Identification of differentially expressed genes and the role of PDK4 in CD14+ monocytes of coronary artery disease

Du, Pan; Guo, Ren; Gao, Keqin; Yang, Shuang; Yao, Baige; Cui, Haobo; Zhao, Ming; Jia, Sujie

doi:10.1042/bsr20204124

Cited by 8 publications

(5 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One group reported that PDK inhibition with dichloroacetate led to decreased protein levels of the pro-inflammatory cytokines IL-1β and TNF-α, as well as the anti-inflammatory cytokine IL-10 (Xuewei Zhu et al, 2020). Another group reported that mRNA levels of PDK4 were higher in CD14 + monocytes from coronary artery disease patients relative to healthy controls, and were further increased by exposure to low density lipoprotein (Du et al, 2021). Thus, there are multiple examples from literature where enhanced oxidative phosphorylation is speculated to be both anti-inflammatory and pro-inflammatory, depending on the context and, likely, the magnitude of change.…”

Section: Discussionmentioning

confidence: 99%

Omega-3 polyunsaturated fatty acids modify glucose metabolism in THP-1 monocytes

Byun,

Armon,

Souza

et al. 2024

Preprint

View full text Add to dashboard Cite

Chronic inflammation is a driving factor in diseases like obesity and type 2 diabetes. Enhanced glucose metabolism, including via oxidative phosphorylation, may contribute to heightened immune activation. A recent clinical trial showed that supplementation with the n-3 fatty acid α-linolenic acid (ALA) reduced oxidative phosphorylation rates in human monocytes. However, the mechanism remains unknown. Therefore, our objective was to explore the direct effects of ALA and docosahexaenoic acid (DHA) on glucose metabolism in a cell culture model and to explore the molecular mechanism. THP-1 monocytes were treated for 48h with 10-40 μM of ALA or DHA and compared with vehicle and oleic acid controls. The Seahorse XFe24 system was used to approximate catabolic rates in the presence of glucose, including glycolysis and oxidative phosphorylation. The latter was validated by respirometry using an Oroboros O2k Oxygraph. Both ALA and DHA treatments reduced oxidative phosphorylation and increased glycolytic rates relative to control conditions. We identified pyruvate dehydrogenase kinase 4 (PDK4), an enzyme that inhibits the conversion of pyruvate to acetyl-CoA, as a possible mechanistic candidate. This gene was significantly upregulated by ALA and, to a greater extent, by DHA. Using fluorescent indicators, we also found that DHA increased reactive oxygen species while ALA had no effect. Our data suggest that ALA and DHA trigger a re-wiring of bioenergetic pathways in monocytes, possibly via the upregulation of PKD4. Given the close relationship between cell metabolism and immune cell activation, this may represent a novel mechanism by which n-3 fatty acids modulate immune function and inflammation.

show abstract

Section: Discussionmentioning

confidence: 99%

Omega-3 polyunsaturated fatty acids modify glucose metabolism in THP-1 monocytes

Byun,

Armon,

Souza

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…PDK4 dysfunction was further plausibly linked with insulin resistance and/or diabetes-associated end-stage renal disease and renal transplantation [ 32 ]. As PDK4 also plays an important role in vascular-related diseases, it might also be associated with nephroangiosclerosis [ 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of PDK4 as Hub Gene for Diabetic Nephropathy Using Co-Expression Network Analysis

Han¹,

Jin²,

Wang³

et al. 2023

Kidney Blood Press Res

View full text Add to dashboard Cite

Introduction: Diabetic nephropathy (DN) is related to type 1 and type 2 diabetes. They are the leading cause of end-stage renal disease (ESRD), but the underling specific pathogenesis of DN is not yet clear. Our study was conducted to explore how DN changed the transcriptome profiles in kidney. Methods: The gene expression profile of micro-dissected glomeruli of 41 type 2 diabetic nephropathy patients and 20 healthy controls were included. The sample data set GSE86804 was obtained from the GEO database. Differentially expressed genes (DEGs) were analyzed in R with the limma package and the important modules were found by weighted gene co-expression network analysis (WGCNA) clustering. The modules were then analyzed based on Gene Ontology (GO) gene set enrichment analysis, and the hub genes were found out. We next validated the hub gene, PDK4, in a cell model of DN. We also constructed the PDK4-related PPI network to investigate the correlation between PDK4 expression and other genes. Results: Heat map and volcano map were drawn to illustrate the mRNA expression profile of 1204 DEGs in both samples of diabetic nephropathy patients and the control group. Using WGCNA, we selected the blue module in which genes showed the strongest correlation with the phenotype and the smallest P-value. We also identified PDK4 as a hub gene. PDK4 expression was upregulated in human diabetic kidney tissue. Moreover, PDK4 was speculated to play a role in glomerular basement membrane development, kidney development according to the enrichment of functions and signaling pathways. Furthermore, PDK4, and two key genes GSTA2 and G6PC protein expression were verified highly expressed in the cell model of DN. Conclusion: During the pathogenesis of DN, many genes may change expression in a coordinated manner. The discovery of PDK4 as key gene using WGCNA is of great significance for the development of new treatment strategies to block the development of DN.

show abstract

“…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%

Section: Construction Of the Tf-hub Gene Regulatory Networkmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Identification of differentially expressed genes and the role of PDK4 in CD14+ monocytes of coronary artery disease

Cited by 8 publications

References 58 publications

Omega-3 polyunsaturated fatty acids modify glucose metabolism in THP-1 monocytes

Omega-3 polyunsaturated fatty acids modify glucose metabolism in THP-1 monocytes

Identification of PDK4 as Hub Gene for Diabetic Nephropathy Using Co-Expression Network Analysis

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

Contact Info

Product

Resources

About