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ABSTRACT: Transcriptome analysis of E.coli treated with different antibiotics

PROVIDER: PRJNA510855 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR8356891_1.fastq.gz	Fastqsanger.gz
	SRR8356891_2.fastq.gz	Fastqsanger.gz
	SRR8356892_1.fastq.gz	Fastqsanger.gz
	SRR8356892_2.fastq.gz	Fastqsanger.gz
	SRR8356893_1.fastq.gz	Fastqsanger.gz

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Mode of action characterization of antibiotics using expression profile fingerprints generated by RNA-sequencing technology

Project description:RNA-seq based transcriptome profiling allows a detailed molecular analysis of the E.coli transcriptome (~4200 genes) after perturbation with different antibiotics. Transcriptome fingerprints enable the identification of gene and pathway level responses to treatment to derive a mechanistic understanding of antibiotics mode of action and to differentiate antibiotics. For this goal, treatment has to be performed in a short time period and with sublethal concentrations of the antibiotics. Sublethal concentrations were chosen based on the EC90 concentration of the antibiotic necessary to change the cellular morphology compared to control conditions. Conclusions: Our study represents the first detailed analysis of E.coli transcriptomes after treatment with different antibiotics, with biologic replicates, generated by RNA-seq technology. The experimental design and data analysis reported here should provide a framework for comparative investigations of expression profiles of known and novel antibiotics. 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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Project description: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.

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