Project description:The gene expression levels in murine bone marrow-derived dendritic cells treated with γ-PGA NPs were examined by oligonucleitide microarray and compared with those in the cells treated with other adjuvants. The gene expression of proinflammatory chemokines, cytokines, and costimulatory molecules was upregulated considerably in DCs treated with γ-PGA NPs. The upregulation pattern was similar to that in DCs treated with LPS but not in DCs treated with unparticulate γ-PGA. The activation of DCs by γ-PGA NPs was confirmed by real-time RT-PCR analysis for the genes related to TLR signaling. The effect of γ-PGA NPs on DCs was not annihilated by treating with polymixin B, an inhibitor of LPS. Furthermore, the immunization of mice with γ-PGA NPs carrying OVA significantly induced Ag-specific CD8+ T cells and Ag-specific production of IL-2, TNF-α, and IFN-γ from the cells. Such activities of γ-PGA NPs were more prominent, when compared to the immunization with OVA plus aluminum hydroxide or OVA plus CFA. These results suggest that γ-PGA NPs induce a CD8+ T cell response through activating innate immunity in a fashion different from that of LPS. Thus, γ-PGA NPs may be an attractive adjuvant to be further developed for vaccine therapy.
Project description:Generation of a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of primary tumor tissue lysates from 86 patients with initially localized renal cell carcinoma (RCC), 75 patients with metastatic RCC treated with sunitinib or pazopanib in the first line and 17 adjacent normal tissues treated at Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic, or University Hospital Pilsen (UHP), Czech Republic, were used to generate the spectral library. Two previously published datasets (dataset A and B) and two newly generated RCC datasets (dataset C and D) were analyzed using the newly generated library showing increased number of quantified peptides and proteins, depending on the size of the library and LC-MS/MS instrumentation. This PRIDE project also includes quantitative analysis results for all four datasets and raw files for dataset C and D. Dataset A is characterized in DOI: 10.1038/nm.3807. It consists of 18 samples from 9 RCC patients involving one cancer and non-cancerous sample per patient. Dataset B is characterized in DOI: 10.3390/biomedicines9091145. It consists of 16 tumor samples and 16 adjacent normal tissues from 16 mRCC patients treated at Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic. Dataset C involves only tumor tissues from dataset B. Half of them responded to sunitinib treatment in the first line three months after treatment initiation and half did not. Dataset D involves 16 RCC patients treated at University Hospital Pilsen (UHP), Czech Republic. All were localized at the time of initial diagnosis, half of the tumors developed distant metastasis in five years after the diagnosis.
Project description:Comparing the performance of methylamine and hydroxylamine on phospho-peptide analysis, particularly in relation to the identification of more complex dose-dependent patterns.
Project description:BV-2 cells treated with different nanoparticles,including rHDL,PEG-PLA NPs,QD,PEG-QD and AuNP.The cells were treated with NPs for 4h.
Project description:Purpose: The goals of this study are to use NGS to perform transcriptome profiling (RNA-seq) to find the changes of the global gene expression programs after viral mimic stimulation in Mouse Embryonic Fibroblasts (MEFs) Methods: MEFs were staimulated by 10 ug/ml Poly(dA:dT) or Poly(I:C) using Lipofectamine 2000 (Invitrogen) as transfection regaent in Opti-MEM medium for 6 hours. Mock groups were treated only with transfection regaent. Total RNA was extracted using RNeasy Mini Kit (Qiagen) according to the manufacturer protocol. Total RNA was used for library construction at the Center for Genomics and Systems Biology, New York University Abu Dhabi, using TruSeq RNA Library Prep Kit v2 (Illumina). Deep-sequencing was performed using Illumina HiSEq 2500 sequencing platform (New York University Abu Dhabi Sequencing Center). Data was processed through the standard RNAseq analysis pipeline at NYUAD. Read aligmnet was performed using tophat2 v2.1.0 against Mus musculus GRCm38.p4 genome version. Raw counts of mapped reads of each genes were derived using HTseq count. DESeq2 was used for differential expression analysis. Results: Using an optimized data analysis workflow, we identified genes up-regulated or down-regulated after anti-viral immunity activation. Gene ontology analysis further identified biological processes, cellular components and molecular functions that were overrepresented in the differentially expressed genes. Conclusions: Our study provides a good reourses for profiling genes regulations after viral immunity activation