TRPM2, PDLIM5, BCL3, CD14, GBA Genes as Feasible Markers for Premature Coronary Heart Disease Risk

Prasongsukarn, Kriengchai; Dechkhajorn, Wilanee; Benjathummarak, Surachet; Maneerat, Yaowapa

doi:10.3389/fgene.2021.598296

Cited by 17 publications

(9 citation statements)

References 67 publications

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“…Expression of BCL3 has been linked to CVD and cancer. 44-46 These examples provide proof of principle that integrating cis -mQTLs with CpGs and traits can reveal biological pathways by linking DNA methylation to a variety of diseases.…”

Section: Discussionmentioning

confidence: 95%

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease

Joehanes

Liu

et al. 2022

Preprint

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DNA methylation commonly occurs at cytosine-phosphate-guanine sites (CpGs) that can serve as biomarkers for many diseases. We analyzed whole genome sequencing data to identify DNA methylation quantitative trait loci (mQTLs) in 4,126 Framingham Heart Study participants. Our mQTL mapping identified 94,362,817 cis-mQTLvariant-CpG pairs (for 210,156 unique autosomal CpGs) at P<1e-7 and 33,572,145 trans-mQTL variant-CpG pairs (for 213,606 unique autosomal CpGs) at P<1e-14. Using cis-mQTL variants for 1,258 CpGs associated with seven cardiovascular disease risk factors, we found 104 unique CpGs that colocalized with at least one cardiovascular disease trait. For example, cg11554650 (PPP1R18) colocalized with type 2 diabetes, driven by a single nucleotide polymorphism (rs2516396). We performed Mendelian randomization (MR) analysis and demonstrated 58 putatively causal relations of CVD risk factor-associated CpGs to one or more risk factors (e.g., cg05337441 [APOB] with LDL; MR P=1.2e-99, and 17 causal associations with coronary artery disease (e.g. cg08129017 [SREBF1] with coronary artery disease; MR P=5e-13). We also showed that three CpGs, e.g., cg14893161 (PM20D1), are putatively causally associated with COVID-19 severity. To assist in future analyses of the role of DNA methylation in disease pathogenesis, we have created a browsable and downloadable resource of mQTLs through the NCBI Molecular QTL Browser.

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Section: Discussionmentioning

confidence: 95%

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease

Joehanes

Liu

et al. 2022

Preprint

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“…A previous study reported that LXN (latexin) [84], LMNA (lamin A/C) [85], PFKFB3 [86], NEU1 [87], TBK1 [88], GRN (granulin precursor) [89], CTSD (cathepsin D) [90], ACADS (acyl-CoA dehydrogenase short chain) [91], IRF7 [92], S1PR1 [93], ZAP70 [94], IDH1 [95], IL15 [96], PIK3R1 [97], OSM (oncostatin M) [98], SOCS3 [99], USP21 [100], CEP19 [101], KDM2A [102], TP53 [103], BRD2 [104], ATP6 [105], BRD4 [106], COX2 [107], RPS6 [108], ND2 [109], CYTB (cytochrome b) [110] and COX1 [111] are altered expressed in obesity. Altered expression of BCL3 [112], TRAF2 [113], NEU1 [114], SNAP29 [115], AGPAT2 [116], LPCAT3 [117], ADORA2B [118], CTSD (cathepsin D) [119], ACADS (acyl-CoA dehydrogenase short chain) [120], ACAD9 [121], E4F1 [122], IRF7 [123], TAF1 [124], S1PR1 [125], RASSF1 [126], ELAC2 [127], RNF146 [128], COX15 [129], SMYD2 [130], IDH1 [131], MTO1 [132], IL15 [133], PIK3R1 [134], ASB1 [135], OSM (oncostatin M) [136], ZNF791 [137], GBA (glucosylceramidase beta) [138], SOCS3 [139], SLC39A7 [140], AKIP1 [141], AMIGO2 [142], GLUL (glutamate-ammonia ligase) [143], SEMA4D [144], KDM2A [145], TP53 [146], JARID2 [147], CTBP1 [148], ATP6 [149], RPL7 [150], HSP90AA1 [151], BRD4 [152], PSMB4 [153], COX2 [154], JUND (JunD proto-oncogene, AP-1 transcription factor subunit) [155], RPS5 [156], RACK1 […”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Vastrad¹,

Vastrad²

2022

Preprint

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Type 1 diabetes mellitus (T1DM) comprise the most common forms of autoimmune disease. The aim of this investigation was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of T1DM. The next generation sequencing (NGS) dataset GSE182870 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. Protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network, and TF-hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, hub genes were validated by using receiver operating characteristic curve analysis. A total of 860 DEGs were screened out from NGS dataset, among which 477 genes were up regulated and 383 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in cellular metabolic process, intracellular anatomical structure and catalytic activity, and the down regulated genes were significantly enriched in cellular nitrogen compound biosynthetic process, protein containing complex and heterocyclic compound binding. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in metabolism of carbohydrates and the down regulated genes were significantly enriched in metabolism of RNA. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and hub genes (MAPK14, RHOC, MAD2L1, TAF1, TRAF2, HSP90AA1, TP53, HSP90AB1, UBA52 and RACK1) were identified. This study provides further insights into the underlying pathogenesis of T1DM.

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“…TherapeuTic advances in cardiovascular disease (CHST15), 131 N-alpha-acetyltransferase 10, NatA catalytic subunit (NAA10), 132 transcription factor 3 (TCF3), 133 calponin 1 (CNN1), 134 TATA-box binding protein associated factor, RNA polymerase I subunit A (TAF1A), 135 acyl-CoA dehydrogenase family member 9 (ACAD9), 136 kelch-like family member 24 (KLHL24), 137 myomesin 2 (MYOM2), 138 tripartite motif containing 63 (TRIM63), 139 catenin alpha 3 (CTNNA3), 140 NLR family CARD domain containing 5 (NLRC5), 141 Kruppel-like factor 9 (KLF9), 142 myosin light chain kinase 3 (MYLK3), 143 RNA-binding motif protein 20 (RBM20), 144 glutathione S-transferase kappa 1 (GSTK1), 145 ubiquinol-cytochrome c reductase, Rieske iron-sulfur polypeptide 1 (UQCRFS1), 146 NADH: ubiquinoneoxidoreductase core subunit S2 (NDUFS2), 147 and cytochrome c oxidase subunit 6B1 (COX6B1) 148 have been reported to be positively correlated with cardiomyopathy. Genes include reticulon 4 (RTN4), 149 neutrophil cytosolic factor 2 (NCF2), 150 Rho GTPase activating protein 9 (ARHGAP9), 151 lipase G, endothelial type (LIPG), 152 BCL3 transcription coactivator (BCL3), 153 heparansulfate proteoglycan 2 (HSPG2), 154 apolipoprotein B receptor (APOBR), 155 integrin subunit alpha 2 (ITGA2), 156 peptidylprolylisomerase A (PPIA), 157 interleukin 1 receptor-associated kinase 1 (IRAK1), 158 vitamin K epoxide reductase complex subunit 1 (VKORC1), 159 renalase, FAD-dependent amine oxidase (RNLS), 160 major histocompatibility complex, class I, F (HLA-F), 161 F-box and leucine-rich repeat protein 17 (FBXL17), 149 collagen type XI alpha 2 chain (COL11A2), 162 and NADH: ubiquinoneoxidoreductase subunit C2 (NDUFC2) 163 are associated with prognosis of coronary heart disease. Genes include C-C motif chemokine receptor 1 (CCR1), 164 mesencephalic astrocyte-derived neurotrophic factor (MANF), 165 HSP90AA1, 166 ARRB2, 167 solute carrier family 39 member 13 (SLC39A13), 168 prolyl 4-hydroxylase subunit alpha 2 (P4HA2), 169 HECT domain E3 ubiquitin protein ligase 3 (HECTD3), 170 canopy FGF signaling regulator 2 (CNPY2), 171 enoyl-CoA hydratase domain containing 2 (ECHDC2), 172 NADH: ubiquinoneoxidoreductase subunit S4 (NDUFS4), 173 and inner membrane mitochondrial protein (IMMT)…”

Section: Carbohydrate Sulfotransferase 15mentioning

confidence: 99%

Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis

Ganekal¹,

Vastrad²,

Vastrad³

et al. 2023

Therapeutic Advances in Cardiovascular Disease

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Background: Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF. Methods: We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein–protein interaction (PPI) network was plotted with Human Integrated Protein–Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. Results: A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B). Conclusions: This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

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TRPM2, PDLIM5, BCL3, CD14, GBA Genes as Feasible Markers for Premature Coronary Heart Disease Risk

Cited by 17 publications

References 67 publications

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis

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