Periostin deficiency attenuates lipopolysaccharide‐ and obesity‐induced adipose tissue fibrosis

Yang, Yong; Zhang, Yanfei; Zhou, Xueliang; Chen, Dandan; Ouyang, Gaoliang; Liu, Yingfu; Cui, Dan

doi:10.1002/1873-3468.14154

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…Signaling pathways include cytokine signaling in immune system [48], extracellular matrix organization [49], diseases of metabolism [50], hemostasis [51], innate immune system [52], metabolism of lipids [53] and metabolism [54] were linked with progression of PCOS. Altered expression of PLA2G5 [55], CASP1 [56], EDNRA (endothelin receptor type A) [57], F2RL1 [58], FOXP3 [59], DRD4 [60], COL6A3 [61], TIMP4 [62], SOCS1 [63], CD74 [64], TGFB1 [65], ATF5 [66], IRF7 [67], IRX3 [68], FOXC2 [69], STX1A [70], IL1RL1 [71], HHIP (hedgehog interacting protein) [72], ELOVL2 [73], BGN (biglycan) [74], POMC (proopiomelanocortin) [75], DOK5 [76], COL1A1 [77], POSTN (periostin) [78], SOD3 [79], ZNF423 [80], FABP5 [81], DDIT4 [82], KCTD15 [83], COL1A2 [84], MGAT2 [85], ENDOG (endonuclease G) [86], HSPA5 [87], CES1 [88], CYP19A1 [89], PLAC8 [90], CD36 [91], GIPR (gastric inhibitory polypeptide receptor) [92], ARG1 [93], MSR1 [94], DOCK2 [95], S1PR1 [96], OXTR (oxytocin receptor) [97], F11R [98], LEPR (leptin receptor) [99], AR (androgen receptor) [100], DKK3 [101], FOXO1 [102], PIK3CB [103], WWP1 [104], PHIP (pleckstrin homology domain interacting protein) [105], MPZL3 [106], FBXO2 [107], GUCY2C [108], ADRA2A [109], CHRNA5 [110], GLP2R [111], SDC3 [112], NFAT5 [113], PON2 [114], PRNP (prion protein) [115], DGKE (diacylglycerol kinase epsilon) [116], ARHGAP21 [117], COQ2 [118], EPHX2 [119] and FAM3C […”

Section: Discussionmentioning

confidence: 99%

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Polycystic ovary syndrome (PCOS) is is associated with infertility, obesity, insulin resistance, hyperinsulinemia, type 2 diabetes mellitus, hypertension, cardiovascular problems, neurological and psychological problems and cancer. The specific mechanism of PCOS and its complications remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of PCOS and its complications. The next generation squancing (NGS) datset GSE199225 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. A protein-protein interaction (PPI) network was constructed and module analysis was performed using HiPPIE and cytoscape. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed for research. The expression of the hub genes was validated using receiver operating characteristic (ROC) curve analysis. We have identified 957 DEGs in total, including 478 up regulated genes and 479 down regulated gene. GO and REACTOME illustrated that DEGs in PCOS were significantly enriched in regulation of molecular function, developmental process, interferon signaling and platelet activation, signaling and aggregation. Finally, through analyzing the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes HSPA5, PLK1, RIN3, DBN1, CCDC85B, DISC1, AR, MTUS2, LYN and TCF4 by the Cytoscape software. This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to PCOS and its complications. These analysis results provide us with novel ideas for finding biomarkers and treatment methods for PCOS and its complications.

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Section: Discussionmentioning

confidence: 99%

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

Vastrad¹,

Vastrad²

2023

Preprint

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“…Among them, MMP9, TIMP1, SPP1, CDC42, COL1A1, COL2A1, COL6A1 and BMP1 were previously correlated with the skeletal development and maturation ( 30 – 35 ). In addition, MMP9, TIMP1, SPP1 and POSTN were shown to play a role in regulating fat metabolism and insulin resistance ( 30 , 36 – 38 ). MMP9, TIMP1, CDC42 and BMP1 were also involved in follicular development ( 39 – 43 ), suggesting that these proteins may be closely related to pubertal development.…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis of proteomics and metabolomics in girls with central precocious puberty

Lan

Chen

2022

Front. Endocrinol.

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BackgroundCentral precocious puberty (CPP) is a multifactorial and complex condition. Traditional studies focusing on a single indicator cannot always elucidate this panoramic condition but these may be revealed by using omics techniques.ObjectiveProteomics and metabolomics analysis of girls with CPP were compared to normal controls and the potential biomarkers and pathways involved were explored.MethodsSerum proteins and metabolites from normal girls and those with CPP were compared by LC-MS/MS. Multivariate and univariate statistical analysis were used to identify the differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs). Functional annotation and pathway enrichment analysis were performed by using GO and KEGG databases, and candidate markers were screened. Finally, bioinformatic analysis was used to integrate the results of proteomics and metabolomics to find the key differential proteins, metabolites and potential biomarkers of CPP.Results134 DEPs were identified in girls with CPP with 71 up- and 63 down-regulated, respectively. Up-regulated proteins were enriched mainly in the extracellular matrix, cell adhesion and cellular protein metabolic processes, platelet degranulation and skeletal system development. The down-regulated proteins were mainly enriched in the immune response. Candidate proteins including MMP9, TIMP1, SPP1, CDC42, POSTN, COL1A1, COL6A1, COL2A1 and BMP1, were found that may be related to pubertal development. 103 DEMs were identified, including 42 up-regulated and 61 down-regulated metabolites which were mainly enriched in lipid and taurine metabolic pathways. KGML network analysis showed that phosphocholine (16:1(9Z)/16:1(9Z)) was involved in arachidonic acid, glycerophospholipid, linoleic acid and α-linolenic acid metabolism and it may be used as a biomarker of CPP.ConclusionsOur study is the first to integrate proteomics and metabolomics to analyze the serum of girls with CPP and we found some key differential proteins and metabolites as well as a potential biomarker for this condition. Lipid metabolism pathways are involved and these may provide a key direction to further explore the molecular mechanisms and pathogenesis of CPP.

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“…ROS1 [250], WNT2 [251], PHLDA2 [252], WNT5A [253], HSD11B2 [254], CLDN1 [255], CYP24A1 [256], HRG (histidine rich glycoprotein) [257], TLR3 [258], THBS1 [259], TRPC6 [260], SELP (selectin P) [261], CLU (clusterin) [262], CDKN1A [263], ITGB1 [264], HLA-E [265], IL6R [266], TIMP1 [267], SERPINA1 [268], CD74 [269], PANX1 [270], STIM1 [271] and CXCR4 [272] were revealed to be correlated with disease outcome in patients with preeclampsia. Previously reported studies have shown that the expression of LBP (lipopolysaccharide binding protein) [273], MC1R [274], CXCL14 [275], RGN (regucalcin) [276], NPY2R [277], MFAP5 [278], WNT5A [279], EDA (ectodysplasin A) [280], THSD7A [281], NEUROD1 [282], SLIT2 [283], PPARGC1A [219], IGF1 [144], OSR1 [284], TLR3 [285], BMP7 [286], POSTN (periostin) [287], LRP1B [288], THBS1 [289], NOTCH2 [290]. LRP1 [291], CLU (clusterin) [292], SMAD3 [91], TGFB1 [293], APP (amyloid beta precursor protein) [294], ITGB2 [295], IL6R [296], TIMP1 [297], CD47 [298], CD74 [299], RARA (retinoic acid receptor alpha) [300], DOCK2 [301], F13A1 [302], IRF7 [303], STIM1 [304], CXCR4 [305], MGLL (monoglyceride lipase) [306], M6PR [307], USP22 [308] and CASP2 [309] were mainly involved in progression of obesity, but these genes might be novel targets for GDM.…”

Section: Discussionmentioning

confidence: 99%

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Gestational diabetes mellitus (GDM) is a metabolic disorder during pregnancy. Numerous biomarkers have been identified that are linked with the occurrence and development of GDM. The aim of this investigation was to identify differentially expressed genes (DEGs) in GDM using a bioinformatics approach to elucidate their molecular pathogenesis. GDM associated expression profiling by high throughput sequencing dataset (GSE154377) was obtained from Gene Expression Omnibus (GEO) database including 28 normal pregnancy samples and 33 GDM samples. DEGs were identified using DESeq2. The gene ontology (GO) and REACTOME pathway enrichments of DEGs were performed by g:Profiler. Protein-protein interaction (PPI) networks were assembled with Cytoscape software and separated into modules using the PEWCC1 algorithm. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were performed with the miRNet database and NetworkAnalyst database. Receiver Operating Characteristic (ROC) analyses was conducted to validate the hub genes. A total of 953 DEGs were identified, of which 478 DEGs were up regulated and 475 DEGs were down regulated. Furthermore, GO and REACTOME pathway enrichment analysis demonstrated that these DEGs were mainly enriched in multicellular organismal process, cell activation, formation of the cornified envelope and hemostasis. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB were identified as key genes in the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB in GDM were validated using ROC analysis. This investigation provides further insights into the molecular pathogenesis of GDM, which might facilitate the diagnosis and treatment of GDM.

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Periostin deficiency attenuates lipopolysaccharide‐ and obesity‐induced adipose tissue fibrosis

Cited by 11 publications

References 31 publications

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

Integrated analysis of proteomics and metabolomics in girls with central precocious puberty

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

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