O-GlcNAcylation disrupts STRA6-retinol signals in kidneys of diabetes

Chen, Chao-Hung; Lin, Kun‐Der; Ke, Liang‐Yin; Liang, Cher‐Wei; Kuo, Wen-Chen; Lee, Mei‐Yueh; Lee, Yu-Li; Hsiao, Pi‐Jung; Hsu, Chih‐Cheng; Shin, Shyi–Jang

doi:10.1016/j.bbagen.2019.03.014

Cited by 14 publications

(12 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Musante et al [437], Han et al [438], Zhang et al [439], Sato et al [440], Yang et al [441], El-Aouni et al [442], Perisic et al [443], Chung et al [444], Wilkening et al [445], Kahvecioglu et al [446], Hu et al [447] and Worthmann et al [448] revealed that ALB (albumin), KLF15, ATF3, LPL (lipoprotein lipase), CUBN (cubilin), ILK (integrin linked kinase), PLEKHH2, TNF (tumor necrosis factor), CCR2, SPON2, LRRC55 and IGFBP1 might be the potential targets for FSGS diagnosis and treatment. Alves et al [449], Ma et al [450], Esposito et al [451], Ai et al [452], Lu et al [453], Bui et al [454], Bao et al [455], Zuo et al [456], Su et al [457], Wang et al [458], Šalamon et al [459], Harskamp et al [460], Ćwiklińska et al [461], Hishida et al [462], Gunay-Aygun et al [463], Simpson et al [464], Svenningsen et al [465], Wang et al [466], Rao et al [467], Chen et al [468], Bedin et al [469], Cao et al [470], Jang et al [471], de Frutos et al [472], Lin et al [473], Wang et al [474], Liu and Zhuang [475], Feng et al [476], Yu et al [477], Reed et al [478], Milillo et al [479], Chen et al [74], Chen et al [480], Vieira et al [76], Charlton et al [481], Shi et al [482], Zhou et al [483], Nazari et al [241], Greene et al [484], Lazar et al [485], Jobst-Schwan et al [486], Du et al [487], Liu et al [85], Liu et al [488], Liu et al [489], Lee et al [490], Wen et al [491], Sakai et al [492], Zhang et al [493], Wang et al [494], CD5L Castelblanco et al [251], Chen et al [495], Swanson et al [496], Ksiazek, [497], Li et al [498], Zhang et a...…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…A previous study has shown that UBASH3A 92 , SIRPG (signal regulatory protein gamma) 93 , IKZF3 94 , CD1D 95 , CD2 96 , CD48 97 , CD247 98 and CYP27B1 99 are liable for progression of type 1 diabetes mellitus. The studies have shown that expression of SIT1 100 , JAML (junction adhesion molecule like) 101 , TIMP1 102 , PRKCB (protein kinase C beta) 103 , MMP7 104 , WNT7B 105 , WNT10A 106 , DUSP1 107 , WT1 108 , APOC3 109 , ERRFI1 110 , HCN2 111 , MME (membrane metalloendopeptidase) 112 , STRA6 113 , SLC12A3 114 and GC (GC vitamin D binding protein) 115 expedites epithelial to mesenchymal transition and renal brosis in DN. Previous studies have found CFD (complement factor D) 116 , DOCK2 117 , LYZ (lysozyme) 118 , CD5L 119 , SCARA5 120 , VCAN (versican) 121 , GDF5 122 , SFRP2 123 , BTG2 124 , ZFP36 125 , GPR3 126 , OLR1 127 , PM20D1 128 and UGT2B7 129 to be expressed in obesity.…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Joshi

Vastrad²,

Joshi

et al. 2022

Preprint

View full text Add to dashboard Cite

Objectives The underlying molecular mechanisms of diabetic nephropathy (DN) have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of DN. Methods We downloaded next generation sequencing (NGS) dataset GSE142025 from Gene Expression Omnibus (GEO) database having 28 DN samples and 9 normal control samples. The differentially expressed genes (DEGs) between DN and normal control samples were analyzed. Biological function analysis of the DEGs was enriched by GO and REACTOME pathway. Then we established the protein-protein interaction (PPI) network, modules, miRNA-DEG regulatory network and TF-DEG regulatory network. Hub genes were validated by using receiver operating characteristic (ROC) curve analysis. Results A total of 549 DEGs were detected including 275 up regulated and 274 down regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in cell activation, integral component of plasma membrane, lipid binding and biological oxidations. Analyzing the PPI network, miRNA-DEG regulatory network and TF-DEG regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 by the Cytoscape software. The ROC curve analysis confirmed that hub genes were of diagnostic value. Conclusions Taken above, using integrated bioinformatics analysis, we have identified key genes and pathways in DN, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for DN.

show abstract

“…Thus, reduced RAR expression may, at least in part, contribute to high-glucose repression of RA/RAR activity in mIMCD-3 cells. In diabetic mouse kidneys, atRA biosynthesis and atRA-dependent gene expression are compromised 73 , at least in part, due to o-GlcNAcylation of proteins crucial for retinoid binding, metabolism and signalling 74 . Furthermore, a biphasic regulation of renal RA/RAR signalling has been suggested by transcriptomic analysis of renal biopsy tissues of early-and late-stage diabetic nephropathy, respectively 75 .…”

Section: Discussionmentioning

confidence: 99%

Collecting duct cells show differential retinoic acid responses to acute versus chronic kidney injury stimuli

Papadimitriou

Romagnani

Angelotti

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Retinoic acid (RA) activates RA receptors (RAR), resulting in RA response element (RARE)-dependent gene expression in renal collecting duct (CD). Emerging evidence supports a protective role for this activity in acute kidney injury (AKI) and chronic kidney disease (CKD). Herein, we examined this activity in RARE-LacZ transgenic mice and by RARE-Luciferase reporter assays in CD cells, and investigated how this activity responds to neurotransmitters and mediators of kidney injury. In RARE-LacZ mice, Adriamycin-induced heavy albuminuria was associated with reduced RA/RAR activity in CD cells. In cultured CD cells, RA/RAR activity was repressed by acetylcholine, albumin, aldosterone, angiotensin II, high glucose, cisplatin and lipopolysaccharide, but was induced by aristolochic acid I, calcitonin gene-related peptide, endothelin-1, gentamicin, norepinephrine and vasopressin. Compared with age-matched normal human CD cells, CD-derived renal cystic epithelial cells from patients with autosomal recessive polycystic kidney disease (ARPKD) had significantly lower RA/RAR activity. Synthetic RAR agonist RA-568 was more potent than RA in rescuing RA/RAR activity repressed by albumin, high glucose, angiotensin II, aldosterone, cisplatin and lipopolysaccharide. Hence, RA/RAR in CD cells is a convergence point of regulation by neurotransmitters and mediators of kidney injury, and may be a novel therapeutic target.

show abstract

O-GlcNAcylation disrupts STRA6-retinol signals in kidneys of diabetes

Cited by 14 publications

References 34 publications

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

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

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Collecting duct cells show differential retinoic acid responses to acute versus chronic kidney injury stimuli

Contact Info

Product

Resources

About