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Next Generation Sequencing Facilitates Quantitative Analysis of E.coli, Wild Type and ΔrecA Transcriptomes

ABSTRACT: Purpose:Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived E.coli transcriptome profiling (RNA-seq) of Wild type and ΔrecA with or without antibiotics treatment. Methods:libraries with different indices were multiplexed and loaded on an Illumina HiSeq instrument, Sequencing was carried out using a 2x150bp paired-end (PE) configuration, image analysis and base calling were conducted by the HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina) on the HiSeq instrument. Main data analysis include Quality Control(Cutadapt), Mapping(Bowtie2 (v2.2.6)),Expression analysis(HTSeq (v0.6.1p1)), Differential expression analysis (DESeq2 Bioconductor package). Results:Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the E.coli genome, The treatment of ampicillin affected the transcriptomic profile of either in the wild type or the ΔrecA strain, compared with that of untreated control cells, with changes to the expression of 4373 and 4286 coding sequences, respectively. Analysis of only the genes with a log2 fold change (log2FC) of ≥ ± 2 showed that 161 and 248 genes were differentially expressed in the ampicillin-treated wild type and ΔrecA strains, respectively. Conclusions:Our study represents the detailed analysis of E.coli transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

ORGANISM(S): Escherichia coli

PROVIDER: GSE179434 | GEO | 2021/07/06

REPOSITORIES: GEO

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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. To understand how ERG1 executes its function during differentiation, we performed RNA-seq experiments in Kcnh2-/- and wild-type rEBs at day 4 Methods: RNA profiles of day 4 wild type (WT) and Kcnh2-/- rEBs were generated by deep sequencing, in duplicate, using Illumina Hiseq. The sequence reads that passed quality filters were analyzed at the transcript isoform level with TopHat followed by Cufflinks. Results: Using an optimized data analysis workflow, we mapped about 35 million sequence reads per sample to the rat genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Kcnh2−/− rESC with TopHat workflow. RNA-seq datashows 211 differentially expressed gene with a fold change ≥2 and p value <0.01, in which 109 genes were downregulated and 102 genes were upregulateds.Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method.Based on GO enrichment analysis results, the deletion of Kcnh2 resulted in the impaired heart development and cell differentiation by inhibiting the expression of heart related genes. KEGG enrichment analysis of all differentially expressed genes yielded the enrichment for several cell differentiation signaling pathways, like PI3K-Akt signaling pathway and Wnt signaling pathway, which is consistent with the experiment results. Conclusions: Our study represents the detailed analysis of transcriptomes of Kcnh2-/- vs. WT rat embryonic stem cell, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

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Next Generation Sequencing Facilitates Quantitative Analysis of E.coli, Wild Type and ΔrecA Transcriptomes

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