&lt;p&gt;&lt;em&gt;CHDH-PNPLA3&lt;/em&gt; Gene–Gene Interactions Predict Insulin Resistance in Children with Obesity&lt;/p&gt;

Chirita-Emandi, Adela; Serban, Costela Lacrimioara; Paul, Corina; Andreescu, Nicoleta; Velea, Iulian; Mihailescu, Alexandra; Serafim, Vlad; Tiugan, Diana-Andreea; Tutac, Paul; Zimbru, Cristian G.; Puiu, Maria; Niculescu, Mihai D.

doi:10.2147/dmso.s277268

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…Obesity has been a common and complex multifactorial disease, which leads to excessive fat accumulation under the combined action of environmental and genetic factors. 24,25 This study revealed the association between FNDC5 (rs16835198), PGC-1α (rs3755863, rs8192678), eating behaviors, physical activity time and overweight or obesity in children and adolescents. There was significant association between FNDC5 (rs16835198) and overweight or obesity, interaction between FNDC5 (rs16835198) and PGC-1α (rs3755863, rs8192678), healthy eating behaviors, moderate-tovigorous physical activity time, and interaction between PGC-1α (rs8192678) and moderate-to-vigorous physical activity time in the occurrence of overweight or obesity in children and adolescents.…”

Section: Discussionmentioning

confidence: 81%

An Association Between FNDC5, PGC-1α Genetic Variants and Obesity in Chinese Children: A Case-Control Study

Wang¹,

Zhang²,

Wu³

et al. 2023

DMSO

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Background Fibronectin type III domain containing protein 5 ( FNDC5 ) gene encodes irisin that regulates adipose tissue metabolism. Peroxide-proliferator-activated receptor γ coactivator 1α ( PGC-1α ) is a powerful promoter of mitochondrial biosynthesis and oxidative metabolism, which plays an important role in inducing heat production and energy consumption of brown fat. PGC1-α expression stimulated an increase in expression of FNDC5 . Purpose The aims of this study were to analyze the association between FNDC5, PGC-1α genetic variants and overweight or obesity in Chinese children and adolescents. Methods A total of 198 children and adolescents with overweight or obesity and 198 children and adolescents with normal weight were screened according to gender and age 1:1. The healthy eating behaviors, moderate-to-vigorous physical activity time were surveyed using food frequency questionnaire and CLASS questionnaire, respectively. Genotypes of FNDC5 and PGC-1α gene were detected using SNaPshot method. Results GT genotype of FNDC 5 (rs16835198) increased the risk of overweight or obesity in boys (OR (95% CI): 1.68 (1.00, 2.93)) based on overdominant model; GG genotype of FNDC 5 (rs16835198) decreased the risk of overweight or obesity in girls and boys (OR (95% CI): 0.45 (0.21,0.97), 0.45 (0.24, 0.83), respectively) based on dominant model; TT genotype of FNDC 5 (rs16835198) increased the risk of overweight or obesity in girls based on recessive model (OR (95% CI): 2.46 (1.19, 5.05)), and based on the additive model (OR (95% CI): 3.82 (1.49, 9.80)). There was significant interaction between FNDC 5 (rs16835198) and PGC -1α (rs3755863, rs8192678), healthy eating behaviors, moderate-to-vigorous physical activity time, interaction between PGC-1α (rs8192678) and moderate-to-vigorous physical activity time in the occurrence of overweight or obesity in Chinese children and adolescents. Conclusion FNDC5 (rs16835198) played an independent or interactive role with PGC -1α (rs3755863, rs8192678), healthy eating behaviors, moderate-to-vigorous physical activity time in the occurrence of overweight or obesity in Chinese children and adolescents.

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Section: Discussionmentioning

confidence: 81%

An Association Between FNDC5, PGC-1α Genetic Variants and Obesity in Chinese Children: A Case-Control Study

Wang¹,

Zhang²,

Wu³

et al. 2023

DMSO

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Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Identification of accurate biomarkers is still particularly urgent for improving the poor survival of chronic obstructive pulmonary disease (COPD) patients. In this investigation, we aimed to identity the potential biomarkers in COPD via bioinformatics and next generation sequencing (NGS) data analysis. In this investigation, the differentially expressed genes (DEGs) in COPD were identified using NGS dataset (GSE239897) from Gene Expression Omnibus (GEO) database. Subsequently, gene ontology (GO) and pathway enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of COPD. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to determine the hub genes, miRNAs and TFs. The receiver operating characteristic (ROC) analysis was performed to determine the diagnostic value of hub genes. A total of 956 overlapping DEGs (478 up regulated and 478 down regulated genes) were identified in the NGS dataset. DEGs were mainly associated with GO functional terms and pathways in cellular response to stimulus. response to stimulus, immune system and neutrophil degranulation. There were 10 hub genes (MYC, LMNA, VCAM1, MAPK6, DDX3X, SHMT2, PHGDH, S100A9, FKBP5 and RPS6KA2) identified by PPI, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis. In conclusion, the DEGs, relative GO terms, pathways and hub genes identified in the present investigation might aid in understanding of the molecular mechanisms underlying COPD progression and provide potential molecular targets and biomarkers for COPD.

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<p><em>CHDH-PNPLA3</em> Gene–Gene Interactions Predict Insulin Resistance in Children with Obesity</p>

Cited by 2 publications

References 52 publications

An Association Between FNDC5, PGC-1α Genetic Variants and Obesity in Chinese Children: A Case-Control Study

An Association Between FNDC5, PGC-1α Genetic Variants and Obesity in Chinese Children: A Case-Control Study

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

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