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Next Generation Sequencing of Wild Type and Hnrnpul1-/- RAW 264.7 Cells Transcriptomes

ABSTRACT: Purpose: The goal of this study is to compare NGS-derived wild type and Hnrnpul1 knockout (Hnrnpul1-/-) RAW 264.7 cells transcriptomes with or without LPS stimulation. Methods: Sequancing was performed by Novogene China Co. Ltd. RNA profiles of wild type and Hnrnpul1-/- RAW 264.7 cells as well as LPS stimulated (10 h) wild type and Hnrnpul1-/- RAW 264.7 cells were generated by deep sequencing using Illumina Novaseq 6000. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Method of TMM was used to normalize the readcount. Negative binomial distribution model was used to calculate the P value, and FDR was calculated by the method of Benjaminiand Hochberg. Results: Using an optimized data analysis workflow, we mapped about 40 million sequence reads per sample to the mouse genome (GRCm38/mm10). Comparing to wild type RAW 264.7 cells, 237 genes were up-regulated and 181 genes were down-regulated in Hnrnpul1-/- cells. At 10 h following LPS stimulation, 341 genes were up-regulated and 288 genes were down-regulated in Hnrnpul1-/- cells. Genes were pre-ranked according to log2FoldChange(KO/WT) followed by GSEA and 6 gene sets were significantly enriched. Significantly differential genes were undergone GO analysis (biological process) and biological process including cell-cell adhesion, positive regulation of cell activation and regulation of response to external stimulus were enriched. Conclusions: Lacking Hnrnpul1 promotes the expression of inflammatory cytokines in LPS stimulated RAW 264.7 cells.

ORGANISM(S): Mus musculus

PROVIDER: GSE196232 | GEO | 2022/06/01

REPOSITORIES: GEO

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Project description:Purpose: To obtain an overview of the metabolic changes associated to the absence of KpsM in Synechocystis, in particular on carbon-related metabolic pathways Methods: The trancriptomes of Synechocystis wild type and kpsM mutant were analyzed by RNA sequencing, using three biological replicates. Cultures were grown until reaching an OD730nm of 1.5. Sequencing libraries were generated using NEBNext® Ultra TM RNA Library Prep Kit for Illumina®. PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina). Raw data (raw reads) of FASTQ format were firstly processed through in-house scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter and poly-N sequences and reads with low quality from raw data. Paired-end clean reads were mapped to the reference genome using HISAT2 software.HTSeq was used to count the read numbers mapped of each gene, including known and novel genes. And then RPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. RPKM, Reads Per Kilobase of exon model per Million mapped reads, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels. Differential expression analysis between two conditions/groups (three biological replicates per condition) was performed using DESeq2 R package. DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochberg’s approach for controlling the False Discovery Rate (FDR). Genes with an adjusted P value < 0.05 found by DESeq2 were assigned as differentially expressed. Results: Approximately 700 genes (out of the 3636 identified and 215 quantified, representing a coverage of 85,12%) were significantly differentially expressed (P 216 value < 0.05) in the kpsM mutant compared to the wild type. Of those, 406 were down217 regulated, while 297 were up-regulated. Conclusions: Overall, our transcriptomic data indicates alterations in the mechanisms of energy production and conversion in the kpsM mutant compared to the wild type. The approach resulted in the identification of altered levels of transcripts belonging to a variety of functional categories and highlighting a number of key metabolic processes affected by the disruption of kpsM, namely photosynthesis, oxidative phosphorylation and carbon metabolism.

Project description:Purpose: Analysis of the regulatory network involved in DSTY in lung cancer cells. Methods: mRNA profiles of A549 knockdown cell group and A549 NC group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate .Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 2555 differential genes, of which 1444 were up-regulated and 1111 were down-regulated in A549 sh1 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in inflammation response,chemotaxis,innate immune response, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,JAK-STAT signaling pathway and PI3K-Akt signaling pathways.We identified 2600 differential genes, of which 1523 were up-regulated and 1077 were down-regulated in A549 sh4 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in chemokine receptor binding,nic hydroxy compound metabolic process,alcohol metabolic process, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,glutathione metabolism and chemical carcinogenesis. Conclusion: Based on RNA-seq analysis, the regulatory network involved in DSTY in Lung cancer .

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Next Generation Sequencing of Wild Type and Hnrnpul1-/- RAW 264.7 Cells Transcriptomes

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