Variability in intrinsic promoter strength underlies the temporal hierarchy of the Caulobacter SOS response induction

Aditya Kamat; Asha Mary Joseph; Deeksha Rathour; Anjana Badrinarayanan

doi:10.1371/journal.pbio.3003557

Abstract

Bacteria encode for gene regulatory networks crucial for sensing and repairing DNA damage. Upon exposure to genotoxic stress, these transcriptional networks are induced in a temporally structured manner. A case in point is of the highly conserved SOS response that is regulated by the LexA repressor. Studies have proposed that affinity of LexA towards promoters of SOS response genes is the primary determinant of its expression dynamics. Here, we describe an additional level of regulation beyond LexA box properties that modulates the SOS response gene expression pattern. Using transcriptomic analyses, we reveal a distinct temporal hierarchy in the induction of SOS-regulated genes in Caulobacter crescentus. We observe that LexA box properties are insufficient in predicting the temporal hierarchy of these genes. Instead, we find that intrinsic promoter strength underlies the order of gene activation, with differential sigma factor association as one of the factors modulating gene expression timing. Our findings highlight a novel regulatory layer in SOS dynamics and underscore the importance of promoter properties in shaping bacterial stress responses.

Citation: Kamat A, Joseph AM, Rathour D, Badrinarayanan A (2025) Variability in intrinsic promoter strength underlies the temporal hierarchy of the Caulobacter SOS response induction. PLoS Biol 23(12): e3003557. https://doi.org/10.1371/journal.pbio.3003557

Academic Editor: Erin Danielle Goley, Johns Hopkins University School of Medicine, UNITED STATES OF AMERICA

Received: January 27, 2025; Accepted: November 26, 2025; Published: December 4, 2025

Copyright: © 2025 Kamat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All RNA-sequencing data are deposited in GEO database with accession numbers GSE311073 and GSE246782. All other relevant data are within the paper and its Supporting information files.

Funding: AB acknowledges funding from the India Alliance Intermediate Grant (Grant number IA/I/21/1/505630; https://www.indiaalliance.org). Funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. AB also acknowledges funding from NCBS-TIFR (Grant number 03/3/2019/R&D-II/DAE/4749; https://www.tifr.res.in). The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AB is a member of PLOS Biology’s Editorial Board. The other authors declare that no competing interests exist.

Abbreviations: BWA, Burrows–Wheeler Aligner; CV, coefficient of variance; PCA, principal component analysis; PWM, position weight matrix; TUs, transcriptional units

Introduction

The SOS response is a highly conserved bacterial stress response that is activated by a wide range of DNA-damaging agents. The damage-inducible nature of the SOS gene network emerges from the regulatory interplay of two proteins, RecA (activator) and LexA (repressor) [1]. In the absence of DNA damage, promoters of genes under SOS regulation are bound and repressed by LexA [2]. Under DNA damage, a RecA nucleoprotein filament formed on single-stranded DNA triggers the rapid auto-cleavage of LexA, resulting in de-repression and induction of SOS response genes [3–5].

The SOS paradigm is best studied in Escherichia coli, which constitutes over 50 genes, many of which are involved in enabling cell survival under genotoxic stress [6,7]. These include diverse mechanisms that repair or reverse DNA damage as well as regulators of cell cycle progression [8]. Precise regulation of the SOS response is essential due to the costs associated with expression of mutagenic error-prone translesion polymerases, toxin-antitoxin systems, and prophages that are also induced when this response is activated [9–12]. Despite the possible fitness costs, the response is important for enabling immediate stress tolerance [13–15] and long-term evolutionary adaptations to a variety of growth environments [16–18].

Several studies over the past years have provided seminal insights into two key features of the SOS response and its regulation via LexA: (i). Temporal hierarchy: SOS response genes exhibit hierarchy in the time taken to reach peak promoter activity, with high-fidelity repair mechanisms peaking earlier as compared to low-fidelity (error-prone) mechanisms [6,19]. Based on this, previous studies suggest a “just-in-time” transcription model for the E. coli SOS response timing which mitigates the mutagenic cost of the response via a delay in maximal promoter activity of these genes [19,20]. According to just-in-time transcription [21,22], expression of various genes within a single regulon is temporally separated in a manner that ensures the induction of certain genes only at specific times, likely to avoid any pleiotropic costs. This phenomenon, observed only at high doses of UV damage, has been attributed to the differential binding affinity of LexA towards promoters of the SOS response genes at specific sequences termed “LexA box” [19]. (ii). Spontaneous activation: The SOS response is inherently leaky, and pulses of SOS gene expression occur throughout the population even in the absence of genotoxic stress. This intrinsic leakiness in the absence of DNA damage can be attributed to variability in spontaneous LexA cleavage across the bacterial population [23], also contributing to heterogeneity in SOS gene expression.

While “just-in-time” transcription organization [21,22,24] is an elegant method for distributing costs, it is unclear if this is applicable in other bacterial systems, and whether LexA/LexA box properties alone are sufficient to fully capture the complexities of gene expression under the SOS response. Indeed, some studies have also implicated other factors in shaping the SOS response dynamics. For example, post-induction, SOS response shows an oscillatory/ pulsatile behavior in single cells that appears to be modulated by products of the umuDC operon that constitute the mutagenic polymerase UmuD′₂C in E. coli [25]. More recently, cell growth and division rates have been shown to contribute the SOS gene expression heterogeneity, resulting in sub-populations with distinct SOS expression levels and associated growth dynamics [26].

Here, we study the temporal dynamics of the genes that are induced under the Caulobacter crescentus SOS response under persistent, sub-lethal doses of DNA damage which allows us to estimate gene expression induction times. We find that genes belonging to the Caulobacter SOS response display temporal hierarchy in their time of induction. Our computational analyses reveal that LexA/ LexA box properties do not influence this hierarchy. Instead, we implicate intrinsic promoter strength as a determinant of temporal variation observed in the induction time of the SOS response genes. Our work suggests a role for differential sigma factor affinity as one of the factors shaping the promoter activity and hence, tuning the hierarchy of SOS response induction. This raises the possibility of context-dependent modularity in the SOS response timing that emerges from variation in sigma factor abundance. Together, our work uncovers a simple regulatory feature that influences the timing of the Caulobacter SOS response to DNA damage.

Results

Comparative analysis of the Caulobacter SOS response under mitomycin-C damage

The Caulobacter SOS response has been well-studied [27,28], thus providing an excellent framework for us to evaluate any temporal hierarchies that may be present in gene expression. Towards this, we first carried out an RNA-sequencing experiment with Caulobacter cells exposed to a sub-lethal dose (0.25 µg/ml) of mitomycin-C (MMC), a potent inducer of DNA damage (intra-strand crosslinks and DNA mono-adducts) (S1A Fig). We collected samples at 0 min (control), and at 20 and 40 min post-MMC exposure (Fig 1A). Our experimental design was aimed at capturing gene expression induction dynamics, and hence based on the following rationale: (A) At this concentration and time of damage exposure recA is essential, while survival of wild type cells is not perturbed. (B) Previous studies have shown that the induction of many SOS genes occurs rapidly and peaks by 40–60 min after exposure to DNA damage. Expression also begins to fall after this peak, a characteristic pattern observed for the SOS response [28,29]. It is possible recovery kinetics additionally contribute to the overall transcriptomic profile at later time points. To ascertain the proportion of genes directly under the SOS response, we set three criteria: 1. Genes regulated by LexA should be induced (de-repressed) in the absence of the LexA repressor even without DNA damage, 2. Gene expression should be dependent on RecA in the presence of damage, and 3. Gene promoter regions should be bound by LexA. For testing the first criteria, we made use of a ΔlexAΔsidA background, where the gene that encodes for SOS-induced cell division inhibitor (SidA) is also deleted [28]. A cell devoid of lexA (ΔlexA) exhibits extreme filamentation due to constitutive expression of sidA, while ΔlexAΔsidA strain background does not yield filamentous cells and significantly rescues the growth phenotype of the ΔlexA strain [27,28].

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Fig 1. Comparative analysis of the Caulobacter SOS response under mitomycin-C damage.

(A) Schematic summarizing the RNA-sequencing experiment. Caulobacter cells were treated with 0.25 μg/ml mitomycin-C for 20 and 40 min (Created in BioRender. Badrinarayanan, A. (2025) https://BioRender.com/grydsln). Samples were collected for transcriptomic analysis before (0 min, control), and at 20 and 40 min post damage induction. (B) Venn diagram representing the genes that meet the listed criteria: 1. Genes induced in wild type cells under MMC damage at 40 min (white circle). 2. Genes induced in ΔlexA in the absence of damage (blue circle). 3. Genes not induced in ΔrecA background under MMC damage at 40 min (gray circle). Genes fulfilling all three criteria are in the gray circle. Number of genes in each category is indicated. (C) [Left] Bar graph indicating whether promoters of the shortlisted genes exhibit binding by the LexA protein as assessed from ChIP-seq analysis [10]. [Right] LexA ChIP-seq profile for genes belonging to the SOS response. Normalized reads (in rpm) are represented for 500 bp upstream and downstream of the gene CDS. The underlying data are available in S1 Data. ChIP-seq data were obtained from the GEO database (GSE76721) [10].

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At 20-min post-MMC treatment, we observed that 27 genes exhibited increased expression (log₂FC > 1, p-value < 0.01, FDR < 0.1) (S4 Table). At 40 min of MMC treatment, this number increased to 64. Out of the 64 genes induced under MMC damage, 58 were found to be under LexA repression, since they were induced in the ΔlexAΔsidA background even in the absence of DNA damage (Fig 1B, S4 Table). Genes sidA and lexA did not exhibit induction in the ΔlexAΔsidA background due to poor coverage resulting from their deletion. However, these genes have been previously shown to be under LexA regulation and were thus included in the list of 58 genes de-repressed in the absence of lexA [27].

We corroborated these observations by assessing whether these genes were regulated by recA as well. We observed that MMC-dependent induction of 56 out of 58 LexA-regulated genes was abolished in a ΔrecA background (Fig 1B, S4 Table). We next tested whether cognate promoters of these potential SOS regulon genes were bound by LexA. For this, we analyzed published Caulobacter LexA ChIP-seq data [10], and found that 47 of these genes exhibited LexA binding in their promoter sequence (Fig 1C). These LexA-regulated genes constituted of 31 transcriptional units (TUs) when we took their operonic arrangements into consideration [30].

Comparison of these genes with a previously annotated Caulobacter SOS response (identified via in silico analysis and microarray experiments [27,28]) showed a significant overlap in the identified SOS genes (Figs 1B and S1B). Our analysis also indicated an addition of 14 new genes (S1B Fig), while 9 genes from the previous studies did not qualify our set criteria (S1B and S1C Fig) with no induction under DNA damage, or in lexA deleted cells. As an exception, ccna_02062 (recN) exhibited de-repression in the ΔlexAΔsidA background but narrowly missed the threshold set for induction under damage (log₂FC_recN = 0.95) (S1C Fig). Additionally, one gene with a LexA-bound promoter (ccna_02877) was induced, but did not cross the FDR threshold of 0.1. We thus exclude this gene from the subsequent Caulobacter SOS regulon analyses. Collectively, we identified 47 genes (summarized in Table S5) showcasing key features for SOS genes that are directly regulated by RecA-LexA.

The Caulobacter SOS response exhibits temporal hierarchy

Our RNA-seq analysis suggested that the Caulobacter SOS response is induced in a structured manner. We observed that 17 out of 31 SOS response promoters (~55%) were induced at 20 min post-exposure to MMC. The remaining exhibited induction 40 min post MMC exposure (Fig 2A). To validate the same, we re-examined the MMC DNA damage time-course microarray data set from [28] (S2A Fig). Consistent with our RNA-seq results, genes that were induced early in the RNA-seq showed similar trends in the microarray analysis as well. A majority of these genes showed a robust and rapid induction, reaching a plateau by 40 min after which decrease in expression could also be seen in some cases. Similarly, genes that were induced late in the RNA-seq also showed late but gradual induction in the microarray, also largely plateauing in expression by 60 min. We thus classified promoters as ‘early’ or ‘late’ based on when their induction was detected in the transcriptomic analysis (Figs 2A, 2B, and S2A). Interestingly, we observed that the promoter regulating the imuABC operon associated with SOS-inducible mutagenesis [31] exhibited early induction. On the other hand, uvrA gene linked with relatively error-free nucleotide excision repair was induced late upon MMC exposure.

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Fig 2. The Caulobacter SOS response exhibits temporal hierarchy.

(A) Volcano plot representing induced genes for wild-type cells exposed to MMC for 20 min (top) and 40 min (bottom). Early and late SOS promoters are highlighted in blue and red, respectively. Cut-off for calling a gene as induced is set at log₂FC>1, p-value < 0.01 and FDR < 0.1, respectively, and are indicated by dashed lines in the volcano plots. The underlying data are available in S1 Data. (B) Table listing early and late SOS response promoters highlighted in Fig 2A. (C) Fluorescence intensity normalized to cell area for P_sidAyfp [left] and P_uvrAyfp [right] cells over 6 hours of MMC exposure. Images were taken every 5 min and intensity over time traces are shown. Dashed line indicates the mean and the shaded region indicates the standard deviation. Time to induction (T_ind—time to doubling of mean initial fluorescence intensity) is indicated on the graph. (0.25 μg/mL MMC, n = 25 cells). The underlying data are available in S1 Data.

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To further verify these observations, we measured the induction kinetics of candidate genes for early (sidA, imuA, recA) and late (uvrA, bapE, ruvC) SOS genes using a transcriptional reporter assay. For this, we designed low-copy plasmid-based reporters where yfp was under the transcriptional control of promoters of each of these candidate genes. Fluorescence intensity normalized to cell area was tracked over time in single cells for these promoter fusion constructs upon exposure to MMC. In line with our RNA-seq analysis, we found that the time to induction of early promoters preceded that of late promoters (Figs 2C, S2B, and S2C). This phenomenon was independent of damage concentration since the induction of P_uvrAyfp lagged that of P_sidAyfp over a wide range of MMC concentrations (Fig S2D and S2E). We refer to this phenomenon as the temporal hierarchy of Caulobacter SOS response induction, which we note is distinct from timing associated with the peaking of the response.

LexA box properties are not predictive of the temporal hierarchy in SOS response induction

We next investigated the factors that shape this temporal hierarchy of the Caulobacter SOS response. Based on studies in E. coli, we first assessed whether differences in affinity of the LexA protein towards SOS response promoters could explain the differences in the observed induction times [19]. The degree of divergence of LexA box sequence from the consensus sequence can be scored and has been previously used as a reliable proxy for LexA affinity [32]. LexA boxes with higher divergence exhibit lower affinity as compared to boxes with lower divergence [7,33]. Thus, affinity of LexA towards the SOS promoters can be estimated from variation in the LexA box sequences. Towards this, we first identified cognate LexA boxes for the Caulobacter SOS response genes. Using MEME analysis, we identified the LexA boxes for individual SOS response promoters (S3A and S3B Fig) [34]. The consensus motif agreed with the previously published motif (GTTCN₇GTTC) (Fig S3B) [27].

We conducted principal component analysis (PCA) to test if the LexA box sequences cluster based on temporal gene expression features. The LexA boxes of early and late SOS genes did not show any distinct separation, suggesting no significant difference in their sequences (Fig 3A). We then scored individual LexA boxes via position weight matrix approach for similarity to the consensus motif. Here, too we did not observe any significant difference between the scores for LexA boxes of early versus late SOS genes (Fig 3B). In further support, we found that the LexA peak heights derived from the ChIP-seq dataset did not vary between early and late SOS promoters (S3C Fig). Collectively, these results suggest that differential affinity of LexA proteins for early and late SOS promoters may not be the underlying explanation for the observed graded response.

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Fig 3. LexA box properties are not predictive of the temporal hierarchy in SOS response induction.

(A) PCA analysis of LexA box sequences for early (blue) and late (red) Caulobacter SOS response genes. Percentage variance explained by each compartment are indicated on the axes. The underlying data are available in S1 Data. (B) Box and scatter plot representing scores of LexA box motifs belonging to early and late SOS response genes. Upper and lower limits of the box represent the 25th and 75th percentile of the data, line within the box denotes the median and the box whiskers represent the upper and lower extremes of the data (for this and all other box plots in this study). Scores for individual LexA boxes of the early and late genes are represented by blue and red points, respectively. t test (unpaired), n.s.—not significant. The underlying data are available in S1 Data. (C) [Top] Box and scatter plot indicating distance of LexA box motif from the CDS of early (in blue) and late (in red) SOS response genes. Mann–Whitney test, n.s. —not significant. [bottom] schematic representing calculation of distances between LexA box motifs and its cognate CDS. The underlying data are available in S1 Data. (D) [Top] ccna_01391 and ccna_02355 possess identical LexA box motifs. [bottom] Heat map of log₂FC values from RNA-seq experiments for ccna_01391 and ccna_02355 over 0, 20, and 40 min post MMC exposure in wild type, and ΔlexAΔsidA (no DNA damage) are shown. Fold change value is shown within each square. (E) Fluorescence intensity normalized to cell area for P_{ccna_01391}yfp [left] and P_{ccna_02355}yfp [right] cells over 3 hours of 0.25 μg/ml MMC exposure. Images were taken every 10 min and intensity over time traces are shown. Dashed line indicates the mean and the shaded region indicates the standard deviation. Time to induction (T_ind—time to doubling of mean initial fluorescence intensity) is indicated on the graph. (0.25 μg/mL MMC, n = 25 cells). The underlying data are available in S1 Data.

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We noted that a majority of SOS response promoters (~97%) carried only a single LexA box. We wondered whether features of the LexA boxes other than sequence or copy number could contribute to the SOS response timing. We thus assessed the distance of the LexA box from its CDS. We found that the distribution of LexA box distance from gene start was similar for early or late SOS genes (Fig 3C). Other LexA box features such as spacer length or spacer GC content were invariant as well (S3A and S3D Fig). Thus, LexA box properties do not appear to be good predictor of SOS response temporal hierarchy.

In support, we identified two early SOS response genes, ccna_01391 and ccna_02355 which possess identical LexA box motifs (GTTCttgttatGTTC) (Fig 3D). Despite this, our RNA-seq experiment showed that the induction kinetics of these two genes under MMC exposure were significantly different (Fig 3D). We assessed the induction kinetics of these two promoters further using our transcriptional reporter assay as described above. Consistent with the RNA-seq data, we observed that the ccna_01391 promoter displayed faster induction when compared with ccna_02355 promoter (Fig 3E). Moreover, in the absence of lexA as well, these genes exhibited variability in their induction levels (Fig 3D). Thus, identical LexA sequences can give rise to varied induction kinetics, suggesting the need to look beyond LexA-associated properties of the SOS response genes.

Intrinsic promoter properties contribute to temporal hierarchy in SOS response gene induction

Our observations so far suggested that variation in LexA box properties did not determine the temporal hierarchy of the Caulobacter SOS response. Given that two SOS response genes with identical LexA boxes showed differences in their induction levels even in the absence of lexA, we wondered whether variation in intrinsic promoter properties was a determinant of the observed temporal hierarchy. In this direction, we assessed whether transcription unit (TU) length or gene distance from the origin of replication were sufficient in explaining temporal differences of the SOS response genes. We found that neither of these properties were different between early and late-induced genes (Fig 4A and 4B). We next tested whether variation in promoter strength shaped the temporal hierarchy of the Caulobacter SOS response. Previously, studies have implicated a role for differential affinity of sigma factors towards its cognate promoters as a source of variance in promoter activity [35,36]. We thus asked whether sigma factor association was different between early and late gene promoters. For this, we analyzed published ChIP-seq data of RpoD (the principal sigma factor, σ⁷⁰ with the SOS response promoters [37,38]. We found that RpoD associated with 88% of the early SOS gene promoters (S4A and S4B Fig; Table S6). However, only 50% of late SOS gene promoters had RpoD enrichment in their promoter sequences, with ChIP-seq profiles also showing lower binding affinity when compared to RpoD bound at early gene promoters (S4B Fig, Table S6). We wondered whether late gene promoters are instead associated with alternate sigma factors. In this direction, we found that two late SOS gene promoters (ccna_00139 (chlI), and ccna_02673 (uvrA)) appeared to be under the regulation of the alternative sigma factor σ³² (RpoH). Both genes showed upregulation in an RNA-seq experiment of a Caulobacter strain over-expressing a stable mutant of σ³² (rpoH^V65A) (S4D Fig) [35]. Moreover, analyzing σ³² ChIP-seq data also revealed σ³² enrichment at the ccna_02673 (uvrA) promoter (S4D Fig) [38]. On the other hand, none of the early SOS promoters showed σ³² enrichment or upregulation upon σ³² overexpression (S4C and S4D Fig, Table S6).

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Fig 4. Intrinsic promoter properties contribute to temporal hierarchy in SOS response gene induction.

(A) Box and scatter plot indicating TU lengths of early (blue) and late (red) SOS response genes. Mann–Whitney test, n.s. —not significant. The underlying data are available in S1 Data. (B) [Left] Schematic representing the calculation of gene distance from Caulobacter replication origin. [Right] Box and scatter plot indicating distance of ori from the CDS of early (blue) and late (red) SOS response promoters. t test (unpaired), n.s. —not significant. The underlying data are available in S1 Data. (C) Log₂FC values for genes in wild-type cells upon 40 min of MMC exposure are plotted against Log₂FC for ΔlexAΔsidA cells relative to wild type in the absence of DNA damage (representing intrinsic promoter strength/β_max). Values for early SOS response promoters are highlighted in blue while values for late genes are highlighted in red. Pearson correlation r² 0.8817 CI [0.8746 to 0.9708] p-value < 0.0001. The underlying data are available in S1 Data. (D) [Left] Box and scatter plot indicating log₂FC for ΔlexAΔsidA cells relative to wild type in the absence of DNA damage, for early (blue) and late (red) SOS response promoters. [Right] Box and scatter plot indicating basal average RPKM for early (blue) and late (red) SOS response promoters. The underlying data are available in S1 Data. (E) [Left] Representative cells showing induction of P_sidA-yfp (candidate early gene) and P_uvrA-yfp (candidate late gene) in wild type and ΔlexAΔsidA backgrounds. [right] Scatter plot shows fluorescence intensity distribution normalized to cell area for the respective transcriptional reporters (n = 100). Significance was estimated via the Mann–Whitney test. The underlying data are available in S1 Data.

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We next assessed promoter strength variation across the entire SOS response to test whether there was a relationship between promoter strength and the observed temporal gene expression hierarchies. Promoter strength of a gene (β_max) is defined by its maximal expression rate [39]. For SOS response genes, expression rate is dependent on LexA levels and hence expression rate would tend to β_max in the absence of LexA protein. Thus, promoter strength for SOS response genes can be estimated based upon expression in the ΔlexAΔsidA background. Transcriptome analysis of the ΔlexAΔsidA strain revealed that the fold change values of SOS response genes exhibited a strong correlation with their extent of induction under DNA damage (R² = 0.89) (Fig 4C). Importantly, early induced promoters showed higher de-repressed expression (promoter strength) as compared to promoters induced late (Fig 4D), while levels of basal gene expression did not vary between early and late promoters (Fig 4D). We corroborated these results with promoter fusion experiments in the ΔlexAΔsidA strain and observed a similar trend for our candidate gene promoters for early (sidA) and late (uvrA) SOS genes (Fig 4E), with higher yfp expression from the sidA promoter when compared with the uvrA promoter. Along with temporal hierarchy, another hallmark of the E. coli SOS response is heterogeneity in gene expression. Strikingly, we also noted that there was significant heterogeneity in the gene expression profiles of ΔlexAΔsidA cells. Comparison of the level of variance from mean (coefficient of variance [CV]) for P_sidA and P_uvrA promoters in wild type cells revealed a high level of heterogeneity in expression from both promoters in the absence (leaky expression) as well as presence of damage (Table S7; CV 0.76 and 0.53 for P_sidA in the absence and presence of damage respectively. CV 0.55 and 0.76 for P_uvrA in the absence and presence of damage respectively). This heterogeneity was also seen in cells lacking the repressor lexA, with CV of 1.3 for P_sidA and CV of 0.9 for P_uvrA. In sum, two key inferences can be drawn from these observations: (a) Hierarchy and heterogeneity in gene expression of the Caulobacter SOS response is observed even in the absence of the LexA protein. (b) The hierarchy is correlated with the promoter strength (Fig 4C and 4D). Taken together, our data suggest that intrinsic promoter strength (possibly shaped by differences in sigma factor binding in case of some promoters) contributes to the temporal hierarchy of Caulobacter SOS response induction.

Discussion

In this study, we investigate the temporal hierarchy observed in the induction of Caulobacter SOS response genes. Our work reveals the role of promoter strength as a determinant of this hierarchy. It is possible that such promoter strength variation forms a fundamental regulatory layer for the development of temporal hierarchy in this pathway across organisms.

We discuss two temporal features of the SOS response, i.e., time to induction (as described in our current study) and time to peak response (as described previously in [19]). These features exhibit some contrasting properties: (i) Variation in time to peak response has been observed distinctly only at higher concentrations of UV damage [19], while our study shows that hierarchy in the time to induction can be observed even at sub-lethal concentrations of DNA damage. (ii) Error-prone SOS genes seem to peak later as compared to error-free genes [6,19]. On the other hand, at least in the case of Caulobacter, error-prone imuABC genes are induced earlier than error-free uvrA. (iii) These temporal features differ in their underlying regulatory logic. While peak time is determined by the affinity of the LexA protein towards the SOS promoters [19], the time to induction is independent of LexA. Going forward, it will be important to dissect the relevance and contribution of these modes of temporal regulation for survival under DNA damage.

A role of intrinsic promoter strength and growth context in shaping the SOS response

The regulatory landscape of E. coli is estimated to have >4000 genes in turn regulated by >200 regulators [40,41]. How do bacteria determine what genes to turn on and when? A crucial determinant of the bacterial transcription landscape is based on the modularity imparted to the RNA polymerase subunit by the sigma factors [42]. During exponential phase, most genes are under the regulation of σ⁷⁰ (RpoD) [43]. Bacteria also possess alternative sigma factors that are dedicated to activate regulon genes under specialized conditions [42–44]. In our experimental regime, we maintain Caulobacter cells in exponential phase. Thus, we hypothesized that the SOS response genes would be under σ⁷⁰-based regulation. Instead, we found that σ⁷⁰ exhibits differential enrichment, predominantly at the early SOS genes as compared to the late SOS genes. Some of the late genes did also exhibit regulation by the alternative sigma factor, σ³² (RpoH) [45,46]. Thus, regulation by different classes of sigma factors may contribute to the temporal hierarchy in gene induction observed in the Caulobacter SOS response. Interestingly, sigma factors have been associated with the emergence of temporal hierarchy in flagellar synthesis pathway of bacteria as well [47,48].

Indeed, several stressors such as oxidative stress, heat stress, nutrient starvation, stationary phase and antibiotic treatment are also known to trigger the SOS response [49–52]. It is possible that a different hierarchical order might be crucial for survival under such conditions. It is tempting to speculate a context-dependent modularity in the SOS response temporal hierarchy based on the sigma factor involved. In support of this hypothesis, the RpoH-regulated late genes of the SOS response exhibit higher expression levels as compared to the RpoD-regulated early genes in an RpoH^V65A over-expression background (S4C and S4D Fig) [35].

Timing the SOS response

Is temporal hierarchy essential for bacterial survival under DNA damage? As discussed above, variation in peak time has been hypothesized to temporally segregate repair mechanisms based on their mutagenic potential [53]. Thus, error-prone polymerases such as UmuC have been observed to exhibit peak response times much later as compared to genes with no mutagenic cost associated with their induction [6,19]. However, this is unlikely to insulate individual bacterial cells from the cost of mutagenesis. Even though error-prone response genes peak late, they exhibit robust induction early, and at the same time as the error-free genes [6]. For example, the umuDC operon of E. coli is induced 5-fold within 5 min of exposure to UV damage [6]. It is possible that there is mutagenesis associated with the early induction of error-prone polymerases in a sub-population of cells. While there may be costs at the single-cell level, this may serve a crucial function in enabling evolvability and population survival. In contrast, when considering time to gene induction we find that bapE, which promotes damage-induced apoptosis, is induced late [29]. Delaying the expression of bapE may enable cells to prevent premature commitment to cell death, and prioritize DNA repair and cell survival instead. However, in light of a potential role for different sigma factors in determining induction times, it is possible that hierarchy may be subject to change depending on the stress and growth conditions. Indeed, along with sigma factors, additional transcriptional repressors or activators could also contribute to the timing and extent of regulation. Our work thus highlights the need to further investigate the temporal hierarchy of the SOS response in the context of different stressors and growth conditions that bacteria may be likely to encounter in their natural environments.

Limitations of present study and future outlook

In this study, we were unable to map the association of many other sigma factors with SOS promoters due to the lack of information on their binding properties. Additionally, we failed to generate transcriptional reporters with LexA binding sites swapped between early and late SOS promoters due to the challenging nature of the sigma factor binding sites and their organization with respect to the LexA binding sites. Such experiments could provide insights to the importance of maintaining hierarchy for SOS function. Finally, recent work has highlighted heterogeneity in the SOS response at the single-cell level. We too observe the same in case of our exemplar “early” and “late” promoters. It is crucial to understand how the population-level structuring of SOS response timing plays out in single cells (and across multiple gene promoters) to assess the effects of the same on cell survival. The development of single-cell RNA-seq techniques in bacteria, combined with single-cell microscopy will be a powerful method for future investigations in this direction [54,55].

Materials and methods

Bacterial strains and growth conditions

All strains, plasmids and oligos used in this study are listed in Table S1-S3, respectively. Caulobacter crescentus NA1000 cells were cultured in peptone yeast extract (PYE) media at 30 °C while shaken at 200 rpm. Antibiotic (kanamycin), DNA damaging agent (MMC) were introduced into the culture at suitable concentrations, wherever required.

Fluorescence microscopy and analysis

Cultures of Caulobacter were grown overnight in PYE media supplemented with kanamycin (5 μg/mL). For time-lapse microscopy, the cultures were diluted to OD₆₀₀ of 0.025. Three hours post-dilution, when OD₆₀₀ reached ~0.1, 2 μL of culture was spotted on 1.5% low-melting GTG agarose pads supplemented with PYE, kanamycin and required level of MMC damage. Cells were then imaged over 6 hours with an interval of 10 min in an OkoLab incubation chamber maintained at 30 °C. All imaging was performed on a Nikon Eclipse Ti-2E epifluorescence microscope equipped with a 60×/1.4 NA oil immersion objective, a motorized stage and an LED light source (pE-4000). The yfp reporter constructs were imaged with an exposure time of 500ms (λ = 490nm, 50% LED power) by an Orca Flash 4.0 camera. Focus was ensured over the course of the time lapse by the Nikon Perfect Focus System. Oufti software was then used for cell segmentation and estimation of normalized fluorescence intensity over time [56]. In order to calculate induction time (T_ind) for the reporter constructs, we fit a logistic curve model (R² > 0.95) to the mean of each induction profiles in Matlab. Induction time was then estimated as the time required for doubling of the mean initial fluorescence intensity (at time = 0). For estimating CV, standard deviation of fluorescence intensity normalized to cell area for a given dataset was divided by the mean of that dataset.

RNA-sequencing

Sample collection.

Overnight cultures of Caulobacter grown in PYE media were diluted to 0.025 OD₆₀₀. When OD₆₀₀ reached ~0.1, 0.25 μg/mL MMC damage was introduced in to these cultures. Caulobacter cells were harvested (2mL/sample) for RNA-seq at 0, 20, 40 min post-damage exposure. The samples were then centrifuged at 10,000g for 5 min following which supernatant was discarded and the pellets were snap frozen and stored at −80 °C until further processed. Data for wild type no damage control was derived from four biological replicates, all other conditions were repeated twice.

mRNA isolation and sequencing.

mRNA was isolated from the collected pellets as per details mentioned in a previous study [57]. Pre-heated trizol (65 °C) was added to each pellet and cells were lysed using a Thermomixer (65 °C, 2,000rpm, for 10 min). Total RNA was extracted from the lysed cells using the Direct-zol RNA MiniPrep kit. Any DNA contamination was then removed via DNAse treatment and RNA was purified using RNA Clean and Concentrator-25 kit. Integrity of the total RNA was tested by the Tapestation instrument. Ribosomal RNA was then depleted using the Thermo RiboMinus kit. mRNA was purified using RNA Clean & Concentrator-5 kit. The mRNA samples were submitted for sequencing to the NCBS next-generation sequencing facility. NEBNext Ultra II Directional RNA Library Prep kit was used for library preparation and samples were sequenced via the Illumina NovaSeq 6000 platform.

RNA-seq analysis.

RNA-seq results were obtained as raw reads in fastq format. The raw reads were mapped to the genome of Caulobacter crescentus NA1000 strain (accession number: NC_011916.1) using the Burrows–Wheeler Aligner (BWA) method (raw read quality ≥ 20). Samtools was used to filter out any multiply mapped reads. Read count per gene was then calculated using Bedtools [58]. Normalization and differential gene expression analysis of these datasets were carried out using the EdgeR package [59]. Genes with log₂FC values > 1, p-value < 0.01, FDR < 0.1 were denoted as induced genes. All analyzed data are provided in Table S4, and raw data are available in GSE311073 and GSE246782. Graphs were plotted in Graphpad prism (10.4.1), and statistical tests were carried out in the same software, supplemented with further analysis with standard Python modules where required.

Survival assay

Cultures of Caulobacter grown overnight in PYE were diluted to OD₆₀₀ of 0.075. Three hours post-dilution, all cultures were normalized to OD₆₀₀ 0.3, and diluted serially in 10-fold increments (10⁻¹–10⁻⁸). 6 μl of each dilution were spotted on PYE plates containing no DNA damage (control) or MMC damage (0.25 µg/mL). The plates were then incubated for 48 hours at 30 °C post which the plates were imaged, and survival was based upon the number of spots grown. All survival assay experiments were repeated twice.

Analysis of ChIP-seq data

Raw reads for ChIP-seq data for Caulobacter LexA, RpoD and RpoH were obtained in fastq format from previous studies [10,35]. These data were analyzed as described previously and briefly described ahead [57]. The raw reads were aligned to the Caulobacter genome (accession number: NC_011916.1) using the BWA tool. Aligned reads were then converted into the .bed format. Bedtools was used in order to estimate coverage at every nucleotide position. These normalized reads were smoothened by applying a Gaussian filter in Matlab, in order to plot individual ChIP-seq profiles for RpoD and RpoH. Peak calling from the analyzed RpoD data was conducted using the Peakzilla software [60]. RpoD peaks were assigned to SOS response genes if their predicted peak summit position was located within ±100 bp of the gene CDS. For calculation of LexA peak heights, maxima of normalized reads counts were estimated.

Bioinformatic analysis of LexA box properties

Identification of LexA box sequences of the SOS response genes.

For identifying LexA boxes in the SOS response promoters, DNA sequences 200 bp upstream and 75 bp downstream of the Caulobacter SOS response gene CDS was subject to analysis by the MEME discovery program [34]. The program identified motifs within the provided sequences that repeated one or more times. All identified LexA boxes from the program are summarized in S3A Fig.

Estimation of LexA box scores.

In order to score individual LexA box sequences with respect to the consensus motif, a position weight matrix (PWM) was generated using the Biostrings package. The PWM score was then calculated using the “PWMscoreStartingAt” command.

Calculation of SOS gene distance from origin.

The location of Caulobacter crescentus NA1000 origin was retrieved from the DoriC database [61,62]. We estimated distance from the origin by calculating the shortest distance of the OriC from the start position of the respective gene.

Supporting information

S1 Fig. Comparative analysis of the Caulobacter SOS response under mitomycin-C damage.

(A) Survival assay of wild type and ΔrecA cells in the presence and absence of MMC (0.25 μg/mL). Representative image shown from three biological replicates. (B) Venn diagram comparing SOS response genes identified in this study with the in silico analysis from Rocha and colleagues, 2008 [27] and microarray at 40 min post MMC damage from Modell and colleagues, [28]. Number of genes in each category are indicated. The underlying data are available in S1 Data. (C) Heat map for log₂FC values of genes previously identified as Caulobacter SOS response genes which do not fulfill criteria put forth in this study. Log₂FC values for an SOS-induced gene (ccna_03319) in wild type cells exposed to MMC damage for 40 min and for ΔlexAΔsidA cells in the absence of damage are shown for comparison.

https://doi.org/10.1371/journal.pbio.3003557.s001

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S2 Fig. The Caulobacter SOS response exhibits temporal hierarchy.

(A) Heat map showing gene expression changes at 20, 40, 60, and 80 min post MMC damage. Data are replotted from microarray analysis in [28]. Genes are marked as early or late based on their time to induction. The underlying data are available in S1 Data. (B) Fluorescence intensity normalized to cell area for P_recAyfp (n = 25) [left] and P_bapEyfp (n = 20) [right] cells over 6 hours of 0.25 μg/mL MMC exposure. Images were taken every 10 min and intensity over time traces are shown. Dashed line indicates the mean and the shaded region indicates the standard deviation. Time to induction (T_ind - time to doubling of mean initial fluorescence intensity) is indicated on the graph. The underlying data are available in S1 Data. (C) As (B) for P_imuAyfp (n = 25) [left] and P_ruvCyfp (n = 20) [right] cells over 6 hours of 0.25 μg/mL MMC exposure. Fluorescence intensity for P_ruvCyfp increases over time but does not cross the threshold set for time to induction within the imaging period. Time to normalized intensity maxima for P_ruvCyfp is indicated with an asterix. The underlying data are available in S1 Data. (D) As (B) for P_sidAyfp [left] and P_uvrAyfp [right] cells over 3 hours of MMC exposure (0.5 μg/mL, n = 25). The underlying data are available in S1 Data. (E) As (D) for cells treated with 0.125 μg/mL MMC, except for an imaging interval of 30 min (n = 20). At these low doses induction of yfp from the uvrA promoter is not detected. The underlying data are available in S1 Data.

https://doi.org/10.1371/journal.pbio.3003557.s002

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S3 Fig. LexA box properties are not predictive of the temporal hierarchy in SOS response induction.

(A) List of LexA box sequence and their box length (in bp) for promoters of all Caulobacter SOS response genes analyzed in present study. (B) Binding consensus motif for LexA, based upon the promoters of the Caulobacter SOS response genes. (C) Box and scatter plot indicating peak height derived from LexA ChIP-seq data for early (blue) and late (red) SOS response genes. Mann–Whitney test, n.s.—not significant. The underlying data are available in S1 Data. (D) Box and scatter plot indicating %GC content of the LexA box spacer for the CDS of early (blue) and late (red) SOS response genes. Mann–Whitney test, n.s. —not significant. The underlying data are available in S1 Data.

https://doi.org/10.1371/journal.pbio.3003557.s003

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S4 Fig. Intrinsic promoter properties contribute to temporal hierarchy in SOS response gene induction.

(A, B) ChIP-seq profiles for RpoD enrichment 500 bp upstream and downstream of the CDS for early [left] and late [right] SOS response genes. % SOS response genes bound by RpoD are indicated adjacent to the ChIP profiles. The underlying data are available in S1 Data. ChIP-seq data were obtained from the GEO database (GSE73925). (C, D) ChIP-seq profiles for RpoD and RpoH enrichment 500 bp upstream and downstream of the CDS of early (sidA) and late (uvrA) SOS response genes. The underlying data are available in S1 Data. ChIP-seq data were obtained from GEO database (GSE73925) [38]. Heat map of log₂FC values from RNA-seq experiments for Caulobacter SOS response genes upon rpoH^V65A over-expression is indicated adjacent to the ChIP profiles. RNA-seq data were obtained from GEO database (GSE102372) [35]. For comparison, log₂FC values for the same genes under 40 min of MMC damage (0.25 μg/mL) are shown.

https://doi.org/10.1371/journal.pbio.3003557.s004

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S1 Table. Strains used in present study.

https://doi.org/10.1371/journal.pbio.3003557.s005

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S2 Table. Plasmids used in present study.

https://doi.org/10.1371/journal.pbio.3003557.s006

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S3 Table. Oligos used in present study.

https://doi.org/10.1371/journal.pbio.3003557.s007

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S4 Table. Genes induced at 20 and 40 min post MMC damage.

Gene expression profile for these genes is provided for recA deletion and lexAsidA deletion cells as well.

https://doi.org/10.1371/journal.pbio.3003557.s008

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S5 Table. List of Caulobacter SOS response genes and their gene annotations.

https://doi.org/10.1371/journal.pbio.3003557.s009

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S6 Table. Summary table of early/late SOS promoters along with LexA box/intrinsic properties.

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S7 Table. Summary of coefficient of variation (CV) values for P_sidAyfp and P_uvrAyfp promoter fusions in wild type (±MMC damage) and ΔlexA background.

https://doi.org/10.1371/journal.pbio.3003557.s011

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S1 Data.

Numerical data underlying graphs and plots shown in main and supplementary figures.

https://doi.org/10.1371/journal.pbio.3003557.s012

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Acknowledgments

Authors are grateful to Prof. Meriem El Karoui and Prof. Andrea Weisse for helpful feedback and advice on the work. The authors acknowledge Neha Sontakke for development of analysis methods for the RNA-seq data.

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Figures

Abstract

Introduction

Results

Comparative analysis of the Caulobacter SOS response under mitomycin-C damage

The Caulobacter SOS response exhibits temporal hierarchy

LexA box properties are not predictive of the temporal hierarchy in SOS response induction

Intrinsic promoter properties contribute to temporal hierarchy in SOS response gene induction

Discussion

A role of intrinsic promoter strength and growth context in shaping the SOS response

Timing the SOS response

Limitations of present study and future outlook

Materials and methods

Bacterial strains and growth conditions

Fluorescence microscopy and analysis

RNA-sequencing

Sample collection.

mRNA isolation and sequencing.

RNA-seq analysis.

Survival assay

Analysis of ChIP-seq data

Bioinformatic analysis of LexA box properties

Identification of LexA box sequences of the SOS response genes.

Estimation of LexA box scores.

Calculation of SOS gene distance from origin.

Supporting information

S1 Fig. Comparative analysis of the Caulobacter SOS response under mitomycin-C damage.

S2 Fig. The Caulobacter SOS response exhibits temporal hierarchy.

S3 Fig. LexA box properties are not predictive of the temporal hierarchy in SOS response induction.

S4 Fig. Intrinsic promoter properties contribute to temporal hierarchy in SOS response gene induction.

S1 Table. Strains used in present study.

S2 Table. Plasmids used in present study.

S3 Table. Oligos used in present study.

S4 Table. Genes induced at 20 and 40 min post MMC damage.

S5 Table. List of Caulobacter SOS response genes and their gene annotations.

S6 Table. Summary table of early/late SOS promoters along with LexA box/intrinsic properties.

S7 Table. Summary of coefficient of variation (CV) values for PsidAyfp and PuvrAyfp promoter fusions in wild type (±MMC damage) and ΔlexA background.

S1 Data.

Acknowledgments

References

S7 Table. Summary of coefficient of variation (CV) values for P_sidAyfp and P_uvrAyfp promoter fusions in wild type (±MMC damage) and ΔlexA background.