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ObjectiveObesity‐associated insulin resistance (IR) is responsible for considerable morbidity and mortality globally. Despite vast genomic data, many areas, from pathogenesis to management, still have significant knowledge gaps. We aimed to characterize visceral adipose tissue (VAT) in obesity and IR through a multi‐omics approach.MethodsWe procured data on VAT samples from the Gene Expression Omnibus (GEO) for the following two groups: 1) populations with obesity (n = 34) versus those without (n = 26); and 2) populations with obesity and IR (n = 15) versus those with obesity but without IR (n = 15). Gene set enrichment, protein‐protein interaction network construction, hub gene identification, and drug‐gene interactions were performed, followed by regulatory network prediction involving transcription factors (TFs) and microRNAs (miRNAs).ResultsInterleukin signaling pathways, cellular differentiation, and regulation of immune response revealed a significant cross talk between VAT and the immune system. Other findings include cancer pathways, neurotrophin signaling, and aging. A total of 10 hub genes, i.e., STAT1, KLF4, DUSP1, EGR1, FOS, JUN, IL2, IL6, MMP9, and FGF9, 24 TFs, and approved hub gene‐targeting drugs were obtained. A total of 10 targeting miRNAs (e.g., hsa‐miR‐155‐5p, hsa‐miR‐34a‐5p) were associated with obesity and IR‐related pathways.ConclusionsOur multi‐omics integration method revealed hub genes, TFs, and miRNAs that can be potential targets for investigation in VAT‐related inflammatory processes and IR, therapeutic management, and risk stratifications.image
ObjectiveObesity‐associated insulin resistance (IR) is responsible for considerable morbidity and mortality globally. Despite vast genomic data, many areas, from pathogenesis to management, still have significant knowledge gaps. We aimed to characterize visceral adipose tissue (VAT) in obesity and IR through a multi‐omics approach.MethodsWe procured data on VAT samples from the Gene Expression Omnibus (GEO) for the following two groups: 1) populations with obesity (n = 34) versus those without (n = 26); and 2) populations with obesity and IR (n = 15) versus those with obesity but without IR (n = 15). Gene set enrichment, protein‐protein interaction network construction, hub gene identification, and drug‐gene interactions were performed, followed by regulatory network prediction involving transcription factors (TFs) and microRNAs (miRNAs).ResultsInterleukin signaling pathways, cellular differentiation, and regulation of immune response revealed a significant cross talk between VAT and the immune system. Other findings include cancer pathways, neurotrophin signaling, and aging. A total of 10 hub genes, i.e., STAT1, KLF4, DUSP1, EGR1, FOS, JUN, IL2, IL6, MMP9, and FGF9, 24 TFs, and approved hub gene‐targeting drugs were obtained. A total of 10 targeting miRNAs (e.g., hsa‐miR‐155‐5p, hsa‐miR‐34a‐5p) were associated with obesity and IR‐related pathways.ConclusionsOur multi‐omics integration method revealed hub genes, TFs, and miRNAs that can be potential targets for investigation in VAT‐related inflammatory processes and IR, therapeutic management, and risk stratifications.image
Pimpled eggs have defective shells, which severely impacts hatching rates and transportation safety. In this study, we constructed single-cell resolution transcriptomic and chromatin accessibility maps from uterine tissues of chickens using single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq). We identified 11 major cell types and characterized their marker genes, along with specific transcription factors (TFs) that determine cell fate. CellChat analysis showed that fibroblasts had the most extensive intercellular communication network and that the chickens laying pimpled eggs had amplified immune-related signaling pathways. Differential expression and enrichment analyses indicated that inflammation in pimpled egg-laying chickens may lead to disruptions in their circadian rhythm and changes in the expression of ion transport-related genes, which negatively impacts eggshell quality. We then integrated TF analysis to construct a regulatory network involving TF–target gene–Gene Ontology associations related to pimpled eggs. We found that the transcription factors ATF3, ATF4, JUN, and FOS regulate uterine activities upstream, while the downregulation of ion pumps and genes associated with metal ion binding directly promotes the formation of pimpled eggs. Finally, by integrating the results of scRNA-seq and scATAC-seq, we identified a rare cell type—ionocytes. Our study constructed single-cell resolution transcriptomic and chromatin accessibility maps of chicken uterine tissue and explored the molecular regulatory mechanisms underlying pimpled egg formation. Our findings provide deeper insights into the structure and function of the chicken uterus, as well as the molecular mechanisms of eggshell formation.
Introduction: The retina is a light-sensitive tissue, and intensive light exposure leads to light-induced retinal damage. It is pointed out that photoreceptor damage is responsible for the decrease in retina function. The aim of this study was to detect the main genes and biological terms which are involved in retinal response to intensive light exposure. Methods: The effect of intensive light on the mouse retina function was searched in the Gene Expression Omnibus (GEO) database. The data of GSE22818 were assessed by the GEO2R program. The significant differentially expressed genes (DEGs) were determined and evaluated via directed protein-protein interaction (PPI) network analysis. The critical significant DEGs were enriched via gene ontology analysis to find the related biological processes, molecular function, and biochemical pathways. Results: Data analysis indicates that the high intensity of light induces gene expression alteration in the retina. 105 significant DEGs were identified as the main responsive genes to light damage in the retina. STAT3, JUN, IL6ST, SOCS3, ATF3, JUNB, FOSL1, CCL2, ICAM1, FGF2, AGT, MYC, LIF, CISH, and EGR1 were introduced as the critical affected genes. STAT3, JUN, IL6ST, SOCS3, and ATF3 and "Positive regulation of the receptor signaling pathway via JAK-STAT" were highlighted as the key elements of molecular events. Conclusion: It can be concluded that regulation of the key DEGs and the dependent biological terms can effectively provide tools to prevent the development of light-induced retinal damage.
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