Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules
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.
Hinweise
Publisher’s Note
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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.
Anzeige
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.
Anzeige
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.
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.
Table 1
The sequences of primers for quantitative RT-PCR
Genes
Forward Primers
Reverse Primers
CEBPD
CGGACTTGGTGCGTCTAAGATG
GCATTGGAGCGGTGAGTTTG
TP73
CCACCACTTTGAGGTCACTTT
CTTCAAGAGCGGGGAGTACG
ESR2
AGCACGGCTCCATATACATACC
TGGACCACTAAAGGAGAAAGGT
TAB1
AACTGCTTCCTGTATGGGGTC
AAGGCGTCGTCAATGGACTC
MAP 3K5
CTGCATTTTGGGAAACTCGACT
AAGGTGGTAAAACAAGGACGG
FN1
CGGTGGCTGTCAGTCAAAG
AAACCTCGGCTTCCTCCATAA
UBD
CCGTTCCGAGGAATGGGATTT
GCCATAAGATGAGAGGCTTCTCC
RUNX1
CTGCCCATCGCTTTCAAGGT
GCCGAGTAGTTTTCATCATTGCC
PIK3R2
AAAGGCGGGAACAATAAGCTG
CAACGGAGCAGAAGGTGAGTG
TNF
CCTCTCTCTAATCAGCCCTCTG
GAGGACCTGGGAGTAGATGAG
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 [32‐36]. 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.
Fig. 1
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)
Fig. 2
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
Table 2
The statistical metrics for key differentially expressed genes (DEGs)
Gene
Symbol
logFC
pValue
adj. P.Val
tvalue
Regulation
Gene Name
SNORD116-16
2.091053
0.012212
0.012212
2.677149
Up
small nucleolar RNA, C/D box 116-16
SULT1C2
1.67612
0.002928
0.002928
3.255886
Up
sulfotransferase family 1C member 2
HLA-DQA1
1.62492
0.022453
0.022453
2.41454
Up
major histocompatibility complex, class II, DQ alpha 1
SNORD9
1.51914
0.032752
0.032752
2.245
Up
small nucleolar RNA, C/D box 9
SPX
1.459547
0.00259
0.00259
3.303822
Up
spexin hormone
FXYD6-FXYD2
1.421933
0.001908
0.001908
3.42214
Up
FXYD6-FXYD2 readthrough
LOC102724951
1.41856
0.047463
0.047463
2.072149
Up
uncharacterized LOC102724951
SNORA11B
1.378113
0.039356
0.039356
2.160257
Up
small nucleolar RNA, H/ACA box 11B
CTAGE4
1.31302
0.038788
0.038788
2.16703
Up
CTAGE family member 4
SNORA9
1.308653
0.027236
0.027236
2.328547
Up
small nucleolar RNA, H/ACA box 9
APOB
1.29598
0.001043
0.001043
3.652911
Up
apolipoprotein B
LCN12
1.258207
0.005394
0.005394
3.01344
Up
lipocalin 12
MIR648
1.254627
0.042035
0.042035
2.129481
Up
microRNA 648
SAPCD1
1.251793
0.000762
0.000762
3.771187
Up
suppressor APC domain containing 1
RPL13AP5
1.217507
0.040849
0.040849
2.142888
Up
ribosomal protein L13a pseudogene 5
TMEM52
1.213987
0.005725
0.005725
2.989444
Up
transmembrane protein 52
LOC285095
1.202993
0.029569
0.029569
2.291487
Up
uncharacterized LOC285095
PRICKLE4
1.154227
0.028453
0.028453
2.308873
Up
prickle planar cell polarity protein 4
ADAD2
1.153307
0.006289
0.006289
2.951413
Up
adenosine deaminase domain containing 2
TMEM145
1.111487
0.011906
0.011906
2.687862
Up
transmembrane protein 145
C1orf185
1.084533
0.01231
0.01231
2.673805
Up
chromosome 1 open reading frame 185
RORB
1.0637
0.008779
0.008779
2.814926
Up
RAR related orphan receptor B
MIA
1.05382
0.049316
0.049316
2.053902
Up
MIA SH3 domain containing
PGAM2
1.04492
0.021559
0.021559
2.432446
Up
phosphoglyceratemutase 2
CECR2
1.02018
0.007304
0.007304
2.890514
Up
CECR2 histone acetyl-lysine reader
BTNL8
1.017967
0.007634
0.007634
2.87239
Up
butyrophilin like 8
UPK1A-AS1
1.01654
0.000323
0.000323
4.090921
Up
UPK1A antisense RNA 1
WNT9B
1.010367
0.001141
0.001141
3.618927
Up
Wnt family member 9B
ARL2-SNX15
0.99786
0.023389
0.023389
2.396471
Up
ARL2-SNX15 readthrough (NMD candidate)
PNCK
0.997553
0.005518
0.005518
3.004258
Up
pregnancy up-regulated nonubiquitousCaM kinase
DNM1P46
0.993367
0.014022
0.014022
2.618583
Up
dynamin 1 pseudogene 46
SNORD116-2
0.993207
0.046271
0.046271
2.084217
Up
small nucleolar RNA, C/D box 116-2
SNORA65
0.979973
0.041933
0.041933
2.13062
Up
small nucleolar RNA, H/ACA box 65
NRXN1
0.9751
0.018721
0.018721
2.494208
Up
neurexin 1
LINC01611
0.971233
0.014969
0.014969
2.590702
Up
long intergenic non-protein coding RNA 1611
CFL1P1
0.965867
0.037627
0.037627
2.181137
Up
cofilin 1 pseudogene 1
FAM27C
0.964833
0.013233
0.013233
2.643197
Up
family with sequence similarity 27 member C
TPSD1
0.9607
0.026678
0.026678
2.337832
Up
tryptase delta 1
STOX1
0.952087
0.00188
0.00188
3.427882
Up
storkhead box 1
ARF4-AS1
0.948007
0.019319
0.019319
2.480515
Up
ARF4 antisense RNA 1
PRPH
0.92522
0.002347
0.002347
3.342103
Up
peripherin
FBXO10
0.923973
0.020523
0.020523
2.454076
Up
F-box protein 10
PGM5P3-AS1
0.923733
0.027972
0.027972
2.316545
Up
PGM5P3 antisense RNA 1
LRRC3-DT
0.920987
0.002375
0.002375
3.337439
Up
LRRC3 divergent transcript
INO80B-WBP1
0.919893
0.002585
0.002585
3.304498
Up
INO80B-WBP1 readthrough (NMD candidate)
SORD2P
0.91266
0.018154
0.018154
2.507572
Up
sorbitol dehydrogenase 2, pseudogene
MYLPF
0.896867
0.035595
0.035595
2.206797
Up
myosin light chain, phosphorylatable, fast skeletal muscle
LINC01999
0.894293
0.015657
0.015657
2.571441
Up
long intergenic non-protein coding RNA 1999
CASQ2
0.890407
0.020198
0.020198
2.461069
Up
calsequestrin 2
ULK4P1
0.887613
0.0333
0.0333
2.237399
Up
ULK4 pseudogene 1
PLPP2
0.881447
0.019592
0.019592
2.474383
Up
phospholipid phosphatase 2
CTD-3080P12.3
0.877473
0.011719
0.011719
2.694519
Up
uncharacterized LOC101928857
KIF19
0.875667
0.021475
0.021475
2.434164
Up
kinesin family member 19
KCNK7
0.871247
0.028119
0.028119
2.314193
Up
potassium two pore domain channel subfamily K member 7
RASL10B
0.867707
0.004269
0.004269
3.107011
Up
RAS like family 10 member B
NECAB1
0.860073
0.030586
0.030586
2.276158
Up
N-terminal EF-hand calcium binding protein 1
LINC02055
0.854973
0.030277
0.030277
2.280768
Up
long intergenic non-protein coding RNA 2055
PRODH
0.85302
0.024345
0.024345
2.378692
Up
proline dehydrogenase 1
ZNF236-DT
0.852393
0.012753
0.012753
2.65886
Up
ZNF236 divergent transcript
KCNE2
0.84654
0.007187
0.007187
2.897075
Up
potassium voltage-gated channel subfamily E regulatory subunit 2
SAM domain, SH3 domain and nuclear localization signals 1
TAFA2
-0.38579
0.046957
0.046957
-2.07723
Down
TAFA chemokine like family member 2
SLC6A7
-0.38559
0.04736
0.04736
-2.07317
Down
solute carrier family 6 member 7
CKMT2-AS1
-0.38548
0.045796
0.045796
-2.08911
Down
CKMT2 antisense RNA 1
MPEG1
-0.38475
0.032517
0.032517
-2.24829
Down
macrophage expressed 1
C19orf18
-0.38407
0.04723
0.04723
-2.07448
Down
chromosome 19 open reading frame 18
KLHL29
-0.38309
0.035953
0.035953
-2.20218
Down
kelch like family member 29
CERCAM
-0.38247
0.019646
0.019646
-2.47319
Down
cerebral endothelial cell adhesion molecule
ETV1
-0.38153
0.029051
0.029051
-2.29948
Down
ETS variant transcription factor 1
BDKRB2
-0.38151
0.040133
0.040133
-2.15114
Down
bradykinin receptor B2
GRHL1
-0.37827
0.012852
0.012852
-2.65557
Down
grainyhead like transcription factor 1
OTULINL
-0.37674
0.028403
0.028403
-2.30967
Down
OTU deubiquitinase with linear linkage specificity like
RGL1
-0.37613
0.000119
0.000119
-4.45727
Down
ral guanine nucleotide dissociation stimulator like 1
RPL13P5
-0.37585
0.048146
0.048146
-2.06535
Down
ribosomal protein L13 pseudogene 5
ZNF488
-0.37547
0.047689
0.047689
-2.06988
Down
zinc finger protein 488
SLC25A14
-0.37434
0.008636
0.008636
-2.82168
Down
solute carrier family 25 member 14
LINC01140
-0.37257
0.004509
0.004509
-3.08519
Down
long intergenic non-protein coding RNA 1140
UGDH
-0.37133
0.02146
0.02146
-2.43449
Down
UDP-glucose 6-dehydrogenase
ENC1
-0.37101
0.018286
0.018286
-2.50442
Down
ectodermal-neural cortex 1
RTL9
-0.36928
0.047629
0.047629
-2.07048
Down
retrotransposon Gag like 9
IQGAP2
-0.36865
0.037295
0.037295
-2.18524
Down
IQ motif containing GTPase activating protein 2
ALDH1L2
-0.36654
0.000623
0.000623
-3.84689
Down
aldehyde dehydrogenase 1 family member L2
GPR34
-0.36515
0.039956
0.039956
-2.15321
Down
G protein-coupled receptor 34
GRAMD1B
-0.36456
0.044696
0.044696
-2.1006
Down
GRAM domain containing 1B
MAN1A1
-0.36373
0.006066
0.006066
-2.96604
Down
mannosidase alpha class 1A member 1
RABIF
-0.36301
0.002326
0.002326
-3.34563
Down
RAB interacting factor
TEC
-0.36224
0.005923
0.005923
-2.97566
Down
tec protein tyrosine kinase
FICD
-0.36185
0.000247
0.000247
-4.19042
Down
FIC domain containing
PLXDC1
-0.36111
0.037452
0.037452
-2.18329
Down
plexin domain containing 1
C1orf146
-0.36015
0.038798
0.038798
-2.1669
Down
chromosome 1 open reading frame 146
DYNC1I1
-0.35903
0.043271
0.043271
-2.11587
Down
dynein cytoplasmic 1 intermediate chain 1
MFAP4
-0.35865
0.014841
0.014841
-2.59438
Down
microfibril associated protein 4
FRMD4B
-0.35817
0.040289
0.040289
-2.14934
Down
FERM domain containing 4B
P2RY13
-0.35797
0.029676
0.029676
-2.28985
Down
purinergic receptor P2Y13
HRH1
-0.35784
0.044282
0.044282
-2.10499
Down
histamine receptor H1
FBN1
-0.35729
0.008031
0.008031
-2.85159
Down
fibrillin 1
COL5A2
-0.35639
0.000402
0.000402
-4.01019
Down
collagen type V alpha 2 chain
ALX4
-0.35556
0.024298
0.024298
-2.37955
Down
ALX homeobox 4
CCDC80
-0.35542
0.001101
0.001101
-3.63236
Down
coiled-coil domain containing 80
SNCAIP
-0.35503
0.013865
0.013865
-2.62339
Down
synuclein alpha interacting protein
IKBIP
-0.354
0.02324
0.02324
-2.3993
Down
IKBKB interacting protein
BICC1
-0.35252
0.004878
0.004878
-3.05381
Down
BicC family RNA binding protein 1
CAMK2N1
-0.34899
0.047228
0.047228
-2.0745
Down
calcium/calmodulin dependent protein kinase II inhibitor 1
TMEM67
-0.34689
0.024989
0.024989
-2.36708
Down
transmembrane protein 67
LIMCH1
-0.34449
0.005071
0.005071
-3.0382
Down
LIM and calponin homology domains 1
GNG2
-0.34436
0.017366
0.017366
-2.52679
Down
G protein subunit gamma 2
TRAM2
-0.34185
0.001914
0.001914
-3.42104
Down
translocation associated membrane protein 2
VLDLR
-0.34162
0.011677
0.011677
-2.69604
Down
very low density lipoprotein receptor
TTYH2
-0.33982
0.049674
0.049674
-2.05044
Down
tweety family member 2
P4HA2
-0.33879
0.00227
0.00227
-3.35509
Down
prolyl 4-hydroxylase subunit alpha 2
FER1L6
-0.33814
0.027267
0.027267
-2.32803
Down
fer-1 like family member 6
GEM
-0.33728
0.005769
0.005769
-2.98636
Down
GTP binding protein overexpressed in skeletal muscle
RCN3
-0.33692
0.02923
0.02923
-2.2967
Down
reticulocalbin 3
IMPAD1
-0.33553
0.005228
0.005228
-3.02597
Down
inositol monophosphatase domain containing 1
SESN1
-0.33492
0.006934
0.006934
-2.91171
Down
sestrin 1
GPR137B
-0.33349
0.036019
0.036019
-2.20133
Down
G protein-coupled receptor 137B
SPARC
-0.33102
0.007711
0.007711
-2.86828
Down
secreted protein acidic and cysteine rich
ZDHHC20
-0.33048
0.021857
0.021857
-2.4264
Down
zinc finger DHHC-type containing 20
CPXM2
-0.32867
0.027253
0.027253
-2.32826
Down
carboxypeptidase X, M14 family member 2
RRN3P2
-0.32859
0.018624
0.018624
-2.49648
Down
RRN3 homolog, RNA polymerase I transcription factor pseudogene 2
SEMA3C
-0.32851
0.017989
0.017989
-2.51154
Down
semaphorin 3C
DACT1
-0.32813
0.03652
0.03652
-2.19495
Down
dishevelled binding antagonist of beta catenin 1
SPDYE17
-0.32802
0.04574
0.04574
-2.08968
Down
speedy/RINGO cell cycle regulator family member E17
LINC01963
-0.32754
0.012373
0.012373
-2.67163
Down
long intergenic non-protein coding RNA 1963
GIN1
-0.32533
0.038359
0.038359
-2.17219
Down
gypsy retrotransposonintegrase 1
HECTD2
-0.32446
0.036485
0.036485
-2.19539
Down
HECT domain E3 ubiquitin protein ligase 2
PTGER4
-0.32374
0.014657
0.014657
-2.5997
Down
prostaglandin E receptor 4
PRNP
-0.32047
0.00413
0.00413
-3.12014
Down
prion protein
MAP 1B
-0.32035
0.015796
0.015796
-2.56765
Down
microtubule associated protein 1B
GAS1RR
-0.31983
0.042327
0.042327
-2.12624
Down
GAS1 adjacent regulatory RNA
ZNF107
-0.31823
0.00885
0.00885
-2.81159
Down
zinc finger protein 107
FSTL3
-0.31787
0.020675
0.020675
-2.45085
Down
follistatin like 3
PKD1L3
-0.31687
0.0431
0.0431
-2.11773
Down
polycystin 1 like 3, transient receptor potential channel interacting
PAG1
-0.31685
0.019249
0.019249
-2.48211
Down
phosphoprotein membrane anchor with glycosphingolipidmicrodomains 1
TMPPE
-0.31551
0.000808
0.000808
-3.74957
Down
transmembrane protein with metallophosphoesterase domain
FRRS1L
-0.31367
0.047263
0.047263
-2.07416
Down
ferric chelate reductase 1 like
WDR78
-0.31163
0.036163
0.036163
-2.19948
Down
WD repeat domain 78
OLFML2B
-0.31035
0.034022
0.034022
-2.22757
Down
olfactomedin like 2B
SH3RF3
-0.31019
0.019582
0.019582
-2.4746
Down
SH3 domain containing ring finger 3
SLC38A6
-0.30913
0.045079
0.045079
-2.09657
Down
solute carrier family 38 member 6
GBP4
-0.30841
0.017695
0.017695
-2.51868
Down
guanylate binding protein 4
ZNF678
-0.30546
0.016314
0.016314
-2.55377
Down
zinc finger protein 678
CYTOR
-0.30436
0.014748
0.014748
-2.59705
Down
cytoskeleton regulator RNA
TDRD15
-0.30397
0.022379
0.022379
-2.416
Down
tudor domain containing 15
ARHGAP18
-0.30123
0.003517
0.003517
-3.18376
Down
Rho GTPase activating protein 18
INSYN2A
-0.29921
0.007796
0.007796
-2.86381
Down
inhibitory synaptic factor 2A
SLC2A10
-0.29836
0.011493
0.011493
-2.7027
Down
solute carrier family 2 member 10
TENM1
-0.29763
0.023018
0.023018
-2.40355
Down
teneurintransmembrane protein 1
COLEC12
-0.29666
0.027788
0.027788
-2.31953
Down
collectin subfamily member 12
MTSS1
-0.29656
0.021939
0.021939
-2.42476
Down
MTSS I-BAR domain containing 1
FSTL1
-0.29568
0.004621
0.004621
-3.07542
Down
follistatin like 1
KLF10
-0.29395
0.015541
0.015541
-2.57463
Down
Kruppel like factor 10
UGP2
-0.29329
0.044865
0.044865
-2.09882
Down
UDP-glucose pyrophosphorylase 2
SLC9A9
-0.29287
0.005573
0.005573
-3.00025
Down
solute carrier family 9 member A9
LPAR1
-0.29287
0.008363
0.008363
-2.83493
Down
lysophosphatidic acid receptor 1
GABRG1
-0.29068
0.037398
0.037398
-2.18397
Down
gamma-aminobutyric acid type A receptor gamma1 subunit
PDLIM1
-0.29033
0.018561
0.018561
-2.49795
Down
PDZ and LIM domain 1
EEA1
-0.29004
0.01712
0.01712
-2.53298
Down
early endosome antigen 1
RUNX1
-0.28986
0.039414
0.039414
-2.15957
Down
RUNX family transcription factor 1
MTHFD1L
-0.28913
0.046147
0.046147
-2.08549
Down
methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1 like
MIR4435-2HG
-0.28849
0.007052
0.007052
-2.90483
Down
MIR4435-2 host gene
FAM118B
-0.28811
0.005667
0.005667
-2.99355
Down
family with sequence similarity 118 member B
CASP6
-0.28807
0.038673
0.038673
-2.16841
Down
caspase 6
MAN2A1
-0.28713
0.008895
0.008895
-2.80947
Down
mannosidase alpha class 2A member 1
LINC02693
-0.28672
0.036645
0.036645
-2.19337
Down
long intergenic non-protein coding RNA 2693
C2CD6
-0.28663
0.049977
0.049977
-2.04753
Down
C2 calcium dependent domain containing 6
SOCS5
-0.28573
0.039472
0.039472
-2.15889
Down
suppressor of cytokine signaling 5
MTF1
-0.28509
0.039683
0.039683
-2.15641
Down
metal regulatory transcription factor 1
ATRN
-0.28445
2.25E-05
2.25E-05
-5.06531
Down
attractin
ENTHD1
-0.28253
0.03304
0.03304
-2.24099
Down
ENTH domain containing 1
ZNF569
-0.28241
0.032207
0.032207
-2.25265
Down
zinc finger protein 569
SYNC
-0.28167
0.026701
0.026701
-2.33744
Down
syncoilin, intermediate filament protein
×
×
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.
Table 3
The enriched GO terms of the up and down regulated differentially expressed genes
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.
Fig. 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 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
Table 5
Topology table for up and down regulated genes
Regulation
Node
Degree
Betweenness
Stress
Closeness
Up
CEBPD
114
0.046829
7961100
0.330434
Up
TP73
111
0.034635
8777680
0.328559
Up
ESR2
101
0.034485
6488550
0.337342
Up
TAB1
99
0.041885
3759452
0.362813
Up
MAP 3K5
92
0.030461
6831818
0.331998
Up
CACNA1A
91
0.035636
5405420
0.325712
Up
CCNA1
80
0.02152
6576136
0.326587
Up
SLC9A3R1
77
0.024066
3777376
0.332877
Up
SREBF1
70
0.01966
4888424
0.327673
Up
TGM2
69
0.023349
3331196
0.360768
Up
VAMP2
59
0.021393
4433628
0.327614
Up
FASN
57
0.012258
2145144
0.352708
Up
PSEN2
53
0.018322
2705190
0.32389
Up
CKB
51
0.012369
2056678
0.335511
Up
ALK
51
0.013768
1544390
0.292392
Up
MED16
46
0.014057
2049900
0.326909
Up
SLC9A3R2
45
0.013157
2184316
0.317821
Up
APOB
43
0.012557
2206980
0.322002
Up
MYOC
42
0.012184
1022522
0.322658
Up
IRAK2
41
0.012813
1784830
0.321293
Up
SLC2A4
40
0.011976
1766080
0.322601
Up
USHBP1
36
0.015448
3575428
0.248925
Up
MRPL11
34
0.010599
1753194
0.322287
Up
CPLX1
33
0.009034
742112
0.287437
Up
PACSIN1
33
0.010298
664252
0.278015
Up
PCBD2
32
0.010339
2852002
0.275204
Up
SLC25A10
30
0.009439
1406902
0.31647
Up
DOT1L
29
0.007619
3660752
0.26943
Up
CDK20
28
0.007513
1387276
0.314207
Up
ACAT2
28
0.007324
722288
0.326091
Up
CDKN2C
28
0.006528
1250162
0.316004
Up
CISH
27
0.005203
494888
0.295879
Up
GINS3
27
0.00745
1311612
0.3176
Up
MVD
26
0.008346
1085242
0.313613
Up
NR1I3
24
0.004449
1369088
0.285591
Up
RAC3
23
0.005847
909650
0.317324
Up
SETD1A
22
0.006802
741452
0.319296
Up
GLUL
21
0.006551
455932
0.338689
Up
NFKBIL1
20
0.007285
2695372
0.255984
Up
CD3EAP
19
0.004472
455916
0.289697
Up
SHANK2
19
0.00448
1859670
0.264621
Up
GPHN
18
0.005963
580752
0.315157
Up
PPP1R16A
17
0.006189
3748574
0.241315
Up
CYP2E1
17
0.00535
689500
0.318654
Up
IL12A
17
0.003913
940482
0.270248
Up
TAF1C
16
0.004869
576732
0.316965
Up
MPST
15
0.004427
490670
0.315703
Up
CNTFR
15
0.004392
3618160
0.24549
Up
DDR1
15
0.00321
479586
0.317379
Up
MKNK2
14
0.002033
289430
0.322116
Up
KCTD17
13
0.003492
916690
0.244208
Up
RGS3
13
0.001735
620256
0.320982
Up
SPHK1
13
0.002877
655002
0.317655
Up
PRPH
13
0.003388
599412
0.319436
Up
ADH1B
13
0.001294
370206
0.275682
Up
SLC25A22
13
0.003141
530858
0.314776
Up
MIB2
12
0.002205
375196
0.315185
Up
ZNF598
12
0.004687
393930
0.312779
Up
GK
12
0.003466
415618
0.314994
Up
RRAD
12
0.0025
148574
0.282166
Up
PFKFB3
12
0.001355
246878
0.314072
Up
SPTBN4
12
0.004741
379826
0.313909
Up
PTPN3
12
0.001814
952130
0.257957
Up
LIN7A
11
0.004011
512222
0.31796
Up
VPS37B
11
0.00232
329104
0.31372
Up
TRMT1L
11
0.002348
529928
0.319184
Up
EDNRA
11
0.001735
273128
0.314885
Up
TBC1D7
10
0.002122
358246
0.3176
Up
AZGP1
10
0.002218
255470
0.31337
Up
WDR62
10
0.001673
347176
0.314912
Up
NRXN1
10
0.003845
1972864
0.226227
Up
CLDN5
9
0.002871
301080
0.313343
Up
TYSND1
9
0.003301
1096284
0.204234
Up
NEK8
9
0.001286
501688
0.317158
Up
MICALL1
9
0.006015
346680
0.317103
Up
TNFRSF8
8
0.001683
558578
0.221218
Up
DDX11
8
0.002534
239432
0.313964
Up
CRHR1
8
0.002228
518378
0.23728
Up
QRICH2
8
0.002787
388592
0.238787
Up
TEAD4
8
0.002413
296338
0.312993
Up
CSPG4
8
0.001645
199988
0.313289
Up
CABP1
8
0.001378
82156
0.281578
Up
SULT1C2
8
0.001233
207860
0.315676
Up
GNG7
8
0.001801
266812
0.312779
Up
HADH
7
0.001386
314576
0.318043
Up
EBP
7
0.002552
384196
0.317186
Up
CSH1
7
8.48E-05
30798
0.243572
Up
C19orf25
7
0.001712
1024492
0.232753
Up
AASS
7
5.38E-04
163226
0.314804
Up
POLM
6
0.001104
522630
0.201203
Up
BSN
6
0.001531
145360
0.312457
Up
KIFC2
6
0.001203
58718
0.27345
Up
POMC
6
0.002233
811808
0.234609
Up
DCXR
6
0.001279
220510
0.31658
Up
EPHB4
6
8.00E-04
275102
0.317572
Up
RAB26
6
0.002046
260892
0.31658
Up
UBTD1
6
0.001963
183788
0.314586
Up
GNG3
6
8.77E-05
19382
0.228452
Up
SRCIN1
6
1.34E-04
36856
0.257593
Up
DLL1
6
0.001102
106292
0.313074
Up
IFI35
6
0.001499
178126
0.313666
Up
EPB41L4B
6
5.42E-04
276788
0.316497
Up
RDH13
5
0.001833
134020
0.312404
Up
SHROOM2
5
3.34E-05
23708
0.250636
Up
PGAM2
5
3.21E-04
99124
0.313101
Up
RASIP1
5
0.001351
121434
0.312404
Up
LDB3
5
5.77E-05
11670
0.240694
Up
STBD1
5
5.68E-04
91162
0.211163
Up
MYLPF
5
0.001107
295946
0.221057
Up
ZNF775
5
0.00111
426814
0.230721
Up
BOK
5
0.001655
134510
0.312404
Up
ARHGEF15
5
0.001681
719112
0.215939
Up
RRP1
5
6.51E-04
120052
0.314749
Up
LLGL2
5
0.001716
131528
0.312404
Up
PKN3
5
0.002193
149884
0.312404
Up
AATK
5
0.001114
414048
0.231497
Up
HELZ2
5
4.97E-04
113146
0.312752
Up
TPRN
4
5.53E-04
316590
0.229602
Up
PMM1
4
6.74E-04
50432
0.317324
Up
SCN4A
4
0.001101
106362
0.254501
Up
MIA
2
0
0
0.289951
Up
GCAT
1
0
0
0.265816
Up
CACNB2
1
0
0
0.241779
Up
PWWP2B
1
0
0
0.265816
Up
CEMP1
1
0
0
0.265816
Down
FN1
689
0.324012
1.05E+08
0.403385
Down
UBD
502
0.200346
87511340
0.362021
Down
RUNX1
96
0.029748
6112420
0.328351
Down
PIK3R2
91
0.025749
6760118
0.337529
Down
TNF
91
0.029868
5372816
0.339382
Down
TRIM63
88
0.033338
4601374
0.331545
Down
FASLG
83
0.03072
3652094
0.356361
Down
FLNC
77
0.027425
3582900
0.356257
Down
DAB2
63
0.01646
2823298
0.354974
Down
PRNP
61
0.022352
8130382
0.290089
Down
RUNX2
57
0.013307
3614466
0.32323
Down
UCHL1
54
0.013451
2855314
0.328973
Down
MAP 1B
45
0.011916
2421086
0.323574
Down
KRT14
44
0.006308
1848344
0.332361
Down
DYNC1I1
41
0.009986
4398264
0.2816
Down
UGP2
39
0.010629
2140698
0.318654
Down
KRT5
39
0.006255
1335540
0.352742
Down
THBS1
37
0.010831
751230
0.351315
Down
SULT1A1
36
0.010429
1425372
0.3207
Down
ITGAV
34
0.009907
738818
0.348595
Down
BLNK
34
0.006063
3165890
0.267709
Down
KRT16
34
0.004495
1291100
0.352231
Down
TUBB2B
34
0.006677
1049214
0.327997
Down
TPM2
31
0.005911
1029436
0.321832
Down
SERPINE1
31
0.006739
1233976
0.3258
Down
CSF1R
30
0.006717
1300016
0.330974
Down
TEC
29
0.006824
858984
0.329152
Down
UGDH
28
0.009574
1182896
0.320334
Down
EEA1
28
0.00918
1154334
0.31573
Down
CYBB
27
0.007148
1130954
0.315922
Down
SNCAIP
25
0.006301
1035622
0.319856
Down
HEXIM1
25
0.006057
1136892
0.320587
Down
PSAT1
24
0.005926
1376392
0.324034
Down
CASP6
23
0.005143
904188
0.326939
Down
CCND2
23
0.004537
916146
0.317683
Down
TFAP2C
23
0.0052
855498
0.321095
Down
C1QA
23
0.006243
387922
0.312967
Down
PDLIM1
22
0.005357
928252
0.314967
Down
FAM118B
22
0.008601
929084
0.314885
Down
MARCKS
22
0.00416
786398
0.318099
Down
KLF10
21
0.005139
897850
0.31669
Down
DACT1
20
0.00252
987976
0.277507
Down
LBP
20
0.004692
404246
0.288597
Down
AP1M2
20
0.005509
738138
0.318989
Down
ETV1
20
0.00302
609754
0.314315
Down
CACNA1C
19
0.004425
753630
0.318849
Down
F2R
19
0.005228
757806
0.314586
Down
WWC1
19
0.004648
1378328
0.261415
Down
SPARC
19
0.002419
271042
0.288871
Down
ETV5
19
0.006356
766338
0.321548
Down
HLA-DRB1
18
0.004301
753340
0.315922
Down
CD2
18
0.004823
668514
0.31658
Down
GTF2A1L
18
0.007186
2863798
0.23688
Down
GEM
17
0.005732
2326034
0.240583
Down
GNG2
16
0.003555
589398
0.314126
Down
IKBIP
16
0.004022
727254
0.319017
Down
METTL21C
15
0.003787
493576
0.315621
Down
ANKRD1
15
0.003619
472178
0.313774
Down
PTPRZ1
15
0.001661
140480
0.283902
Down
TRPV4
15
0.002549
481158
0.316662
Down
KCNA2
15
0.00436
1061814
0.239903
Down
ITGB1BP2
14
0.001989
240810
0.285502
Down
FSTL1
14
0.00427
2031070
0.23759
Down
CYP2C9
14
0.001662
302482
0.26514
Down
MTHFD1L
14
0.002585
651008
0.31647
Down
IQGAP2
13
0.002809
325554
0.314045
Down
PAG1
13
0.001736
135246
0.277571
Down
F13A1
13
0.001735
158432
0.30128
Down
FCGR2B
13
0.001066
801788
0.251639
Down
PALLD
13
0.003467
531744
0.313532
Down
CD28
13
0.00323
284756
0.32323
Down
RABIF
13
0.004967
429618
0.312832
Down
GRB14
12
0.002983
685690
0.249385
Down
LIMCH1
12
0.003625
2733430
0.254786
Down
FBN1
11
0.002676
383518
0.248755
Down
SAMSN1
11
8.53E-04
133970
0.288871
Down
EPHB3
11
0.003278
542624
0.316607
Down
ENC1
11
0.001583
423908
0.317324
Down
TRIM50
11
0.00292
496854
0.312886
Down
SERPINE2
11
0.002799
2273346
0.242664
Down
HLA-DQA1
11
0.002544
326930
0.314369
Down
ARRDC4
10
0.001535
375436
0.315157
Down
RND3
10
0.00448
330498
0.313182
Down
VLDLR
10
0.004556
320522
0.314804
Down
SOCS5
10
0.003457
380282
0.320869
Down
BAMBI
10
0.004199
323112
0.313909
Down
NAIP
9
0.003621
141406
0.276246
Down
DRD1
9
0.003299
1100044
0.229184
Down
GLB1
9
0.002193
292422
0.314858
Down
HGF
9
0.001854
1215984
0.238226
Down
ALX4
9
0.003365
1011538
0.2046
Down
COL3A1
9
5.70E-04
68058
0.284833
Down
MTF1
9
0.002252
733044
0.272103
Down
LOX
8
0.001541
584690
0.240345
Down
CACNG2
8
0.002377
668336
0.245242
Down
ANGPT2
8
8.97E-04
173688
0.314641
Down
INHBA
8
0.001672
81898
0.210214
Down
CCDC136
8
9.01E-04
360770
0.220509
Down
CCL18
8
0.001487
441826
0.255876
Down
PMEPA1
7
5.77E-04
135816
0.312967
Down
STAB1
7
0.001896
241374
0.264544
Down
KCNA1
7
6.54E-04
95830
0.211727
Down
CRABP2
7
4.03E-04
99166
0.313209
Down
PTGFRN
7
0.002199
667760
0.225681
Down
ASAH1
7
0.001266
221440
0.315457
Down
RCN3
7
0.00299
297670
0.314072
Down
FRZB
7
0.002208
481180
0.22015
Down
SLIT2
6
0.001658
181082
0.312967
Down
CLEC4M
6
0.001712
388848
0.245111
Down
TNFRSF12A
6
0.001027
142760
0.312671
Down
INSM1
6
0.001234
305024
0.249044
Down
SFRP2
6
6.16E-04
195012
0.229515
Down
SESN1
6
4.86E-04
138422
0.313801
Down
JAZF1
5
2.77E-04
135572
0.315922
Down
LINGO1
5
0.001129
296570
0.207901
Down
SLC5A1
5
5.97E-04
499752
0.26091
Down
SERF1A
5
4.54E-04
225474
0.3168
Down
TLR7
5
6.25E-04
167802
0.209634
Down
AMPD1
5
5.58E-04
409250
0.245374
Down
HPD
5
7.74E-04
98924
0.313936
Down
ICAM5
1
0
0
0.244667
Down
ADRA1B
1
0
0
0.262695
×
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.
Fig. 4
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
Table 6
miRNA - target gene and TF - target gene interaction
Regulation
Target Genes
Degree
MicroRNA
Regulation
Target Genes
Degree
TF
Up
FASN
147
hsa-mir-4314
Up
SREBF1
94
ATF4
Up
SREBF1
81
hsa-mir-5688
Up
FASN
71
CUX1
Up
CKB
72
hsa-mir-583
Up
SLC9A3R1
63
MBD2
Up
CACNA1A
69
hsa-mir-632
Up
CKB
50
IRF4
Up
ESR2
61
hsa-mir-3176
Up
TGM2
50
SIN3A
Up
TAB1
58
hsa-mir-4438
Up
VAMP2
32
GABPA
Up
SLC9A3R1
56
hsa-mir-6133
Up
PSEN2
16
TGIF2
Up
CEBPD
56
hsa-mir-4433a-3p
Up
CEBPD
15
SAP30
Up
MAP 3K5
53
hsa-mir-4753-3p
Up
TP73
11
WRNIP1
Up
VAMP2
39
hsa-mir-2355-5p
Up
TAB1
6
KLF9
Up
TGM2
27
hsa-mir-375
Up
CCNA1
3
SUPT5H
Up
TP73
22
hsa-mir-7114-3p
Up
ESR2
1
IRF1
Up
CCNA1
19
hsa-mir-7-5p
Down
PIK3R2
73
ZNF143
Up
PSEN2
9
hsa-mir-29b-2-5p
Down
FLNC
53
SMARCE1
Up
ALK
6
hsa-mir-132-3p
Down
RUNX1
53
ZBTB7A
Down
MAP 1B
249
hsa-mir-1299
Down
FN1
45
CREB1
Down
RUNX1
125
hsa-mir-4530
Down
TRIM63
22
RELA
Down
PRNP
106
hsa-mir-4477a
Down
TNF
20
NFIC
Down
FN1
105
hsa-mir-606
Down
PRNP
19
KLF11
Down
DAB2
75
hsa-mir-1343-3p
Down
MAP 1B
13
CREM
Down
RUNX2
52
hsa-mir-944
Down
FASLG
5
BACH1
Down
PIK3R2
48
hsa-mir-1912
Down
UBD
3
TEAD3
Down
UCHL1
45
hsa-mir-20a-3p
Down
DYNC1I1
2
ZNF71
Down
FLNC
35
hsa-mir-455-3p
Down
UCHL1
2
ZNF610
Down
UBD
34
hsa-mir-214-5p
Down
RUNX2
1
NR2F6
Down
FASLG
25
hsa-mir-7849-3p
Down
TRIM63
24
hsa-mir-3660
Down
TNF
15
hsa-mir-130a-3p
Down
KRT14
11
hsa-mir-1343-3p
Down
DYNC1I1
9
hsa-mir-129-2-3p
×
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.
Fig. 5
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
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.
Fig. 6
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
×
Anzeige
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.
Fig. 7
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.
Fig. 8
Structures of designed molecules
Table 7
Docking results of designed molecules on over expressed proteins
Sl. No/ Code
CEBPD
TP73
ESR2
TAB1
MAP 3K5
PDB: 4LY9
PDB:2XWC
PDB: 2IOG
PDB: 5NZZ
PDB: 5UP3
Total Score
Crash (-Ve)
Polar
Total Score
Crash (-Ve)
Polar
Total Score
Crash (-Ve)
Polar
Total Score
Crash (-Ve)
Polar
Total Score
Crash (-Ve)
Polar
TZPS1
6.566
-2.405
3.689
7.9803
-1.081
5.097
7.0797
-2.8787
1.281
5.0431
-0.6948
4.499
6.5667
-2.4057
3.689
TZPS2
6.800
-1.167
3.419
6.1601
-1.083
6.357
7.8372
-2.6932
2.462
4.6726
-1.1262
1.907
6.8009
-1.1671
3.419
TZPS3
6.468
-1.228
3.868
6.0227
-1.072
5.427
7.3618
-3.0167
1.176
4.8565
-1.1463
2.469
6.4686
-1.2285
3.868
TZPS4
7.715
-1.553
4.079
6.9313
-0.970
3.418
6.9469
-3.4753
1.281
5.5469
-1.4023
3.550
7.7156
-1.5531
4.079
TZPS5
6.453
-2.306
3.410
7.6894
-0.931
4.526
8.4667
-3.7554
4.692
4.3198
-1.0241
1.117
6.4536
-2.3064
3.410
TZPS6
6.461
-1.855
3.560
6.4527
-0.984
4.601
7.4944
-2.9796
1.224
4.9436
-1.2781
2.196
6.4616
-1.8552
3.560
TZPS7
8.534
-0.828
5.290
7.0007
-0.724
4.207
7.5843
-2.8172
1.999
4.7368
-1.3057
1.168
8.5349
-0.8288
5.290
TZPS8
11.489
-2.122
7.200
9.3291
-0.883
7.126
8.2146
-3.2986
3.067
6.7141
-0.8055
4.503
11.4898
-2.1229
7.200
TZPS9
7.152
-2.611
2.884
6.6449
-1.505
3.473
10.6699
-4.5053
5.965
5.0364
-1.7434
2.868
7.1521
-2.6116
2.884
TZPS10
10.851
-1.162
8.210
9.2527
-2.282
9.981
4.6334
-5.6706
3.280
6.8487
-1.4021
5.550
10.8518
-1.1628
8.210
TZPS11
7.549
-1.776
5.977
7.7814
-0.869
6.651
6.5663
-3.9855
2.880
5.2886
-1.4319
2.135
7.549
-1.7765
5.977
TZPS12
6.421
-1.515
3.412
5.2317
-1.441
5.132
7.4944
-2.9796
1.224
4.9436
-1.2781
2.196
6.4616
-1.8552
3.560
TZP13
7.906
-2.434
4.218
7.2083
-0.547
5.237
8.0517
-2.3234
2.222
4.697
-1.0273
2.204
7.9062
-2.4344
4.218
TZP14
7.674
-3.098
4.391
7.9457
-0.894
5.848
6.2749
-3.9247
1.839
4.7119
-0.9488
1.085
7.6749
-3.0982
4.391
TZP15
7.183
-2.936
3.636
7.1064
-0.980
4.143
8.5779
-3.6225
2.525
5.0291
-0.944
2.222
7.1835
-2.9367
3.636
TZP16
6.406
-1.533
1.860
6.3759
-0.991
3.531
8.2194
-1.9538
2.196
4.8598
-0.9268
3.062
6.4065
-1.5333
1.860
TZP17
8.690
-2.478
5.875
6.6083
-1.099
3.064
8.6505
-2.9891
3.094
4.948
-1.1025
3.928
8.6902
-2.4782
5.875
TZP18
6.770
-1.638
3.659
6.8998
-0.879
4.661
7.4019
-3.3357
2.698
5.0051
-1.0824
2.177
6.7708
-1.6383
3.659
TZP19
8.628
-1.406
5.189
8.3366
-1.139
5.462
8.8114
-4.448
5.183
4.7401
-1.8841
2.637
8.6283
-1.4062
5.189
TZP20
12.212
-2.219
8.540
8.9043
-0.806
6.430
10.7167
-2.127
5.792
7.3594
-1.4906
5.170
12.2126
-2.2192
8.540
TZP21
8.030
-1.557
5.434
8.2243
-1.099
6.851
8.2053
-4.0771
1.467
5.207
-1.2243
3.859
8.0309
-1.5573
5.434
TZP22
11.013
-2.175
8.175
9.4828
-0.737
9.663
11.0511
-5.3734
6.934
5.7841
-0.7944
5.493
11.0134
-2.175
8.175
TZP23
7.965
-3.168
6.847
8.5304
-1.107
7.803
8.0344
-2.5889
4.518
4.3368
-1.305
2.127
7.9652
-3.1688
6.847
TZP24
6.770
-1.638
3.659
6.8998
-0.879
4.661
7.4019
-3.3357
2.698
5.0051
-1.0824
2.177
6.7708
-1.6383
3.659
Standard Pioglitazone
9.8244
-2.396
5.097
9.834
-0.633
8.476
10.1314
-1.7567
2.8556
7.4321
-0.7906
4.505
9.8284
-2.396
5.097
Fig. 9
3D Binding of molecule TZP22with 2IOG
Fig. 10
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.
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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.
Acknowledgements
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.
Conflict of interest
The authors declare that they have no conflict of interest.
Informed consent
No informed consent because this study does not contain human or animals participants.
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This article does not contain any studies with human participants or animals performed by any of the authors.
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Competing interests
The authors declare that they have no competing interests.
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Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules
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G. Prashanth Basavaraj Vastrad Anandkumar Tengli Chanabasayya Vastrad Iranna Kotturshetti
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