Sodium periodate efficiently catalyzed the three-component Biginelli reaction of an aldehyde, Į ȕ-keto ester or ȕ-keto ketone, and urea or thiourea at room temperature under solvent-free conditions and afforded the corresponding 3,4-dihydropyrimidine-2-(1H)-ones in excellent yields.
It is well known that as the king of cancer, pancreatic ductal adenocarcinoma (PDAC) has malignant biological behavior and poor prognosis. The interaction between pancreatic stellate cells and PDAC cells promotes PDAC development. The aim of this study was to describe gene characteristics in pancreatic stellate cell (PSCs) after cross-talk with BXPC-3 and unravel their underlying mechanisms. The expression profiling analysis of genes in PSCs was performed after 48 h co-culture with primary BXPC-3. The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis and gene ontology (GO) analysis were performed and the differentially expressed genes (DEGs) were identified by Agilent GeneSpring GX program. In total, 1804 DEGs were filtered out in PSCs, including 958 up-regulated genes and 846 down-regulated genes. GO analysis showed that the up-regulated DEGs were significantly enriched in biological processes (BP) such as defense response, immune system processes and immune response, while the down-regulated DEGs were significantly enriched in biological regulation and cytoskeleton organization. KEGG pathway analysis showed that 28 pathways were up-regulated and 5 were down-regulated. By constructing PPI network, we selected 10 key genes (IL6, IL8, IL1B, BCL2, CCL2, CSF2, KIT, ICAM1, PTPRC and IGF1) and significantly enriched pathways. In conclusion, the current study suggests that the filtered DEGs contribute to our understanding of the molecular mechanisms underlying the interaction between PSCs and pancreatic cancer cells, and might be used as molecular targets to further the study the role of tumor microenvironment in the PDAC progression.
Salmonella enterica ser. Pullorum is one of the most easily re-infecting pathogens in poultry production because of its mechanism of escaping from immune elimination. We used the transcriptome method to investigate the variation in gene expression in chicken spleen resulting from the interaction between hosts and S. Pullorum in the survival process. The expression of various genes related to the maturation and activation of B cells was activated before S. Pullorum was eliminated, which might help S. Pullorum escape from the elimination process. The suppression of some genes involved in the fusion of autophagosomes and lysosomes, such as MYO6, was identified and may be regulated by the secretion systems of S. Pullorum. In addition, a large proportion of these differentially expressed genes could be localized in the identified quantitative trait loci regions associated with the antibody response to bacteria. Collectively, these identified genes provided an outline for further understanding the interaction between chicken immune cells and S. Pullorum in chicken spleen.
Background. The clinical outcomes of low-grade glioma (LGG) are associated with T cell infiltration, but the specific contribution of heterogeneous T cell types remains unclear. Method. To study the different functions of T cells in LGG, we mapped the single-cell RNA sequencing results of 10 LGG samples to obtain T cell marker genes. In addition, bulk RNA data of 975 LGG samples were collected for model construction. Algorithms such as TIMER, CIBERSORT, QUANTISEQ, MCPCOUTER, XCELL, and EPIC were used to depict the tumor microenvironment landscape. Subsequently, three immunotherapy cohorts, PRJEB23709, GSE78820, and IMvigor210, were used to explore the efficacy of immunotherapy. Results. The Human Primary Cell Atlas was used as a reference dataset to identify each cell cluster; a total of 15 cell clusters were defined and cells in cluster 12 were defined as T cells. According to the distribution of T cell subsets (CD4+ T cell, CD8+ T cell, Naïve T cell, and Treg cell), we selected the differentially expressed genes. Among the CD4+ T cell subsets, we screened 3 T cell-related genes, and the rest were 28, 4, and 13, respectively. Subsequently, according to the T cell marker genes, we screened six genes for constructing the model, namely, RTN1, HERPUD1, MX1, SEC61G, HOPX, and CHI3L1. The ROC curve showed that the predictive ability of the prognostic model for 1, 3, and 5 years was 0.881, 0.817, and 0.749 in the TCGA cohort, respectively. In addition, we found that risk scores were positively correlated with immune infiltration and immune checkpoints. To this end, we obtained three immunotherapy cohorts to verify their predictive ability of immunotherapy effects and found that high-risk patients had better clinical effects of immunotherapy. Conclusion. This single-cell RNA sequencing combined with bulk RNA sequencing may elucidate the composition of the tumor microenvironment and pave the way for the treatment of low-grade gliomas.
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