Alteration of DNA supercoiling serves as a trigger of short-term cold shock repressed genes of E. coli

Abstract

Cold shock adaptability is a key survival skill of gut bacteria of warm-blooded animals. Escherichia coli cold shock responses are controlled by a complex multi-gene, timely-ordered transcriptional program. We investigated its underlying mechanisms. Having identified short-term, cold shock repressed genes, we show that their responsiveness is unrelated to their transcription factors or global regulators, while their single-cell protein numbers' variability increases after cold shock. We hypothesized that some cold shock repressed genes could be triggered by high propensity for transcription locking due to changes in DNA supercoiling (likely due to DNA relaxation caused by an overall reduction in negative supercoiling). Concomitantly, we found that nearly half of cold shock repressed genes are also highly responsive to gyrase inhibition (albeit most genes responsive to gyrase inhibition are not cold shock responsive). Further, their response strengths to cold shock and gyrase inhibition correlate. Meanwhile, under cold shock, nucleoid density increases, and gyrases and nucleoid become more colocalized. Moreover, the cellular energy decreases, which may hinder positive supercoils resolution. Overall, we conclude that sensitivity to diminished negative supercoiling is a core feature of E. coli's short-term, cold shock transcriptional program, and could be used to regulate the temperature sensitivity of synthetic circuits.

Figure 1.

Workflow illustration. (I) Identification of short-term cold shock repressed (CSR) genes from RNA-seq in optimal and cold shock conditions. We also performed flow cytometry of protein levels of 30 cold shock repressed genes using a YFP fusion library (34). (II) Identification of strongly supercoiling sensitive genes by RNA-seq following gyrase inhibition by novobiocin, followed by an assessment of the correlation between the genes’ responses to both novobiocin and cold shock. (III) Measurements of biophysical parameters to estimate cell energy (ATP), morphology, and the engagement of gyrase and RNAP with the nucleoid. (IV) Schematic illustration of cold shock repressed genes behaviour and corresponding kinetic model in optimal and cold shock conditions. In optimal conditions the global state of the DNA is negatively supercoiled (20,115). During cold shock, the promoters’ locking propensity increases, due to DNA relaxation (i.e. reduced overall negative DNA supercoiling), likely caused by reduced topoisomerases’ efficiency, particularly gyrase. The signs ‘−’ and ‘+’ represent local, negative and positive supercoiling, respectively. Created with BioRender.com.

Figure 2.

Effects of temperature shifts on cellular morphology, physiology, and global transcriptional regulators. (A) Growth curves at 10°C, 15°C, 20°C, 25°C and 30°C following a temperature shift, set to be minute 0. (B) Mean RpoS concentration during cold shock (15°C) and optimal conditions after 180 min, and during stationary growth (i.e. after 700 min). (C) Pulse width over time following temperature shifts (Methods Section Bacterial strains, growth conditions, and gene expression measurements). (D) Mean concentration of GyrA, GyrB, TopA, TopB and RpoB proteins over time after shifting temperature to 15°C. The vertical error bars are the standard error of the mean (SEM) from three biological repeats.

Figure 3.

Characterization of cold shock repressed (CSR) genes. (A) Bar plot of the variability, CV², of the fitness of all genes of E. coli’s genome (‘All genes’, dark blue bar), cold shock repressed cohort (‘CSR’, light blue bar), randomly selected cohort (‘Random’, light green bar) and a randomly selected cohort with the same size and same biological function (‘Random EF’, purple bar), where EF stands for ‘equal function’. The inset shows the mean fitness (in %) for each cohort. (B) Distribution of cold shock repressed (CSR) genes in operons. (C) Scatter plot between the |LFC_CS| of pairs of cold shock repressed genes downstream and upstream in the same operon during cold shock. (D) |LFC_CTRL| of cold shock repressed genes downstream in the operon plotted against the |LFC_CTRL| of cold shock repressed genes upstream in the same operon at optimal temperatures. Dashed lines are the null models (Supplementary Section XIX). We performed an ANCOVA test for the null hypothesis that the line and the dashed line are not statistical distinguishable. P-value <0.05 rejects the null hypothesis.

Figure 4.

Relationship between CV² and mean protein numbers over time, at different temperatures. Blue corresponds to cold shock conditions, while green corresponds to optimal conditions. (A) Squared coefficient of variation (CV²) versus mean protein numbers of 30 cold shock repressed genes (Supplementary file X1). Data at 120 and 180 min was merged as they did not differ (Supplementary Figure S15). We performed a 2-sample t-test to test the null hypothesis that Ω at 30°C and 15°C does not differ. The test rejected the null hypothesis (P-value of 0.02). (B) Box plot of relative Ω over time (set to 1 at t = 0 min) at ‘control’ and ‘cold shock’ temperatures (Supplementary File X1). The red line in the box is the median. The distance between the bottom and top of each box is the interquartile range. The vertical black bars are the range between the minimum and maximum value at each moment. For control and cold shock temperatures, we fit the best fitting function. An F-test on the regression model failed to reject the null hypothesis that the first order polynomial does not significantly improve the fitting compared to a 0-order polynomial (P-value = 0.06). The lines are the best-fit functions that maximize R².

Figure 5.

Correlation between empirical and predicted skewness (SKEW). (A) Cold shock temperatures (15°C, 12°C and 10°C). Skewness is predicted using Equation (2) and the empirical values of Ω (Section Short-term responses of cold shock repressed genes can be partially explained by operon organization and by (p)ppGpp sensitivity). (B) Control temperatures (30°C, 25°C and 20°C). Meanwhile, empirical data on skewness is extracted from single-cell distributions obtained by flow cytometry (Supplementary File X1) after being corrected for background noise. Blue dashed line is the estimated lower bound (Supplementary Section XV). Grey circles are data points excluded from the fitting due to being below or crossing the noise floor.

Figure 6.

Nature of the short-term cold shock responses. (A) The three models considered differ in transcription (reaction (1.1) for the one rate-limiting step model, reactions (1.2) for the two rate-limiting steps model, and reactions (1.3) for the ON–OFF model), while having the same reactions for translation and RNA and protein decay (reactions (2), (3) and (4), respectively). The inset shows the conditions that the rate constants must respect to impose identical mean protein numbers for each model. (B) CV² of protein numbers (relative to the one step model) from in silico predictions, assuming the parameter values in Supplementary Table S3. Vertical error bars are the SEM. (C) Scatter plots of |LFC_NOVO| after adding novobiocin (relative to a control condition, absent of novobiocin) versus the |LFC_CS| after shifting to cold shock. The data informs on the 3915 genes (grey circles), for whom there is RNA-seq on both cold shock and novobiocin responses. The blue circles are the 367 cold shock repressed genes. As a null model, we randomized both |LFC| values of each gene (black dashed line). We also created cohorts of randomly selected, non cold shock repressed (non-CSR) genes, whose average mean LFC_CS was similar to that of cold shock repressed genes (violet dots show the example results of 1 of the 1000 randomly assembled cohorts). Best fit lines obtained by OLS. We performed an F-test on the linear regression model, to test for the null hypothesis that the first order polynomial does not significantly improve the fitting compared to a zero order polynomial. If p-values <0.05, the null hypothesis is rejected, and the best fit line is a first order polynomial. (D) Flow cytometry data on the effects of novobiocin over time on Ω. Data best fit by a sigmoid, , where L is the maximum value, x₀ is the sigmoid midpoint, and a is the curve steepness, which we set to 0.1 in order to maximize the R² (we also attempted to fit polynomials up to several orders, but none fitted better). Ω values obtained for each time point, by fitting the single cell data with the function CV² = Ω/M (34,74). Vertical error bars are the SEM.

Figure 7.

Biophysical parameters during cold shock. (A) Nucleoid areas relative to the cell areas following cold shock (CS) over time. The error bars are the SEM. More than 500 cells analyzed per time moment (different cells analyzed in each time moment). (B) Mean ratio of fluorescence intensities from GyrA-YFP inside the nucleoid and the total GyrA-YFP inside the cell. (C) Mean ratio of fluorescence intensities between RpoB-YFP inside the nucleoid and the total RpoB-YFP inside the cell. In (B) and (C) more than 400 cells were analyzed per condition. The red error bars are the SEM and the black error bars are the standard deviation (STD).