key: cord-0279644-royfbe80 authors: Vastrad, Basavaraj; Vastrad, Chanabasayya title: Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis date: 2021-09-25 journal: bioRxiv DOI: 10.1101/2021.09.24.461631 sha: 74f3815eea20406c54e80ddd2cdc45458e296763 doc_id: 279644 cord_uid: royfbe80 Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications. Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 infection is one of the leading causes of respiratory tract infection [1] . COVID-19 is the fast spreading infectious disease worldwide, with a more than 200 million confirmed cases and 4.2 million confirmed death during August 2021 across nearly 200 countries according to the information given by the World Health Organization (WHO). The incidence rate of COVID-19 is high, which seriously affects the patient's health complications, which includes diabetes mellitus [2] , cardiovascular complications [3] , neurological complications [4] and kidney complications [5] . However, the molecular mechanism underlying many COVID-19 and its associated complications causes remains unclear, resulting in a lack of effective treatment [6] . The higher prevalence and limited treatments of COVID-19 infection and its associated complications lead to considerable public health and economic difficulty [7] . Therefore, more reliable prognostic biomarkers should be explored as a target for improving the treatment effect and better understanding the underlying molecular mechanism of COVID-19 infection and its associated complications. 41] , IFN-AhR signaling [42] and STING pathway [43] [44] are involved in the COVID-19 infection and its associated complications. Therefore, it is significant to further explore the molecular mechanisms of COVID-19 infection and its associated complications, and find molecular targets that can be used to diagnose early, prevent early, and treat early. Next-generation sequencing (NGS) has thoroughly heightened the understanding of COVID-19 infection and its associated complications [45] . It provides a platform for public databases and exploring the molecular mechanisms of COVID-19 infection and its associated complications. A high-quality NGS could potentially link molecular biomarkers to the advancement, diagnosis, and treatment of COVID-19 infection and its associated complications. In this investigation, we identified DEGs in expression profiling by high throughput sequencing data GSE156063 [46] from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) [47] . We performed Gene Ontology (GO) and REACTOME functional and pathway enrichment analyses, and constructed and analyzed a protein-protein interaction (PPI) network and isolated modules from PPI network, miRNA-hub gene regulatory network and TFhub gene regulatory network were constructed and analyzed. Diagnostic values of hub genes were determined by receiver operating characteristic curve (ROC) analysis. Hub genes and the clinical prognosis of patients with COVID-19 infection and its associated complications were compared and analyzed with the objective of identifying novel prognostic biomarkers and therapeutic targets. The expression profiling by high throughput sequencing GSE156063 [46] was downloaded from the GEO database and was used in this investigation. The dataset consisted of 93 COVID 19 samples and 100 non COVID 19 samples. The platform used was the GPL24676 Illumina NovaSeq 6000 (Homo sapiens). The limma in R Bioconductor software package [48] was used to perform the identification of DEGs between COVID 19 samples and non COVID 19 samples. To precise the analysis of statistically significant genes and false-positives, we using the adjusted P-value and Benjamini and Hochberg false discovery rate method [49] . An adj P value < 0.05, and a |log FC (fold change) | > 1 for up regulated genes and a |log FC (fold change) | <-0.5 for down regulated genes were defined as the thresholds for DEG screening. The heatmaps of DEGs from each dataset were plotted by the gplots package in the R software. The DEGs are presented as volcano plots, generated using ggplot2 in the R software. The g:Profiler (http://biit.cs.ut.ee/gprofiler/) [50] is an online functional annotation tool to provide a comprehensive understanding of biological information of genes and proteins. GO term (http://www.geneontology.org) enrichment analysis is used broadly to classify the characteristic biological attributes of genes, gene products, and sequences, including biological process (BP), cell components (CC) and molecular function (MF) [51] . REACTOME (https://reactome.org/) [52] pathway enrichment analysis demonstrates the enriched signaling pathways in DEGs. P<0.05 was considered to represent statistical significance. The IMEx interactome (https://www.imexconsortium.org/) is a public database harboring known and predicted protein-protein interactions [53] . Protein-protein interaction (PPI) is an indispensable approach for research on protein functions as it helps to clarify the interactions among proteins. In this study, the IMEx interactome database was used to construct a PPI network with an interaction score > 0.4. The network was then visualized using the Cytoscape 3.8.2 (http://www.cytoscape.org/) [54] . Moreover, Network Analyzer [55] , which is another plug-in of Cytoscape, was employed to study key nodes in the network with 4 methods includes node degree [56] , betweenness centrality [57] , stress centrality [58] and closeness centrality [59] are completed to explore the hub genes that were contained in the PPI network. Significant modules and hub genes were further analyzed and visualized with app PEWCC1 (http://apps.cytoscape.org/apps/PEWCC1) [60] plugged in Cytoscape. The two significant modules were displayed to show the density of nodes by IMEx interactome. MiRNAs controls gene expression under defined disease conditions through interaction with hub genes was analyzed. We applied miRNet database (https://www.mirnet.ca/) [61] to integrate 14 miRNA databases (TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0). We visualized miRNA-hub gene regulatory network by employing Cytoscape 3.8.2 [54] . TFs controls gene expression under defined disease conditions through interaction with hub genes was analyzed. We applied NetworkAnalyst database (https://www.networkanalyst.ca/) [62] to integrate TF database (JASPER). We visualized TF-hub gene regulatory network by employing Cytoscape 3.8.2 [54] . Receiver operating characteristic (ROC) analysis was used to assess the diagnostic value of hub genes in this research using pROC package in R statistical software [63] . Area under curve (AUC) analysis was implemented to determine the diagnostic value of hub genes. The AUC value > 0.8 showed a good diagnostic value for COVID 19. A total of 738 DEGs (415 up regulated genes and 323 down regulated genes) were identified between COVID 19 samples and non COVID 19 samples according to methods described above. The heatmap of the DEGs has been shown in Fig. 1 . The up and down regulated genes were listed in Table 1 . The volcano plot showed ( Fig. 2 ) that DEGs (up and down regulated genes) with the most significant logFC, and were used to further identify potential biomarkers. The consistently up and down regulated genes were clustered via the online website g:Profiler for the GO and REACTOME pathway enrichment analyses of DEGs in COVID 19. Three GO category results are presented in Table 2 . GO terms cover BP, MF and CC categories. As to the BP, DEGs were significantly enriched in immune system process, response to stimulus, cell communication and localization. For the CC, DEGs were enriched in the cell periphery, plasma membrane, cytosol and intracellular anatomical structure. In terms of the MF, DEGs were enriched in binding, identical protein binding, protein binding and ion binding. Pathway enrichment results are presented in Table 3 . The REACTOME pathway enrichment analysis revealed that DEGs were highly associated with the The TF-hub gene regulatory network is shown in Fig. 6 , which has 59 nodes (hub genes:84; TFs:328) and 2719 interactions. The nodes with higher degrees are listed in Table 5 , including ESR1 was the potential target of 22 TFs (ex; STAT3); IRF7 was the potential target of 14 TFs (ex; KLF5); IRF8 was the potential target of 12 TFs (ex; MAX); EIF2AK2 was the potential target of 10 TFs (ex; POU2F2); FYN was the potential target of 10 TFs (ex; ARID3A); BCL6 was the potential target of 17 TFs (ex; SREBF2); CEBPB was the potential target of 12 TFs (ex; E2F6); EGR1 was the potential target of 12 TFs (ex; SRF); RXRA was the potential target of 12 TFs (ex; GATA3); ARRB2 was the potential target of 12 TFs (ex; USF1). The ROC curves (Fig.7) Although many relevant studies of COVID-19 infection and its associated complications have been performed, early diagnosis, efficacy of treatment and prognosis for COVID-19 infection and its associated complications remain poorly determined. For prognosis, diagnosis and treatment, it is necessary to further understand the molecular mechanisms resulting in occurrence and advancement. Due to the advancement of NGS technology, the genetic modifications due to disease development can be detected, indicating gene targets for diagnosis, therapy and prognosis of infectious diseases. In the present investigation, a total of 738 DEGs between COVID-19 samples and non COVID-19 samples were identified, consisting of 415 up regulated and 323 down regulated genes. CXCL11 [64] , CXCL10 [65] , ISG15 [66] , SIGLEC1 [67] and CXCL9 [64] are identified as a new type of COVID-19 susceptibility genes. CXCL10 might play a role in progression of diabetes mellitus [68] . CXCL10 [69] , ISG15 [70] , SIGLEC1 [71] , CXCL9 [72] , IFIT3 [73] , KLF15 [74] and SPRR3 [75] have a potential role in the diagnosis and treatment of cardiovascular diseases. CXCL10 [76] , SIGLEC1 [77] and IL1R2 [78] have been suggested to be associated with neurological diseases. CXCL9 [79] , SIGLEC1 [80] , IL1R2 [81] , KLF15 [82] and CD207 [83] are important in the development of kidney diseases.. GO and REACTOME pathway enrichment pathway analyses were performed using g:Profiler. Immune system [84] , HDACs deacetylate histones [85] , adaptive immune system [86] , innate immune system [87] and neutrophil degranulation [88] were linked with progression of COVID-19. Immune system [89] , HDACs deacetylate histones [90] , adaptive immune system [91] , infectious disease [92] [294] , GYS1 [295] , ETS2 [296] , WDR1 [297] , THBD (thrombomodulin) [298], ALPL (alkaline phosphatase, biomineralization associated) [299] , FOSL2 [300] and GPX3 [301] contributes to the progression of cardiovascular diseases. CCL8 [302] , LAG3 [303], CXCL13 [304] , CCL2 [305] , DDX58 [306] , C1QA [307], C1QB [308] , IFNG (interferon gamma) [309] , STAT1 [310] , CCR5 [311], HLA-DMB [312] , CMKLR1 [313] , LAMP3 [314] , HLA-F [315] , HLA-G [316], MDK (midkine) [317] , IRF4 [318] , C4A [319], EIF2AK2 [320] , HLA-DRB5 [321] , GSDMB (gasdermin B) [322] , CD244 [323] , C1R [324] , CTLA4 [325] , CD38 [326] , CX3CL1 [327] , HLA-DRB1 [328] , AIM2 [329] , TLR7 [330], IRF8 [331], TMEM176B [332] , BCL11B [333] , TAP2 [334] , APOD (apolipoprotein D) [335] , CXCL12 [336] , CFH (complement factor H) [337] , IL32 [338], NLRC5 [339] , CD33 [340] , ACE2 [341] , KCNA3 [342] , ABCB1 [343] , GABRB2 [344] . MLC1 [345] , KCNA2 [346] , TIMP4 [347] [388] , ATP6V1B2 [389] , PTEN (phosphatase and tensin homolog) [390] , PELI1 [391] , ZMIZ1 [392] , NDRG2 [393] , FOXO3 [394] , DDIT4 [395] , PINK1 [396] , CPEB4 [397] , ADIPOR1 [398] , GLUL (glutamate-ammonia ligase) [399] , PRKCD (protein kinase C delta) [400] , USF2 [401] , SGK1 [402] , WDR45 [403] , RAB7A [404] , MAPK1 [405] , FTH1 [406] [454] , ADM (adrenomedullin) [455] , C5AR1 [456] , IL6R [457] and SDCBP (syndecan binding protein) [458] were found to be involved in the progression of COVID-19. GZMB (granzyme B) [459] , OAS1 [460] , CCL2 [461] , IDO1 [462] , CD80 [463] , GZMA (granzyme A) [464] , IFNG (interferon gamma) [465] , IFIH1 [466] , STAT1 [467], CCR5 [468] , ADCY5 [469] , MT2A [470] , DPP4 [471] , FASLG (Fas ligand) [472] , HLA-DMB [473] , CD3D [474], CMKLR1 [475] , ICOS (inducible T cell costimulator) [476] [545] , CMIP (c-Maf inducing protein) [546] , STATH (statherin) [547] , FOSL2 [548], GPX3 [549] , CAMK1D [550] and RNASEK (ribonuclease K) [551] were identified to be closely associated with diabetes mellitus. The hub genes from PPI network and modules defined in this study might lead to the progression of potential diagnostic, prognostic, or therapeutic biomarkers for COVID-19 infection and its associated complications. UBE2D1, FOS (Fos proto-oncogene, AP-1 transcription factor subunit), IFIT1, GBP1, GABARAPL1, GABARAP (GABA type A receptor-associated protein), RASSF5 and MAP1LC3B were might be the novel biomarkers for COVID-19 infection and its associated complications. miRNA-hub gene regulatory network and TF-hub gene regulatory network can be regarded as key to the understanding COVID-19 infection and its associated complications and might also lead to new therapeutic approaches. hsa-mir-4484 [552] , hsa-mir-4511 [553], STAT3 [554] , KLF5 [555] , SRF (Serum response factor) [556] , GATA3 [557] and USF1 [558] have been implicated as a principal mediator of kidney diseases. Studies have reported that hsa-mir-190b [559], hsamir-4693-5p [560] , hsa-mir-1470 [560] , hsa-mir-1825 [561] , STAT3 [562] , SREBF2 [563] and SRF (Serum response factor) [564] are necessary for neurological diseases. hsa-mir-4693-5p [565] and STAT3 [432] . 2008 ] were identified to be closely associated in patients with diabetes mellitus. PABPC1, RXRA (retinoid X receptor alpha), hsamir-3668, MAX (myc-associated factor X), POU2F2 and ARID3A were might be the novel biomarkers for COVID-19 infection and its associated complications. In conclusion, the current investigation provided a comprehensive bioinformatics analysis of DEGs in COVID-19 infection. Analysis of these genes provided information regarding the molecular mechanisms of COVID-19 infection and its associated complications, and significant biomarkers or targets for the diagnosis and treatment of COVID-19 infection and its associated complications. However, further molecular biological experiments are required to confirm the function of the DEGs and pathways in COVID-19 infection and its associated complications. 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The African-American population with a low allele frequency of SNP rs1990760 (T allele) in IFIH1 predicts less IFN-beta expression and potential vulnerability to COVID-19 infection In silico analysis of altered expression of long noncoding RNA in SARS-CoV-2 infected cells and their possible regulation by STAT1, STAT3 and interferon regulatory factors Puchhammer-Stöckl E. Deletion of the NKG2C receptor encoding KLRC2 gene and HLA-E variants are risk factors for severe COVID-19 CCR5 inhibition in critical COVID-19 patients decreases inflammatory cytokines, increases CD8 T-cells, and decreases SARS-CoV2 RNA in plasma by day The potential association between PARP14 and SARS-CoV-2 infection (COVID-19) Dipeptidyl peptidase-4 (DPP4) inhibition in COVID-19 Complement activation and increased expression of Syk, mucin-1 and CaMK4 in kidneys of patients with COVID Soluble HLA-G is upregulated in serum of patients with severe COVID-19 Physiology of Midkine and Its Potential Pathophysiological Role in COVID-19 CD38 in the age of COVID-19: a medical perspective Could SARS-CoV-2 blocking of ACE2 in endothelial cells result in upregulation of CX3CL1, promoting thrombosis in COVID-19 patients X-linked recessive TLR7 deficiency in ~1% of men under 60 years old with life-threatening COVID-19 Association of Interleukin 7 Immunotherapy With Lymphocyte Counts Among Patients With Severe Coronavirus Disease COVID-19 and APOL1: Understanding Disease Mechanisms through Clinical Observation Clinical Implications of IL-32, IL-34 and IL-37 in Atherosclerosis: Speculative Role in Cardiovascular Manifestations of COVID-19 Murch SH. Common determinants of severe Covid-19 infection are explicable by SARS-CoV-2 secreted glycoprotein interaction with the CD33-related Siglecs, Siglec-3 and Siglec-5/14 Targeting ACE2 for COVID-19 Therapy: Opportunities and Challenges Re-analysis of Single Cell Transcriptome Reveals That the NR3C1-CXCL8-Neutrophil Axis Determines the Severity of COVID-19 The zinc finger transcription factor, KLF2, protects against COVID-19 associated endothelial dysfunction Prognostic Value of Bioactive Adrenomedullin in Critically Ill Patients with COVID-19 in Germany: An Observational Cohort Study Association of COVID-19 inflammation with activation of the C5a-C5aR1 axis Genetically proxied interleukin-6 receptor inhibition: opposing associations with COVID-19 and pneumonia Single-cell RNA sequencing identify SDCBP in ACE2-positive bronchial epithelial cells negatively correlates with COVID-19 severity The serine protease granzyme B as an inflammatory marker, in relation to the insulin receptor cleavage in human obesity and type 2 diabetes mellitus OAS1 is not associated with MHC class I hyperexpression in the islets of donors with T1D regardless of disease duration Palmitate induces a pro-inflammatory response in human pancreatic islets that mimics CCL2 expression by beta cells in type 2 diabetes Loss of IDO1 Expression From Human Pancreatic β -Cells Precedes Their Destruction During the Development of Type 1 Beta cellspecific CD80 (B7-1) expression disrupts tissue protection from autoantigen-specific CTL-mediated diabetes Granzyme A Deficiency Breaks Immune Tolerance and Promotes Autoimmune Diabetes Through a Type I Interferon-Dependent Pathway Interferon-gamma receptor signaling is dispensable in the development of autoimmune type 1 diabetes in NOD mice Association between interferon-induced helicase (IFIH1) rs1990760 polymorphism and seasonal variation in the onset of type 1 diabetes mellitus Is CCR5-Delta32 mutation associated with diabetic nephropathy in type 2 diabetes? Genetic variants in GCKR, GIPR, ADCY5 and VPS13C and the risk of severe sulfonylurea-induced hypoglycaemia in patients with type 2 diabetes Polymorphism as a Risk Factor for Chronic Kidney Disease and Diabetes: Cross-Sectional and Cohort Studies Use of DPP4 inhibitors in Italy does not correlate with diabetes prevalence among COVID-19 deaths Fas and Fas ligand expression in inflamed islets in pancreas sections of patients with recent-onset Type I diabetes mellitus Non-HLA genetic factors and insulin dependent diabetes mellitus in the Japanese Aerobic exercise decreases chemerin/CMKLR1 in the serum and peripheral metabolic organs of obesity and diabetes rats by increasing PPARγ ICOS mediates the development of insulin-dependent diabetes mellitus in nonobese diabetic mice Associations of SUCNR1, GRK4, CAMK1D gene polymorphisms and the susceptibility of type 2 diabetes mellitus and essential hypertension in a northern Chinese Han population Association of HLA-G 3' untranslated region variants with type 1 diabetes mellitus Association of TCR/CD3, PTPN22, CD28 and ZAP70 gene polymorphisms with type 1 diabetes risk in Tunisian population: family based association study UBASH3A deficiency accelerates type 1 diabetes development and enhances salivary gland inflammation in NOD mice TAP1 I333V gene polymorphism and type 1 diabetes mellitus: a meta-analysis of 2248 cases CTLA4 promoter polymorphisms are associated with canine diabetes mellitus Autoimmunity to CD38 and GAD in Type I and Type II diabetes: CD38 and HLA genotypes and clinical phenotypes The effects of TLR3, TRIF and TRAF3 SNPs and interactions with environmental factors on type 2 diabetes mellitus and vascular complications in a Han Chinese population CD247, a novel T cell-derived diagnostic and prognostic biomarker for detecting disease progression and severity in patients with type 2 diabetes Association of NOS2 and NOS3 gene polymorphisms with susceptibility to type 2 diabetes mellitus and diabetic nephropathy in the Chinese Han population Diabetes induces macrophage dysfunction through cytoplasmic dsDNA/AIM2 associated pyroptosis Association of CFH and CFB gene polymorphisms with retinopathy in type 2 diabetic patients Toll-like receptor 7 deficiency suppresses type 1 diabetes development by modulating B-cell differentiation and function Associations of Salivary BPIFA1 Protein in Chronic Periodontitis Patients with Type 2 Diabetes Mellitus Neither an intronic CA repeat within the CD48 gene nor the HERV-K18 polymorphisms are associated with type 1 diabetes The rs11755527 polymorphism in the BACH2 gene and type 1 diabetes mellitus: case control study in a Brazilian population CD226 gene polymorphism (rs763361 C>T) is associated with susceptibility to type 1 diabetes mellitus among Egyptian children Serum concentration of angiogenic (CXCL1, CXCL12) and angiostasis (CXCL9, CXCL10) CXC chemokines are differentially altered in normal and gestational diabetes mellitus associated pregnancies Impaired upregulation of Stat2 gene restrictive to pancreatic β -cells is responsible for virus-induced diabetes in DBA/2 mice Serum levels of IL-32 in patients with type 2 diabetes mellitus and its relationship with TNF-α and IL-6 DPP4 and ACE2 in Diabetes and COVID-19: Therapeutic Targets for Cardiovascular Complications? Structure and function of CD72 in the non-obese diabetic (NOD) mouse Hyperglycemia-induced transcriptional regulation of ROCK1 and TGM2 High expression of CD52 in adipocytes: a potential therapeutic target for obesity with type 2 diabetes MMP-9 (rs17576)and TIMP-1 (rs6609533) gene polymorphisms with susceptibility to type 2 diabetes A novel compound heterozygous variant of the SLC12A3 gene in Gitelman syndrome with diabetes and the choices of the appropriate hypoglycemic drugs: a case report Level with Basal Active Glucagon-Like Peptide 1 Level in Patients with Type 2 IRS-1 genetic polymorphism (r.2963G>A) in type 2 diabetes mellitus patients associated with insulin resistance Association of CYP19A1 and CYP1A2 genetic polymorphisms with type 2 diabetes mellitus risk in the Chinese Han population PROX1 gene CC genotype as a major determinant of early onset of type 2 diabetes in slavic study participants from Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation study Assessing the association of rs7574865 STAT4 gene variant and type 1 diabetes mellitus among Egyptian patients Scarel-Caminaga RM. Functional haplotype in the Interleukin8 (CXCL8) gene is associated with type 2 Diabetes Mellitus and Periodontitis in Brazilian population MiRNA-92a protects pancreatic B-cell function by targeting KLF2 in diabetes mellitus Tangduqing Granules Attenuate Insulin Resistance and Abnormal Lipid Metabolism through the Coordinated Regulation of PPARγ and DGAT2 in Type 2 Diabetic Rats Roshanzamir N, Hassan-Zadeh V. Methylation of Specific CpG Sites in IL-1β and IL1R1 Genes is Affected by Hyperglycaemia in Type 2 Diabetic Patients Egr1 mediates retinal vascular dysfunction in diabetes mellitus via promoting p53 transcription Two novel functional single nucleotide polymorphisms of ADRB3 are associated with type 2 diabetes in the Chinese population Association of Superoxide dismutases (SOD1 and SOD2) and Glutathione peroxidase 1 (GPx1) gene polymorphisms with type 2 diabetes mellitus Group A Member 1 (Nr4a1) and Liver Linase B1 (LKB1) Mitigates Type Sirtuin 1 (SIRT1) Axis and Inhibiting Nuclear Factor-kappa B (NF-κB) Activation Elevation of a novel angiogenic factor, leucine-rich-α2-glycoprotein (LRG1), is associated with arterial stiffness, endothelial dysfunction, and peripheral arterial disease in patients with type 2 diabetes Alteration of forkhead box O (foxo4) acetylation mediates apoptosis of podocytes in diabetes mellitus MAP4K4 and IL-6+ Th17 cells play important roles in non-obese type 2 diabetes Differential effects of MYH9 and APOL1 risk variants on FRMD3 Association with Diabetic ESRD in African Americans Early-Onset Diabetes Mellitus in a Patient With a Chromosome 13q34qter Microdeletion Including IRS2 SULF2 strongly prediposes to fasting and postprandial triglycerides in patients with obesity and type 2 diabetes mellitus Resveratrol mitigates pancreatic TF activation and autophagymediated beta cell death via inhibition of CXCL16/ox-LDL pathway: A novel protective mechanism against type 1 diabetes mellitus in mice No Association Between a Genetic Variant of FOXO3 and Risk of Type 2 Diabetes Mellitus in the Elderly Population of North India Type 2 diabetes-induced hyposalivation of the submandibular gland through PINK1/Parkin-mediated mitophagy β -cell-specific overexpression of adiponectin receptor 1 does not improve diabetes mellitus in Akita mice Genetic Variant at the GLUL Locus Predicts All-Cause Mortality in Patients With Type 2 Diabetes SGK1 is regulated by metabolic-related factors in 3T3-L1 adipocytes and overexpressed in the adipose tissue of subjects with obesity and diabetes Autophagyregulating TP53INP2 mediates muscle wasting and is repressed in diabetes Opposite Genetic Effects of CMIP Polymorphisms on the Risk of Type 2 Diabetes and Obesity: A Family-Based Study in China microRNA-143-3p contributes to inflammatory reactions by targeting FOSL2 in PBMCs from patients with autoimmune diabetes mellitus Evaluation of the oxidative stress-related genes ALOX5, ALOX5AP, GPX1, GPX3 and MPO for contribution to the risk of type 2 diabetes mellitus in the Han Chinese population Associations of SUCNR1, GRK4, CAMK1D gene polymorphisms and the susceptibility of type 2 diabetes mellitus and essential hypertension in a northern Chinese Han population Analysis of SLC16A11 Variants in 12,811 American Indians: Genotype-Obesity Interaction for Type 2 Diabetes and an Association With RNASEK Expression Profiling analysis of circulating microRNA in peripheral blood of patients with class IV lupus nephritis Complement C5 activation promotes type 2 diabetic kidney disease via activating STAT3 pathway and disrupting the gut-kidney axis FIH-1-modulated HIF-1α C-TAD promotes acute kidney injury to chronic kidney disease progression via regulating KLF5 signaling Serum response factor, a novel early diagnostic biomarker of acute kidney injury A Novel Role for GATA3 in Mesangial Cells in Glomerular Development and Injury Comprehensive investigation of aberrant microRNAs expression in cells culture model of MnCl2-induced neurodegenerative disease Prediction of Single-Nucleotide Polymorphisms within microRNAs Binding Sites of Neuronal Genes Related to Multiple Sclerosis: A Preliminary Study Expression profile of plasma microRNAs and their roles in diagnosis of mild to severe traumatic brain injury Axis Mediates Neuroinflammatory Cascade across Schwann Cell-Neuron-Microglia SREBF-2 polymorphism influences white matter microstructure in bipolar disorder Interference of neuronal activity-mediated gene expression through serum response factor deletion enhances mortality and hyperactivity after traumatic brain injury miRNAs in SARS-CoV 2: A Spoke in the Wheel of Pathogenesis Differentially expressed miRNAs in circulating exosomes between atrial fibrillation and sinus rhythm A subset of circulating microRNAs is expressed differently in patients with myocardial infarction Evaluation of exosomal miRNAs as potential diagnostic biomarkers for acute myocardial infarction using next-generation sequencing PARP1 interacts with STAT3 and retains active phosphorylated-STAT3 in nucleus during pathological myocardial hypertrophy SREBF1c and SREBF2 gene polymorphisms are associated with acute coronary syndrome and blood lipid levels in Mexican population Deficiency of GATA3-Positive Macrophages Improves Cardiac Function Following Myocardial Infarction or Pressure Overload Hypertrophy USF1 contributes to high serum lipid levels in Dutch FCHL families and U.S. whites with coronary artery disease MicroRNA profiling and their pathways in South African individuals with prediabetes and newly diagnosed type 2 diabetes mellitus Circulating MicroRNAs in Elderly Type 2 Diabetic Patients miR-125a-5p ameliorates hepatic glycolipid metabolism disorder in type 2 diabetes mellitus through targeting of STAT3. Theranostics FIH-1-modulated HIF-1α C-TAD promotes acute kidney injury to chronic kidney disease progression via regulating KLF5 signaling Association study of SREBF-2 gene polymorphisms and the risk of type 2 diabetes in a sample of Iranian population Genetic variation of the transcription factor GATA3, not STAT4, is associated with the risk of type 2 diabetes in the Bangladeshi population Upstream transcription factor 1 (USF1) in risk of type 2 diabetes: association study in 2000 Dutch Caucasians I thank Charles Langelier, University of California San Francisco, Medicine, San Francisco, CA, USA, very much, the author who deposited their profiling by high throughput sequencing dataset GSE156063, into the public GEO database.