LncRNA SNHG12 suppresses adipocyte inflammation and insulin resistance by regulating the HDAC9/Nrf2 axis.

Obesity is often associated with low-grade inflammation. The incidence of obesity has increased annually worldwide, which seriously affects human health. A previous study indicated that long noncoding RNA SNHG12 was downregulated in obesity. Nevertheless, the role of SNHG12 in obesity remains to be elucidated. In this study, qRT-PCR, western blot, and ELISA were utilized to examine the gene and protein expression. Flow cytometry was employed to investigate the M2 macrophage markers. RNA pull-down assay and RIP were utilized to confirm the interactions of SNHG12, hnRNPA1, and HDAC9. Eventually, a high-fat diet-fed mouse model was established for in vivo studies. SNHG12 overexpression suppressed adipocyte inflammation and insulin resistance and promoted M2 polarization of macrophages that was caused by TNF-α treatment. SNHG12 interacted with hnRNPA1 to downregulate HDAC9 expression, which activated the Nrf2 signaling pathway. HDAC9 overexpression reversed the effect of SNHG12 overexpression on inflammatory response, insulin resistance, and M2 phenotype polarization. Overexpression of SNHG12 improved high-fat diet-fed mouse tissue inflammation. This study revealed the protective effect of SNHG12 against adipocyte inflammation and insulin resistance. This result further provides a new therapeutic target for preventing inflammation and insulin resistance in obesity.

Gut microbiome combined with metabolomics reveals biomarkers and pathways in central precocious puberty.

BACKGROUND: Central precocious puberty (CPP) is a common disease in prepubertal children and results mainly from disorders in the endocrine system. Emerging evidence has highlighted the involvement of gut microbes in hormone secretion, but their roles and downstream metabolic pathways in CPP remain unknown. METHODS: To explore the gut microbes and metabolism alterations in CPP, we performed the 16S rRNA sequencing and untargeted metabolomics profiling for 91 CPP patients and 59 healthy controls. Bioinformatics and statistical analyses, including the comparisons of alpha and beta diversity, abundances of microbes, were undertaken on the 16S rRNA gene sequences and metabolism profiling. Classifiers were constructed based on the microorganisms and metabolites. Functional and pathway enrichment analyses were performed for identification of the altered microorganisms and metabolites in CPP. RESULTS: We integrated a multi-omics approach to investigate the alterations and functional characteristics of gut microbes and metabolites in CPP patients. The fecal microbiome profiles and fecal and blood metabolite profiles for 91 CPP patients and 59 healthy controls were generated and compared. We identified the altered microorganisms and metabolites during the development of CPP and constructed a machine learning-based classifier for distinguishing CPP. The Area Under Curves (AUCs) of the classifies were ranged from 0.832 to 1.00. In addition, functional analysis of the gut microbiota revealed that the nitric oxide synthesis was closely associated with the progression of CPP. Finally, we investigated the metabolic potential of gut microbes and discovered the genus Streptococcus could be a candidate molecular marker for CPP treatment. CONCLUSIONS: Overall, we utilized multi-omics data from microorganisms and metabolites to build a classifier for discriminating CPP patients from the common populations and recognized potential therapeutic molecular markers for CPP through comprehensive analyses.