SARS-CoV-2 spike protein induces the cytokine release syndrome by stimulating T cells to produce more IL-2
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ABSTRACT:
PROVIDER: PRJNA1010223 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25781527_1.fastq.gz | Fastqsanger.gz | |||
| SRR25781527_2.fastq.gz | Fastqsanger.gz | |||
| SRR25781528_1.fastq.gz | Fastqsanger.gz | |||
| SRR25781528_2.fastq.gz | Fastqsanger.gz | |||
| SRR25781529_1.fastq.gz | Fastqsanger.gz |
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Project description:Cytokine release syndrome (CRS) is one of the leading causes of mortality in COVID-19 patients caused by the SARS-CoV-2 coronavirus. However, the mechanism of CRS induced by SARS-CoV-2 is vague. This study shows that dendritic cells loaded with spike protein of SARS-CoV-2 stimulate T cells to release much more IL-2, which subsequently cooperates with spike protein to facilitate peripheral blood mononuclear cells to release IL-1β, IL-6, and IL-8. The aim of this sequencing is to find the key molecular of IL-2 synergistic spike protein releasing much more inflammatory factors in PBMCs and to explore the molecular mechanism.
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Project description:We have generated a wholly defined spike-in dataset for Agilent microarrays consisting of 12 arrays with more than 2000 differentially expressed, and approximately 3600 background, cRNAs. The composition of this “Ag Spike”dataset is identical to that of our previous Platinum Spike dataset (GSE21344) and therefore allows direct cross-platform comparison. Comparison between the Ag Spike and Platinum Spike studies shows high agreement between results obtained using the Affymetrix and Agilent platforms.
2010-10-23 | GSE24866 | GEO
Project description:We have generated a wholly defined spike-in dataset for Agilent microarrays consisting of 12 arrays with more than 2000 differentially expressed, and approximately 3600 background, cRNAs. The composition of this âAg Spikeâdataset is identical to that of our previous Platinum Spike dataset (GSE21344) and therefore allows direct cross-platform comparison. Comparison between the Ag Spike and Platinum Spike studies shows high agreement between results obtained using the Affymetrix and Agilent platforms. We used the same 28 PCR pools that had been used before to generate the Platinum-Spike dataset (GSE21344). These pools contain PCR products from 5725 Drosophila Gene Collection release 1.0 (DGCr1) cDNA clones. We mixed PCR products from all pools together with specified relative abundance to generate two samples representing the A and B conditions. Both samples were in vitro transcribed and then labeled with Cy3 and Cy5 fluorescent dye. Labeled A samples were hybridized with B samples labeled with reverse dye to three Agilent Drosophila Gene Expression microarrays. The process from in vitro transcription to hybridization was repeated twice. At the end we generated 12 arrays that were randomly placed in three independent slides to remove potential systematic error due to array position.
2010-10-23 | E-GEOD-24866 | biostudies-arrayexpress
Project description:Spike-in normalization is a powerful approach to assess global changes in data obtained from genomic mapping of DNA-associated proteins by methods such as ChIP-sequencing (ChIP-seq) or CUT&RUN. While multiple spike-in methods provide detailed documentation, the implementation of these approaches often omit critical quality control steps and veer from the established procedures. Here, we show that proper application of spike-in normalization can increase quantification accuracy across a spectrum of conditions. Next, we outline how misuse of spike-in approaches can create erroneous biological interpretations and provide guidelines to minimize pitfalls when applying this approach to normalize data from protein-DNA interaction results.
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Project description:Purpose: To elucidate how SARS-CoV-2 spike protein affects macrophage inflammatory responses Methods: Peritoneal macrophages were isolated from Myeloid-specific IKKβ knockout mice (IKKβΔMye) and control littermates (IKKβ F/F). Then the macrophages were treated with lentivirus expressing Spike protein for 24 hours Results: Deficiency of IKKβ nearly abolished spike protein-associated geneset scores and DEGs-associated key inflammatory pathways. Conclusions: These results indicate that SARS-CoV-2 Spike protein induces strong inflammatory responses in macrophages, potentially through activation of canonical IKKβ/NF-κB signaling
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Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.
2012-03-03 | E-GEOD-36217 | biostudies-arrayexpress
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| PRJEB49553 | ENA
Project description:ENCODE Spike-in ChIP-chip Experiments Keywords: ChIP-chip comparison
2006-08-08 | GSE5053 | GEO
Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being “recalibrated” (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration.
2012-03-03 | GSE36217 | GEO