Genistein inhibits high fat diet-induced obesity through miR-222 by targeting BTG2 and adipor1

Gan, Mailin; Shen, Linyuan; Wang, Shujie; Guo, Zhixian; Zheng, Ting; Tan, Yimin; Yuan, Fan; Liu, Lin; Chen, Lei; Jiang, Anan; Li, Xuewei; Zhang, Shunhua; Zhu, Li

doi:10.1039/c9fo00861f

Cited by 48 publications

(36 citation statements)

References 45 publications

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“…Husemoen et al [ [94], Tang et al [95], Goodier et al [96], Petyuk et al [97], Roux et al [98], Castrogiovanni et al [99], Suleiman et al [100] [111], Ma et al [112], Chabbert et al [113], Abramsson et al [114], Aeby et al [115] and Roll et al [116] found that the expression of DCC (DCC netrin 1 receptor), PLP1, SNX19, SH3RF1, TNFRSF1A, NCSTN (nicastrin), DGCR2, NPAS2, CDNF (cerebral dopamine neurotrophic factor), SMCR8, HSPA2, STUB1, CHID1, ATP13A2, SQSTM1, LIG3, SP4, ACSL6, ERN1, ATF6B, LRFN2, NRG3, LRRTM3, GABRA2, ADAM30, GABRR2, TSHZ3, LOXL1, SCN1B and SRPX2 are associated with the prognosis of patients with cognitive impairment, but these genes might be novel target for T1DM. Recent studies found that KCP (kielin cysteine rich BMP regulator) [117], NOG (noggin) [118], COL6A3 [119], BTG2 [120], RPS6 [121], KLF15 [122], KLF3 [123], ZFP36 [124], ETV5 [125], TLE3 [126], NNMT (nicotinamide Nmethyltransferase) [127], WDTC1 [128], ZFHX3 [129], SIAH2 [130], MBOAT7 [131], RUNX1T1 [132], MAPK4 [133], KLF9 [134], SELENBP1 [135], HELZ2 [136], ELK1 [137], SERTAD2 [138], CRTC3 [139], ABCB11 [140], TACR1 [141], SLC22A11 [142], PER3 [143], P2RX5 [144], MF...…”

Section: Discussionmentioning

confidence: 99%

“…GABRR2, TSHZ3, LOXL1, SCN1B and SRPX2 are associated with the prognosis of patients with cognitive impairment, but these genes might be novel target for T1DM. Recent studies found that KCP (kielin cysteine rich BMP regulator) [117], NOG (noggin) [118], COL6A3 [119], BTG2 [120], RPS6 [121], KLF15 [122], KLF3 [123], ZFP36 [124], ETV5 [125], TLE3 [126], NNMT (nicotinamide N-methyltransferase) [127], WDTC1 [128], ZFHX3 [129], SIAH2 [130], MBOAT7 [131], RUNX1T1 [132], MAPK4 [133], KLF9 [134], SELENBP1 [135], HELZ2 [136], ELK1 [137], SERTAD2 [138], CRTC3 [139], ABCB11 [140], TACR1 [141], SLC22A11 [142], PER3 [143], P2RX5 [144], MFAP5 [145], FGL1 [146], OLFM4 [147], NTN1 [148], ESR1 [149], ABCB1 [150], VAV3 [151] and LAMB3 [152] plays an important role in the occurrence and development of obesity, but these genes might be novel target for T1DM. STAR (steroidogenic acute regulatory protein) [153], IL1RN [154], AQP5 [155], EGR1 [156], SFTPD (surfactant protein D) [157], KLF10 [158], PODXL (podocalyxin like) [159], FOXN3 [160], IL6R [161], PBX1 [162], APOD (apolipoprotein D) [163], ACVR2B [164], CD34 [165], INSR (insulin receptor) [166], APOA5 [167], STAR (steroidogenic acute regulatory protein) [168], PDK4 [169], GLS (glutaminase) [170], FKBP5 [171], SLC6A15 [172], MT2A [173], SLC38A4 [174], AQP7 [175], ABHD15 [176], ABCA1 [177], ZNRF1 [178], PPP1R3B [179], MAOA (monoamine oxidase A) [180], UBE2E2 [181], RNASEK (ribonuclease K) [182], PREX1 [183], DGKG (diacylglycerol kinase gamma) [184], POS...…”

Section: Discussionmentioning

confidence: 99%

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Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Bm¹,

Cm²

2021

Preprint

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Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were then performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis and RT-PCR confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Bm¹,

Cm²

2021

Preprint

View full text Add to dashboard Cite

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“…It has been reported that adiponectin receptor 1 (adipoR1) is another potential target of miR‐222. [ 43,44 ] Adiponectin is an adipokine that reduces insulin resistance by stimulating fatty acid oxidation and GLUT4 membrane translocation. [ 45 ] Thus, responders could show reduced amount of adipoR1 and thus reduced adiponectin action.…”

Section: Discussionmentioning

confidence: 99%

Inter‐Individual Variability in Insulin Response after Grape Pomace Supplementation in Subjects at High Cardiometabolic Risk: Role of Microbiota and miRNA

Ramos-Romero

Léniz

Martínez-Maqueda

et al. 2020

Molecular Nutrition Food Res

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Scope Dietary polyphenols have shown promising effects in mechanistic and preclinical studies on the regulation of cardiometabolic alterations. Nevertheless, clinical trials have provided contradictory results, with high inter‐individual variability. This study explores the role of gut microbiota and microRNAs (miRNAs) as factors contributing to the inter‐individual variability in polyphenol response. Methods and Results 49 subjects with at least two factors of metabolic syndrome are divided between responders (n = 23) or non‐responders (n = 26), depending on the variation rate in fasting insulin after grape pomace supplementation (6 weeks). The populations of selected fecal bacteria are estimated from fecal deoxyribonucleic acid (DNA) by quantitative real‐time polymerase chain reaction (qPCR), while the microbial‐derived short‐chain fatty acids (SCFAs) are measured in fecal samples by gas chromatography. MicroRNAs are analyzed on a representative sample, followed by targeted miRNA analysis. Responder subjects show significantly lower (p < 0.05) Prevotella and Firmicutes levels, and increased (p < 0.05) miR‐222 levels. Conclusion After evaluating the selected substrates for Prevotella and target genes of miR‐222, these variations suggest that responders are those subjects exhibiting impaired glycaemic control. This study shows that fecal microbiota and miRNA expression may be related to inter‐individual variability in clinical trials with polyphenols.

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“…SIT1 [93], JAML (junction adhesion molecule like) [94], TIMP1 [95], PRKCB (protein kinase C beta) [96], MMP7 [97], WNT7B [98], WNT10A [99], DUSP1 [100], WT1 [101], APOC3 [102], ERRFI1 [103], HCN2 [104], MME (membrane metalloendopeptidase) [105], STRA6 [106], SLC12A3 [107] and GC (GC vitamin D binding protein) [108] expedites epithelial to mesenchymal transition and renal brosis in DN. Previous studies have found CFD (complement factor D) [109], DOCK2 [110], LYZ (lysozyme) [111], CD5L [112], SCARA5 [113], VCAN (versican) [114], GDF5 [115], SFRP2 [116], BTG2 [117], ZFP36 [118], GPR3 [119], OLR1 [120], PM20D1 [121] and UGT2B7 [122] to be expressed in obesity, but these genes might be liable for advancement of DN. Polymorphic FCRL3 [123], FCGR2B [124], COMP (cartilage oligomeric matrix protein) [125], ERFE (erythroferrone) [126] and NPHS1 [127] expression can be altered by in ammation, which might involved in nephropathy.…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in diabetic nephropathy

Joshi

Vastrad²,

Joshi

et al. 2020

Preprint

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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. We selected expression profiling by high throughput sequencing dataset GSE142025 from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between DN and normal control samples were analyzed with limma package. Gene ontology (GO) and REACTOME enrichment analysis were performed using ToppGene. Then we established the protein-protein interaction (PPI) network, miRNA-DEG regulatory network and TF-DEG regulatory network. The diagnostic values of hub genes were performed through receiver operating characteristic (ROC) curve analysis. Finally, the candidate small molecules as potential drugs to treat DM were predicted using molecular docking studies. Through expression profiling by high throughput sequencing dataset, 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, response to hormone, cell surface, integral component of plasma membrane, signaling receptor binding, lipid binding, immunoregulatory interactions between a lymphoid and a non-lymphoid cell and biological oxidations. DEGs with high degree of connectivity (MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1) were selected as hub genes from protein-protein interaction (PPI) network, miRNA-DEG regulatory network and TF-DEG regulatory network. The ROC curve analysis confirmed that hub genes were high diagnostic values. Finally, the significant small molecules were obtained based on molecular docking studies. Our results indicated that MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 could be the potential novel biomarkers for GC diagnosis prognosis and the promising therapeutic targets. The present study may be crucial to understanding the molecular mechanism of DN initiation and progression.

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Genistein inhibits high fat diet-induced obesity through miR-222 by targeting BTG2 and adipor1

Abstract: Genistein may regulate lipid metabolism in adipose tissue of obese mice by regulating the expression of miR-222 and its target genes, BTG2 and adipor1.

Cited by 48 publications

References 45 publications

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Inter‐Individual Variability in Insulin Response after Grape Pomace Supplementation in Subjects at High Cardiometabolic Risk: Role of Microbiota and miRNA

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in diabetic nephropathy

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