Tumor-derived microvesicles did not induce apoptosis in Jurkat T-cells. In contrast, NSCLC cell lines down-regulated CD3 epsilon but not CD3 zeta chain expression in Jurkat T-cells; this effect was induced by
soluble factors but not by microvesicles. In lung adenocarcinoma patients, significant decreases of MFI values for CD3 epsilon, but not CD3 zeta, were found in CD4+T and CD8+T cells from pleural effusion compared to peripheral blood and in peripheral blood of patients compared to healthy donors.\n\nConclusions Our data do not support the tumor counterattack hypothesis for NSCLC. Nonetheless, down-regulation of CD3 epsilon in T-cells induced by NSCLC cells might lead to T-cell dysfunction.”
“MicroRNAs (miRNAs) are involved in posttranscriptional gene Cell Cycle inhibitor regulation by repressing the expression
of their target genes through inhibition of translation and/or cleavage of mRNAs. In plants, the target genes of miRNAs usually belong to large gene families, and miRNA regulation is gained for individual genes throughout gene family evolution. To explore the selective effect of miRNA regulation on their target genes, we investigated the pattern of polymorphism and interspecific divergence in 11 multigene families that include target genes of ancient miRNAs in Arabidopsis thaliana. We found that the levels of polymorphism and divergence in target genes are significantly reduced, whereas those for nontarget genes are very similar to the genomic averages. This pattern is particularly clear at synonymous sites; we found that the reduction of MI-503 nmr synonymous variation is caused by selection for optimal codons, which increase BAY 57-1293 mw the efficiency and accuracy of translation. Based on these results, we conclude that tuning via miRNA regulation has a strong impact on the evolution of target genes through which highly sophisticated regulation systems have been established.miRNA system, gene duplication, codon usage bias.”
“Proteins evolve at very different rates and, most notably, at rates inversely
proportional to the level at which they are produced. The relative frequency of highly expressed proteins in the proteome, and thus their impact on the cell budget, increases steeply with growth rate. The maximal growth rate is a key life-history trait reflecting trade-offs between rapid growth and other fitness components. We show that the maximal growth rate is weakly affected by genetic drift. The negative correlation between protein expression levels and evolutionary rate and the positive correlation between expression levels of highly expressed proteins and growth rates, suggest that investment in growth affects the evolutionary rate of proteins, especially the highly expressed ones. Accordingly, analysis of 61 families of orthologs in 74 proteobacteria shows that differences in evolutionary rates between lowly and highly expressed proteins depend on maximal growth rates.