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BACKGROUND:Ferroptosis-related genes have been found to play an important role in the pathogenesis of rheumatoid arthritis.However,there is currently a lack of immune expression of ferroptosis-related signature genes in rheumatoid arthritis and the construction of competing endogenous RNA(CeRNA)interaction networks.Machine learning,as a powerful signature gene selection algorithm based on bioinformatics,can more accurately identify ferroptosis-related signature genes that dominate the pathogenesis of rheumatoid arthritis. OBJECTIVE:To screen ferroptosis-related signature genes in rheumatoid arthritis using bioinformatics and machine learning methods,and to analyze the correlation between ferroptosis-related signature genes and immune infiltration and the construction of CeRNA network of ferroptosis-related signature genes. METHODS:Rheumatoid arthritis-related microarrays were obtained from the GEO database,and ferroptosis-related genes and their differential gene expression were extracted using R language.The differentially expressed genes were screened using machine learning methods.The LASSO regression and SVM-RFE methods were used for signature gene screening,and the genes filtered by both were re-intersected to finally obtain the signature genes in rheumatoid arthritis.Receiver operating characteristic curves were used to assess the accuracy of the screened signature genes for disease diagnosis.Immune infiltration of rheumatoid arthritis and normal synovial tissues was analyzed using the CIBERSORT algorithm,and the correlation between the signature genes and immune cells was analyzed.Finally,the CeRNA network of ferroptosis-related signature genes for rheumatoid arthritis was constructed and the disease signature genes were validated. RESULTS AND CONCLUSION:A total of 150 ferroptosis-related genes in rheumatoid arthritis were obtained,including 55 up-regulated genes and 95 down-regulated genes.GO and KEGG enrichment analyses identified 18 GO significantly correlated entries and 30 KEGG entries respectively,mainly involving metal ion homeostasis,ferric ion homeostasis and oxidative stress response.Machine learning analysis finally identified disease signature genes GABARAPL1 and SAT1.GSEA analysis found that adipocytokine signaling pathway,drug metabolism cytochrome P450,fatty acid metabolism,PPAR signaling pathway,tyrosine metabolism were mainly concentrated when GABARAPL1 was highly expressed,and chemokine signaling pathway,intestinal immune network on IGA production were mainly concentrated when SAT1 was highly expressed.Immune infiltration analysis found that nine immune cells were significantly different in rheumatoid arthritis and normal synovial tissues,in which plasma cells,T-cell CD8,and T-cell follicular helper were highly expressed and the rest were lowly expressed in the disease group.Single gene and immune cell correlation analysis found that GABARAPL1 was positively correlated with dendritic resting cells,activated NK cells,and macrophage M1,with the most significant correlation with dendritic resting cells,while SAT1 was positively correlated with T cell CD4 and γδ T cells and negatively correlated with NK resting cells.GSVA analysis found that SAT1 was upregulated in ascorbic acid and aldehyde metabolism,while downregulated in B-cell receptor signaling pathway,Toll-like receptor signaling pathway,T-cell receptor signaling pathway,and natural killer cell-mediated cytotoxicity.GABARAPL1 showed a down-regulation trend in PPAR signaling pathway,metabolism of nicotinate and nicotinamide,tryptophan metabolism,fatty acid metabolism,and steroid biosynthesis.Sixty long non-code RNAs may play a key role in the development of rheumatoid arthritis.To conclude,the occurrence of rheumatoid arthritis is significantly correlated with the abnormal expression of rheumatoid arthritis-induced ferroptosis-related signature genes,and the signature genes induce disease development via relevant signaling pathways.By analyzing rheumatoid arthritis-related long non-code RNAs-mediated ceRNA networks,potential therapeutic targets and signaling pathways can be identified to further elucidate its pathogenesis and provide a reference basis for subsequent experimental studies.
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Objective The basic biological, echocardiography and gene sequencing parameters of mice overexpressing Slit2 gene (Slit2-Tg mice) were collected and evaluated, and to provide a reference for the application of Slit2-Tg mice in biomedical research. Methods Slit2-Tg and C57BL/6 J mice were inbred. The genotypes of the mice were determined by a PCR assay. The blood samples were collected for blood routine and biochemical tests. The tissues of main organs were collected for protein expression and pathological analysis. Echocardiography and transcriptome sequencing was carried out for analyzing the heart function and gene expression, respectively. Results The litter size was significantly higher in the Slit2-Tg mice than in C57BL/6 J mice. Human Slit2 gene and protein expressions were detected in the main organs of Slit2-Tg mice. Organ coefficient of spleen was significantly increased in Slit2-Tg mice, but the tissue structure appeared normal. There were significant changes in the counts of erythrocytes, platelets, eosinophils, and biochemistry of glucose, globulin, urea nitrogen, triglycerides, HDL, and atherosclerosis index. Echocardiography showed no significant differences in the morphology and function of the Slit2-Tg hearts except in the left ventricular anterior wall thickness at the end-diastolic state. Compared with the C57BL/6 J mice, 535 genes out of 17513 genes in the Slit2-Tg hearts were increased or decreased, mainly involving 15 biological process or signal transduction pathways. Conclusions This study has collected the biological parameters of Slit2-Tg mice and suggests that this model animal is suitable for the studies of cardiovascular diseases.
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Objective To provide a basis for clinical diagnosis,a serum metabonomic dynamic study was carried out on the Tg2576 mouse model at different stages of Alzheimer's disease(AD) whose pathological progress is similar to that of human AD patients.Methods Serum samples of Tg2576 mice were collected at the early(6 months) and late(12 months) stages of Alzheimer's disease.The 1H NMR spectra of the serum samples were collected and the metabolic characteristics were analyzed by multivariate analysis.Results Significant differences in serum metabonomics were found in the transgenic Tg2576 mice and C57 mice at 6 and 12 months of age,and there were significant metabolic changes in Tg2576 mice at different stages of Alzheimer's disease.Compared with C57 mice,the Tg2576 mice at early stage of Alzheimer's disease showed higher levels of serum lactate,myo-inositol and amino acids(such as leucine,isoleucine,alanine),and lower levels of lipids,choline,phosphorylcholine,glycerol phosphorglcholine,betaine,glycine and glucose.At the late stage of Alzheimer's disease,the transgenic Tg2576 mice had higher levels of lactate,myo-inositol and alanine,while the serum levels of lipids,choline,phosphorylcholine,glycerophosphorylcholine,betaine,and glycine continued to drop.Meanwhile glutamine and creatine levels started to decline.By comparing the early and late serum metabolites of Alzheimer's disease,serum metabonomic profiles of the late stage of Alzheimer's disease indicated an up-regulation of lactate,myo-inositol and alanine,and a down-regulation of lipids,choline,phosphorylcholine and glycerophosphorylcholinelevels.Moreover,the levels of lactate,lipids,choline,phosphorylcholine and glycerophosphorylcholine showed statistical significance at the early stage of AD,and they were closely correlated with the severity of Alzheimer's disease.Conclusions The above results show that the changes of lactate,myo-inositol and alanine are positively-correlated with the development of AD,while the serum levels of lipids,choline,phosphorylcholine and glycerophosphorylcholine are inversely-proportional to the severity of AD.These metabolites are dynamically and progressively changed along with the disease progression,which hopefully may serve as early metabolic markers for the diagnosis of AD in clinical practice.
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Objective To establish a mouse model of diethylnitrosamine(DEN)-induced hepatocellular carcinoma (HCC),and to explore the effects of two different diet formulas on the establishment of DEN-induced HCC model.Methods SPF C57BL/6 mice (8 males and 8 females) were injected intraperitoneally with 25 mg/kg DEN at day 14 to establish a HCC model.The mice were divided into two groups after weaning.One group was fed with the SPF class rodents cereal-based diet,another group was fed with AIN-93G formula diet.The mice were sacrificed at the age of 9 months.The livers were weighed and the growth of liver cancer was observed and recorded.Results All the mice in the cereal-based diet group developed HCC as expected.The body weight and liver mass of the mice in the AIN-93G diet group were significantly lower than that of the cereal-based diet group.The incidence of HCC,and the number and size of tumor nodules were also significantly lower in the AIN-93G diet group than that in the cereal-based diet group.Conclusions DEN-induced HCC model has been successfully established in mice fed with cereal-based diet,while mice fed with AIN93-G diet prevented the development of DEN-induced HCC,and their body weight was decreased significantly,suggesting that dietary factors play a key role in establishment of animal disease models.
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ER, and least marked when IR