HIF-1/AKT Signaling-Activated PFKFB2 Alleviates Cardiac Dysfunction and Cardiomyocyte Apoptosis in Response to Hypoxia

Feng, Wenjing; Lv, Wei; Liu, Wenhui; Fu, Caihua

doi:10.1536/ihj.20-315

Cited by 16 publications

(9 citation statements)

References 46 publications

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“…Recent studies have shown that altered expression of DDX58 [413], STAT1 [414], TLR4 [415], CYP2D6 [416], JAK2 [417], TLR2 [418], DUSP6 [419], HDAC9 [420], LATS2 [421], CA2 [150], HIPK3 [422], CCR2 [423], GAB1 [120], UFL1 [424], OGT (O-linked N-acetylglucosamine (GlcNAc) transferase) [425], PRKAR1A [265], SIRT1 [426], FGL2 [427], TET2 [428], ASCC2 [429], BIN1 [430], HSPB1 [431], IGFBP4 [432], TRPM4 [433] and LGALS3 [434] might be associated with the progression of heart failure. STAT1 [435], TLR4 [436], ABCA1 [437], PTGS2 [68], CYP2D6 [438], CR1 [439], PDK4 [440], RNF213 [441], ZDHHC17 [70], TLR8 [442], PDGFC (platelet derived growth factor C) [443], TLR2 [444], CYP1B1 [445], HDAC9 [446], IL1RN [447], GCH1 [448], EGR1 [449], ZEB2 [450], PLA2G7 [451], CCR2 [452], GCLC (glutamate-cysteine ligase catalytic subunit) [258], VEGFA (vascular endothelial growth factor A) [453], CD46 [454], NFKBIZ (NFKB inhibitor zeta) [455], LDLR (low density lipoprotein receptor) [456], TLR6 [457], SIRT1 [458], NOD2 [459], FGL2 [460], IDH1 [461], TET2 [462], PFKFB2 [463], KDM6A [464], IKZF2 [465], ZNF606 [466], PF4 [467], CCR7 [468], RUNX3 [469], TCF7 [470], PPBP (pro-platelet basic protein) [471], IGFBP4 [280], HSPG2 [472] and LGALS3 [236] expression might be regarded as an indicator of susceptibility to CAD. STAT1 [473], TLR4 [367], JAK2 [474], PDGFC (platelet derived growth factor C) […”

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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“…Inhibiting mitochondrial autophagy could lead to mitochondrial pathway-dependent apoptosis (Dorn and Kitsis 2015 ; Gao et al. 2021 ). Studies have confirmed that the earliest genes involved in apoptosis are the Bcl-2 family, which contains Bcl-2 and Bax (Kalkavan and Green 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Banxia-Houpu decoction diminishes iron toxicity damage in heart induced by chronic intermittent hypoxia

Song

Zhao

Zhen

et al. 2022

Pharmaceutical Biology

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Context Obstructive sleep apnoea (OSA) causes chronic intermittent hypoxia (CIH), which results in mitochondrial dysfunction and generates reactive oxygen species (ROS) in the heart. Excessive free iron could accelerate oxidative damage, which may be involved in this process. Banxia-Houpu decoction (BHD) was reported to improve the apnoea hypopnoea index in OSA patients, but the specific mechanism was still unclear. Objective To investigate whether BHD could reduce CIH-induced heart damage by regulating iron metabolism and mitochondrial function. Materials and methods C57BL/6N mice were randomly divided into control, CIH and BHD groups. Mice were exposed to CIH (21 − 5% O 2 , 20 times/h, 8 h/d) and administered BHD (3.51, 7.01 and 14.02 g/kg, intragastrically) for 21 d. Cardiac and mitochondrial function, iron levels, apoptosis and mitophagy were determined. Results BHD (7.01 g/kg) significantly improved cardiac dysfunction, pathological change and mitochondrial structure induced by CIH. BHD increased the Bcl-2/Bax ratio (1.4-fold) and inhibited caspase 3 cleavage in CIH mice (0.45-fold). BHD activated mitophagy by upregulating Parkin (1.94-fold) and PINK1 (1.26-fold), inhibiting the PI3K-AKT-mTOR pathway. BHD suppressed ROS generation by decreasing NOX2 (0.59-fold) and 4-HNE (0.83-fold). BHD reduced the total iron in myocardial cells (0.72-fold) and mitochondrial iron by downregulating Mfrn2 (0.81-fold) and MtFt (0.78-fold) proteins, and upregulating ABCB8 protein (1.33-fold). Rosmarinic acid, the main component of Perilla Leaf in BHD, was able to react with Fe 2+ and Fe 3+ in vitro . Discussion and conclusions These findings encourage the use of BHD to resist cardiovascular injury and provide the theoretical basis for clinical treatment in OSA patients.

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“…There is no doubt that PFKFB2 affects the activity of the glycolytic process. Myocardial hypoxia can activate the HIF/AKT signaling pathway, regulate the increased expression of PFKFB2 , and then improve cardiac function after MI and reduce cardiomyocyte apoptosis by reprogramming cellular glycolysis ( 22 ). At the same time, some researchers found that activated neutrophils and M1 macrophages in myocardial tissue during AMI showed the characteristics of enhanced glycolysis ( 23 , 24 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood

Yang

et al. 2022

Front. Cardiovasc. Med.

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ObjectiveThis study aims to analyze the gene expression profile of peripheral blood in different stages of myocardial infarction (MI) by transcriptome sequencing, and to study the gene expression characteristics of peripheral blood after MI.MethodsDifferentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify genes and modules associated with old myocardial infarction (OMI). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation were applied to analyze the potential functions of genes. Hub genes were identified by Random Forest Classifier. CIBERSORT was used to provide an estimate of the abundance of 22 immune cells in peripheral blood. Quantitative polymerase chain reaction (qPCR) was used to detect gene expression levels in clinical samples. The cellular components (CC) of peripheral blood were counted by an automatic hematology analyzer.ResultsThrough differential gene analysis and co-expression network analysis, 11 candidate genes were obtained. A random forest classifier identified 10 hub genes. Immune cell distribution of peripheral blood was found that T cell CD4 memory resting, NK cells resting, Dendritic cells activated, Mast cells resting, Monocytes and Neutrophils were correlated with OMI. Spearman correlation analysis found that PFKFB2 is related to the above immune cells. Low expression of PFKFB2 in peripheral blood of OMI was detected in clinical samples, and the relationship between PFKFB2 and peripheral blood immune cell counts was analyzed, which showed monocytes were associated with PFKFB2 in our study.ConclusionPFKFB2 was low expressed in OMI, and related to the distribution of immune cells. PFKFB2 may play a key role in reflecting the transition from AMI to OMI, and predicting the distribution of immune cells, which provided a new perspective for improving myocardial fibrosis and adverse remodeling.

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HIF-1/AKT Signaling-Activated PFKFB2 Alleviates Cardiac Dysfunction and Cardiomyocyte Apoptosis in Response to Hypoxia

Cited by 16 publications

References 46 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

Banxia-Houpu decoction diminishes iron toxicity damage in heart induced by chronic intermittent hypoxia

Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood

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