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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE112899 | GEO | 2019/08/07

REPOSITORIES: GEO

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Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (mailto:nshoresh@broad.mit.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track displays maps of chromatin state generated by the Broad/MGH ENCODE group using ChIP-seq. Chemical modifications (methylation, acetylation) to the histone proteins present in chromatin influence gene expression by changing how accessible the chromatin is to transcription. The ChIP-seq method involves first using formaldehyde to cross-link histones and other DNA-associated proteins to genomic DNA within cells. The cross-linked chromatin is subsequently extracted, mechanically sheared, and immunoprecipitated using specific antibodies. After reversal of cross-links, the immunoprecipitated DNA is sequenced and mapped to the human reference genome. The relative enrichment of each antibody-target (epitope) across the genome is inferred from the density of mapped fragments. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf ChIP-seq: Cells were grown according to the approved ENCODE cell culture protocols. Cells were fixed in 1% formaldehyde and resuspended in lysis buffer. Chromatin was sheared to 200-700 bp using a Diagenode Bioruptor. Solubilized chromatin was immunoprecipitated with antibodies against each of the histone antibodies listed above. Antibody-chromatin complexes were pulled-down using protein A-sepharose (or anti-IgM-conjugated agarose for RNA polymerase II), washed and then eluted. After cross-link reversal and proteinase K treatment, immunoprecipitated DNA was extracted with phenol-chloroform, ethanol precipitated, treated with RNAse and purified. One to ten nanograms of DNA were end-repaired, adapter-ligated and sequenced by Illumina Genome Analyzers as recommended by the manufacturer. Alignment: Sequence reads from each IP experiment were aligned to the human reference genome (GRCh37/hg19) using MAQ with default parameters, except '-C 11' and '-H output_file', which outputs up to 11 additional best matches for each read (if any are found) to a file. This information was used to filter out any read that had more than 10 best matches on the genome. Note: It is likely that instances where multiple reads align to the same position and with the same orientation are due to enhanced PCR amplification of a single DNA fragment. No attempt has been made, however, to remove such artifacts from the data, following ENCODE practices. Signal: Fragment densities were computed by counting the number of reads overlapping each 25 bp bin along the genome. Densities were computed using igvtools count with default parameters (in particular, '-w 25' to set window size of 25 bp and '-f mean' to report the mean value across the window), except for '-e' set to extend the reads to 200 bp, and the .wig output was converted to bigWig using wigToBigWig from the UCSC Kent software package. Peaks: Discrete intervals of ChIP-seq fragment enrichment were identified using Scripture, a scan statistics approach, under the assumption of uniform background signal. All data sets where processed with '-task chip', and with '-windows 100,200,500,1000,5000,10000,100000'. (No mask file nor the '-trim' option have been used.) The resulting called segments were then further filtered to remove intervals that are significantly enriched only because they contain smaller enriched intervals within them. This post-processing step has been implemented using Matlab. The use of the post-processing step allowed very large enriched intervals (of the order of Mbps for H3K27me3, for instance) to be detected, as well as much smaller intervals, without the need to tailor the parameters of Scripture based on prior expectations.

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

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