Aberrant PDK4 Promoter Methylation Preceding Hyperglycemia in a Mouse Model

Putra, Sulistyo Emantoko Dwi; Singajaya, Stephanie; Thesman, Ferensia; Pranoto, Dicky Andhika; Sanjaya, Ricky; Vianney, Yoanes Maria; Artadana, Ida Bagus Made

doi:10.1007/s12010-019-03143-6

Cited by 4 publications

(3 citation statements)

References 27 publications

(21 reference statements)

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“…Therefore, PDK is an important regulator of PDC, which changes glucose metabolism [ 7 ]. Genetic inactivation of PDK4 is reported to improve hepatic insulin resistance-induced diabetes [ 20 ]. Lower PDK4 expression was found in preadipocytes from obese humans with type 2 diabetes [ 21 ].…”

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

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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.

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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

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“…Pathways include metabolism [42], transport of small molecules [43] and immune system [44] are linked with progression of FSGS. PDK4 [45], ALB (albumin) [46], EGR1 [47], RYR3 [48], APOH (apolipoprotein H) [49], CYP27B1 [50], ESM1 [51], GATA6 [52], PCK1 [53], TET2 [54], AFP (alpha fetoprotein) [55], CACNA1D [56], ATF3 [57], EGF (epidermal growth factor) [58], LPL (lipoprotein lipase) [59], PPARGC1A [60], PLG (plasminogen) [61], NR4A1 [62], STRA6 [63], MLXIPL (MLX interacting protein like) [64], PLA2G6 [65], RGS2 [66], GPS2 [67], SOX5 [68], GLUL (glutamate-ammonia ligase) [69], RYK (receptor like tyrosine kinase) [70], NFKBIA (NFKB inhibitor alpha) [71], LGR4 [72], SPRY2 [73], TRPC1 [74], KL (klotho) [75], GCLC (glutamate-cysteine ligase catalytic subunit) [76], NOX4 [77], CD69 [78], SLC19A2 [79], S100A12 [80], MT1A [81], SORCS1 [82], FKBP5 [83], AFM (afamin) [84], CA3 [85], MAOA (monoamine oxidase A) [86], ND1 [87], ATP6 [88], CHGA (chromogranin A) [89], ICOS (inducible T cell costimulator) [90], DBH (dopamine beta-hydroxylase) [91], CD5 [92], LTA (lymphotoxin alpha) [93], IFNG (interferon gamma) [94], MPO (myeloperoxidase) [95], CD70 [96], CD300E [97], COLEC12 [98], TLR10 [99], LCN2 [100], SLAMF7 [101], TREM2 [102], ITGAL (integrin subunit alpha L) [103], CD27 [104], JAK3 [105], CCR5 [106], FCN1 [107], IL1RN [108], CX3CR1 [109], PDCD1 [110], TRPM2 [111], PLEK (pleckstrin) [112], CD101 [113],...…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Focal segmental glomerulosclerosis (FSGS) is a renal disease leading threat to human health around the world. Here we aimed to explore novel molecular and potential therapeutic targets in FSGS through adopting integrated bioinformatics tools. Next generation sequencing (NGS) data of GSE197307 was available from Gene Expression Omnibus (GEO) database. Furthermore, differentially expressed genes (DEGs) were screened using the DESeq2 package in R software. Gene ontology (GO) and REACTOME pathway enrichment analyses of DEGs were performed via g:Profiler. Then, the protein-protein interaction (PPI) network, miRNA- hub gene regulatory network and TF-hub gene regulatory network were constructed using the HIPPIE, miRNet and NetworkAnalyst databases. Hub genes were validated using the receiver operating characteristic curve (ROC) analysis. By performing DEGs analysis, 488 up regulated genes and 488 down regulated genes were successfully identified from GSE197307, respectively. And they were mainly enriched in the terms of multicellular organismal process, cell periphery, protein binding, metabolism, immune system process, signaling receptor binding and immune system. Based on the data of protein-protein interaction (PPI), miRNA-hub gene regulatory network and TF-hub gene regulatory network, the top hub genes were ranked, including ILK, DDX5, MATR3, ALB, FOS, MKI67, PLK1, TNF, LCK and GTSE1. Bioinformatics analysis of NGS data identified signaling pathways and hub genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in FSGS.

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“…High-fat diets also inactivated insulin receptor substrate (IRS-1) in liver and caused inflammation in mice models [15]. Methylation studies on PDK4 also revealed that high-fat-diet-induced methylation on a specific CpG site before the onset of hyperglycemia as one proof of epigenetic regulation plays an important role in metabolic disorder [16].…”

Section: Introductionmentioning

confidence: 99%

The genetic basis of high-carbohydrate and high-monosodium glutamate diet related to the increase of likelihood of type 2 diabetes mellitus: a review

et al. 2020

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Diabetes is one of the most common metabolic diseases. Aside from the genetic factor, previous studies stated that other factors such as environment, lifestyle, and paternal-maternal condition play critical roles in diabetes through DNA methylation in specific areas of the genome. One of diabetic cases is caused by insulin resistance and changing the homeostasis of blood glucose control so glucose concentration stood beyond normal rate (hyperglycemia). High fat diet has been frequently studied and linked to triggering diabetes. However, most Asians consume rice (or food with high carbohydrate) and food with monosodium glutamate (MSG). This habit could lead to pathophysiology of type 2 diabetes mellitus (T2D). Previous studies showed that high-carbohydrate or high-MSG diet could change gene expression or modify protein activity in body metabolism. This imbalanced metabolism can lead to pleiotropic effects of diabetes mellitus. In this study, the authors have attempted to relate various changes in genes expression or protein activity to the high-carbohydrate and high-MSG-induced diabetes. The authors have also tried to relate several genes that contribute to pathophysiology of T2D and proposed several ideas of genes as markers and target for curing people with T2D. These are done by investigating altered activities of various genes that cause or are caused by diabetes. These genes are selected based on their roles in pathophysiology of T2D.

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Aberrant PDK4 Promoter Methylation Preceding Hyperglycemia in a Mouse Model

Cited by 4 publications

References 27 publications

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

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

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

The genetic basis of high-carbohydrate and high-monosodium glutamate diet related to the increase of likelihood of type 2 diabetes mellitus: a review

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