This study aimed to explore the temperature-related pathogenic mechanism of Ralstonia solanacearum infection in tomato (Lycopersicon esculentum Mill.). Based on bioinformatics analysis of microarray dataset (GSE33657), the co-differentially expressed genes (co-DEGs) ribonucleic acids were identified in R. solanacearum GMI1000-infected L. esculentum Mill., which was cultured at 20°C and 28°C, in rich medium containing casamino acids, peptone, and glucose (CPG) and planta. In total, 63 upregulated co-DEGs and 57 downregulated co-DEGs were identified between 20°C and 28°C in the CPG and planta groups. Protein–protein interaction network revealed 70 protein interaction pairs and 59 nodes. Notably, iolG, iolE, ioll and RSc1248 played critical roles in the network. The subcellular localization and functional annotation showed that the increased expressed proteins were mainly localized in the inner cell membrane, while those with decreased expression were localized in the cytoplasm. Furthermore, these proteins were mainly enriched in regulation of DNA-templated transcription. RSc1154 and RhlE were predicted to be temperature-related pathogenic genes for R. solanacearum in tomato. Furthermore, phosphorelay signal transduction system function might play an important role in R. solanacearum infection. The candidate genes were verified by quantitative real-time PCR, and the results were consistent with gene expression profile.
We aimed to characterize the stomach adenocarcinoma (STAD) microbiota and its clinical value using an integrated analysis of the microbiome and transcriptome. Microbiome and transcriptome data were downloaded from the Cancer Microbiome Atlas and the Cancer Genome Atlas databases. We identified nine differentially abundant microbial genera, including Helicobacter, Mycobacterium, and Streptococcus, which clustered patients into three subtypes with different survival rates. In total, 74 prognostic genes were screened from 925 feature genes of the subtypes, among which five genes were identified for prognostic model construction, including NTN5, MPV17L, MPLKIP, SIGLEC5, and SPAG16. The prognostic model could stratify patients into different risk groups. The high-risk group was associated with poor overall survival. A nomogram established using the prognostic risk score could accurately predict the 1, 3, and 5 year overall survival probabilities. The high-risk group had a higher proportion of histological grade 3 and recurrence samples. Immune infiltration analysis showed that samples in the high-risk group had a higher abundance of infiltrating neutrophils. The Notch signaling pathway activity showed a significant difference between the high- and low-risk groups. In conclusion, a prognostic model based on five feature genes of microbial subtypes could predict the overall survival for patients with STAD.
In this work, a colorimetric sensor array based on six specific color reactions was developed and used for the determination of sulfur dioxide content. The qualitative and quantitative analysis of sulfur dioxide residues in real samples was achieved.
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