Project description:Quantitative proteomics in DIA mode was used to analysis the proteomic profile of 24 weeks from the sorafenib treated and vehicle treated monkeys.
Project description:In this study, we investigated how nicotinamide (NAM) affects cellular metabolism in TNBC cells and also such altered cellular metabolism by NAM is linked to cytotoxic functions through an integrated analysis of proteome and transcriptome data generated from three different TNBC cell lines, BT20, MB468, and MB231. By analyzing mRNA-seq data from control and NAM treated TNBC cells (3 replicates in each of 3 cell lines), we identified differentially expressed genes (DEGs), and associated cellular process. The up-regulated genes were associated with the processes related to cell death (programmed cell death, autophagy, and apoptotic mitochondrial changes) and mitochondria (organization, pH reduction/oxidation-reduction/NADP metabolism, response to starvation, respiratory chain, and lipid/oxoacid metabolism). On the other hand, the processes related to immune response (NF-kB and MAPK signaling, cell adhesion/migration, and response to cytokine) and cell proliferation were down-regulated consistently in the three TNBC cells.
Project description:Purpose: We found neutrophil extracellular traps (NETs) can affect the differentiation of naïve CD4+ T cells. This sequencing aims to explore the gene profile change of neutrophil extracellular traps (NETs) on naïve CD4+ T cells. Methods: Total RNA was extracted from the T cells treated with NETs or vehicle for 72h and send to sequencing. Sequencing libraries were generated using NEBNext®UltraTMRNA Library Prep Kit for Illumina® (NEB, USA) and sequenced on an Illumina platform. Paired-end reads were then generated and evaluated for quality control. Clean raw data with high quality were aligned to the reference genome using the Spliced Transcripts Alignment to a Reference (STAR) software. For quantification, FeatureCouts was used to count the read numbers mapped of each gene. Differential expression analysis between two groups (three biological replicates per condition) was performed using DESeq2 R package. Results: Using an optimized data analysis workflow, we mapped around 40 million sequence reads per sample with total mapping rate around 96%. Total 23,249 transcripts in the NETs and Vehicle group were identified. Approximately 7% of the transcripts (1675) showed differential expression between the with p value <0.05. Top differential expressed genes were then enriched based on KEGG and GO database. Altered expression genes in the most enrichment pathways were confirmed with qRT–PCR and western blot, demonstrating the high degree of sensitivity of the RNA-seq method. The top differential expressed genes provide new insigts for the mechanism of T cell differentiation affected by NETs. Conclusions: Our study represents the first detailed analysis of naïve CD4+ T cells treated with NETs. Most differentital expressed genes were enriched in metabolic pahtways, which prompted us to further explore the related mechanism of the novel phenotype.