Protein phosphorylation database and prediction tools.

Protein phosphorylation, one of the main protein post-translational modifications, is required for regulating various life activities. Kinases and phosphatases that regulate protein phosphorylation in humans have been targeted to treat various diseases, particularly cancer. High-throughput experimental methods to discover protein phosphosites are laborious and time-consuming. The burgeoning databases and predictors provide essential infrastructure to the research community. To date, >60 publicly available phosphorylation databases and predictors each have been developed. In this review, we have comprehensively summarized the status and applicability of major online phosphorylation databases and predictors, thereby helping researchers rapidly select tools that are most suitable for their projects. Moreover, the organizational strategies and limitations of these databases and predictors have been highlighted, which may facilitate the development of better protein phosphorylation predictors in silico.

Biomarkers associated with severity classification of asthma identified by comprehensive bioinformatics analysis / 中华预防医学杂志

Using an integrated bioinformatics approach to find novel biomarkers that can predict asthma severity. From June 2022 to December 2022, this clinical medical study was conducted and completed in the Department of Allergy, Zhongnan Hospital of Wuhan University. The gene chip dataset GSE43696 was screened and downloaded from the high-throughput Gene Expression Omnibus (GEO) database, and the gene chip data preprocessing was completed using package "affy" in R and "rma" algorithm in turn. Use the the "edgeR" and "limma" packages to screen out the differentially expressed genes (DEGs) between normal controls, mild to moderate asthma patients and severe asthma patients, and then use the "clusterProfiler" package to perform GO enrichment analysis and KEGG pathway enrichment analysis of DEGs, finally use the STRING website to construct a protein-protein interaction (PPI) network of DEGs to further screen key genes. Using the R language "WGCNA" package, the weighted gene co-expression network analysis (WGCNA) was performed on the dataset GSE43696, and the modules significantly related to the severity of asthma were screened out, then the hub genes were obtained by intersecting the WGCNA analysis results with the DEGs screened by PPI. Datasets GSE43696 and GSE63142 were used to verify the expression of hub genes, and the diagnostic value was evaluated according to the ROC curve, then the potential function of hub genes in dataset GSE43696 was further clarified by gene set enrichment analysis (GSEA). The results showed that a total of 251 DEGs were screened, including 39 in the normal group and mild to moderate asthma group, 178 in the normal group and severe asthma group, and 34 in the mild to moderate asthma group and severe asthma group, mainly involved in biological processes such as response to toxic substance, response to oxidative stress, extracellular structure organization, extracellular matrix organization. Two modules significantly correlated with asthma severity were screened out (red module, P=7e-6, r=0.43; pink module, P=5e-8, r=-0.51), and finally six hub genes were obtained, including B3GNT6, CEACAM5, CCK, ERBB2, CSH1 and DPPA5. The comparison of gene expression levels and ROC curve analysis of datasets GSE43696 and GSE63142 further verified the six hub genes, which may associated with o-glycan biosynthesis, alpha linolenic acid metabolism, linoleic acid metabolism, pentose and glucoronate interconversions. In conclusion, through a variety of bioinformatics analysis methods, this study identified six hub genes significantly related to the severity of asthma, which potentially provided a new direction for the prediction and targeted therapy of asthma.

Biomarkers associated with severity classification of asthma identified by comprehensive bioinformatics analysis / 中华预防医学杂志

Using an integrated bioinformatics approach to find novel biomarkers that can predict asthma severity. From June 2022 to December 2022, this clinical medical study was conducted and completed in the Department of Allergy, Zhongnan Hospital of Wuhan University. The gene chip dataset GSE43696 was screened and downloaded from the high-throughput Gene Expression Omnibus (GEO) database, and the gene chip data preprocessing was completed using package "affy" in R and "rma" algorithm in turn. Use the the "edgeR" and "limma" packages to screen out the differentially expressed genes (DEGs) between normal controls, mild to moderate asthma patients and severe asthma patients, and then use the "clusterProfiler" package to perform GO enrichment analysis and KEGG pathway enrichment analysis of DEGs, finally use the STRING website to construct a protein-protein interaction (PPI) network of DEGs to further screen key genes. Using the R language "WGCNA" package, the weighted gene co-expression network analysis (WGCNA) was performed on the dataset GSE43696, and the modules significantly related to the severity of asthma were screened out, then the hub genes were obtained by intersecting the WGCNA analysis results with the DEGs screened by PPI. Datasets GSE43696 and GSE63142 were used to verify the expression of hub genes, and the diagnostic value was evaluated according to the ROC curve, then the potential function of hub genes in dataset GSE43696 was further clarified by gene set enrichment analysis (GSEA). The results showed that a total of 251 DEGs were screened, including 39 in the normal group and mild to moderate asthma group, 178 in the normal group and severe asthma group, and 34 in the mild to moderate asthma group and severe asthma group, mainly involved in biological processes such as response to toxic substance, response to oxidative stress, extracellular structure organization, extracellular matrix organization. Two modules significantly correlated with asthma severity were screened out (red module, P=7e-6, r=0.43; pink module, P=5e-8, r=-0.51), and finally six hub genes were obtained, including B3GNT6, CEACAM5, CCK, ERBB2, CSH1 and DPPA5. The comparison of gene expression levels and ROC curve analysis of datasets GSE43696 and GSE63142 further verified the six hub genes, which may associated with o-glycan biosynthesis, alpha linolenic acid metabolism, linoleic acid metabolism, pentose and glucoronate interconversions. In conclusion, through a variety of bioinformatics analysis methods, this study identified six hub genes significantly related to the severity of asthma, which potentially provided a new direction for the prediction and targeted therapy of asthma.