Effects of SCN9A gene modification on Na+ channel and the expression of nerve growth factor in a rat model of diarrhea‑predominant irritable bowel syndrome

Cai, Yong‐Yan; Li, Chen; Zhang, Yan; Ma, Na; Li, Fangfang

doi:10.3892/mmr.2017.8061

Cited by 2 publications

(3 citation statements)

References 27 publications

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Section: Discussionmentioning

confidence: 99%

“…MMP9 [133], S100A12 [134], SOCS3 [135], MMP8 [136], CRISP3 [137], S100A9 [138], RETN (resistin) [139], IL1RN [140], TLR4 [141], GGT1 [142], CTSD (cathepsin D) [143], FASLG (Fas ligand) [144], CCR4 [145], FCRL3 [146] and ABCF1 [147] were revealed and regarded as diagnostic biomarker in pancreatitis. Accumulating evidence shows that MMP9 [133], S100A12 [148], HP (haptoglobin) [149], OSM (oncostatin M) [150], PRDM5 [151], TSPO (translocator protein) [152], IL18RAP [153], ADAMDEC1 [154], IL1RN [155], SCN9A [156], FADS2 [157], NCF4 [158], SERPINB1 [159], TNFRSF13B [160], IL2RB [161], DLG5 [162], FASLG (Fas ligand) [163], GPR68 [164], IL2RA [161], TCF4 [165], TAGAP (T cell activation RhoGTPase activating protein) [166], ABCB1 [167], FCRL3 [168], ITGB7 [169], PTGER4 [170] and TRAF3IP2 [171] are altered expression in gastrointestinal complications. Recent studies reported that MMP9 [133], S100A12 [172], IGF2 [173], GPR84 [174], SOCS3 [175], IGFBP2 [176], HP (haptoglobin) [177], MMP8 [178], OSM (oncostatin M) [179], TLR5 [180], S100A9 [181], PLIN5 [182], NRG1 [183], LCN2 [184], TSPO (translocator protein) [185], PLAU (plasminogen activator, urokinase) [186], PPBP (pro-platelet basic protein) [187], UPP1 [188], ALOX5 [189], IL1RN [190], CYP1B1 [191], DGAT2 [192], MSRA (methionine sulfoxidereductase A) [193], ADAM9 [194], CPEB4 [195], IRS2 [196], FADS2 [197], SLC22A4 [198], PFKFB3 [199], CTSD (cathepsin D) […”

Section: Discussionmentioning

confidence: 99%

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Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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“…Additionally, the other genes involved in the muscle contraction pathway, SCN1A , and SCN9A , can affect sodium channel and expression of nerve growth factor (Cai et al . 2018). In this module GH , GHRHR and SCN4A are downstream of GGaluGA198627 which was found to be associated with the MGR trait.…”

Section: Discussionmentioning

confidence: 99%

Identification of new genes and quantitative trait locis associated with growth curve parameters in F2 chicken population using genome‐wide association study

et al. 2021

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Summary The markers which are correlated with the growth curve parameters help in understanding the characteristics of individual growth during the rearing of livestock. This study aimed to identify a set of biomarkers through a GWAS for growth curve parameters in crossbred chickens using the Illumnia 60K chicken SNP Beadchip. Growth data were collected from a total of 301 birds from cross of a broiler line and native chickens. Using the Gompertz–Laird model, two growth curve parameters, the instantaneous growth rate per day (L) and the coefficient of relative growth or maturing index (k), were estimated. The L and k were used to estimate five derived parameters, namely asymptotic (mature) body weight, body weight at inflection point, age at the inflection point, average growth rate and maximum growth rate. These parameters were considered as phenotypic values in the GWAS based on generalized linear models. The results of the GWAS indicated 21 significant markers, which were located near or within 46 genes. A number of these genes, such as GH, RET, GRB14, FTSJ3 and CCK, are important for growth and meat quality in chickens, and some of them are growth related in other species such as sheep and cattle (GPI, XIRP2, GALNTL6, BMS1, THSD4, TRHDE, SHISA9, ACSL6 and DYNC1LI2). The other genes are associated with developmental biological pathways. These genes are particuarly related to body weight, average daily gain and growth QTL. The results of this study can shed light on the genetic mechanism of biological functions of growth factors in broiler chickens, which is useful for developing management practices and accelerating genetic progress in breeding programs.

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Effects of SCN9A gene modification on Na+ channel and the expression of nerve growth factor in a rat model of diarrhea‑predominant irritable bowel syndrome

Cited by 2 publications

References 27 publications

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Identification of new genes and quantitative trait locis associated with growth curve parameters in F2 chicken population using genome‐wide association study

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