Résumé
BACKGROUND:The pathogenesis of osteoporosis is complex,and its essence is the weakening of bone formation and the enhancement of bone absorption caused by various reasons,resulting in the imbalance of bone metabolism.In recent years,N6-methyladenosine has been found(N6-methyladenosine,m6A)methylation can prevent and treat osteoporosis by regulating bone metabolism. OBJECTIVE:Taking the regulation of bone metabolism by m6A methylation as an entry point,to systematically sort out and summarize the research progress of m6A methylation in osteoporosis,so as to provide certain theoretical reference bases for the search of new therapeutic targets for osteoporosis. METHODS:CNKI,WanFang,VIP,PubMed,MEDLINE,Nature,and Cochrane databases were retrieved for relevant literature published from database inception to 2023.The keywords were"osteoporosis,m6A methylation,bone metabolism,bone marrow mesenchymal stem cells,osteoblasts,osteoclasts"in Chinese and English.Duplicates and obsolete non-referenced documents were excluded,and a total of 73 standard papers were included for further review. RESULTS AND CONCLUSION:m6A methylation can affect the activity and differentiation of bone marrow mesenchymal stem cells,osteoblasts,and osteoclasts through various pathways to regulate bone metabolism and prevent osteoporosis.The regulatory process of m6A methylation is extremely complex,and its related proteins play different roles in different cells.Even in the same kind of cells,the same type of proteins may have radically different roles,regulating different physiological and pathological processes.
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Objective:To analyze identification of copper death gene related subtypes,construction of prognosis model and influence of immune infiltration in osteosarcoma(OS)on basis of copper death gene.Methods:Survival and prognosis of OS associated copper death gene were analyzed combining by TARGET and GEO database.OS was divided into different subtypes of copper death by consistent clustering method.SSGSEA was used to analyze difference of immune cells in classification of copper death.Setting P value= 0.05 and q value=0.05,GO and KEGG enrichment analysis were performed on differential genes of copper death typing.Prognosis model was constructed according to results of Lasso regression analysis and cross validation,risk assessment analysis and ROC curve were used to evaluate accuracy of model prediction.Combined with clinical characteristics,nomograms were constructed to predict survival time of patients,and risk differences were analyzed.Immune cell infiltration and tumor microenvironment analysis were performed on OS samples."pRRophetic"package in R software was used to analyze drug sensitivity of OS samples.Results:FDX1,GLS,DLAT and PDHB as high-risk genes for OS prognosis were identified.According to copper death classification of OS samples,OS could be divided into two types:CRGclusterA and CRGclusterB.CRGclusterA was associated with Th2 cells,and CRGclusterB was associated with Th1 cells.Most OS copper death genes were highly expressed in CRGclusterA.Immune cell infiltration analysis results showed that γδ T cells,resting mast cells and resting dendritic cells were positively correlated with risk score,while CD8 T cells were negatively correlated with risk score.Drug sensitivity analysis showed that OS showed higher sensitivity to Elesclomol and GW.441756.Conclusion:Two subtypes of CRGclusterA and CRGclusterB are identified in this study.Four high-risk prognostic genes FDX1,GLS,DLAT and PDHB are identified,providing new insights into prognostic evaluation and immunotherapy target candidates for OS.
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Objective:To explore the molecular mechanism of Danggui Niantong Decoction in the treatment of gouty arthritis (GA) based on network pharmacology and molecular docking.Methods:By selecting for the active components and targets of Danggui Niantong Decoction with TCMSP, and retrieving the GeneCards, OMIM, PharmGKB and DrugBank databases to obtain GA related targets. The potential targets of Danggui Niantong Decoction in the treatment of GA were obtained by the intersection of mappings. The regulation network of Chinese medicine compound and protein-protein interaction network of Danggui Niantong Decoction were constructed by Cytoscape software, and the targets of Danggui Niantong Decoction in the treatment of GA were analyzed by GO and KEGG enrichment by David Database. Finally, molecular docking was performed by using Autodock software.Results:There are 198 active components that could treat GA in Danggui Niantong Decoction. The key active components are Quercetin and Kaempferol. There are 46 key targets, the core targets are NFE2L2, HMOX1, PPARA, PTGS2, IL1β, CXCL8. GO enrichment suggests that the key genes are primarily involved in many biological processes such as Inflammatory response regulation, response to oxidative stress, Fatty acid metabolism process, steroid metabolism, lipopolysaccharide response and reactive oxygen species metabolism. KEGG pathway indicates that Danggui Niantong Decoction mainly acted on IL-17 signal pathway, HIF-1 signal pathway, TNF signal pathway and AGE-RAGE signal pathway. Molecular docking shows that the active components of Danggui Niantong Decoction and action target of GA can combine toghether with high efficiency, and the structure is stable.Conclusion:Danggui Niantong Decoction has multi-component, multi pathway and multi-protein characteristics. Danggui Niantong Decoction can treat GA by regulating immune inflammatory reaction and oxidative stress reaction.
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With the advent of the post-genomic era, metabolic engineering of microorganisms plays an increasingly important role in industrial production. The genome-scale metabolic model (GSMM) integrates all known metabolic information in the organism to provide an optimal platform for global understanding of the metabolic state of the organism and rational guidance for metabolic engineering. As a model strain, Lactococcus lactis NZ9000 plays an important role in industrial fermentation, but there is still no specific genome-scale metabolic model for it. Based on genomic function annotation and comparative genomics, we constructed the first genome-scale metabolic model iWK557 of L. lactis NZ9000, which contains 557 genes, 668 metabolites, and 840 reactions, and further verified at both qualitative and quantitative levels, to provide a good tool for rationally guiding metabolic engineering.