Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli
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ABSTRACT:
PROVIDER: PRJNA881952 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR21622600_1.fastq.gz | Fastqsanger.gz | |||
| SRR21622600_2.fastq.gz | Fastqsanger.gz | |||
| SRR21622601_1.fastq.gz | Fastqsanger.gz | |||
| SRR21622601_2.fastq.gz | Fastqsanger.gz | |||
| SRR21622602_1.fastq.gz | Fastqsanger.gz |
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Project description:Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing under three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of new TF auto-regulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses under different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.
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Project description:Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.
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Project description:Genomic Perturbations Reveal Distinct Regulatory Networks in Intrahepatic Cholangiocarcinoma
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Project description:This dataset contains transcription profiles of TOP10 E.coli transformed with 85 rewired network plasmids first described in: Evolvability and hierarchy in rewired bacterial gene networks. Nature 452:840-5 (2008). The data are described and analysed in an accompanying paper Baumstark et al.,The propagation of perturbations in shuffled bacterial gene networks (Submitted, 2015). 260 raw data microarrays are provided in total, representing biological triplicates (a,b and c; independent colonies grown from the same transformation, under standard growth conditions; LB, 16h). This is done for each of 255 promoter-ORF constructs (e.g. appY-crp, etc.) and 5 control construct microarrays (empty plasmid; Co). Co controls are provided for comparison, to show the relative effect of the rewiring genetic perturbation. The final processed data compares the number of perturbed genes, comparing between the average expression values of each rewired construct and Co. Methods: The 85 rewiring plasmids (Isalan et al, 2008) were transformed into E. coli TOP10 cells and grown under standardised conditions: bacteria were freshly plated onto LB Agar plates (with 100 μg/ml Ampicillin and 50 μg/ml Streptomycin) and incubated overnight at 37oC to form colonies. Single colonies (<3 days old) were used to inoculate 2 ml of LB in 14 ml culture tubes, containing 100 μg/ml Ampicillin and 50 μg/ml Streptomycin. Constructs were grown for 16h at 37oC, at 220 rpm in an orbital shaker. 10 μg of extracted total bacterial RNA (integrity number > 7.0) was used with Affymetrix GeneChip E. coli Genome 2.0 Arrays. Key to names: RAW DATA samples 1-260 _Co_1a.CEL = Control Co, colony a _Co_1b.CEL = Control Co, colony b etc. appY_O_30a.CEL = appY-promoter only, colony a appY_O_30b.CEL = appY-promoter only, colony b etc. appY_crp_34a.CEL = rewired construct, appY-promoter expressing crp ORF, colony a appY_crp_34b.CEL = rewired construct, appY-promoter expressing crp ORF, colony b appY_crp_34b.CEL = rewired construct, appY-promoter expressing crp ORF, colony c etc. PROCESSED DATA sample 261: probe_set_expression_value_norm_all - normalised data for all samples, for all E. coli K12 genes sample 262: probe_set_expression_value_norm_MG1655 - normalised data for all samples, for E. coli MG1655 subset of genes
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