Abstract
Background
Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus.
Methods
To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules.
Results
A total of 820 DEGs were identified between healthy obese and metabolically unhealthy obese, among 409 up regulated and 411 down regulated genes. The GO enrichment analysis results showed that these DEGs were significantly enriched in ion transmembrane transport, intrinsic component of plasma membrane, transferase activity, transferring phosphorus-containing groups, cell adhesion, integral component of plasma membrane and signaling receptor binding, whereas, the REACTOME pathway enrichment analysis results showed that these DEGs were significantly enriched in integration of energy metabolism and extracellular matrix organization. The hub genes CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, which might play an essential role in obesity associated type 2 diabetes mellitus was further screened.
Conclusions
The present study could deepen the understanding of the molecular mechanism of obesity associated type 2 diabetes mellitus, which could be useful in developing therapeutic targets for obesity associated type 2 diabetes mellitus.
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Introduction
Obesity associated type 2 diabetes is one of the most common metabolic disorder worldwide [1]. Type 2 diabetes mellitus is characterized by insulin deficiency due to pancreatic β-cell inactivation and insulin resistance [2]. Genetic factors, hyperinsulinemia, atherogenic dyslipidemia, glucose intolerance, hypertension, prothrombic state, hyperuricemia and polycystic ovary syndrome are the key risk factors for the occurrence and progression of type 2 diabetes mellitus [3]. Obesity associated type 2 diabetes mellitus affects the vital organs such as heart [4], brain [5], kidney [6] and eye [7]. Etiology and advancement of obesity associated type 2 diabetes mellitus is more complex and still understandable. Therefore, it is essential to understand the precise molecular mechanisms associated in the progression of obesity associated type 2 diabetes mellitus and thus to establish valid diagnostic and therapeutic strategies.
Current evidence has shown that genetic predisposition plays a key role in the advancement of obesity associated type 2 diabetes mellitus [8]. Recently, several genes and pathways have been found to participate in the occurrence and advancement of obesity associated type 2 diabetes mellitus [9], including FGF21 [10], pro-opiomelanocortin (POMC) [11], PI3K/AKT pathway [12] and JAK/STAT pathway [13]. However, the current knowledge is insufficient to explain and understand how these crucial genes and signaling pathways are associated with advancement of obesity associated type 2 diabetes mellitus. Therefore, there is a great need to find new prognostic and diagnostics biomarkers, and to advance novel techniques to enlighten the molecular mechanisms controlling the progression of obesity associated type 2 diabetes mellitus.
Bioinformatics analysis of expression profiling by high throughput sequencing data has shown great promise to discover potential key genes and signaling pathways with significant roles in metabolic disorder [14], to identify new prognostic and diagnostics biomarkers, and biological processes implicated in obesity associated type 2 diabetes mellitus. In this investigation, using bioinformatics analysis, we aimed to investigate expression profiling by high throughput sequencing data to determine differentially expressed genes (DEGs) and significant pathways in obesity associated type 2 diabetes mellitus. After searching the Gene Expression Omnibus (GEO) database [15], we selected RNA sequencing dataset GSE143319 for identifying DEGs for obesity associated type 2 diabetes mellitus. This dataset gives more information about obesity associated type 2 diabetes mellitus elevates patient’s risk of nonalcoholic steatohepatitis (NASH), cardiovascular disease and cancer. Gene Ontology (GO) and pathway enrichment analysis were performed. A hub and target genes were identified from protein-protein interaction (PPI) network, modules, miRNA-target genes regulatory network and TF-target gene regulatory network. Subsequently, hub genes were validated by using receiver operating characteristic (ROC) curve and RT- PCR analysis. Finally, molecular docking studies performed for prediction of small drug molecules.
Materials and Methods
RNA sequencing data
The expression profiling by high throughput sequencing dataset GSE143319 deposited by Ding et al [16] into the GEO database were obtained on the GPL20301 platform (Illumina HiSeq 4000 (Homo sapiens)). This dataset is provided for 30 samples, including 15 samples of metabolically healthy obese and 15 samples of a metabolically unhealthy obese.
Identification of DEGs
The limma [17] in R bioconductor package was utilized to screen DEGs between metabolically healthy obese and metabolically unhealthy obese. These DEGs were identified as important genes that might play an important role in the development of obesity associated type 2 diabetes mellitus. The cutoff criterion were ∣log fold change (FC)∣ > 0.2587 for up regulated genes, ∣log fold change (FC)∣ < -0.2825 for down regulated genes and adjusted P value < 0.05.
GO and pathway enrichment analyses
ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) [18], which is a useful online database that integrates biologic data and provides a comprehensive set of functional annotation information of genes as well as proteins for users to analyze the functions or signaling pathways. GO (https://geneontology.org/) [19] enrichment analysis (biologic processes [BP], cellular components [CC], and molecular functions [MF]) is a strong bioinformatics tool to analyze and annotate genes. The REACTOME (https://reactome.org/) [20] is a pathway database resource for understanding high-level gene functions and linking genomic information from large scale molecular datasets. To analyze the function of the DEGs, biologic analyses were performed using GO and REACTOME pathway enrichment analysis via ToppGene online database.
PPI network construction and module analysis
IMEX interactome (https://www.imexconsortium.org/) [21] online PPI database was using to identify the hub gene information in PPI network. Analyzing the interactions and functions of DEGs might give information about the controlling the progression of obesity associated type 2 diabetes mellitus. Cytoscape (version 3.8.2) (www.cytoscape.org) is a bioinformatics platform for constructing and visualizing PPI network [22]. Therefore, the topological properties includes node degree [23], betweenness centrality [24], stress centrality [25], closeness centrality [26] are analyzed in using Java plug-in Network Analyzer to obtain hub genes in the PPI network. The plug-in PEWCC1 of Cytoscape was applied to detect densely connected regions in PPI network. The significant modules in the PPI network was selected using PEWCC1 (https://apps.cytoscape.org/apps/PEWCC1) [27]. 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.
Target gene – miRNA regulatory network construction and analysis
Obesity associated type 2 diabetes mellitus relating miRNAs and experimentally validated target genes were identified from miRNet database (https://www.mirnet.ca/) [28]. Obesity associated type 2 diabetes mellitus relating miRNAs and target genes were identified through target genes - miRNA regulatory network. Then the target genes - miRNA regulatory network was constructed and visualized by using Cytoscape software.
Target gene – TF network regulatory construction and analysis
Obesity associated type 2 diabetes mellitus relating TFs and experimentally validated target genes were identified from TFs database NetworkAnalyst database (https://www.networkanalyst.ca/) [29]. Obesity associated type 2 diabetes mellitus relating TFs and target genes were identified through target genes - TF regulatory network. Then the target genes -TF regulatory network was constructed and visualized by using Cytoscape software.
Receiver operating characteristic (ROC) curve analysis
The ROC curve was used to calculate classifiers in bioinformatics applications. To further assess the predictive accuracy of the hub genes, ROC analysis was performed to discriminate metabolically healthy obese from metabolically unhealthy obese. ROC curves for hub genes were generated using pROC in R [30] based on the obtained DEGs and their expression profiling by high throughput sequencing dataset. The area under the curve (AUC) was evaluated and used to compare the diagnostic value of hub genes.
Validation of the expression levels of candidate genes by RT-PCR
Quantitative RT-PCR was conducted to validate the expressions of these hub genes in obesity associated type 2 diabetes mellitus. Total RNAs were extracted from Primary Subcutaneous Pre adipocytes; Normal Human cell line (ATCC® PCS-210-010™) and 3T3-L1 cells (ATCC® CL-173) using TRI Reagent® (Sigma, USA) according to instruction, followed by reverse transcription with Reverse transcription cDNA kit (Thermo Fisher Scientific, Waltham, MA, USA) and cDNA amplification through 7 Flex real-time PCR system (Thermo Fisher Scientific, Waltham, MA, USA). The expressions of hub genes were normalized to against beta actin expression. The data were calculated by the 2−ΔΔCt method [31]. A primer used in the current investigation was listed in Table 1.
Molecular docking studies
The Surflex-Docking docking studies for the designed molecules were performed using module SYBYL-X 2.0 perpetual software. Using ChemDraw Tools, the molecules were sketched and imported and saved into sdf format using open free software from Babel. The co-crystallised protein structures of CEBPD, TP73, ESR2, TAB1 and MAP 3K5 of its PDB code 3L4W, 2XWC, 1U3Q, 5NZZ & 2CLQwas extracted from Protein Data Bank [32,33,34,35,36]. Together with the TRIPOS force field, GasteigerHuckel (GH) charges were added to all designed derivatives for the structure optimization process. Furthermore, energy minimization was carried out using MMFF94s and MMFF94 algorithm process. The processing of protein was accomplished after the incorporation of protein. The co-crystallized ligand and all water molecules were expelled from the crystal structure; more hydrogen was added and the side chain was optimized. TRIPOS force field was used to minimize complexity of structure. The interaction efficiency of the compounds with the receptor was expressed in kcal / mol units by the Surflex-Dock score. The best spot between the protein and the ligand was inserted into the molecular region. The visualization of ligand interaction with receptor is done by using discovery studio visualizer.
Results
Identification of DEGs
As presented in the cluster heat map of Fig. 1, total 820 DEGs, comprising 409 up regulated and 411 down regulated genes, were identified between metabolically healthy obese samples and metabolically unhealthy obese samples. DEGs were illustrated by volcano plot (Fig.2), and the top up regulated and down regulated DEGs are listed in Table 2.
Heat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A15 = metabolically healthy obese samples; B1 – B15 = metabolically unhealthy obese samples)
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
Gene ontology and pathway enrichment analyses
DEGs were divided into up regulated genes and down regulated genes. GO and REACTOME pathway enrichment analysis were conducted for DEGs. Results of GO categories were presented by functional groups, which include BP, CC, and MF, and are listed in Table 3. In group BP, up regulated genes enriched in regulation of ion transmembrane transport and oxoacid metabolic process, while the down regulated genes enriched in cell adhesion and response to endogenous stimulus. For group CC, up regulated genes enriched in intrinsic component of plasma membrane and mitochondrion, while down regulated genes enriched in integral component of plasma membrane and supra molecular fiber. In addition, GO results of group MF showed that up regulated genes enriched in transferase activity, transferring phosphorus-containing groups and transporter activity and down regulated genes enriched in signaling receptor binding and molecular transducer activity. Several significant enriched pathways were acquired through REACTOME pathway analysis (Table 4). The enriched pathways for up regulated genes included integration of energy metabolism and neuronal system, while, down regulated genes enriched in extracellular matrix organization and GPCR ligand binding.
PPI network construction and module analysis
PPI network complex consisted of 3648 nodes and 6305 edges, wherein node and edge represented gene and interaction between genes (Fig.3a). Moreover, CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF were identified as hub genes and are listed in Table 5. In addition, module analysis was conducted to detect the highly connected regions of PPI network, and two significant modules were identified (Fig.3b and Fig.3c). Further GO and pathway enrichment analysis revealed that genes in these modules were mostly implicated in regulation of ion transmembrane transport, oxoacid metabolic process, intrinsic component of plasma membrane, extracellular matrix organization and supra molecular fiber.
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 4 nodes and 6 edges for up regulated genes c The most significant module was obtained from PPI network with 6 nodes and 10 edges for down regulated genes. Up regulated genes are marked in green; down regulated genes are marked in red
Target gene – miRNA regulatory network construction and analysis
The target genes - miRNA regulatory network was constructed, including 1982 miRNAs and 245 target genes. As shown in the integrated target genes - miRNA regulatory network (Fig.4), FASN targeted 147 miRNAs (ex, hsa-mir-4314), SREBF1 targeted 81 miRNAs (ex, hsa-mir-5688), CKB targeted 72 miRNAs (ex, hsa-mir-583), CACNA1A targeted 69 miRNAs (ex, hsa-mir-632), ESR2 targeted 61 miRNAs (ex, hsa-mir-3176), MAP 1B targeted 249 miRNAs (ex, hsa-mir-1299), RUNX1 targeted 125 miRNAs (ex, hsa-mir-4530), PRNP targeted 106 miRNAs (ex, hsa-mir-4477a), FN1 targeted 105 miRNAs (ex, hsa-mir-606) and DAB2 targeted 75 miRNAs (ex, hsa-mir-1343-3p6) and are listed in Table 6.
Target gene - miRNA regulatory network between target genes. The blue color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red
Target gene-TF s regulatory network construction and analysis
The target genes -TF regulatory network was constructed, including 333 TFs and 204 target genes. As shown in the integrated target genes -TF regulatory network (Fig. 5), SREBF1 targeted 94 TFs (ex, ATF4), FASN targeted 71 TFs (ex, CUX1), SLC9A3R1 targeted 63 TFs (ex, MBD2), CKB targeted 50 TFs (ex, IRF4), TGM2 targeted 50 TFs (ex, SIN3A), PIK3R2 targeted 73 TFs (ex, ZNF143), FLNC targeted 53 TFs (ex, SMARCE1), RUNX1 targeted 53 TFs (ex, ZBTB7A), FN1 targeted 45 TFs (ex, CREB1) and TRIM63 targeted 22 TFs (ex, RELA) and are listed in Table 6.
Target gene - TF regulatory network between target genes. The gray color triangle nodes represent the key TFs; up regulated genes are marked in green; down regulated genes are marked in red
Receiver operating characteristic (ROC) curve analysis
The ROC curve analysis was used to assess the predictive accuracy of hub genes. AUC was determined and used to prefer the most appropriate cut-off gene expression levels. ROC curves and AUC values are presented in Fig. 6. All AUC values exceeded 0.72, while the up regulated genes CEBPD, TP73, ESR2, TAB1 and MAP 3K5, and down regulated genes FN1, UBD, RUNX1, PIK3R2 and TNF had AUC values > 0.75.
ROC curve analyses of hub genes. a CEBPD b TP73 c ESR2 d TAB1 e MAP 3K5 f FN1 g UBD h RUNX1 i PIK3R2 j TNF
Validation of the expression levels of candidate genes by RT-PCR
To further verify the expression level of hub genes in obese samples, RT-PCR was performed to calculate the mRNA levels of the ten hub genes identified in the present study (CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF) in obese samples. As illustrated in Fig. 7, the expression of CEBPD, TP73, ESR2, TAB1, MAP 3K5 were significantly up regulated in obese samples compared with normal control tissues, while FN1, UBD, RUNX1, PIK3R2 and TNF were significantly down regulated in obese samples compared with normal control tissues. The present RT-PCR results were in line with the prior bioinformatics analysis, suggesting that these hub genes might be associated with progression of obesity associated type 2 diabetes mellitus.
RT-PCR analyses of hub genes. A) CEBPD B) TP73C) ESR2 D) TAB1 E) MAP 3K5 F) FN1 G) UBD H) RUNX1 I) PIK3R2 J) TNF
Molecular docking studies
In the current research, the docking simulation was conducted to recognize the active site conformation and major interactions responsible for complex stability with the binding sites receptor. Drug design software Sybyl X 2.1 was used to perform docking experiments on novel molecules containing thiazolidindioneheterocyclic ring. Molecules containing the heterocyclic ring of thiazolidinedione are constructed based on the pioglitazone structure and are most widely used alone or in conjunction with other anti-diabetic drugs. Obesity associated type 2 diabetes mellitus is a chronic disorder that prevents insulin from being used by the body the way it should. It's said that people with obesity associated type 2 diabetes mellitus have insulin resistance, oral hypoglycaemic agents are used either alone or in combination of two or more drugs. Pioglitazone (Glitazones) are commonly used either alone or in combination in obesity associated type 2 diabetes mellitus. The one protein in each over expressed genes in obesity associated type 2 diabetes mellitus are selected for docking studies. The X-RAY crystallographic structure of one protein from each over-expressed genes of CEBPD, TP73, ESR2, TAB1 and MAP 3K5, and their co-crystallized PDB code of 4LY9, 2XWC, 2IOG, 5NZZ and 5UP3 respectively were selected for docking. The examinations of the designed molecules were performed to recognize the potential molecule. The foremost of the designed molecules obtained C-score greater than 6 and are said to be active. A total of 24 designed molecules few molecules have excellent good binding energy (C-score) greater than 8 respectively. Few of the designed molecules obtained good binding scores such as molecule TZP20, TZPS8, TZP22, TZPS10 (Fig.8) obtained binding core of 12.212, 11.489, 11.013 and 10.851 with 5UP3 and molecule TZP22, TZPS8, TZPS10 obtained binding score of 9.482, 9.329 and 9.252 with 2XWC and molecule TZP20, TZPS10 obtained binding score 7.359 and 6.848 with 5NZZ and molecule TZP22, TZP21, TZPS9 obtained binding score 11.053, 10.716 and 10.669 with 2IOG respectively. The molecule TZP23, TZPS5, TZPS2 obtained bind score 4.336 to 4.319 with 5NZZ and molecule TZPS10 of binding core 4.633 with 2IOG respectively. The binding score of the predicted molecules are compared with that of the standard pioglitaone obtained bind score of 10.1314, 9.834, 9.8244, 9.8284 and 7.4321 with 2IOG, 2XWC, 4LY9, 5UP3 and 5NZZ, the values are depicted in Table 7. The molecule TZP22 obtained good binding score with all proteins and hydrogen bonding and other bonding interactions with amino acids with protein code 2IOG are depicted by 3D (Fig.9) and 2D (Fig.10) figures.
Structures of designed molecules
3D Binding of molecule TZP22with 2IOG
2D Binding of molecule TZP22with 2IOG
Discussion
Obesity associated type 2 diabetes mellitus is the most common aggressive metabolic disorder [37]. However, the most key challenge in treating obesity associated type 2 diabetes mellitus is the presence of complexity [38]. Although previous investigations have reported various potential molecular markers linked with the advancement of obesity associated type 2 diabetes mellitus, the molecular mechanism underlying its pathogenesis has not been generally studied [39]. In the present investigation, a total of 820 DEGs were identified, containing 409 up regulated genes and 411 down regulated genes. SULT1C2 [40] and UBD (ubiquitin D) [41] were responsible for progression of kidney diseases, but these genes might be liable for advancement of obesity associated type 2 diabetes mellitus. HLA-DQA1 was associated with progression of T2DM [42]. SPX (spexin hormone) [43] and APOB (apolipoprotein B) [44] are a critical proteins plays an important role in obesity associated type 2 diabetes mellitus.
The GO and pathway enrichment analysis of DEG are closely related to obesity associated type 2 diabetes mellitus. Genes such as KCNE5 [45], SHANK3 [46], CASQ2 [47], EDNRA (endothelin receptor type A) [48], EPHB4 [49], ALPK3 [50], WNT11 [51], IRAK2 [52], FBN1 [53], SFRP2 [54], CLCA2 [55], NEXN (nexilin F-actin binding protein) [56], PALLD (palladin, cytoskeletal associated protein) [57], DAB2 [58], NRP2 [59], THBS2 [60], CSF1R [61], KCNA2 [62], CACNA1C [63], F2R [64], UCHL1 [65], CCL18 [66], ITGB1BP2 [67] and FMOD (fibromodulin) [68] were reportedly involved in cardio vascular diseases, but these genes might be key for progression of obesity associated type 2 diabetes mellitus. Hu et al. [69], Liu et al. [70], Eltokhi et al. [71], Cai et al. [72], Pfeiffer et al. [73], Lin et al. [74], Royer-Zemmour et al. [75], Pastor et al. [76], Goodspeed et al. [77], Zhang et al. [78], Rogers et al. [79], Su et al. [80] and Foale et al. [81] reported that NRXN1, CRHR1, SHANK2, PSEN2, CKB (creatine kinase B), CD200R1, SRPX2, PTPRZ1, SLC6A1, GABRB2, KCNA1, ASAH1 and LINGO1 were the genes expressed in progression of neuropsychiatric disorders, but these genes might be involved in advancement of obesity associated type 2 diabetes mellitus. Reports indicate that genes include SPHK2 [82], NPC1L1 [83], CNTFR (ciliaryneurotrophic factor receptor) [84], SLC2A4 [85], EDA (ectodysplasin A) [86], TGM2 [87], GCK (glucokinase) [88], FASN (fatty acid synthase) [89], FAP (fibroblast activation protein alpha) [90], PRNP (prion protein) [91], LYVE1 [92], SERPINE1 [93], TNF (tumor necrosis factor) [94], FASLG (Fas ligand) [95], HGF (hepatocyte growth factor) [96], FNDC5 [97], LBP (lipopolysaccharide binding protein) [98] and LOX (lysyl oxidase) [99] were the genes expressed in obesity associated type 2 diabetes mellitus. Hirai et al [100], Vuori et al [101], Porta et al [102], Nomoto et al [103] and Blindbæk et al [104] demonstrates that VAMP2, CACNB2, SLC19A3, PFKFB3 and MFAP4 were the genes essential for progression of type 1 diabetes, but these genes might be key for advancement of obesity associated type 2 diabetes mellitus. Genes such as CACNA1A [105], ALK (ALK receptor tyrosine kinase) [106], SLC4A4 [107], STOX1 [108], COL3A1 [109], VNN1 [110], SLC4A7 [111], BDKRB2 [112], DRD1 [113] and LPAR1 [114] have reported significantly linked with hypertension, but these genes might be crucial for progression of obesity associated type 2 diabetes mellitus. Genes such as KCNE2 [115], DLL1 [116], ACVR1C [117], RGS3 [118], MLXIPL (MLX interacting protein like) [119], PAG1 [120], SLC2A10 [121] and GRB14 [122] play important role in type 2 diabetes mellitus progression. A recent investigation has indicated that genes such as GPIHBP1 [123], FGFRL1 [124], DAPK2 [125], MAP 3K5 [126], ANKK1 [127], GK (glycerol kinase) [128], SPHK1 [129], GNG3 [130], FSTL3 [131], SLIT2 [132], CCDC80 [133], RND3 [134], PTGER4 [135], RUNX1 [136], ADAM12 [137], OLR1 [138], THBS1 [139], CD28 [140], TRPV4 [141], ATRN (attractin) [142], MRC1 [143], SEMA3C [144], HTR2B [145], NOX4 [146], TACR1 [147], BAMBI [148], PDGFD (platelet derived growth factor D) [149], APLN (apelin) [150], MFAP5 [151] and LUM (lumican) [152] are associated with a development of obesity. A previous investigation found that genes such asDDR1 [153], TAB1 [154], NEK8 [155], SERPINE2 [156], FCGR2B [157], ANGPT2 [158], FN1 [159], SOCS5 [158], SMOC2 [160], CD2 [161] and SCN9A [162] expression were associated with a kidney diseases, but these genes might be responsible for advancement of obesity associated type 2 diabetes mellitus.
In addition, an investigation reported that hub genes serve an essential role in maintaining the entire PPI network and its modules are indispensable. 10 hub genes, including CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, were identified as the key genes responsible for progression of obesity associated type 2 diabetes mellitus. Investigation has demonstrated that CEBPD (CCAAT enhancer binding protein delta) is involved in obesity [163]. An investigation by Domingues-Montanari et al. [164] demonstrated that key gene ESR2 was involved in the progression of cardio vascular disease, but this gene might be responsible for progression of obesity associated type 2 diabetes mellitus. TP73, PIK3R2, SLC9A3R1, KRT5, KRT14 and TFAP2C are novel biomarkers for pathogenesis of obesity associated type 2 diabetes mellitus.
The miRNA-target gene regulatory network and TF-target gene regulatory network highlighted in the current investigation provides new theoretical guidance for further exploring the molecular mechanism of obesity associated type 2 diabetes mellitus and provides a new perspective for understanding the underlying biological processes of this diseases, and miRNA and TF targeted therapy. Eberlé et al [165], Cheng et al [166], Cavallari et al [167], Qi et al [168] and Yan et al [169] indicated that SREBF1, MBD2, IRF4, CREB1 and RELA (Nuclear factor-kB) were the genes responsible for advancement of obesity associated type 2 diabetes mellitus. Matsha et al [170] and Ding et al [171] demonstrated that hsa-mir-1299 and hsa-mir-4530 were the miRNAs liable for progression of type 2 diabetes mellitus. Hall et al [172] and Salazar-Mendiguchía et al [173] reported that FLNC (filamin C) and TRIM63 were the genes involved in progression of cardio vascular disease, but these genes might be essential for development of obesity associated type 2 diabetes mellitus. Xiao et al [174], Stratigopoulos et al [175] and Zhou et al [176] noted that ATF4, CUX1 and ZBTB7A were the genes responsible for advancement of obesity. MAP 1B, hsa-mir-4314, hsa-mir-5688, hsa-mir-583, hsa-mir-632, hsa-mir-3176, hsa-mir-4477a, hsa-mir-606, hsa-mir-1343-3p6, SIN3A, ZNF143 and SMARCE1 are the novel biomarkers for pathogenesis of obesity associated type 2 diabetes mellitus.
However, this investigation had distinct limitations. First, the mechanisms of several hub genes in the pathological process of obesity associated type 2 diabetes mellitus remain unclear, permit further investigation. Moreover, the potency of our small molecule drug screening in diminishing side effects remains to be assessed.
In conclusion, with the integrated bioinformatics analysis for expression profiling by high throughput sequencing in obesity associated type 2 diabetes mellitus, ten hub genes associated with the pathogenesis and prognosis of obesity associated type 2 diabetes, including CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF. These hub genes were associated with progression of obesity associated type 2 diabetes mellitus and first five (CEBPD, TP73, ESR2, TAB1 and MAP 3K5) of them might be linked with targeted therapy. These hub genes might be regarded as new diagnostic and prognostic biomarkers for obesity associated type 2 diabetes mellitus. However, further in-depth investigation (in vivo and in vitro experiment) is necessary to elucidate the biological function of these genes in obesity associated type 2 diabetes mellitus.
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. [(GSE143319) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143319]
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I thank Jun Yoshino, Washington University School of Medicine, Medicine, St. Louis, USA, very much, the author who deposited their profiling by high throughput sequencing dataset, GSE143319, into the public GEO database.
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Prashanth, G., Vastrad, B., Tengli, A. et al. Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules. BMC Endocr Disord 21, 80 (2021). https://doi.org/10.1186/s12902-021-00718-5
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DOI: https://doi.org/10.1186/s12902-021-00718-5