Effects of the Prdx2 depletion on blood pressure and life span in spontaneously hypertensive rats

Mahal, Zinat; Fujikawa, Koichi; Matsuo, H.; Zahid, Hasan M.; Koike, Masamichi; Misumi, Masaki; Kaneko, Takehito; Mashimo, Tomoji; Ohara, Hirotaka; Nabika, Toru

doi:10.1038/s41440-019-0207-9

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…CEACAM1 [340], ACSL1 [341], TLR4 [342], ABCA1 [343], TLR5 [344], CYP2D6 [345], JAK2 [346], NOTCH2 [347], DDX3X [348], NCOA4 [349], EGR1 [350], IQGAP2 [351], GCLC (glutamate-cysteine ligase catalytic subunit) [352], VEGFA (vascular endothelial growth factor A) [353], ITGB1 [354], LDLR (low density lipoprotein receptor) [355], TLR6 [316], SIRT1 [356], FGL2 [357], TET2 [358], PHF2 [328], VEGFB (vascular endothelial growth factor B) [359], SELENOM (selenoprotein M) [360], TRPM4 [361], OLFM2 [362] and ATAD3A [363] are thought to be involved in non-alcoholic fatty liver disease. Altered expression of ATOH8 [364], STAT1 [365], ARG1 [366], TLR4 [367], VNN1 [368], ABCA1 [369], IFIH1 [370], PTGS2 [371], F2RL1 [289], CYP2D6 [372], PDK4 [373], RNF213 [374], JAK2 [375], NOTCH2 [376], PDGFC (platelet derived growth factor C) [377], TLR2 [378], CYP1B1 [379], IL1RN [380], GCH1 [381], EGR1 [382], HIF1A [383], PLA2G7 [384], CCR2 [385], GAB1 [386], VEGFA (vascular endothelial growth factor A) [387], OGT (O-linked N-acetylglucosamine (GlcNAc) transferase) [388], OXR1 [389], IRF9 [390], FMR1 [391], LDLR (low density lipoprotein receptor) [392], SIRT1 [393], NOD2 [394], ATP13A3 [395], VCAN (versican) [396], FGL2 [397], TET2 [398], KDM6A [399], KLHL2 [400], CAVIN1 [401], TNFRSF4 [402], PF4 [403], VEGFB (vascular endothelial growth factor B) [330], CCR7 [404], PRDX2 [405], HSPB1 [406], TCF4 […”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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“…[97] and CYP3A5 [98] have been shown in schizophrenia. Recent studies showed that SLC6A9 [99], FKBP1B [100], S100A12 [101], SLC6A19 [102], TXN (thioredoxin) [103], TLR9 [104], HP (haptoglobin) [105], ARG1 [106], PINK1 [107], B2M [108], C5AR1 [109], MYADM (myeloid associated differentiation marker) [110], CBS (cystathionine beta-synthase) [111], GPX1 [112], SIAH2 [113], PRDX2 [114], RDH8 [115], CYP11B2 [116], RARRES2 [117], NOX1 [118], IL33 [119], OTC (ornithine transcarbamylase) [120], CYP1A1 [121], NFATC4 [122], TSLP (thymic stromal lymphopoietin) [123], WNT4 [124], MGP (matrix Gla protein) [125], FGFBP1 [126], GHR (growth hormone receptor) [127], ERBB3 [128], GFAP (glial fibrillary acidic protein) [129], CCDC40 [130], CACNB2 [131], CD34 [132], NOTCH3 [133], TERT (telomerase reverse transcriptase) [134], GNB3 [135], TP73 [136], RYR2 [137], ENPEP (glutamyl aminopeptidase) [138], SCN7A [139], WNK4 [140], SFRP5 [141], GDF15 [142], CAV1 [143], KCNA5 [144], FOXC1 [145], ASIC1 [146], VASH2 [147], CXCL8 [148], PAPPA2…”

Section: Discussionmentioning

confidence: 99%

“…Altered expression of SLC6A9 [56] and RHD (Rh blood group D antigen) [57], CHRFAM7A [58], ANXA3 [59], SLC1A5 [60], KCNH2 [61], HP (haptoglobin) [62], SELENBP1 [63], SNCA (synuclein alpha) [64], TGM2 [65], PINK1 [66], B2M [67], QPCT (glutaminyl-peptide cyclotransferase) [68], CBS (cystathionine beta-synthase) [69], NQO2 [70], GLRX5 [71], BASP1 [72], GAS7 [73], GPX1 [74], OLIG2 [75], RPTN (repetin) [76], IL33 [77], SOX10 [78], GRIK1 [79], ZFPM2 [80], SHANK3 [81], ERBB3 [82], ARC (activity regulated cytoskeleton associated protein) [83], GFAP (glial fibrillary acidic protein) [84], PLAT (plasminogen activator, tissue type) [85], GRIK5 [86], CACNB2 [87], NRXN2 [88], TERT (telomerase reverse transcriptase) [89], GNB3 [90], L1CAM [91], EGR3 [92], CAV1 [93], CACNA1B [94], MAGI1 [95], KIR2DL1 [96], PAH (phenylalanine hydroxylase) [97] and CYP3A5 [98] have been shown in schizophrenia. Recent studies showed that SLC6A9 [99], FKBP1B [100], S100A12 [101], SLC6A19 [102], TXN (thioredoxin) [103], TLR9 [104], HP (haptoglobin) [105], ARG1 [106], PINK1 [107], B2M [108], C5AR1 [109], MYADM (myeloid associated differentiation marker) [110], CBS (cystathionine beta-synthase) [111], GPX1 [112], SIAH2 [113], PRDX2 [114], RDH8 [115], CYP11B2 [116], RARRES2 [117], NOX1 [118], IL33 [119], OTC (ornithine transcarbamylase) [120], CYP1A1 [121], NFATC4 [122], TSLP (thymic stromal lymphopoietin) [123], WNT4 [124], MGP (matrix Gla protein) [125], FGFBP1 […”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

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“…Besides the above, we recently created peroxiredoxin 2 ( Prdx2 ) KO SHR to investigate whether the deletion of an antioxidant gene exacerbates cerebro-cardiovascular phenotypes of SHR [ 115 ]. Consequently, Prdx2 KO SHR had greater basal BP compared with WT SHR.…”

Section: Rat Modelsmentioning

confidence: 99%

Genetic Modifications to Alter Blood Pressure Level

OHARA

Nabika

2022

Biomedicines

Self Cite

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Genetic manipulation is one of the indispensable techniques to examine gene functions both in vitro and in vivo. In particular, cardiovascular phenotypes such as blood pressure cannot be evaluated in vitro system, necessitating the creation of transgenic or gene-targeted knock-out and knock-in experimental animals to understand the pathophysiological roles of specific genes on the disease conditions. Although genome-wide association studies (GWAS) in various human populations have identified multiple genetic variations associated with increased risk for hypertension and/or its complications, the causal links remain unresolved. Genome-editing technologies can be applied to many different types of cells and organisms for creation of knock-out/knock-in models. In the post-GWAS era, it may be more worthwhile to validate pathophysiological implications of the risk variants and/or candidate genes by creating genome-edited organisms.

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Effects of the Prdx2 depletion on blood pressure and life span in spontaneously hypertensive rats

Cited by 17 publications

References 17 publications

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Genetic Modifications to Alter Blood Pressure Level

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