Background The contribution of mitogen-activated protein kinase (MAPK) cascades to plant growth and development has been widely studied, but this knowledge has not yet been extended to the medicinal plant Salvia miltiorrhiza, which produces a number of pharmacologically active secondary metabolites. Results In this study, we performed a genome-wide survey and identified six MAPKKK kinases (MAPKKKKs), 83 MAPKK kinases (MAPKKKs), nine MAPK kinases (MAPKKs) and 18 MAPKs in the S. miltiorrhiza genome. Within each class of genes, a small number of subfamilies were recognized. A transcriptional analysis revealed differences in the genes’ behaviour with respect to both their site of transcription and their inducibility by elicitors and phytohormones. Two genes were identified as strong candidates for playing roles in phytohormone signalling. A gene-to-metabolite network was constructed based on correlation analysis, highlighting the likely involvement of two of the cascades in the synthesis of two key groups of pharmacologically active secondary metabolites: phenolic acids and tanshinones. Conclusion The data provide insight into the functional diversification and conservation of MAPK cascades in S. miltiorrhiza.
Phenolic acids are the main active ingredients in Salvia miltiorrhiza, which can be used for the treatment of many diseases, particularly cardiovascular diseases. It is known that salicylic acid (SA) can enhance phenolic acid content, but the molecular mechanism of its regulation is still unclear. The nonexpresser of PR genes 1 (NPR1) plays a positive role in the SA signaling pathway. In this study, we identified a SmNPR1 gene that responds to SA induction and systematically investigated its function. We found that SmNPR1 positively affected phenolic acid biosynthesis. Then, we identified a novel TGA transcription factor, SmTGA2, which interacts with SmNPR1. SmTGA2 positively regulates phenolic acid biosynthesis by directly up-regulating SmCYP98A14 expression. After double-gene transgenic analysis and other biochemical assays, it was found that SmNPR1 and SmTGA2 work synergistically to regulate phenolic acid biosynthesis. In addition, SmNPR4 forms a heterodimer with SmNPR1 to inhibit the function of SmNPR1, and SA can alleviate this effect. Collectively, these findings elucidate the molecular mechanism underlying the regulation of phenolic acid biosynthesis by SmNPR1-SmTGA2/SmNPR4 modules and provide novel insights into the study of the SA signaling pathway regulating plant secondary metabolism.
Background The pathogenic mechanisms of children asthma and the relationship between children asthma and children wheeze are complex and not fully understood. The purpose of this study was to identify the pathways and hub genes along with common differentially expressed genes (DEGs) between children asthma and children wheeze, and to explore the specific insights for the clinical asthma and wheeze therapies in children. Methods The GSE123750 dataset was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the children asthma and children wheeze groups were identified using the “Limma” package in the R language. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, along with Gene Set Enrichment Analysis (GSEA), were performed to explore potential signaling pathways associated with the DEGs. Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to identify gene network modules associated with children asthma using microarray data. Functional enrichment analysis was performed on the co-expression genes within specific highlighted modules. Results A total of 334 DEGs were identified in children with asthma compared to those with wheezing. Furthermore, five KEGG pathways, namely Ribosome, Oocyte meiosis, p53 signaling pathway, B cell receptor signaling pathway, and Cellular senescence, exhibited significant enrichment. The protein-protein interaction (PPI) network analysis in Cytoscape highlighted four hub genes: TOP2A, CDK1, CENPA, and KIF11. GSEA results indicated a positive correlation between asthma and down-regulation of early-stage progenitor T lymphocyte genes, up-regulation of plasma cell genes in bone marrow and blood, deadenylation-dependent mRNA decay, and down-regulation of genes in monocytes. The WGCNA analysis identified the pink module as being highly associated with asthma. Genes within this key module were primarily associated with organ or tissue-specific immune response, mucosal immune response, and the ribosome signaling pathway according to GO and KEGG pathway enrichment analyses. Furthermore, a protein-protein interaction (PPI) network analysis using STRING and Cytoscape revealed one hub gene, IMPA1, among the key genes. Conclusions In conclusion, our study not only advances the current understanding of childhood asthma but also provides valuable insights for identifying potential biomarkers to improve early diagnosis of asthma in preschool children with wheezing. These findings can assist clinicians in making informed decisions regarding early treatment interventions.
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