RESUMO
BACKGROUND: The persistent presence of inflammation is a recognized pathogenic mechanisms of diabetic foot ulcers (DFUs). We aimed to investigate the expression of PLIN1 in tissues from DFU patients and assess its potential association with inflammation-induced damage. METHODS: We performed transcriptome sequencing and correlation analysis of the foot skin from patients with or without DFUs. Additionally, we examined the correlation between PLIN1 and related inflammatory indicators by analyzing PLIN1 expression in tissue and serum samples and through high-glucose stimulation of keratinocytes (HaCaT cells). RESULTS: PLIN1 is upregulated in the tissue and serum from DFU patients. Additionally, PLIN1 shows a positive correlation with leukocytes, neutrophils, monocytes, C-reactive protein, and procalcitonin in the serum, as well as IL-1ß and TNF-α in the tissues. Experiments with Cells demonstrated that reduced expression of PLIN1 leads to significantly decreased expression of iNOS, IL-1ß, IL-6, IL-18, and TNF-α. PLIN1 may mediate wound inflammatory damage through the NF-κB signaling pathway. CONCLUSION: Our findings suggest that PLIN1 mediates the inflammatory damage in DFU, offering new prospects for the treatment of DFU.
Assuntos
Diabetes Mellitus , Pé Diabético , Humanos , Pé Diabético/genética , Pé Diabético/patologia , Fator de Necrose Tumoral alfa/genética , Fator de Necrose Tumoral alfa/metabolismo , Pele/patologia , Inflamação/metabolismo , Queratinócitos/metabolismo , Diabetes Mellitus/metabolismo , Perilipina-1/metabolismoRESUMO
BACKGROUND: Perinatal exposure to maternal obesity predisposes offspring to develop obesity later in life. Immune dysregulation in the hypothalamus, the brain center governing energy homeostasis, is pivotal in obesity development. This study aimed to identify key candidate genes associated with the risk of offspring obesity in maternal obesity. METHODS: We obtained obesity-related datasets from the Gene Expression Omnibus (GEO) database. GSE135830 comprises gene expression data from the hypothalamus of mouse offspring in a maternal obesity model induced by a high-fat diet model (maternal high-fat diet (mHFD) group and maternal chow (mChow) group), while GSE127056 consists of hypothalamus microarray data from young adult mice with obesity (high-fat diet (HFD) and Chow groups). We identified differentially expressed genes (DEGs) and module genes using Limma and weighted gene co-expression network analysis (WGCNA), conducted functional enrichment analysis, and employed a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to pinpoint candidate hub genes for diagnosing obesity-associated risk in offspring of maternal obesity. We constructed a nomogram receiver operating characteristic (ROC) curve to evaluate the diagnostic value. Additionally, we analyzed immune cell infiltration to investigate immune cell dysregulation in maternal obesity. Furthermore, we verified the expression of the candidate hub genes both in vivo and in vitro. RESULTS: The GSE135830 dataset revealed 2868 DEGs between the mHFD offspring and the mChow group and 2627 WGCNA module genes related to maternal obesity. The overlap of DEGs and module genes in the offspring with maternal obesity in GSE135830 primarily enriched in neurodevelopment and immune regulation. In the GSE127056 dataset, 133 DEGs were identified in the hypothalamus of HFD-induced adult obese individuals. A total of 13 genes intersected between the GSE127056 adult obesity DEGs and the GSE135830 maternal obesity module genes that were primarily enriched in neurodevelopment and the immune response. Following machine learning, two candidate hub genes were chosen for nomogram construction. Diagnostic value evaluation by ROC analysis determined Sytl4 and Kncn2 as hub genes for maternal obesity in the offspring. A gene regulatory network with transcription factor-miRNA interactions was established. Dysregulated immune cells were observed in the hypothalamus of offspring with maternal obesity. Expression of Sytl4 and Kncn2 was validated in a mouse model of hypothalamic inflammation and a palmitic acid-stimulated microglial inflammation model. CONCLUSION: Two candidate hub genes (Sytl4 and Kcnc2) were identified and a nomogram was developed to predict obesity risk in offspring with maternal obesity. These findings offer potential diagnostic candidate genes for identifying obesity-associated risks in the offspring of obese mothers.