profiling by high throughput sequencing dataset GSE123658 was downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) [21]. CPM count normalization performed on the original dataset GSE123658 from GEO databse using package edgeR package [22], voom function [23], and Limma [24] of R software. The data was produced using a GPL18573 Illumina NextSeq 500 (Homo sapiens). The GSE123658 dataset contained data from 82 samples, including 39 T1D patients’ samples and 43 healthy donors’ samples.

Identification of DEGs

The identification of DEGs between 39 T1D patients’ samples and 43 healthy donors’ samples was performed using Limma package in R bioconductor. lmFit function in the limma package to construct linear model for individual gene [25]. makeContrasts function in the limma package to compose similarity between T1D and healthy donors groups (log fold-changes) are obtained as contrasts of these fitted linear model. eBayes is a function in limma package which figure out empirical Bayes predicts of DEGs [26]. topTable function in limma package to obtain a table of the most significant Up and down regulated genes from a eBayes model fit. To correct the discovery of statistically important molecular biomarkers and limitations of false-positives, we using the adjusted P-value and Benjamini and Hochberg false discovery rate method [27]. Fold-change (FC) and adjust p-values were used to found DEGs. A |log2FC|  > 0.94 for up regulated genes, |log2FC|  -0.39 for down regulated genes and P-value  <  0.05 were used as considered statistically significant. The volcano plot was implemented using ggplot2 package [28], and the heat map was established using gplots package in R language.

Gene Ontology (GO) and pathway enrichment analyses of DEGs

Gene Ontology (GO) (http://www.geneontology.org) analysis is a routine analysis for annotating genes and determining biological component, including biological process (BP), cellular component (CC) and molecular function (MF) [29]. REACTOME (https://reactome.org/) [30] pathway database is applied for classification by correlating gene sets into their respective pathways. The ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) [31] is a gene functional classification tool that objective to provide a extensive set of functional annotation tools for authors to recognize the biological explanation behind large lists of genes. P < 0.05 was find statistically significant.

PPI network construction and module analysis

The online Human Integrated Protein-Protein Interaction rEference (HIPPIE) (http://cbdm.uni-mainz.de/hippie/) [32] online database was using to predicted the PPI network information. Analyzing the interactions and functions between DEGs may provide information about the mechanisms of generation and development of disease (PPI score  >  0.4). Cytoscape 3.8.0 (http://www.cytoscape.org/) [33] is a bioinformatics platform for constructing and visualizing molecular interaction networks. The Network Analyzer Cytoscape plug-in was used to find hub genes were screened using highest node degree [34], betweenness centrality [35], stress centrality [36] and closeness centrality [37] methods and hub genes were further analyzed for pathway and GO enrichment analysis. The plug-in PEWCC1 (http://apps.cytoscape.org/apps/PEWCC1) [38] of Cytoscape was applied to detect densely connected regions in PPI networks. The PPI networks were constructed using Cytoscape and the most key module in the PPI networks was preferred using PEWCC1. The criteria for selection were set as follows: Max depth = 100, degree cut-off = 2, Node score cut-off = 0.2, PEWCC1 scores >5, and K-score = 2.

Construction of miRNA - target regulatory network

miRNet database (https://www.mirnet.ca/) [39] online database was used to predict miRNAs that targeted the DEGs associated with T1D. The DEGs were selected according to the screening criterion of P value <0.05. The results were exported into the Cytoscape software for analysis. The target genes - miRNA regulatory network was constructed through network topology prosperities. The node degree was determined using the Network analysis plugin, and miRNAs with a node degree >12.

Construction of TF - target regulatory network

NetworkAnalyst database (https://www.networkanalyst.ca/) [40] online database was used to predict TFs that targeted the DEGs associated with T1D. The DEGs were selected according to the screening criterion of P value <0.05. The results were exported into the Cytoscape software for analysis. The target genes - TF regulatory network was constructed through network topology prosperities. The node degree was determined using the Network analysis plugin, and TFs with a node degree >12.

Validation of hub genes

Receiver operating characteristic (ROC) curve analysis [41] was implemented to calculate the sensitivity and specificity of the DEGs for T1D diagnosis using the R package by using the generalized linear model (GLM) in machine learning algorithms [42]. An area under the curve (AUC) value was determined and used to label the ROC effect. The RIN-m5F (ATCC CRL-11605) cell line procured from ATCC for T1D. RINm5F cell line was grown in RPMI-1640 medium added with 10% fetal bovine serum and penicillin/streptomycin. Incubate this cell line at 37 °C in a 5% CO2 in humidified cell culture incubator. The HIT­T15 (ATCC CRL­1777) cell line procured from ATCC for normal. CRL-1777 cell line was grown in Ham’s F12K medium added with 10% fetal bovine serum,87.5%; dialyzedhorse serum, 2 mM L­glutamine, 1.5 g/L sodium bicarbonate, and penicillin/streptomycin. Incubate the this cell line at 37 °C in a 5% CO2 in humidified cell culture incubator. In RT-PCR analysis, total RNA was isolated from in vitro cultured cells of diabetics and normal using a TRI Reagent (Sigma, USA). cDNA was synthesized using 2.0 μg of total RNA with the FastQuant RT kit (with gDNase; Tiangen Biotech Co., Ltd.). The relative mRNA expression was measured in the QuantStudio 7 Flex real-time PCR system (Thermo Fisher Scientific, Waltham, MA, USA). The relative expression levels were determined by the 2-ΔΔCt method and normalized to internal control β-actin [42]. All RT-PCR reactions were performed in triplicate. The primers used to explore mRNA expression of ten hub genes were shown in Table 1.

Table 1 The sequences of primers for quantitative RT-PCR
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Molecular Docking studies

The surflex-docking studies of the designed molecule, standard on over expressed genes of PDB protein were performed using SYBYL-X 2.0 perpetual software. All the designed molecules and standard were outlined using ChemDraw Software, imported and saved in sdf. templet using open babel free software. The protein structures of EGFR (epidermal growth factor receptor) and its co-crystallised protein of PDB code 2XYJ, 2XYX and GRIN2B its co-crystallised protein of PDB code and its co-crystallised protein of PDB code 5EWL, 6E7V were extracted from Protein Data Bank [43,44,45,46]. Together with the TRIPOS force field, GasteigerHuckel (GH) charges were added to all designed molecules and standard for the structure optimization process. Additionally, using MMFF94s and MMFF94 algorithm methods, energy minimization was done. The protein preparation was carried out after incorporation of protein. The co-crystallized ligand and all water molecules were removed from the crystal structure; more hydrogen’s were added and the side chain was set. TRIPOS force field was used for the minimization of structure. The compounds’ interaction efficiency with the receptor was represented by the Surflex-Dock score in kcal / mol units. The interaction between the protein and the ligand, the best pose was incorporated into the molecular area. The visualization of ligand interaction with receptor is done by using discovery studio visualizer.

Results

Identification of DEGs

With a threshold of P-value <0.05 and absolute value of |log2FC|  > 0.94 for up regulated genes, |log2FC|  -0.39 for down regulated genes, DEGs were identified from dataset. 284 DEGs were screened from the GSE123658 dataset, which consisted of 142 up regulated genes and 142 down regulated genes and only top ten up regulated genes and down regulated genes are listed in Table 2. Volcano plots were used to visualize differential expression of genes between the T1D group and healthy donors group (Fig.1). A heat map showed expression profiling of DEGs in the analysis result (Fig.2).

Table 2 The statistical metrics for key differentially expressed genes (DEGs)
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Fig. 1
figure 1

Volcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes

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Fig. 2
figure 2

Heat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A43 = healthy donors’ samples; B1 – B14 = T1D samples)

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Gene Ontology (GO) and pathway enrichment analyses of DEGs

To identify the pathways which had the most significant involvement with the genes identified, up regulated and down regulated genes are listed in Table 3 and Table 4. DEGs were submitted into ToppGene for GO and REACTOME pathway enrichment analysis. GO enrichment analysis revealed that in BP terms, the up regulated genes were mainly enriched in cell-cell signaling and reproductive process, whereas down regulated genes were mainly enriched in vesicle fusion and biological adhesion. In CC terms, up regulated genes were mainly enriched in integral component of plasma membrane and supramolecularfiber, whereas down regulated genes were mainly enriched in whole membrane and cell junction. In MF terms, up regulated genes were mainly enriched in signaling receptor activity and structural molecule activity, whereas down regulated genes were mainly enriched in lipid binding and GTPase binding. REACTOME pathway analysis demonstrated that up regulated genes were significantly enriched in signaling by GPCR and muscle contraction, whereas down regulated genes were mainly enriched in innate immune system and cytokine signaling in immune system.

Table 3 The enriched pathway terms of the up and down regulated differentially expressed genes
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Table 4 The enriched GO terms of the up and down regulated differentially expressed genes
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PPI network construction and module analysis

Interactions between the identified up and down regulated genes were reported by constructing a PPI network. In total, there were 5017 nodes and 8133 edges in the network (Fig.3a). According to node degree, betweenness centrality, stress centrality and closeness centrality levels, the top four hub nodes were: EGFR (degree, 1343; betweenness, 0.514227; stress, 4.35E+08; closeness, 0.441893), GRIN2B (degree, 195; betweenness, 0.008701; stress, 14,535,992; closeness, GJA1 (degree,170 0.03151; betweenness, 64,939,702; stress, 0.31364; closeness, 0.282164), CAP2 (degree, 108; betweenness, 0.004218; stress, 888,128; closeness, 0.325945), MIF (degree, 99; betweenness, 0.025028; stress, 7,627,912; closeness, 0.362124), POLR2A (degree, 512; betweenness, 0.095104; stress, 2E+08; closeness, 0.3738), PRKACA (degree, 350; betweenness, 0.104658; stress, 44,280,174; closeness, 0.378052), GABARAP (degree, 322; betweenness, 0.144708; stress, 36,234,824; closeness, 0.358401), TLN1 (degree, 223; betweenness, 0.027406; stress, 67,578,124; closeness, 0.35998), and PXN (degree, 173; betweenness, 0.029203; stress, 29,047,866; closeness,0.360318) and are listed in Table 5. Significant modules were subsequently constructed with 12 nodes and 23 edges for up regulated genes (Fig.3b) and 5 nodes and 10 edges for down regulated genes, which gained the highest PEWCC1 score (Fig.3c). Subsequent functional enrichment analysis revealed that the genes in these modules were mainly enriched in cell-cell signaling and cytokine signaling in immune system.

Fig. 3
figure 3

PPI network and the most significant modules of DEGs. a The PPI network of DEGs was constructed using Cytoscape. b The most significant module was obtained from PPI network with 12 nodes and 23 edges for up regulated genes. c The most significant module was obtained from PPI network with 5 nodes and 10 edges for down regulated genes. Up regulated genes are marked in green; Down regulated genes are marked in red

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Table 5 Topology table for up and down regulated genes
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Prediction of key miRNAs

The regulatory relationships between the target genes and their miRNAs were established using Cytoscape, which showed that the single gene was regulated by multiple miRNAs is shown in Fig.4a. Subsequently, 106 miRNAs (ex, hsa-mir-4257) targeting GRIN2B, 83 miRNAs (ex, hsa-mir-564) targeting EGFR, 66 miRNAs (ex, hsa-mir-587) targeting DKK1, 64 miRNAs (ex, hsa-mir-941) targeting GJA1, 54 miRNAs (ex, hsa-mir-561-3p) targeting RGS4, 190 miRNAs (ex, hsa-mir-4300) targeting TLN1, 139 miRNAs (ex, hsa-mir-5694) targeting IGF2R, 117 miRNAs (ex, hsa-mir-378b) targeting POLR2A, 113 miRNAs (ex, hsa-mir-3918) targeting ARHGAP1 and 102 miRNAs (ex, hsa-mir-6719-3p) targeting HIP1, and were verified in miRNet database are listed in Table 6. Integrating with the result of REACTOME pathway analysis, it was indicated that these key target genes - miRNA network was mainly involved in the signaling by GPCR and innate immune system.

Fig. 4
figure 4

a Target gene - miRNA regulatory network between target genes and miRNAs. b Target gene - TF regulatory network between target genes and TFs. Up regulated genes are marked in green; down regulated genes are marked in red; The chocolate color triangle nodes represent the key miRNAs; the blue color triangle nodes represent the key miRNAs

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Table 6 miRNA - target gene and TF - target gene interaction
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Prediction of key TFs

The regulatory relationships between the target genes and their TFs were established using Cytoscape, which showed that the single gene was regulated by multiple TFs is shown in Fig.4b. Subsequently, 19 TFs (ex, FOXD1) targeting RGS4, 16 TFs (ex, GATA2) targeting EYA1, 12 TFs (ex, FOXL1) targeting GRIA4, 11 TFs (ex, TP53) targeting CCL19, 11 TFs (ex, JUND) targeting PRL, 15 TFs (ex, STAT3) targeting PRKACA, 14 TFs (ex, TFAP2A) targeting GAB2 , 12 TFs (ex, KLF5) targeting HIP1, 10 TFs (ex, PPARG) targeting PXN and 9 TFs (ex, HINFP) targeting RGL2 were verified in NetworkAnalyst database are listed in Table 6. Integrating with the result of REACTOME pathway analysis, it was indicated that these key target genes - TF network was mainly involved in the signaling by GPCR and innate immune system.

Validation of hub genes

As these 4 genes are remarkably expressed in T1D, we executed a ROC curve analysis to calculate their sensitivity and specificity for the diagnosis of T1D. As shown in Fig. 5 EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN achieved an AUC value of >0.982, demonstrating that these genes have high sensitivity and specificity for T1D diagnosis. We further used RT-PCR to detect the mRNA expression of the hub gene. The 10 hub genes contain two up regulated genes (EGFR, GRIN2B, GJA1, CAP2 and MIF) and two down regulated gene (POLR2A, PRKACA, GABARAP, TLN1 and PXN). The RT-PCR data showed that although the trend of expression patterns of these 10 hub genes were consistent, among these up regulated genes, only EGFR, GRIN2B, GJA1, CAP2 and MIF were significantly up regulated in T1D. In addition, the expression of POLR2A, PRKACA, GABARAP, TLN1 and PXN were reduced in T1D (Fig. 6).

Fig. 5
figure 5

ROC curve analyses of hub genes. a EGFR. b GRIN2B. c GJA1. d CAP2. e MIF. f POLR2A. g PRKACA. h GABARAP. i TLN1. j PXN

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Fig. 6
figure 6

RT-PCR analyses of hub genes. a EGFR. b GRIN2B. c GJA1. d CAP2. e MIF. f POLR2A. g PRKACA. h GABARAP. i TLN1. j PXN

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Molecular Docking studies

The docking simulations are conducted in the present study is to identify the active site conformation and major interactions responsible for complex stability with the receptor binding sites. In T1D over expression of genes are identified and their proteins of x-ray crystallographic structure are selected from PDB for docking studies. The drugs containing thiazolidindione ring pioglitazone are most commonly used either alone or in combination with other antidibetic drugs. The docking studies of pioglitazone (as standard) and designed molecules containing the heterocyclic ring of thiazolidinedione have been carried out using Sybyl X 2.1 drug design software. The docking studies were performed to know the biding interaction of pioglitazone standard and designed molecules on identified over expressed genes of protein. The X- RAY crystallographic structure of two proteins from each over expressedgenes of epidermal growth factor receptor (EGFR), cyclase associated actin aytoskeleton aegulatory arotein 2 (CAP2), glutamate inotropic receptor NMDA type subunit 2B (GRIN2B), gap junction protein plpha 1 (GJA1) and macrophage migration inhibitory factor (MIF) and one protein from each of their co-crystallised PDB code 2XYJ, 4 K41, 5EWL, 2ZW3 and 4WR8 respectively were selected for the docking studies to identify and predict the potential molecule based on the binding score with the protein and effective against type 1 diabetes mellitus. A total of 54 molecules were designed and the binding score greater than six are believed to be good, few molecules obtained excellent binding score (C-score) with particular protein greater than 10The designed molecules obtained binding or score c- score less than 5 are TSPZP19, TBPZ38, TBPZ41 and TSIO4, TSPZ12, TBIO32, TBPZ40 TBPZ41 TBPZ42 TBPZ43 TBPZ44 TBPZ45 TBPZP46 and TSIO2, TSIO3, TSIO5, TSPZP20, TSPZP23, TBIO32, TBPZP48, TBPZP53 and TSIO1, TSIO9, TSPZ11, TSPZ12, TSPZ13, TSPZ14, TSPZ18, TSPZP19, TSPZP20, TSPZP23, TSPZP26, TSPZP27, TBIO28, TBIO30, TBIO31 TBIO32, TBIO35, TBIO36, TBPZ37, TBPZ39, TBPZ40, TBPZ41, TBPZ44, TBPZ45, TBPZP47, TBPZP49, TBPZP50 with PDB code of protein 2XYJ and 5EWL and 2ZW3 and 4WR8 respectively. The molecules obtained binding score 5 to 7 are TSIO1, TSIO2, TSIO3, TSIO4, TSIO5, TSIO7, TSIO8, TSIO9, TSPZ10, TSPZ11, TSPZ12, TSPZ13, TSPZ14, TSPZ16, TSPZ17, TSPZ18, TSPZP20, TSPZP21, TSPZP24, TSPZP25, TSPZP26, TBIO28, TBIO29, TBIO31, TBIO32, TBIO33, TBIO35, TBIO36, TBPZ39, TBPZ40, TBPZ42, TBPZ43, TBPZ40, TBPZ42, TBPZ43, TBPZ44, TBPZ45, TBPZP47, TBPZP48, TBPZP49, TBPZP50, TBPZP52, TBPZP53, TBPZP54 and TSIO1, TSIO2, TSIO3, TSIO5, TSIO6, TSIO7 TSIO8, TSIO9, TSPZ10, TSPZ11, TSPZ13, TSPZ14, TSPZ15, TSPZ16, TSPZ17, TSPZ18, TSPZP19, TSPZP20, TSPZP21 TSPZP22, TSPZP23, TSPZP24, TSPZP25, TSPZP26, TSPZP27, TBIO28, TBIO29, TBIO30, TBIO31, TBIO33, TBIO34, TBIO35, TBIO36, TBPZ37, TBPZ38, TBPZ39, TBPZ40, TBPZ42, TBPZ43, TBPZ44, TBPZ45, TBPZP46, TBPZP47, TBPZP49, TBPZP50, TBPZP51, TBPZP52, TBPZP53 and TSIO1, TSIO4, TSIO7, TSIO8, TSIO9, TSPZ10, TSPZ11, TSPZ12, TSPZ13, TSPZ14, TSPZ15, TSPZ16, TSPZ17, TSPZ18, TSPZP19, TSPZP21, TSPZP22, TSPZP24, TSPZP25, TSPZP26, TSPZP27, TBIO28, TBIO29, TBIO30, TBIO31, TBIO34, TBIO35, TBIO36, TBPZ37, TBPZ38, TBPZ39, TBPZ40, TBPZ41, TBPZ42, TBPZ43, TBPZ44, TBPZ45, TBPZP46 TBPZP47, TBPZP49, TBPZP50, TBPZP52 and TSIO2, TSIO3, TSIO4, TSIO5, TSIO6, TSIO7, TSIO8, TSPZ10, TSPZ15, TSPZ16, TSPZ17, TSPZP21, TSPZP22, TSPZP25, TBIO29, TBIO33, TBIO34, TBPZ38 TBPZ42, TBPZ43, TBPZP46, TBPZP48 with PDB protein 2XYJ and 5EWL and 2ZW3 and 4WR8 respectively. The molecules obtained c-score greater than 7 and less than 10 are TSIO6, TSPZ15, TSPZP22, TSPZP23, TSPZP27, TBIO30, TBIO34, TBPZ37, TBPZP46, TBPZP51, PIO (STD) and the molecules TSIO1, TSIO2, TSIO3, TSIO4, TSIO5, TSIO6, TSIO7, TSIO9, TSPZ10, TSPZ11, TSPZ12, TSPZ13, TSPZ14,TSPZ16, TSPZ17, TSPZ18, TSPZP19, TSPZP20, TSPZP21, TSPZP22, TSPZP23, TSPZP25, TSPZP26, TSPZP27, TBIO28, TBIO29, TBIO31, TBIO32, TBIO33, TBIO34, TBIO36, TBPZ37, TBPZ38, TBPZ39, TBPZ40, TBPZ41, TBPZ42, TBPZ43, TBPZ44, TBPZ45, TBPZP46 TBPZP47, TBPZP48, TBPZP49, TBPZP50, TBPZP51, TBPZP52, TBPZP53, TBPZP54, PIO (STD) and the molecules TSIO6, TBIO33, TBPZP51 and the molecule TBPZP51 with PDB code of 2XYJ and 4 K41 and 2ZW3 and 4WR8 respectively. Pioglitazone was used as a standard; it obtained good binding score of 8.396 and 7.648with two PDB proteins 2XYJ and 4WR8 respectively.

Discussion

Pathological complications of T1D remains undetermined, hyperglycemia develops to play a key role [47]. Although T1D is rare compared to type 2 diabetics, it might affect in any part of the body, such as the eyes, kidneys, heart, peripheral and autonomic nervous systems [48]. Considering the poor prognosis of T1D, understanding the specific molecular biomarkers of the disease is important for initial diagnosis and therapy to increase survival rates. In this investigation, we examined the expression profiling by high throughput sequencing dataset GSE123658 including T1D group and healthy donors group to identify the molecular mechanism of T1D and seek some molecular biomarkers. Bioinformatics analysis of these biological factors is applied to explore genes that are favorable to treatment.

In total 284 DEGs, 142 up regulated and 142 down regulated genes were identified. Polymorphic gene ARMS2 has an important role in the advancement of T1D [49]. Polymorphic gene AS3MT was reported to be associated with progression of T1D [50]. Wang et al [51] also reported that CRHR2 are abnormally expressed in hypertension patients, but this gene might be induces hypertension in T1D patients. Investigation has indicated that RNF122 decrease expression is associated with hyperactivity disorder [52]; this finding is consistent with our results and indicates that RNF122 might be involved in the development of T1D. A previous investigation found that autophagy regulating TP53INP2 gene expression was associated with development of T1D [53]. Polymorphic gene CRTC2 is well known for its critical role in type 2 diabetes [54], but this polymorphic gene might be responsible for advancement of T1D.

GO and REACTOME pathway enrichment analyses were performed to explore interactions among the DEGs. These DEGs were mainly enriched in cell-cell signaling, integral component of plasma membrane, signaling receptor activity, signaling by GPCR, vesicle fusion, whole membrane, lipid binding and innate immune system. Altered MYH6 gene was known to play a role in congenital heart defects [55], but this gene might be induces T1D in patients with congenital heart defects. McKenna et al [56] found that AVP (arginine vasopressin) play essential roles in the T1D. Yue et al [57] concluded that high GRIA4 expression was correlated with abdominal aortic aneurysm progression, but this gene might be answerable for advancement of T1D in patients with abdominal aortic aneurysm. Kochetova el at [58] found that GRIN2B is a significant biomarker for type 2 diabetes compared to healthy controls, but it was confirmed for the first time in our study that GRIN2B expression in T1D indicates a good prognosis. Recent investigations demonstrated that DCC (DCC netrin 1 receptor) gene can mediate angiogenesis and plays an important role in diabetic kidney disease [59]. Ruiz de Azua et al [60] indicated that RGS4 was associated progression of type 2 diabetes, but this gene might be essential for T1D progression. A previous investigation demonstrated that GJA1 was associated with the progression of atrial fibrillation [61], but might be important gene signatures of T1D in patients with atrial fibrillation. Faienza et al [62] and Galán et al [63] demonstrated that, GREM1 and EGFR (epidermal growth factor receptor) might be important for predicting treatment response in T1D. TRHR (thyrotropin releasing hormone receptor) is considered a potential biomarker of hypertension [64], but this gene might be associated with development of T1D in patients with hypertension. PTPRT (protein tyrosine phosphatase receptor type T) has been reported to serve a role in obesity associated insulin resistance [65], but this gene might be involved in progression of T1D. Research has demonstrated that FCAMR (Fc fragment of IgA and IgM receptor) gene could contribute to progression of atherosclerosis [66], but this gene might be crucial for progression of T1D in patients with atherosclerosis. Sun et al. [67] revealed that CCL19 was associated with diabetic nephropathy in patients with T1D. DSP (desmoplakin) has been reported to be expressed in cardiomyopathy [68], but this gene might be linked with progression of T1D in patients with cardiomyopathy. Xu et al. [69] have demonstrated that ITIH4 are related with coronary heart disease, but this gene might be responsible for advancement of T1D in patients with coronary heart disease. Miyashita et al [70] demonstrate that GAB2 plays a role in Alzheimer disease progression, but this gene might be crucial for T1D in patients with Alzheimer disease. McCann et al. [71] showed that IGF2R played an important role in T1D. Previous studies reported that the expression of MYADM (myeloid associated differentiation marker) induced hypertension [72], but this gene might be liable for advancement of T1D in patients with hypertension. Fan et al. [73] and Chan et al. [74] revealed that TPCN2 and APOL1 were associated with type 2 diabetes, but these genes might be responsible for development of T1D. Previous investigation had confirmed that MEFV (MEFV innate immuity regulator, pyrin) play critical roles in ischemic heart disease [75], but this gene might be essential for progression of T1D in patients with ischemic heart disease. Wang et al [76] state that the expression of DTX4 is important event in obesity, but this gene might be linked with progression of T1D in patients with obesity.

We analyzed the protein–protein interactions (PPI) and modules of the DEGs involved in T1D. Table 5 summarizes the PPI network hub genes (five up regulated and five down regulated) that were identified in the T1D, which included EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN. A recent study showed that protein expression levels of CAP2 were up regulated in cardiomyopathy patients [77], but this gene might be responsible for T1D in patients with cardiomyopathy. MIF (macrophage migration inhibitory factor) [78] and KIF1A [79] were reported to be associated with T1D. Recent research suggested that PXN (paxillin) is involved in hypertension [80], but this gene might be associated with progression of T1D in patients with obesity. POLR2A, PRKACA, GABARAP, TLN1 and CIITA (class II major histocompatibility complex transactivator) might be considered as a novel biomarkers associated with the development of T1D.

Target gene - miRNA regulatory network and target gene - TF regulatory network analysis demonstrated that DEGs interacted directly or indirectly. The more edges associated with genes, miRNAs and TFs indicated the more potential selection for the targets. Table 6 summarizes the target gene - miRNA regulatory network and target gene - TF regulatory network (target genes, miRNAs and TFs) that were identified in the T1D, which included GRIN2B, EGFR, DKK1, GJA1, RGS4, TLN1, IGF2R, POLR2A, ARHGAP1, HIP1, RGS4, EYA1, CCL19, PRL, PRKACA, GAB2, HIP1, PXN , RGL2, hsa-mir-4257, hsa-mir-564, hsa-mir-587, hsa-mir-941, hsa-mir-561-3p, hsa-mir-4300, hsa-mir-5694, hsa-mir-378b, hsa-mir-3918, hsa-mir-6719-3p, FOXD1, GATA2, FOXL1, TP53, JUND, STAT3, TFAP2A, KLF5, PPARG and HINFP. STAT3 had been reported to be involved in the pathogenesis of T1D [81]. Polymorphic gene GATA2 has been reported to be crucial for the progression of coronary artery disease [82], but this gene might be responsible for advancement of T1D in patients with coronary artery disease. PRKACA (protein kinase cAMP-activated catalytic subunit alpha), DSP (desmoplakin), hsa-mir-4257, hsa-mir-564, hsa-mir-4300, hsa-mir-5694, RGS4, FOXD1, EYA1, TFAP2A and GAB2 might be considered as a novel biomarkers associated with the development of T1D.

In addition, we also performed validation of hub genes by ROC analysis and RT-PCR. Results showed that these hub genes differentiated T1D group from healthy donors group, and may be candidates for diagnostic biomarkers and new therapeutic target. Moreover, EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN are involved in progression of T1D.

Among all few molecules of TSIO8, TSPZ15, TSPZP24, TBIO30, TBIO35 (Fig.7) obtained excellent binding score of 11.084 with PDB code 4 K41, the values are depicted in Table 7. The molecule TSIO8 (Fig. 8) has highest binding score its interaction with protein 4 K41, and formed hydrogen bond interaction of 4″’ –NH2 group with CYS-217, the hetero atom 1 oxygen and 3″ aromatic hydroxyl group (−OH) formed hydrogen bonding interactions with same amino acid LYS-213. The 3″ hydroxyl group also farmed hydrogen bond interaction with ASP-157 respectively. The sulphur of thiazolidinone ring 1′-S formed two hydrogen bond interactions with different amino acids LEU-16 & GLY-15 and 4′-C ring carbonyl formed interaction with GLY-302 respectively. The molecule also formed pi-pi interactions of electrons of aromatic ring with TYR-306, and pi-sigma interaction with GLU-214 and 2′ C=O of thiazolidine ring formed unfavourable interaction with calcium 401 (Ca) following 2′ C=O formed carbon-hydrogen interaction with GLY-13, All interactions with amino acids and metal are depicted by 3D (Fig.9) and 2D (Fig.10) figures.

Fig. 7
figure 7

Structures of Designed Molecules

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Table 7 Docking results of Designed Molecules on Over Expressed Proteins
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Fig. 8
figure 8

Structure of the Designed Molecule (TSIO8) obtained Highest Binding Score with PDB

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Fig. 9
figure 9

3D Binding of Molecule TSIO8 with 4 K41

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Fig. 10
figure 10

2D Binding of Molecule TSIO8 with 4 K41

Full size image

In conclusion, the present investigation was designed to identify DEGs that may be involved in the progression of T1D. A total of 284 DEGs and 10 hub genes were identified and might be regarded as diagnostic biomarkers and new therapeutic target for T1D. Together, EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN might be effective targets in T1D, but more experimental investigations and clinical trials are needed.

Availability of data and materials

The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE123658) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE123658)]

References

  1. Atkinson MA, Eisenbarth GS. Type 1 diabetes: new perspectives on disease pathogenesis and treatment. Lancet. 2001;358(9277):221–9. https://doi.org/10.1016/S0140-6736(01)05415-0.

    Article  CAS  PubMed  Google Scholar 

  2. Costacou T. The Epidemiology of Cardiovascular Disease in Adults with Type 1 Diabetes. Curr Diabetes Rev. 2017;13(6):520–7. https://doi.org/10.2174/1573399812666160927122643.

    Article  PubMed  Google Scholar 

  3. Silverstein J, Klingensmith G, Copeland K, et al. Care of children and adolescents with type 1 diabetes: a statement of the American Diabetes Association. Diabetes Care. 2005;28(1):186–212. https://doi.org/10.2337/diacare.28.1.186.

    Article  PubMed  Google Scholar 

  4. Foulis AK, Farquharson MA, Meager A. Immunoreactive alpha-interferon in insulin-secreting beta cells in type 1 diabetes mellitus. Lancet. 1987;2(8573):1423–7. https://doi.org/10.1016/s0140-6736(87)91128-7.

    Article  CAS  PubMed  Google Scholar 

  5. Haak T, Gölz S, Fritsche A, Füchtenbusch M, Siegmund T, Schnellbächer E, Klein HH, Uebel T, Droßel D. Therapy of Type 1 Diabetes. Exp Clin Endocrinol Diabetes. 2019;127(S 01):S27–38. https://doi.org/10.1055/a-0984-5696.

    Article  CAS  PubMed  Google Scholar 

  6. Acharjee S, Ghosh B, Al-Dhubiab BE, Nair AB. Understanding type 1 diabetes: etiology and models. Can J Diabetes. 2013;37(4):269–76. https://doi.org/10.1016/j.jcjd.2013.05.001.

    Article  PubMed  Google Scholar 

  7. Pociot F, Lernmark Å. Genetic risk factors for type 1 diabetes. Lancet. 2016;387(10035):2331–9. https://doi.org/10.1016/S0140-6736(16)30582-7.

    Article  CAS  PubMed  Google Scholar 

  8. Butalia S, Kaplan GG, Khokhar B, Rabi DM. Environmental Risk Factors and Type 1 Diabetes: Past, Present, and Future. Can J Diabetes. 2016;40(6):586–93. https://doi.org/10.1016/j.jcjd.2016.05.002.

    Article  PubMed  Google Scholar 

  9. Nisticò L, Buzzetti R, Pritchard LE, et al. The CTLA-4 gene region of chromosome 2q33 is linked to, and associated with, type 1 diabetes. Belgian Diabetes Registry. Hum Mol Genet. 1996;5(7):1075–80. https://doi.org/10.1093/hmg/5.7.1075.

    Article  PubMed  Google Scholar 

  10. Wang CY, She JX. SUMO4 and its role in type 1 diabetes pathogenesis. Diabetes Metab Res Rev. 2008;24(2):93–102. https://doi.org/10.1002/dmrr.797.

    Article  CAS  PubMed  Google Scholar 

  11. Hussein AG, Mohamed RH, Alghobashy AA. Synergism of CYP2R1 and CYP27B1 polymorphisms and susceptibility to type 1 diabetes in Egyptian children. Cell Immunol. 2012;279(1):42–5. https://doi.org/10.1016/j.cellimm.2012.08.006.

    Article  CAS  PubMed  Google Scholar 

  12. Qian C, Guo H, Chen X, Shi A, Li S, Wang X, Pan J, Fang C. Association of PD-1 and PD-L1 Genetic Polymorphyisms with Type 1 Diabetes Susceptibility. J Diabetes Res. 2018;2018:1614683. https://doi.org/10.1155/2018/1614683.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wu X, Zhu X, Wang X, Ma J, Zhu S, Li J, Liu Y. Intron polymorphism in the KIAA0350 gene is reproducibly associated with susceptibility to type 1 diabetes (T1D) in the Han Chinese population. Clin Endocrinol. 2009;71(1):46–9. https://doi.org/10.1111/j.1365-2265.2008.03437.x.

    Article  CAS  Google Scholar 

  14. Ingaramo PI, Ronco MT, Francés DE, Monti JA, Pisani GB, Ceballos MP, Galleano M, Carrillo MC, Carnovale CE. Tumor necrosis factor alpha pathways develops liver apoptosis in type 1 diabetes mellitus. Mol Immunol. 2011;48(12–13):1397–407. https://doi.org/10.1016/j.molimm.2011.03.015.

    Article  CAS  PubMed  Google Scholar 

  15. Liu H, Xu R, Kong Q, Liu J, Yu Z, Zhao C. Downregulated NLRP3 and NLRP1 inflammasomes signaling pathways in the development and progression of type 1 diabetes mellitus. Biomed Pharmacother. 2017;94:619–26. https://doi.org/10.1016/j.biopha.2017.07.102.

    Article  CAS  PubMed  Google Scholar 

  16. Güzel D, Dursun AD, Fıçıcılar H, Tekin D, Tanyeli A, Akat F, Topal Çelikkan F, Sabuncuoğlu B, Baştuğ M. Effect of intermittent hypoxia on the cardiac HIF-1/VEGF pathway in experimental type 1 diabetes mellitus. Anatol J Cardiol. 2016;16(2):76–83. https://doi.org/10.5152/akd.2015.5925.

    Article  CAS  PubMed  Google Scholar 

  17. Carmann C, Lilienthal E, Weigt-Usinger K, Schmidt-Choudhury A, Hörster I, Kayacelebi AA, Beckmann B, Chobanyan-Jürgens K, Tsikas D, Lücke T. The L-arginine/NO pathway, homoarginine, and nitrite-dependent renal carbonic anhydrase activity in young people with type 1 diabetes mellitus. Amino Acids. 2015;47(9):1865–74. https://doi.org/10.1007/s00726-015-2027-9.

    Article  CAS  PubMed  Google Scholar 

  18. Gbr AA, Abdel Baky NA, Mohamed EA, Zaky HS. Cardioprotective effect of pioglitazone and curcumin against diabetic cardiomyopathy in type 1 diabetes mellitus: impact on CaMKII/NF-κB/TGF-β1 and PPAR-γ signaling pathway. Naunyn Schmiedebergs Arch Pharmacol. 2020. https://doi.org/10.1007/s00210-020-01979-y.

  19. Zhao LP, Alshiekh S, Zhao M, Carlsson A, Larsson HE, Forsander G, Ivarsson SA, Ludvigsson J, Kockum I, Marcus C, et al. Next-Generation Sequencing Reveals That HLA-DRB3, −DRB4, and -DRB5 May Be Associated With Islet Autoantibodies and Risk for Childhood Type 1 Diabetes. Diabetes. 2016;65(3):710–8. https://doi.org/10.2337/db15-1115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mao K, Geng W, Liao Y, Luo P, Zhong H, Ma P, Xu J, Zhang S, Tan Q, Jin Y. Identification of robust genetic signatures associated with lipopolysaccharide-induced acute lung injury onset and astaxanthin therapeutic effects by integrative analysis of RNA sequencing data and GEO datasets. Aging. 2020;12(18):18716–40. https://doi.org/10.18632/aging.104042.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Clough E, Barrett T. The Gene Expression Omnibus Database. Methods Mol Biol. 2016;1418:93–110. https://doi.org/10.1007/978-1-4939-3578-9_5.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40. https://doi.org/10.1093/bioinformatics/btp616.

    Article  CAS  PubMed  Google Scholar 

  23. Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. https://doi.org/10.1186/gb-2014-15-2-r29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. https://doi.org/10.1093/nar/gkv007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Simonne DH, Martini A, Signorile M, Piovano A, Braglia L, Torelli P, Borfecchia E, Ricchiardi G. THORONDOR: a software for fast treatment and analysis of low-energy XAS data. J Synchrotron Radiat. 2020;27(Pt 6):1741–52. https://doi.org/10.1107/S1600577520011388.

    Article  PubMed  Google Scholar 

  26. Landau W, Niemi J, Nettleton D. Fully Bayesian analysis of RNA-seq counts for the detection of gene expression heterosis. J Am Stat Assoc. 2019;114(526):610–21. https://doi.org/10.1080/01621459.2018.1497496.

    Article  CAS  PubMed  Google Scholar 

  27. Solari A, Goeman JJ. Minimally adaptive BH: A tiny but uniform improvement of the procedure of Benjamini and Hochberg. Biom J. 2017;59(4):776–80. https://doi.org/10.1002/bimj.201500253.

    Article  PubMed  Google Scholar 

  28. Maag JLV. gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2. F1000Res. 2018;7:1576. Published 2018 Sep 28. https://doi.org/10.12688/f1000research.16409.2.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Thomas PD. The Gene Ontology and the Meaning of Biological Function. Methods Mol Biol. 2017;1446:15–24. https://doi.org/10.1007/978-1-4939-3743-1_2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F. May B et al The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649–55. https://doi.org/10.1093/nar/gkx1132.

    Article  CAS  PubMed  Google Scholar 

  31. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(Web Server issue):W305–11. https://doi.org/10.1093/nar/gkp427.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 2017;45(D1):D408–14. https://doi.org/10.1093/nar/gkw985.

    Article  CAS  PubMed  Google Scholar 

  33. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B. Ideker T Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. https://doi.org/10.1101/gr.1239303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Przulj N, Wigle DA, Jurisica I. Functional topology in a network of protein interactions. Bioinformatics. 2004;20(3):340–8. https://doi.org/10.1093/bioinformatics/btg415.

    Article  CAS  PubMed  Google Scholar 

  35. Nguyen TP, Liu WC, Jordán F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179. Published 2011 Oct 31. https://doi.org/10.1186/1752-0509-5-179.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Shi Z, Zhang B. Fast network centrality analysis using GPUs. BMC Bioinformatics. 2011;12:149. https://doi.org/10.1186/1471-2105-12-149.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Fadhal E, Gamieldien J, Mwambene EC. Protein interaction networks as metric spaces: a novel perspective on distribution of hubs. BMC Syst Biol. 2014;8:6. https://doi.org/10.1186/1752-0509-8-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zaki N, Efimov D, Berengueres J. Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics. 2013;14:163. https://doi.org/10.1186/1471-2105-14-163.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Fan Y, Xia J. miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol Biol. 2018;1819:215–33. https://doi.org/10.1007/978-1-4939-8618-7_10.

    Article  CAS  PubMed  Google Scholar 

  40. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234–41. https://doi.org/10.1093/nar/gkz240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. https://doi.org/10.1186/1471-2105-12-77.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) Method. Methods. 2001;25:402–8. https://doi.org/10.1006/meth.2001.1262.

    Article  CAS  PubMed  Google Scholar 

  43. Liao C, Sitzmann M, Pugliese A, Nicklaus MC. Software and resources for computational medicinal chemistry. Future Med Chem. 2011;3(8):1057–85. https://doi.org/10.4155/fmc.11.63.

    Article  CAS  PubMed  Google Scholar 

  44. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3:33. Published 2011 Oct 7. https://doi.org/10.1186/1758-2946-3-33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gong Z, Xie Z, Qiu J, Wang G. Synthesis, Biological Evaluation and Molecular Docking Study of 2-Substituted-4,6-Diarylpyrimidines as α-Glucosidase Inhibitors. Molecules. 2017;22(11):1865. https://doi.org/10.3390/molecules22111865.

    Article  CAS  PubMed Central  Google Scholar 

  46. Grewal AS, Kharb R, Prasad DN, Dua JS, Lather V. Design, synthesis and evaluation of novel 3,5-disubstituted benzamide derivatives as allosteric glucokinase activators. BMC Chem. 2019;13(1):2. https://doi.org/10.1186/s13065-019-0532-8.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Salvi GE, Kandylaki M, Troendle A, Persson GR, Lang NP. Experimental gingivitis in type 1 diabetics: a controlled clinical and microbiological study. J Clin Periodontol. 2005;32(3):310–6. https://doi.org/10.1111/j.1600-051X.2005.00682.x.

    Article  PubMed  Google Scholar 

  48. Twetman S, Petersson GH, Bratthall D. Caries risk assessment as a predictor of metabolic control in young Type 1 diabetics. Diabet Med. 2005;22(3):312–5. https://doi.org/10.1111/j.1464-5491.2005.01419.x.

    Article  CAS  PubMed  Google Scholar 

  49. Toni M, Hermida J, Toledo E, Goñi MJ, Díez GN. Role of CFH and ARMS2 polymorphisms in retinopathy and coronary artery disease in type 1 diabetes. An Sist Sanit Navar. 2012;35(3):425–32. https://doi.org/10.23938/ASSN.0098.

    Article  CAS  PubMed  Google Scholar 

  50. Drobná Z, Del Razo LM, García-Vargas GG, Sánchez-Peña LC, Barrera-Hernández A, Stýblo M, Loomis D. Environmental exposure to arsenic, AS3MT polymorphism and prevalence of diabetes in Mexico. J Expo Sci Environ Epidemiol. 2013;23(2):151–5. https://doi.org/10.1038/jes.2012.103.

    Article  CAS  PubMed  Google Scholar 

  51. Wang LA, Nguyen DH, Mifflin SW. CRHR2 (Corticotropin-Releasing Hormone Receptor 2) in the Nucleus of the Solitary Tract Contributes to Intermittent Hypoxia-Induced Hypertension. Hypertension. 2018;72(4):994–1001. https://doi.org/10.1161/HYPERTENSIONAHA.118.11497.

    Article  CAS  PubMed  Google Scholar 

  52. Garcia-Martínez I, Sánchez-Mora C, Soler Artigas M, et al. Gene-wide Association Study Reveals RNF122 Ubiquitin Ligase as a Novel Susceptibility Gene for Attention Deficit Hyperactivity Disorder. Sci Rep. 2017;7(1):5407. https://doi.org/10.1038/s41598-017-05514-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sala D, Ivanova S, Plana N, et al. Autophagy-regulating TP53INP2 mediates muscle wasting and is repressed in diabetes. J Clin Invest. 2014;124(5):1914–27. https://doi.org/10.1172/JCI72327.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Keshavarz P, Inoue H, Nakamura N, Yoshikawa T, Tanahashi T, Itakura M. Single nucleotide polymorphisms in genes encoding LKB1 (STK11), TORC2 (CRTC2) and AMPK alpha2-subunit (PRKAA2) and risk of type 2 diabetes. Mol Genet Metab. 2008;93(2):200–9. https://doi.org/10.1016/j.ymgme.2007.08.125.

    Article  CAS  PubMed  Google Scholar 

  55. Granados-Riveron JT, Ghosh TK, Pope M, et al. Alpha-cardiac myosin heavy chain (MYH6) mutations affecting myofibril formation are associated with congenital heart defects. Hum Mol Genet. 2010;19(20):4007–16. https://doi.org/10.1093/hmg/ddq315.

    Article  CAS  PubMed  Google Scholar 

  56. McKenna K, Morris AD, Ryan M, et al. Renal resistance to vasopressin in poorly controlled type 1 diabetes mellitus. Am J Physiol Endocrinol Metab. 2000;279(1):E155–60. https://doi.org/10.1152/ajpendo.2000.279.1.E155.

    Article  CAS  PubMed  Google Scholar 

  57. Yue J, Zhu T, Yang J, et al. CircCBFB-mediated miR-28-5p facilitates abdominal aortic aneurysm via LYPD3 and GRIA4. Life Sci. 2020;253:117533. https://doi.org/10.1016/j.lfs.2020.117533.

    Article  CAS  PubMed  Google Scholar 

  58. Kochetova OV, Avzaletdinova DS, Korytina GF, Morugova TV, Mustafina OE. The association between eating behavior and polymorphisms in GRIN2B, GRIK3, GRIA1 and GRIN1 genes in people with type 2 diabetes mellitus. Mol Biol Rep. 2020;47(3):2035–46. https://doi.org/10.1007/s11033-020-05304-x.

    Article  CAS  PubMed  Google Scholar 

  59. Jiao X, Zhang D, Hong Q, et al. Netrin-1 works with UNC5B to regulate angiogenesis in diabetic kidney disease. Front Med. 2020;14(3):293–304. https://doi.org/10.1007/s11684-019-0715-7.

    Article  PubMed  Google Scholar 

  60. Ruiz de Azua I, Scarselli M, Rosemond E, et al. RGS4 is a negative regulator of insulin release from pancreatic beta-cells in vitro and in vivo. Proc Natl Acad Sci U S A. 2010;107(17):7999–8004. https://doi.org/10.1073/pnas.1003655107.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Wang P, Qin W, Wang P, et al. Genomic Variants in NEURL, GJA1 and CUX2 Significantly Increase Genetic Susceptibility to Atrial Fibrillation. Sci Rep. 2018;8(1):3297. https://doi.org/10.1038/s41598-018-21611-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Faienza MF, Ventura A, Delvecchio M, et al. High Sclerostin and Dickkopf-1 (DKK-1) Serum Levels in Children and Adolescents With Type 1 Diabetes Mellitus. J Clin Endocrinol Metab. 2017;102(4):1174–81. https://doi.org/10.1210/jc.2016-2371.

    Article  PubMed  Google Scholar 

  63. Galán M, Kassan M, Choi SK, et al. A novel role for epidermal growth factor receptor tyrosine kinase and its downstream endoplasmic reticulum stress in cardiac damage and microvascular dysfunction in type 1 diabetes mellitus. Hypertension. 2012;60(1):71–80. https://doi.org/10.1161/HYPERTENSIONAHA.112.192500.

    Article  CAS  PubMed  Google Scholar 

  64. García SI, Porto PI, Dieuzeide G, et al. Thyrotropin-releasing hormone receptor (TRHR) gene is associated with essential hypertension. Hypertension. 2001;38(3 Pt 2):683–7. https://doi.org/10.1161/01.hyp.38.3.683.

    Article  PubMed  Google Scholar 

  65. Feng X, Scott A, Wang Y, et al. PTPRT regulates high-fat diet-induced obesity and insulin resistance. PLoS One. 2014;9(6):e100783. Published 2014 Jun 20. https://doi.org/10.1371/journal.pone.0100783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ward-Caviness CK, Neas LM, Blach C, et al. A genome-wide trans-ethnic interaction study links the PIGR-FCAMR locus to coronary atherosclerosis via interactions between genetic variants and residential exposure to traffic. PLoS One. 2017;12(3):e0173880. https://doi.org/10.1371/journal.pone.0173880.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Sun J, Wang J, Lu W, et al. MiR-325-3p inhibits renal inflammation and fibrosis by targeting CCL19 in diabetic nephropathy. Clin Exp Pharmacol Physiol. 2020. https://doi.org/10.1111/1440-1681.13371.

  68. Yang Z, Bowles NE, Scherer SE, et al. Desmosomal dysfunction due to mutations in desmoplakin causes arrhythmogenic right ventricular dysplasia/cardiomyopathy. Circ Res. 2006;99(6):646–55. https://doi.org/10.1161/01.RES.0000241482.19382.c6.

    Article  CAS  PubMed  Google Scholar 

  69. Xu H, Shang Q, Chen H, et al. ITIH4: A New Potential Biomarker of "Toxin Syndrome" in Coronary Heart Disease Patient Identified with Proteomic Method. Evid Based Complement Alternat Med. 2013;2013:360149. https://doi.org/10.1155/2013/360149.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Miyashita A, Arai H, Asada T, et al. GAB2 is not associated with late-onset Alzheimer's disease in Japanese. Eur J Hum Genet. 2009;17(5):682–6. https://doi.org/10.1038/ejhg.2008.181.

    Article  CAS  PubMed  Google Scholar 

  71. McCann JA, Xu YQ, Frechette R, Guazzarotti L, Polychronakos C. The insulin-like growth factor-II receptor gene is associated with type 1 diabetes: evidence of a maternal effect. J Clin Endocrinol Metab. 2004;89(11):5700–6. https://doi.org/10.1210/jc.2004-0553.

    Article  CAS  PubMed  Google Scholar 

  72. Sun L, Lin P, Chen Y, et al. miR-182-3p/Myadm contribute to pulmonary artery hypertension vascular remodeling via a KLF4/p21-dependent mechanism. Theranostics. 2020;10(12):5581–99. https://doi.org/10.7150/thno.44687.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Fan Y, Li X, Zhang Y, et al. Genetic Variants of TPCN2 Associated with Type 2 Diabetes Risk in the Chinese Population. PLoS One. 2016;11(2):e0149614. https://doi.org/10.1371/journal.pone.0149614.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Chan GC, Divers J, Russell GB, et al. FGF23 Concentration and APOL1 Genotype Are Novel Predictors of Mortality in African Americans With Type 2 Diabetes. Diabetes Care. 2018;41(1):178–86. https://doi.org/10.2337/dc17-0820.

    Article  CAS  PubMed  Google Scholar 

  75. Hermansson C, Lundqvist A, Wasslavik C, Palmqvist L, Jeppsson A, Hultén LM. Reduced expression of NLRP3 and MEFV in human ischemic heart tissue. Biochem Biophys Res Commun. 2013;430(1):425–8. https://doi.org/10.1016/j.bbrc.2012.11.070.

    Article  CAS  PubMed  Google Scholar 

  76. Wang Z, Dai Z, Pan Y, Wu S, Li Z, Zuo C. E3 ubiquitin ligase DTX4 is required for adipogenic differentiation in 3T3-L1 preadipocytes cell line. Biochem Biophys Res Commun. 2017;492(3):419–24. https://doi.org/10.1016/j.bbrc.2017.08.083.

    Article  CAS  PubMed  Google Scholar 

  77. Aspit L, Levitas A, Etzion S, Krymko H, Slanovic L, Zarivach R, Etzion Y, Parvari R. CAP2 mutation leads to impaired actin dynamics and associates with supraventricular tachycardia and dilated cardiomyopathy. J Med Genet. 2019;56(4):228–35. https://doi.org/10.1136/jmedgenet-2018-105498.

    Article  CAS  PubMed  Google Scholar 

  78. Ismail NA, El Baky AN, Ragab S, Hamed M, Hashish MA, Shehata A. Monocyte chemoattractant protein 1 and macrophage migration inhibitory factor in children with type 1 diabetes. J Pediatr Endocrinol Metab. 2016;29(6):641–5. https://doi.org/10.1515/jpem-2015-0340.

    Article  CAS  PubMed  Google Scholar 

  79. Baptista FI, Pinto MJ, Elvas F, Almeida RD, Ambrósio AF. Diabetes alters KIF1A and KIF5B motor proteins in the hippocampus. PLoS One. 2013;8(6):e65515. https://doi.org/10.1371/journal.pone.0065515.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Veith C, Marsh LM, Wygrecka M, Rutschmann K, Seeger W, Weissmann N, Kwapiszewska G. Paxillin regulates pulmonary arterial smooth muscle cell function in pulmonary hypertension. Am J Pathol. 2012;181(5):1621–33. https://doi.org/10.1016/j.ajpath.2012.07.026.

    Article  CAS  PubMed  Google Scholar 

  81. Parackova Z, Vrabcova P, Zentsova I, et al. Enhanced STAT3 phosphorylation and PD-L1 expression in myeloid dendritic cells indicate impaired IL-27Ralpha signaling in type 1 diabetes. Sci Rep. 2020;10(1):493. https://doi.org/10.1038/s41598-020-57507-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Muiya NP, Wakil S, Al-Najai M, et al. A study of the role of GATA2 gene polymorphism in coronary artery disease risk traits. Gene. 2014;544(2):152–8. https://doi.org/10.1016/j.gene.2014.04.064.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgement

I thank Felipe Leal Valentim,INSERM/UPMC, Unité Immunologie-Immunopathologie-Immunothérapie, Paris, France very much, the author who deposited their microarray dataset, GSE123658, into the public GEO database.

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Authors and Affiliations

  1. Department of General Medicine, Basaveshwara Medical College, Chitradurga, Karnataka, 577501, India

    G. Prashanth

  2. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka, 582103, India

    Basavaraj Vastrad

  3. Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Mysuru and JSS Academy of Higher Education & Research, Mysuru, Karnataka, 570015, India

    Anandkumar Tengli

  4. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, Karanataka, 580001, India

    Chanabasayya Vastrad

  5. Department of Ayurveda, Rajiv Gandhi Education Society’s Ayurvedic Medical College, Ron, Karanataka, 582209, India

    Iranna Kotturshetti

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Contributions

PG - Methodology and validation. BV - Writing original draft, and review and editing. AT - Formal analysis and validation. CV - Software and investigation. IK - Supervision and resources. The authors read and approved the final manuscript.

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Correspondence to Chanabasayya Vastrad.

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Prashanth, G., Vastrad, B., Tengli, A. et al. Identification of hub genes related to the progression of type 1 diabetes by computational analysis. BMC Endocr Disord 21, 61 (2021). https://doi.org/10.1186/s12902-021-00709-6

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Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

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