Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Characterization of three CpG ODN classes in plasmacytoid dendritic cells (pDCs)

ABSTRACT: Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare distinct immunostimulatory activities of three classes of CpG ODNs in pDCs using RNA-seq. mRNA profiles of murine pDCs induced by three classes of CpG ODNs were generated by RNA-Seq studies. Methods: mRNA profiles of murine pDCs induced by three classes of CpG ODNs were generated by deep sequencing, in triplicate, using Illumina Hiseq Xten. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: HISAT based on Burrows-Wheeler transform and Ferragina-Manzini and Bowtie2 followed by RSEM. Results: Using an optimized data analysis workflow, we mapped > 60 million total clean reads per sample to the mouse genome (build mm10), with a clean reads Q20 score > 98.58% and a mapping rate to the reference genome of each sample varying from 92.3% to 96.56%. The Pearson correlation coefficient between each sample revealed that CpG-A and CpG-C have the highest correlation. A total of 21,573 genes were detected as being expressed. More than 5,000 genes were downregulated and 2,000 genes were upregulated in CpG ODNs stimulated groups relative to control group, with a fold change ≥ 2 and Q value <0.001. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes uncovered several genes that may contribute to characterize the features of three classes of CpG ODNs in pDCs. Conclusions: Our study represents the first detailed analysis of pDC transcriptomes stimulated with three classes of CpG ODNs, with biological replicates, generated by RNA-seq technology.

ORGANISM(S): Mus musculus

PROVIDER: GSE142688 | GEO | 2022/12/24

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Characterization of three CpG ODN classes in B cells

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare distinct immunostimulatory activities of three classes of CpG ODNs in B cells using RNA-seq. mRNA profiles of murine B cells induced by three classes of CpG ODNs were generated by RNA-Seq studies. Methods: mRNA profiles of murine B cells induced by three classes of CpG ODNs were generated by deep sequencing, in triplicate, using BGISEQ. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: HISAT based on Burrows-Wheeler transform and Ferragina-Manzini and Bowtie2 followed by RSEM. Results: Using an optimized data analysis workflow, we mapped > 60 million total clean reads per sample to the mouse genome (build mm10), with a clean reads Q20 score >97% and a mapping rate to the reference genome of each sample varying from 89.33% to 93.29%. The Pearson correlation coefficient between each sample revealed that the all samples had a high correlation. In particular, CpG-B and CpG-C have the highest correlation. A total of 28,020 genes were detected as being expressed. More than 10,000 genes were downregulated and 1,000 genes were upregulated in CpG ODNs stimulated groups relative to control group, with a fold change ≥ 2 and Q value <0.001. In addition, only 91 genes were upregulated and 99 genes were downregulated in CpG-C group relative to CpG-B group. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes uncovered several genes that may contribute to characterize the features of three classes of CpG ODNs in B cells. Conclusions: Our study represents the first detailed analysis of B-cell transcriptomes stimulated with three classes of CpG ODNs, with biological replicates, generated by RNA-seq technology.

2022-12-24 | GSE142689 | GEO

IRF-5 and IRF-8 govern the response of Human Plasmacytoid Dendritic Cells following TLR9 ligation

2016-06-01 | GSE70429 | GEO

IRF-5 and IRF-8 govern the response of Human Plasmacytoid Dendritic Cells following TLR9 ligation

Project description:Synthetic oligonucleotides (ODNs) containing CpG motifs stimulate human plasmacytoid dendritic cells (pDCs) to produce type-1 interferons (IFN) and pro-inflammatory cytokines. Previous studies demonstrated that interferon regulatory factors (IRFs) played a central role in mediating CpG-induced pDC activation. This work explores the inverse effects of IRF-5 and IRF-8 on CpG dependent gene expression. Results from RNA interference and microarray studies indicate that IRF-5 up-regulates TLR9-driven gene expression whereas IRF-8 down-regulates the same genes. Several findings support the conclusion that IRF-8 inhibits TLR9 dependent gene expression by directly blocking the activity of IRF-5. First, the inhibitory activity of IRF-8 is only observed when IRF-5 is present. Second, proximity ligation analysis shows that IRF-8 and IRF-5 co-localize within the cytoplasm of resting human pDC and co-translocate to the nucleus after CpG stimulation. Taken together, these findings suggest that two transcription factors with opposing functions control TLR9 signaling in human pDCs. CAL-1 cells were transfected with siRNA targeting IRF-5 (IRF-5si) and left unstimulated (n=4, technical repeats) or stimulated with K-type CpG ODN (n=4, technical repeats). CAL-1 cells were transfected with siRNA targeting IRF-8 (IRF-8si) and left unstimulated (n=4, technical repeats) or stimulated with K-type CpG ODN (n=4, technical repeats). CAL-1 cells were transfected with control siRNA (Contsi) and left unstimulated (n=4, technical repeats) or stimulated with K-type CpG ODN (n=4, technical repeats).

2016-06-01 | E-GEOD-70429 | biostudies-arrayexpress

Transcriptomic network of Tulipa gesneriana during petal senescence

Project description:Purpose: We aimed to dissect transcriptomic responses of Tulipa gesneriana during petal senescence and characterize function of senescence related genes. Methods: A total amount of 3 µg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. Index of the Arabidopsis genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis between each two different stages was performed using the DESeq R package (1.18.0). Results:In total, 15 samples with three biological replicates per stage combination were used for RNA sequencing analysis. At least 6 G clean bases were generated for each sample. Comparative analysis identified genes involved in flower development and/or fllower senescence.

2022-11-02 | GSE136183 | GEO

Overexpression of a Medicago truncutula homeodomain finger protein, MtPHD6, enhances drought tolerance in Arabidopsis

Project description:Purpose: We aimed to dissect function of Medicago truncutula homeodomain finger protein, MtPHD6 in Arabidopsis Methods: A total amount of 3 μg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. Index of the Arabidopsis genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of drought stress versus control condition was performed using the DESeq R package (1.18.0). Results:In total, four samples with three biological replicates per genotype/treatment combination were used for RNA sequencing analysis. At least 2 G clean bases were generated for each sample. Comparative analysis revealed different networks which were modulated by drought stress and MtPHD6 transgene in Arabidopsis seedlings

2020-01-08 | GSE134945 | GEO

Correlation between immune lymphoid cells and plasmacytoid DCs in colon cancer

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to use RNA-seq to compare ILC3s and pDCs gene expression in colon cancer. Results: Using an optimized data analysis workflow, More than >60 million clean reads were obtained from each sample group after elimination of low-quality reads. A total of 14,943 in ILC3s and 10,840 in pDCs DEGs were found up-regulated and 4,213 in ILC3s and 11,549 DEGs in pDC s down-regulated on comparison of ILC3s and pDCs from tumor samples and controls samples transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl−/− retina, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusion: These findings highlighting the important roles of ILC3s and pDCs in the processes of tumor progression and inhibition in colon cancer promote the development of new strategies for inducing antitumor immune responses in metastatic and recurrent colon cancer.

2021-03-16 | GSE127934 | GEO

Transcriptomic response of bermudagrass to 7 days of drought, salt, heat and submergence

Project description:Purpose: We aimed to dissect response of bermudagrass to drought, salt, submergence and heat stresses and identify stress responsive genes inbermudagrass. Methods: A total amount of 3 µg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated.Paired-end clean reads were aligned to the reference genome of TAIR10 and rice protein sequence from MSU (version_7.0) using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then RPKM of each gene was calculated . Differential expression analysis of abiotic stress versus control condition was performed using the DESeq R package (1.18.0). Results:In total, 12 samples with two biological replicates per treatment were used for RNA sequencing analysis. At least 2 G clean bases were generated for each sample. Comparative analysis identified genes modulated by different abiotic stress treatments.

2022-11-01 | GSE140362 | GEO

Transcriptome sequencing of 36 rice samples was used to identify the cold tolerance mechanism of rice

Project description:Purpose: In this study, RNA-seq was used to analyzed the molecular mechanism of cold tolerance in rice Methods: Rice 9311 (cold intolerant) and DC907 (cold tolerant) were used as experimental materials for transcriptome sequencing on Illumina HIseq platform. Fastq is used to evaluate the quality of the original data, and NGS QC Toolkit is used to filter the original data to obtain clean reads. Clean reads were compared to the rice 9311 reference genome using HISAT2 software. Put the genome mapping results of all sequenced reads data together, assemble them with cufflinks, and then compare them with known gene models with cuffcompare. The gene expression level of each sample was analyzed by HTSeq software. All treatments were analyzed for differential expression using DEseq. Results: A total of 36 samples were used for RNA sequencing analysis, with three biological replicates per treatment. Each sample generates at least 2G data. The genes regulated by cold stress treatment (1,3 days) and recovery treatment (1,3 days) were determined by comparative analysis.

2022-04-06 | GSE182563 | GEO

Transcriptomic response of bermudagrass to heat stress treatments for 2 and 12h

Project description:Purpose: We aimed to dissect heat response of bermudagrass and identify heat stress responsive genes. Methods: A total amount of 3 µg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. Index of the Arabidopsis genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of drought stress versus control condition was performed using the DESeq R package (1.18.0). Results:In total, six samples with two biological replicates per genotype/treatment combination were used for RNA sequencing analysis. At least 2 G clean bases were generated for each sample. Comparative analysis identified genes modulated by short term heat (2h) and long term heat (12h) treatments.

2021-12-21 | GSE136182 | GEO

Melatonin promotes Arabidopsis root growth in an IAA dependent manner [7d]

Project description:Purpose: We aimed to compare transcriptomic changes after melatonin (MT) and IAA treatments in Arabidopsis and dissected cross-talk between MT and IAA Methods: A total amount of 1 μg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq2000 platform and paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. Index of the Arabidopsis genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of drought stress versus control condition was performed using the DESeq R package (1.18.0). Results:In total, six samples with two biological replicates per genotype/treatment combination were used for RNA sequencing analysis. At least 2 G clean bases were generated for each sample. Comparative analysis identified coregulated genes by melatonin and IAA in Arabidopsis seedlings

2021-08-11 | GSE167524 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data