Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Abstract

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.

Fig. 1. Genome-wide promoter activity profiles of TFKD measured by PPTP-seq.

a Schematic of a regulatory network. Perturbing regulators and the recorded responses of genes are used to infer regulatory interactions. b Reporter plasmids used to quantify promoter activity under CRISPRi-based regulator perturbation. A native promoter was cloned upstream of the gfp gene, and a sgRNA was inserted downstream of a constitutive promoter. c Massively parallel promoter activity measurements for a combinatory library. A combinatory library of more than 2.5 × 10⁵ sgRNA-promoter pairs was sorted into 16 bins according to their GFP expression levels. The sgRNA and promoter regions in each bin were sequenced to estimate perturbed promoter activity for each sgRNA-promoter pair. d Sorted promoter activities of all promoters. The gray and red dots respectively represent promoter activities in strains with TF-targeting sgRNAs and negative control sgRNAs. The black line represents sorted median promoter activities across all TFKD conditions. The blue lines indicate 2-fold changes from the median activities. a.u. arbitrary units. Source data are provided as a Source Data file.

Fig. 2. Genome-wide promoter responses to TFKD in E. coli.

a Promoter activity changes by TFKD. Dashed lines indicate cutoffs for statistically significant (q < 0.01) and substantial (>1.7-fold change) effects. Each dot represents a TF-promoter pair. Upregulation and downregulation by TFKD are shown in red and blue, respectively. A few known interacting TF-promoter pairs are labeled. b Histogram of the number of regulated promoters per TF. Inset in (b) shows histograms over a smaller range. c Histogram of the number of regulating TFs per promoter. d Fractions of constant promoters and variable promoters in each COG category. All COG categories of genes in an operon controlled by a promoter are assigned to the promoter. The dashed line indicates the average fraction of constant promoters over all COG categories. Statistical significance is determined by one-sided Fisher’s exact test. **P < 0.01. Source data are provided as a Source Data file.

Fig. 3. Perturbation effects of transcription factors on their promoters.

a Perturbation-response network of TFs constructed using PPTP-seq data in minimal glucose medium. b Autoregulation of TFs identified by PPTP-seq in minimal glucose medium. Promoter activity fold changes upon the knockdown of TF controlled by the promoter. TF gene names marked in red were selected for validation. Source data are provided in Supplementary Data 5.

Fig. 4. Promoter activity changes in one-carbon metabolism.

a Promoter activity changes in response to metR and metJ knockdown by CRISPRi. Hcy and SAM control the activity of MetR and MetJ, respectively. NA not applicable, KD knockdown, GTP Guanosine-5’-triphosphate, DHPPP 6-hydroxymethyl-7,8-dihydropterin pyrophosphate, PABA para-aminobenzoic acid, DHP dihydropteroate, DHF dihydrofolate, THF tetrahydrofolate, dUMP deoxyuridine monophosphate, dTMP deoxythymidine monophosphate, Met L-methionine, fMet N-formylmethionine, Hcy L-homocysteine, SAM S-adenosylmethionine, SAH S-adenosylhomocysteine, Rib-Hcy S-ribosyl-L-homocysteine. b TF-dependent promoter activity changes for metA, metE, and metK. Each row represents a promoter, and each column stands for a TFKD condition. c Validation of MetR targets. Promoter activities were measured in a metR knockdown strain and, as a control, in a wild-type E. coli strain. Data are presented as means ± SD of three replicates from different days. a.u. arbitrary units. Source data are provided as a Source Data file.

Fig. 5. Integrative analysis of promoter response and TF binding.

a Comparison of TF perturbation-response results from PPTP-seq and TF binding results. b Fraction of TF-promoter pairs that have binding evidence. c Distribution of fraction of regulated promoters with corresponding TFBS for each TF. d–h Factors that may affect whether a potentially bound TF on a promoter affects the promoter activity. For each TF-promoter binding interaction, the binding site location in DAP-seq (d), TF concentration measured by Ribo-seq (e), TF concentration measured by mass spectrometry (f), relative binding strength per TF measured by DAP-seq (g), relative binding strength per TF measured by gSELEX (h), and relative binding strength per promoter measured by DAP-seq (i) were considered. The violin plot shows the distribution of data, the central dot in the box represents the median, the box bounds represent the 25th and 75th percentiles, and whiskers represent the minima to maxima values. The number of TFBSs is indicated below. Benjamini–Hochberg adjusted P-values were calculated by the Wilcoxon rank sum test. Source data are provided in Supplementary Data 6.

Fig. 6. Condition-specific promoter responses to TFKD.

a Comparison of TF perturbation-response results from PPTP-seq at different growth conditions. b Known TF-promoter interactions from RegulonDB showed different regulation under different growth media. Source data are provided as a Source Data file.