Reactive Blue 19 samples
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA448637 | ENA |
REPOSITORIES: ENA
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2020-07-22 | GSE153931 | GEO
Project description:The contribution of CD4+ T cells to protective or pathogenic immune responses to SARS-CoV-2 infection remains unknown. Here, we present a large-scale single-cell transcriptomic analysis of viral antigen-reactive CD4+ T cells from 40 COVID-19 patients. In hospitalized patients compared to non-hospitalized patients, we found increased proportions of cytotoxic follicular helper (TFH) cells and cytotoxic T helper cells (CD4-CTLs) responding to SARS-CoV-2, and reduced proportion of SARS-CoV-2-reactive regulatory T cells (TREG). Importantly, in hospitalized COVID-19 patients, a strong cytotoxic TFH response was observed early in the illness which correlated negatively with antibody levels to SARS-CoV-2 spike protein. Polyfunctional T helper (TH)1 and TH17 cell subsets were underrepresented in the repertoire of SARS-CoV-2-reactive CD4+ T cells compared to influenza-reactive CD4+ T cells. Together, our analyses provide so far unprecedented insights into the gene expression patterns of SARS-CoV-2-reactive CD4+ T cells in distinct disease severities.
2020-10-06 | GSE152522 | GEO
Project description:Purpose: In an effort to better understand the mechanism of blue light inhibition in Staphylococcus aureus, a whole transcriptome analysis of S. aureus isolate BUSA2288 was performed using RNA-seq to analyze the differential gene expression in response to blue light exposure. Methods: RNA was extracted from S. aureus cultures pooled from 24 one ml well samples illuminated with a dose of 250 J/cm2 of 465 nm blue light, and from control cultures grown in the dark. Complementary DNA (cDNA) were generated from the RNA samples and then sequenced using the Illumina MiSeq Next Generation Sequencer. The combined results of 2 independent experiments were analyzed using the Pairwise Analysis tools in GeneSifter®. The genes were normalized by Mapped Reads using EdgeR statistics including a Benjamini and Hochberg false discovery rate correction. Quality was set at a minimum number of 10 reads and a p value of 0.05. The lower threshold for change was 5 fold. Results: Transcriptomic comparisons using a cutoff of 5 fold identified a total of 28 down-regulated genes and 6 up-regulated genes in the samples that were exposed to blue light. All but 6 of the differentially regulated genes fall into 8 functional categories: amino acid biosynthesis, cell envelope components, cellular processes, central intermediary metabolism, energy metabolism, protein synthesis, regulatory function, and transport and binding proteins. Five genes encode conserved proteins of unknown function. Conclusions: Blue light has been shown to be bactericidal against S. aureus and is a potential alternative therapy for antibiotic resistant organisms. The mechanism for the inactivation of bacteria is hypothesized to involve ROS. We have found evidence that supports this hypothesis that involves multiple pathways including essential metabolic pathways and known virulence pathways.
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Project description:Proteomics analysis to identify the differential expression of proteins in normal and blue light-exposed ARPE-19 cells
2020-01-22 | PXD017259 | iProX
Project description:Light quality is an important abiotic factor that affects growth and development of photosynthetic organism. In this study, D. salina was exposed to red (660 nm) and blue light (450 nm), and cell growth, pigments, and transcriptome were analyzed. The RNA of D. salina was sequenced and transcriptomic response of algal cells after transitioning from white light to red and blue light was investigated. Genes encoding for enzymes involved in photosynthesis were down-regulated, whereas genes involved in the metabolism of carotenoid were up-regulated. Genes encoding for photoprotective enzymes related to reactive oxygen species scavenging were up-regulated under both red and blue light. The present transcriptomic study would assist in the comprehensive understanding of carotenoid biosynthesis of D. salina.
2020-03-01 | GSE128019 | GEO
Project description:Freshwater samples (Beaches samples) Raw sequence reads
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Project description:We investigated light dependent gene expression changes in the marine ochrophyte Nannochloropsis oceanica CCMP1779. These algae have several putative blue light photoreceptors but appear to lack red light photoreceptors. To study early light signaling in N. oceanica and avoid as much as possible secondary downstream events, we quantified gene expression changes in dark-adapted cells after a short blue or red light pulse. More genes were differentially expressed under blue than under red light. In addition, fold change in expression was smaller for the red light-treated samples. For example, the median fold change of induced genes was 3 for blue light and 2.5 for red light. Moreover, hierarchical cluster analysis showed that gene expression after red light treatment was more similar to the dark control than after blue light treatment.
2022-01-05 | GSE164714 | GEO
Project description:To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) rRNA was removed by using RNase H method, 2) QAIseq FastSelect RNA Removal Kit was used to remove the Globin RNA, 3) The purified fragmented cDNA was combined with End Repair Mix, then add A-Tailing Mix, mix well by pipetting, incubation, 4) PCR amplification, 5) Library quality control and pooling cyclization, 6) The RNA library was sequenced by MGI2000 PE100 platform with 100bp paired-end reads. Analysis steps: 1) RNA-seq raw sequencing reads were filtered by SOAPnuke (Li et al., 2008) to remove reads with sequencing adapter, with low-quality base ratio (base quality < 5) > 20%, and with unknown base (’N’ base) ratio > 5%. 2) Reads aligned to rRNA by Bowtie2 (v2.2.5) (Langmead and Salzberg, 2012) were removed. 3) The clean reads were mapped to the reference genome using HISAT2 (Kim et al., 2015). Bowtie2 (v2.2.5) was applied to align the clean reads to the transcriptome. 4)Then the gene expression level (FPKM) was determined by RSEM (Li and Dewey, 2011). Genes with FPKM > 0.1 in at least one sample were retained.
2021-03-24 | E-MTAB-10298 | biostudies-arrayexpress