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Transcriptome of human T cell stimulated with anti-CD3 antibody.

ABSTRACT: Transcriptome of human T cell stimulated with anti-CD3 antibody.

PROVIDER: PRJNA449397 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR6974030_1.fastq.gz	Fastqsanger.gz
	SRR6974030_2.fastq.gz	Fastqsanger.gz
	SRR6974023_1.fastq.gz	Fastqsanger.gz
	SRR6974023_2.fastq.gz	Fastqsanger.gz
	SRR6974024_1.fastq.gz	Fastqsanger.gz

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Project description:Purpose: The aim of this study is the evaluation of differential gene expression profile by RNA-seq in response to anti-CD3 treatment in CD3 cells obtained from blood samples of healthy donors, to achieve a better understanding of the antibodies molecular mechanisms of action. In this study T cells were treated in the complex PBMC milieu, in a tentative to mimetize the natural ambient that occurs in a clinical administration of therapeutic anti-CD3. Methods: Human peripheral blood mononuclear cells were purified from healthy volunteers blood and were cultured in the presence of the monoclonal antibody OKT3 or a recombinant fragment of humanized anti-CD3 (FvFcR) or recombinant fragment chimeric anti-CD3 (FvFcM). After 72 hours, CD3+ T cells were isolated by negative selection using magnetic beads and total RNA was extracted with the RNeasy kit (Qiagen). Paired-end cDNA reads (150bp) were generated using the HiSeq 2500 Sequencing system (Illumina) located at the NGS Overseas Project Manager, Next Generation Sequencing Division, Macrogen, Inc. (Seoul, Korea). Quality check of the paired-end reads was performed using FASTQC. The reads were aligned to the human genome refence downloaded from the UCSC Genome Browser database using open source segemehl_0_2_0. The aligned files were ordered and indexed using Samtools followed by read count using HTSeq-count. Statistical analysis was done using the R environment for statistical computing. Gene model quantification were performed using the Bioconductor package DESeq2. Results: Using an optimized data analysis workflow, we mapped about 50 million sequence reads per sample to the human genome and identified 7089 transcripts in the T cell stimulated with OKT3, 2425 transcripts with FvFc R and 1406 with FvFc M. RNA-seq data confirmed stable expression of some known housekeeping genes, and 3 of these were validated with qRT-PCR. We found 860 genes there are equally regulated among the treatments, considering a padj ≤ 0.05. Altered expression of 35 genes was confirmed with qRT-PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Conclusions: In conclusion, we had used a deep transcriptome sequencing method for comparing three anti-CD3 antibodies in terms of gene ontology enrichment and immunological markers expression. The present data showed that both recombinant antibodies induced a compatible expression profile, suggesting they could be tested as substitute in human pre-clinical trials. Moreover, the proposed methodology is amenable to be more generally applied for molecular comparison purposes.

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Transcriptome of human T cell stimulated with anti-CD3 antibody.

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