
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

39257796
10.1101/2024.09.01.610008
preprint
1
Article
Negative feedback of cyclic di-GMP levels optimizes switching between sessile and motile lifestyles in Vibrio cholerae
http://orcid.org/0000-0003-3471-0257
Rangarajan Aathmaja Anandhi 1*
http://orcid.org/0000-0001-8829-8494
Schroeder Jeremy W. 2*
Hurto Rebecca L. 2
http://orcid.org/0000-0003-2441-5916
Severin Geoffrey B. 3
http://orcid.org/0000-0002-1846-5063
Pell Macy E. 1
http://orcid.org/0000-0002-6360-005X
Hsieh Meng-Lun 1
http://orcid.org/0000-0003-2336-7836
Waters Christopher M. 1#
http://orcid.org/0000-0002-5821-4226
Freddolino Lydia 24#
1. Department of Microbiology, Genetics and Immunology, Michigan State University, East Lansing, MI, USA
2. Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
3. Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
* Equal contribution

# address correspondence to Christopher M. Waters at watersc3@msu.edu or Lydia Freddolino at lydsf@umich.edu
01 9 2024
2024.09.01.610008https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
nihpp-2024.09.01.610008.pdf
The signaling molecule cyclic di-GMP (cdG) controls the switch between bacterial motility and biofilm production, and fluctuations in cellular levels of cdG have been implicated in Vibrio cholerae pathogenesis. Intracellular concentrations of cdG are controlled by the interplay of diguanylate cyclase (DGC) enzymes, which synthesize cdG to promote biofilms, and phosphodiesterase (PDE) enzymes, which hydrolyse cdG to drive motility. To track the complete regulatory logic of how V. cholerae responds to changing cdG levels, we followed a timecourse of overexpression of either the V. harveyi diguanylate cyclase QrgB or a variant of QrgB lacking catalytic activity (QrgB*). We find that QrgB increases cdG levels relative to QrgB* for 30 minutes after overexpression, but the effect of QrgB on cdG levels plateaus at 30 minutes, indicating tight adaptive control of cdG levels. In contrast, loss of VpsR, a master regulator activating biofilm formation upon binding to cdG, leads to higher baseline levels of cdG and continuously increasing cdG through 60 minutes after QrgB induction, revealing the existence of a negative feedback loop on cdG levels operating through VpsR. Through a combination of RNA polymerase ChIP-seq, RNA-seq, and genetic approaches, we show that transcription of a gene encoding a PDE, cdgC, is activated by VpsR at high cdG concentrations, mediating this negative feedback on cdG levels. We further identify a transcript encoded within, and antisense to, the cdgC open reading frame which we name sRNA negative regulator of CdgC (SnrC). RNA polymerase ChIP-seq and RNA-seq demonstrate SnrC to be expressed specifically under conditions of high cdG in the absence of VpsR. Ectopic SnrC expression increases cdG levels in a manner dependent on CdgC, demonstrating that its effect on cdG levels is likely through interference with CdgC production. Further, although cells lacking cdgC exhibit enhanced biofilm formation, these mutants are outcompeted by wild type V. cholerae in colonization assays that reward a combination of attachment, dispersal, and motility behaviors. These results underscore the importance of negative feedback regulation of cdG to maintain appropriate homeostatic levels for efficient transitioning between biofilm formation and motility, both of which are necessary over the course of the V. cholerae infection cycle.
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pmcIntroduction

Vibrio cholerae is the causative agent of cholera, a severe diarrheal disease which affects millions of people and causes nearly 100,000 deaths annually, with the number of deaths increasing significantly in recent years according to WHO reports [1]1. V. cholerae can occupy numerous environmental niches, from metazoan digestive tracts to aquatic habitats, requiring rapid lifestyle shifts between a sessile, biofilm state and a motile, free-living state for successful survival and propagation [2–5]. In the majority of bacteria, an important signaling molecule that directs the transition between motility and biofilms is the second messenger cyclic di-GMP (cdG), with high cdG almost universally driving bacteria toward biofilm association and low cdG promoting unicellular lifestyles [3,5,6].

cdG is recognized by numerous transcription factors, riboswitches, and sRNAs to regulate gene expression both transcriptionally and post-transcriptionally [6,7]. In V. cholerae, two master regulators promote biofilm formation when they bind cdG: VpsR and VpsT [8,9]. cdG-bound VpsR activates transcription of vpsT, and both VpsR and VpsT activate the production of biofilm matrix components when they are bound to cdG, including vpsL, vpsU, rbmAC, rbmF, and bap-1 [10–17]. At the same time, cdG binds to and inactivates the master regulator of flagellar gene expression, FlrA [18,19]. Although VpsR and VpsT dependent regulation of biofilm related genes is well-studied, knowledge of other genes regulated by VpsR and VpsT in the presence of cdG and their temporal dynamics is incomplete.

Several mechanistic aspects of the switch from biofilm formation to dispersal in V. cholerae have been identified. For example, low levels of cdG enhance the retraction of MSHA pili, detachment from surfaces through the LapGD module, activation of FlrA to promote expression of flagellar genes, and downregulation of extracellular polysaccharide synthesis genes, altogether resulting in detachment from surfaces, dispersal from biofilms, and increased motility [3,4,20,21]. However, the upstream regulatory logic affecting cdG metabolism and directing these molecular events during the switch from biofilm formation to dispersal is poorly understood.

In order to define the regulatory paths connecting cdG levels to the multitude of phenotypes described above, we applied a series of genome-wide studies on gene regulation and expression upon strong induction of a non-native diguanylate cyclase (DGC), Vibrio harveyi QrgB. We identified a genetic circuit that is important for negative feedback on cdG levels to balance biofilm formation and dispersal. Our results highlight an unexpectedly central role of regulation of the phosphodiesterase (PDE) CdgC. While transcription of cdgC is activated by cdG-bound VpsR, high cdG levels promote expression of a newly identified antisense transcript we have named snrC that likely antagonizes cdgC translation. The integration of information on temporal variations of cdG levels at cdgC highlights the importance of negative feedback regulation in V. cholerae to maintain cdG homeostasis at an appropriate level for optimal transitioning between biofilm formation and dispersal.

Results

Feedback regulation of cdG levels drives time-dependent regulation of flagellar and biofilm genes

To map the cdG dependent transcriptome of V. cholerae and determine the dynamics of gene regulation in response to increased cdG production, we performed a timecourse of induction of cdG synthesis using QrgB, a well characterized DGC from V. harveyi which is known to induce physiologically relevant cdG levels in V. cholerae [22,23]. All experiments were performed in a ΔvpsL background to avoid clumping of cells forming biofilms at high cell density; thus all strains noted below (including the “wild type”) are vpsL−. Samples were harvested at 0, 15, 30, and 60 minutes after induction of QrgB. At each timepoint, we also harvested equivalent samples from control cells expressing QrgB*, which is a catalytically inactive variant of QrgB which cannot produce cdG. At each timepoint we measured intracellular levels of cdG and performed RNA-seq and RNA polymerase (RNAP) ChIP-seq. Identical experiments were performed using ΔvpsR and ΔvpsT mutants to determine the impact of these transcription factors on the cdG transcriptome.

In wild type and ΔvpsT, cdG levels increased at 15 and 30 minutes post-induction of QrgB relative to QrgB*, but plateaued at 60 minutes (Fig. 1A–C), indicating that even upon overexpression of a DGC, V. cholerae has mechanisms to limit cdG accumulation. In contrast, ΔvpsR cells had a higher concentration of cdG at the initial timepoint, and this concentration remained elevated at all timepoints when compared to wild type and ΔvpsT cells (Fig. 1A,C). Induction of the inactive QrgB* variant did not increase cdG levels in any background (Fig. 1B); rather, cdG concentrations steadily decreased over the course of growth in QrgB*-expression cells, which is consistent with these strains transitioning to the high-cell density quorum state where cdG concentrations are reduced [22]. These results suggest that cdG levels are regulated by a negative feedback loop that depends on VpsR.

We next examined the temporal dynamics of gene expression in response to cdG. At each timepoint we compared transcript abundance (measured via RNA-seq) following induction of QrgB vs. QrgB*. In broad terms, this experiment captured the known biological responses to cdG, as exemplified by both a gradual increase in the expression of biofilm genes over time (Fig. 1D) and a reduction in expression of flagellar genes immediately following induction (Fig. 1F).

Closer inspection of trends in cdG transcriptome dynamics revealed biofilm gene induction to be entirely dependent on VpsR, as expected, whereas VpsT was required for a subset of transcriptional changes including induction of the vps-II operon and enhanced induction of the vps-I operon (Fig. 1D, E). Flagellar expression dynamics provided further evidence of a negative feedback loop on cdG levels that depends on VpsR. Flagellar genes were repressed in a pulsatile manner in wild type cells, with cdG leading to the strongest repression of class III flagellar genes at 15 minutes (Fig. 1F), and class IV genes at 30 minutes. By contrast, the ΔvpsR mutant exhibited continuously strengthening repression of class III and IV flagellar genes throughout our timecourse (Fig. 1F). Together, these data suggest that a VpsR-dependent negative feedback loop centered on cdG acts to limit both cdG levels and flagella gene repression during the cdG response.

CdgC is regulated by VpsR in the presence of cdG

The V. cholerae genome contains 62 enzymes predicted to encode DGCs or PDEs [24]. This includes 31 proteins with GGDEF domains involved in synthesis of cdG; 12 and 9 with EAL and HD-GYP domains, respectively, each of which could hydrolyse cdG; and 10 proteins containing both GGDEF and EAL domains [reviewed in [6]]. In order to identify the gene responsible for the VpsR-dependent negative feedback on cdG levels, we assessed the effect of cdG induction on expression of transcripts encoding proteins with GGDEF, EAL, or HD-GYP domains in our RNA-seq datasets.

The GGDEF/EAL domain containing protein CdgC stood out as the sole clear candidate for negative feedback on cdG levels, as it uniquely displayed cdG and time-dependent upregulation in wild type and ΔvpsT cells when comparing QrgB to QrgB* that was absent in the ΔvpsR mutant (Fig. 2A). Although there is an apparent positive spike in the effect of cdG on CdgC expression after 15 minutes of induction in ΔvpsR cells, we note that this apparent increase does not represent increased expression in the QrgB condition per se, but rather, in the QrgB* condition, ΔvpsR cells display very low CdgC expression at this timepoint (Fig. S1). Our results are consistent with previous studies that have demonstrated CdgC expression to be downregulated in a vpsR mutant compared to wild type, and the cdgC promoter to possess a VpsR binding motif [11,13]. As a follow-up to our RNA-seq analysis, we used a transcriptional fusion of gfp to the cdgC promoter (PcdgC), originally published in [22]. Results using PcdgC:gfp were consistent with our RNA-seq analysis, showing that cdgC transcription increased in wild type cells overexpressing QrgB relative to QrgB*, but this response was lost in the ΔvpsR mutant (Fig. 2B).

VpsR controls cyclic di-GMP levels through CdgC

Though CdgC contains both GGDEF and EAL domains, it lacks the catalytic residues required to produce cdG, and upon overexpression it acts as a PDE through its EAL domain to decrease cdG, thus increasing motility [25]. In addition, deletion of cdgC results in higher biofilm formation potentially due to loss of this PDE [22,26]. We therefore hypothesized that induction of cdgC by cdG could promote degradation of cdG in wild type and ΔvpsT cells, accounting for the observed negative feedback loop. Consistent with this hypothesis, expression of QrgB in a ΔcdgC mutant resulted in high cdG levels that were not decreased at 60 minutes (Fig. 2C), analogous to what we observed in the ΔvpsR mutant (Fig. 1A). Conversely, overexpression of CdgC decreased the levels of cdG in ΔvpsR cells to levels similar to wild type (Fig. 2C). These results indicate that CdgC is responsible for maintaining low levels of cdG in wild type cells during the cdG response; that VpsR, independent of VpsT, induces CdgC expression to reduce cdG levels; and that CdgC expression is necessary and sufficient for VpsR-dependent feedback moderation of cdG levels.

An antisense RNA targeting cdgC is induced by VpsR during the cdG response and regulates CdgC

Having demonstrated that cdgC induction by VpsR in a cdG-dependent manner initiates a negative feedback loop, we next investigated transcription at the cdgC locus. RNAP ChIP-seq showed that increasing cdG levels led to clear increases in RNAP occupancy immediately 5′ to cdgC specifically in wild type cells, and not in ΔvpsR cells (Fig. 3A). In addition to cdG- and VpsR-dependent changes in cdgC promoter occupancy, we identified potential promoters or RNAP pausing sites within the cdgC locus (Fig. 3A, arrows). The effect of cdG on RNAP occupancy at these sites changed over time and was clearly impacted by the presence of VpsR. We therefore took a targeted approach to analyzing our RNA-seq data, paying particular attention to potential transcripts arising from within, but distinct from, the cdgC gene.

Visual inspection of RNA-seq fragment pileups at the cdgC locus qualitatively revealed the presence of an antisense RNA transcribed from the cdgC locus. Expression of the antisense RNA, hereafter termed sRNA negative regulator of CdgC (SnrC), increased in the ΔvpsR mutant late in the cdG response, suggesting that VpsR is a negative regulator of this sRNA (Fig. 3B).

We hypothesized that SnrC may counteract cdgC expression, thus adding another potential level of regulation. To test this hypothesis, we overexpressed SnrC from a plasmid along with QrgB in wild type cells. Overexpression of SnrC increased the levels of cdG, potentially by counteracting cdgC expression (Fig. 3D). Equivalent expression of control transcripts RpoA RNA and the complement of SnrC (sense-snrC) resulted in low cdG levels (Fig. 3D). The cdG levels remained high in a ΔcdgC mutant and were unaffected by SnrC, sense-snrC, or RpoA RNA, indicating that the overexpression of SnrC increases cdG levels specifically through CdgC (Fig. 3E). We note that although the adjusted p-values for the comparison of SnrC abundance during QrgB induction vs. QrgB* induction at each timepoint do not meet our usual statistical significance threshold, in light of our biological follow-up experiments, we are confident that our RNA-seq analysis revealed biologically meaningful regulation of snrC, and that SnrC represents a bona fide antisense RNA that likely targets the cdgC transcript (with lack of significant changes in the RNA-seq analysis likely reflecting a type II error).

Feedback by CdgC promotes growth during QrgB induction

Elevated intracellular cdG levels exert a detrimental impact on the growth of V. cholerae [27]. We hypothesized that the sustained high levels of cdG caused by the absence of CdgC or VpsR would be especially disadvantageous for V. cholerae growth. We thus assessed the growth of wild type, ΔvpsR, ΔcdgC, and ΔcdgC ΔvpsR mutants while inducing QrgB with increasing IPTG concentrations. Notably, at the highest IPTG concentrations tested (0.625 and 1.25 mM) the ΔcdgC, ΔvpsR, and ΔvpsR ΔcdgC mutants exhibited reduced growth compared to wild type. In contrast, at the lower IPTG concentrations (0.0 and 0.15 mM), no discernible difference in growth emerged between wild type and mutants, implying that constitutively elevated cdG levels curtails V. cholerae growth and such levels of cdG are controlled by VpsR and CdgC (Fig. 4A). Our growth curves also further affirmed the epistatic nature of VpsR and CdgC, as the ΔvpsR ΔcdgC double mutant displayed practically identical growth to the ΔvpsR mutant even at the highest tested level of IPTG (Fig. 4A, right panel). As noted above, each of these strains are in a ΔvpsL mutant background, ruling out the possibility that the observed growth defects are due to biofilm formation itself or an artifact of cellular aggregation.

CdgC allows balanced biofilm formation and dispersal behaviors to enhance fitness in fluctuating environments

High and low cdG levels drive attachment and dispersal, respectively. Following dispersal, bacteria may colonize new surfaces. Both biofilm formation and dispersal are crucial for survival and successful dissemination of V. cholerae during human infection and colonization in aquatic reservoirs [4,28]. Prior work demonstrated that biofilm formation is increased in a cdgC mutant [25]. Although loss of CdgC has been associated with increased biofilm formation, we hypothesized that ΔcdgC cells could have a fitness cost in physiologically relevant environments due to decreased dispersal from biofilms owing to dysregulated cdG levels. That is to say, we predict that cells lacking CdgC will over-commit to entering, and subsequently remain in, a biofilm when cdG levels increase. We therefore predicted that loss of the cdG negative feedback loop in ΔcdgC cells should lead to decreased fitness in a fluctuating environment requiring well-balanced biofilm formation/dispersal dynamics.

To test whether CdgC promoted fitness in fluctuating environments through balancing biofilm formation and dispersal, we performed a competition assay modeled after biofilm evolution experiments created by the Cooper laboratory [29,30]. We competed wild type lac− strains against ΔcdgC lac+ strains based on their ability to disperse from biofilms coating a polystyrene bead to form a new biofilm on fresh beads. The competition assay was performed by serially passaging biofilm-coated beads into sterile media containing a fresh sterile bead every 24 hours (Fig. 4B). The process was continued for 9 days, and each day the bead that was not passaged to fresh media was vortexed in fresh solution and plated, and the ratio of wild type colonies (white on X-gal-containing plates) to ΔcdgC colonies (blue on X-gal-containing plates) was calculated by comparing the colony forming units (CFUs) of each color. As shown in Fig. 4C, regardless of the starting ratio of wild type vs. ΔcdgC cells, wild type was able to outcompete ΔcdgC, potentially due to poor dispersal of cells lacking CdgC.

As a control we competed the wild type lac− strains and ΔcdgC lac+ strains in equivalent media without beads, which showed no change in the ratio of wild type to ΔcdgC cells over time, indicating there is no difference in the growth rate of wild type and cdgC mutant cells when grown planktonically in minimal media (Fig. 4C). We also complemented the cdgC gene along with its native promoter region by placing them in the lacZ locus in the ΔcdgC mutant. After complementing cdgC under control of its native promoter, there was no difference in fitness between wild type lac+ cells and ΔcdgC lacZ::PcdgC:cgdC cells (Fig. 4C). This indicates that the cdgC gene per se is responsible for loss of fitness in our competition assay, effectively ruling out any possibility that deletion of cdgC caused other effects. Taken together, these data suggest that the PDE activity of CdgC is responsible for limiting the levels of cdG, priming the population of cells to efficiently switch between biofilm formation and dispersal, which increases the fitness of V. cholerae in dynamic environments.

Discussion

Both biofilm formation and dispersal are important for the successful colonization and dispersal of V. cholerae in the human gut and in other environments [4,31]. Hyper biofilm forming mutants possessing high intracellular cdG display decreased colonization of mice when compared to wild type, potentially due to poor dispersal [32]. It was also recently shown that high levels of cdG in V. fischeri impair squid colonization [33]. In this work we identified and characterized two interconnected regulatory circuits that influence the balance of V. cholerae biofilm formation and dispersal in response to cdG induction.

For the first circuit, we showed that through VpsR, which is the master regulator of the biofilm response to cdG, the PDE CdgC enforces negative feedback on cdG levels. Previous findings have demonstrated CdgC to be differentially regulated in a vpsR mutant, the cdgC promoter to possess a predicted VpsR binding site, and cdgC expression to be upregulated during increased global cdG levels in several conditions [11,13,34]. Here, we showed that the cdG → VpsR → CdgC ⊣ cdG regulatory path (Fig. 5) serves as a pulse generator, bringing about a short-duration rise in cdG levels after cdG induction by an external cue. This property of the cdG → VpsR → CdgC negative feedback loop is apparent both from our LC-MS analysis of cdG levels in wild type vs. ΔvpsR cells, and by our RNA-seq analysis of flagellar genes exhibiting a pulse of repression in wild type and ΔvpsT cells, but not in ΔvpsR cells (Fig. 1). We additionally showed that induction of cdgC by VpsR provides increased fitness to V. cholerae when biofilm formation and dispersal must exist in balance with each other (Fig. 4).

Positive feedback on cdG levels was recently proposed to occur through VpsT-dependent activation of the DGC VpvC [35]. However, we did not detect evidence for induction of VpvC expression under the conditions of our experiments (Fig. 2A, locus tag VC2454). We find it likely that differing laboratory conditions may explain our identification of a negative feedback loop, whereas others have posited that positive feedback occurs; the two may both exist, but under differing environmental conditions. Indeed, the potential existence of opposing feedback loops on cdG that can be turned on or off depending on environmental cues is intriguing, and it would underscore the benefits of balancing cdG levels until the proper signals are in place to fully commit to motility, biofilm production, or dispersal. Alternatively, our study used V. cholerae strain C6706 while [35] utilized strain E7946, which could be responsible for the different results.

The second circuit that we discovered and characterized involves a newly-identified small RNA, SnrC. Although mechanistic details of snrC regulation are not yet clear, we have demonstrated certain broad aspects of snrC regulation and the effect of snrC expression on cdG levels. snrC is expressed under conditions with very high cdG concentrations (ΔvpsR +QrgB), and SnrC acts in trans to inhibit CdgC in vivo to increase cdG levels. We note that although we detected SnrC expression in ΔvpsR cells, we do not favor a model for VpsR repressing snrC expression. Rather, we view loss of VpsR as the indirect mechanism by which sufficiently high cdG levels were achieved to induce snrC expression. Altogether, we assert that VpsR, through its induction of CdgC maintains brief cdG pulses (alongside its many other cdG-dependent activities) so as to balance biofilm formation and dispersal. SnrC, when expressed, would extend the duration of any cdG pulse, potentially indefinitely, to tip the balance toward biofilm formation. Thus, the extended SnrC-mediated branch of the circuit provides an independent path for modulating the duration and intensity of the CdgC-controlled cdG pulse (Fig. 5).

The balance between biofilm formation and dispersal that CdgC promotes yields greater fitness in fluctuating environments that require cells not to over-commit to the biofilm state during elevated cdG. However, there are environments in which the balance must be tipped strongly, and perhaps quickly, toward biofilm formation. Above we briefly described how SnrC could shift V. cholerae toward longer cdG pulses and biofilm formation. Another possible mechanism to extend cdG pulses, to increase cdG pulse amplitude, or both, could come in the form of direct regulation of PDE activity of CdgC. The degenerate GGDEF domain of CdgC may bind cdG to modulate PDE activity, and CdgC contains a sensing domain that may modulate PDE activity in response to an unknown signal. The first 156 residues of the structural prediction of CdgC (AFDB model AF-Q9KLF9-F1) model the CdgC sensing domain with high confidence [36,37]. Structural alignment of the sensing domain against structures in the Protein Data Bank using Foldseek [38] revealed that the sensing domain of CdgC is similar to light-sensing GAF domains found on the DGC Dcsbis from Pseudomonas aeruginosa [39] and Cph2 from Synechocystis sp. PCC 6803, the DGC and EAL activity of which is likely to be modulated by light sensed by the GAF domains in Cph2 [40]. CdgC PDE activity may be modulated by an as-yet unknown signal. Therefore, the GAF domain of CdgC and control of CdgC by SnrC are likely to represent two orthogonal points at which additional signals are integrated into the cdG metabolic network, such that, depending on the environment in which V. cholerae resides, cdG pulse duration and amplitude can be quickly tuned to adapt appropriately to either be motile or to form a biofilm.

Understanding how the genetic circuits in the cdG metabolic network identified here impact various aspects of V. cholerae pathogenesis will require further investigation of both the signal that may modulate CdgC activity and regulation of snrC expression. In addition, while we demonstrated here that a proper balance of biofilm formation and dispersal through VpsR and CdgC enhances fitness, it will be useful to understand precisely when SnrC and CdgC play their respective roles in cdG metabolism.

Methods

Growth conditions and gene manipulations

All the strains are derivatives of V. cholerae O1 El Tor biotype strain C6706str2. The list of strains, plasmids and oligonucleotides used in this study are listed in supplementary table S1–S3. CW2034 (ΔvpsL) is designated as “wild type” in all of the experiments and all other strains were derived from this background except for biofilm assays in Fig. 4C for which wild-type C6706str2 (vpsL+) was used. The strains were grown in LB broth at 35°C with shaking. Antibiotics were used at the following concentrations; kanamycin (50 μg/ml) and ampicillin (50 μg/ml). 1 mM IPTG was used for induction of QrgB of QrgB*. Gene deletions and insertions were performed using homologous recombination and the pKAS32 suicide plasmid as described previously [42]. Overexpression plasmids were constructed in Ptac inducible vector pEVS143 using PCR and Gibson assembly and verified by sequencing.

Quantification of intracellular cdG levels

CdG quantification was performed using UPLC-MS/MS as described previously [27]. The cells were grown to OD600 of 1 after which 1 mM IPTG was added and samples were taken at 0, 15, 30 and 60 minutes after the addition of IPTG. Briefly 1 ml of cultures were spun down at 20000 r.c.f for 2 min, resuspended with 100 μl nucleotide extraction solution (40% acetonitrile, 40% methanol, 0.1% formic acid, and 19.9% water) and incubated at −20°C for 20 minutes. Following this, the samples were centrifuged at 20000 r.c.f for 15 minutes and the supernatant was transferred to a fresh tube and dried under vacuum. The samples are then resuspended in 100 μl HPLC grade water, and 10 μl of this solution was injected into the UPLC-MS/MS system (Waters, Xevo).

Electrospray ionization using multiple reaction monitoring in negative-ion mode at m/z 689.16→344.31 was used to detect cdG. The MS parameters were as follows: capillary voltage, 3.5 kV; cone voltage, 50 V; collision energy, 34 V; source temperature, 110 °C; desolvation temperature, 350 °C; cone gas flow (nitrogen), 50 L/h; desolvation gas flow (nitrogen), 800 L/h; collision gas flow (nitrogen), 0.15 mL/min; and multiplier voltage, 650 V. Waters BEH C18 2.1 × 50 mm column with a flow rate of 0.3 mL/min with the following gradient was used: solvent A (10 mM tributylamine plus 15 mM acetic acid in 97:3 water:methanol) to solvent B (methanol): t = 0 min; A-99%:B1%, t = 2.5 min; A-80%:B-20%, t = 7.0 min; A-35%:B-65%, t = 7.5 min; A-5%:B-95%, t = 9.01 min; A-99%:B-1%, t = 10 min (end of gradient). 125, 62.5, 31.25, 15.62, 7.81, 3.625, 1.9 nM of cdG (Biolog) was used to generate a standard curve for calculating the cdG concentrations in the sample. The intracellular concentration was determined by dividing the total moles of cdG by the intracellular volume of bacteria. The intracellular volume was determined by multiplying the number of CFU in each sample by the volume of one bacterium which was previously determined to be 6.46 × 10−16 L.

CdgC Promoter-GFP analysis

The expression analysis was performed in Costar 96-well black bottom plates. Overnight cultures were diluted 1:100 in triplicates in LB broth with Kanamycin (50 μg/ml) and Ampicillin (50 μg/ml) and grown to an OD600 of 0.1. Following this, IPTG was added to the final concentration of 1 mM and GFP was measured with Ex/Em of 488/509 nm using an Envision Multi-label plate reader (Perkin-Elmer) at 0, 15, 30 and 60 minutes.

Growth curve assays

Overnight cultures were diluted 1:500 into 150 μL LB supplemented with ampicillin (100 μg/ml) with 0 mM, 0.125 mM, 0.625 mM or 1.25 mM IPTG for the induction of QrgB. Growth was determined by measuring OD600 every 15 minutes for 24 hours at 35°C with shaking using an Infinite F500 Microplate Reader (Tecan).

Biofilm competition assay

The biofilm dispersal assay was performed as described previously [29] with modifications as indicated below. Wild type lacZ− and ΔcdgC lacZ+ or wild type lacZ+ strain and ΔcdgC lacZ::PcdgC-cdgC strains were used for biofilm competition assays. The cells were inoculated in the ratio of 1:1, 9:1 or 1:9 to OD600 0.05 in 10 ml of minimal media (1x M9 salts, 0.4% glucose, 2 mM MgSO4, 100 μM CaCl2) containing polymyxin B (40 μg/mL) in 4 well sterile plates (127.8 × 85.5 mm, Thermo Scientific). V. cholerae C6706 is naturally resistant to polymyxin B, and it was included to minimize contamination. Biofilm competition assay was performed as described in Figure 4B. Two polystyrene beads of 7 mm diameter (Costar) were placed 3 cm apart in a single well and statically incubated at 35°C for 24 hours. After 24 hours, the bead on the left side was washed in PBS by gently shaking the beads in 8 mL PBS in a petri dish (60 × 15 mm) for 1 minute, transferred to a 2 mL tube, and vortexed with 1 mL LB for 2 minutes. Dilutions of the resulting suspension were plated on LB agar containing polymyxin B with X-gal and IPTG and subsequently scored for blue and white colonies. The bead on the right was also washed with PBS similarly and placed into a new plate on the left side containing fresh minimal media with polymyxin B and a fresh bead was placed 3 cm away on the right side. After 24 hours the same process was repeated, following the same cycle for a total of 9 days. For control experiments a similar setup was used without the beads.

Cell growth and crosslinking for RNA-seq and RNAP ChIP-seq

For each biological replicate of each genotype, cells were grown in LB supplemented with a final concentration of 100 μg/ml ampicillin. Overnight cultures were diluted to an OD600 = 0.05 in 500 mL of LB pre-warmed to 35°C containing 100 μg/ml ampicillin. Cultures were incubated at 35°C with constant shaking at 200 rpm and cultured to an OD600 = 1.0, at which time the 0 minute timepoint aliquots were rapidly collected as follows: 200 μL of culture was mixed with 1 mL of DNA/RNA shield (Zymo Research Corporation) to be used for RNA extraction (described later) and 100 mL of culture was aliquoted to a pre-warmed 250 mL flask and treated with 75 μg/mL rifampicin.

Immediately following initial time 0 sample collection, IPTG was added to the remaining 400 mL of original culture to a final concentration of 1 mM to induce expression of the qrgB allele. At 15, 30, and 60 minutes after IPTG was introduced, additional aliquots of culture were removed and processed as described above. For each timepoint, rifampicin treatment was allowed to proceed at 200 rpm shaking at 35°C for 10 minutes, at which time 40mL of rifampin-treated culture was added to each of two 50 mL conical tubes containing 1080 μL of fresh formaldehyde to induce crosslinking. Crosslinking was allowed to proceed for 5 minutes at room temperature with constant shaking, after which time the reaction was quenched by the addition of 8 mL of 2 M glycine. Quenched reactions were incubated for 10 minutes at room temperature with constant shaking, placed on ice for 10 minutes, then cells were collected by centrifugation for 5 minutes at 5,000 × g at 4°C. Crosslinked cell pellets from the matched 50 mL conical tubes were resuspended in 10 mL cold PBS and combined. The combined crosslinked cells were then harvested by centrifugation for 5 minutes at 5,000 × g at 4°C, resuspended in a second 10 mL PBS aliquot, and again harvested by centrifugation for 5 minutes at 5,000 × g at 4°C. The twice washed cross linked cell pellet was then resuspended in 400 μL of cold PBS, split to three 1.8 mL tubes and the final cell samples were collected via centrifugation for 5 minutes at 4°C, 10,000 × g. The supernatant was aspirated and pellets were flash frozen in an ethanol-dry ice bath, then stored at −80°C.

RNA-seq

Total nucleic acid was extracted from cells, which had been lysed and preserved in DNA/RNA Shield and stored at −80°C, using an RNA Clean & Concentrator-96 kit (Zymo Research Corporation). Briefly, 300 μL of cell lysate was added to tubes containing 600 μL of RNA binding buffer and 900 μL of 100% ethanol, and vortexed. Each sample was bound to a filter (one well) of a 96 well spin column plate 700 μL at a time until all of the sample was bound. Washes were performed per manufacturer’s protocol. Samples were eluted using 25 μL of water.

DNA was removed by digestion with Baseline-ZERO DNase (LGC Biosearch Technologies). All of the samples (~23 μL each) were added to DNA lo-bind tubes containing 60 μL of water and 10 μL of 10X Baseline-ZERO DNase Buffer. After vortexing, 2 μL of Murine RNase Inhibitor (NEB) and 5 μL of Baseline-ZERO DNase were added to each sample. After gentle mixing, tubes were incubated 37°C for 30 minutes.

Reactions were cleaned again using a Zymo RNA Clean and Concentrate Kit-96 (Zymo Research) according to the manufacturer’s instructions, and eluted with 25 μL of water]. The QuantiFluor RNA System (Promega) was used to determine RNA concentration of samples.

For each sample, 0.5 μg of RNA was diluted to 11 μL and used as input material for NEBNext rRNA Depletion Kit for Bacteria (NEB). Depletion was performed per kit directions. Library preparation was performed using NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB) per kit directions for intact RNA (15 minute fragmentation) except that diluted (1:100) NEBNext Unique Dual Index UMI Adaptors DNA Set 1 were used instead of the NEBNext Adaptor for Illumina. Pooled libraries were subjected to Illumina sequencing on a NextSeq platform with 38 × 37 bp paired end reads.

RNAP ChIP-seq

Crosslinked cell pellets were resuspended in 630 μL of lysis buffer [10 mM Tris pH 8.0, 1x cOmplete™, EDTA-free Protease Inhibitor Cocktail (Roche), 50 mM NaCl, 52 units/mL Ready-Lyse (Epicentre)] and incubated 15 minutes at 30°C. Samples were placed on ice, then sonicated in ice bath at 25% amplitude for 20 seconds (5 seconds on, 5 seconds off). To each sample, 5.4 μL of 100 mM MnCl2, 4.5 μL 100 mM CaCl2, 12 μL of RNase A (10mg/mL) and 12 μL of DNase I (Fisher) was added and mixed. Samples were incubated on ice for 30 minutes before 50 μL of 500 mM EDTA was mixed in to stop DNase I digestion. Samples were clarified by centrifugation (16k r.c.f.,10 minutes, 4°C), then liquid was split into new microfuge tubes (50 μL for input; 350 μL for RNA polymerase ChIP; ~ 325 μL for other purposes). To each input sample, 450 μL of ChIP elution buffer (50 mM Tris pH 8.0, 10mM EDTA pH 8.0, 1% SDS) was added and mixed in. Inputs were maintained on ice until (at the end of day) placed at 65°C for 12 hours to reverse cross-links.

To each RNApol ChIP sample, an equal volume of 2X IP buffer (200 nM Tris pH 8.0, 600 mM NaCl, 4% Triton X100) with 2x cOmplete™, EDTA-free Protease Inhibitor Cocktail and 10 μL of purified anti-E. coli RNA Polymerase β Antibody (1μg/μL, BioLegend clone 8RB13) was added. ChIP samples were incubated overnight on a nutating platform at 4°C. After incubation overnight, 50 μL of pre-washed once with 200 μL 1X IP buffer. Dynabeads protein G magnetic beads (Invitrogen) were added to each ChIP sample and incubated 2 hours on a nutating platform at 4°C. Beads from ChIP samples were washed, in series, with 1 mL of Buffer A (100 mM Tris pH8, 250mM LiCl, 0.2% Triton x-100, 1mM EDTA), Buffer B (10 mM Tris pH8, 500mM LiCl, 0.1% Triton x-100, 1mM EDTA, sodium deoxycholate), Buffer C (10 mM Tris pH8, 500mM LiCl, 0.1% Triton x-100, 1mM EDTA), 1X IP buffer with 1 mM EDTA added, and 1X TE. Beads from ChIP samples were resuspended in 500 μL ChIP elution buffer, heated to 65°C for 30 minutes with 600 rpm shaking, and pulled out of solution by a magnetic stand. The eluted ChIP samples were transferred to new tubes and placed at 65°C for 12 hours to reverse cross-links.

After reversal of cross-links, all sample types were digested for 2 hours with RNaseA (100mg/sample) and then 2 hours with proteinase K (200mg/sample). This was followed by phenol/chloroform extraction [add 500μL of phenol/chloroform/isoamyl alcohol (25:24:1), mix, separate layers (2 minutes at 21,130 r.c.f.), transfer water layer to new tube with 500 μL of chloroform/isoamyl alcohol (24:1), mix, separate layers (2 minutes at 21130 r.c.f.), and then transfer water layer to new empty tube]. DNA from samples was precipitated by the addition of 1/25 volume of 5M NaCl, 2 μL of Glycoblue coprecipitant (Invitrogen), and 3 volumes of 100% ethanol, mixed, incubating 1 hour at 4°C, then >2 hr at −20°C. DNA was pelleted via centrifugation (16,000 r.c.f., 15 minutes, 4°C), washed once with ice-cold 95% ethanol, and air dried. Pellets were brought up in 0.1X TE (100 μL for input, 20 μL for IPOD or RNApol ChIP). QuantiFluor dsDNA dye (Promega) was used to determine DNA concentration prior to sequencing library preparation.

For each sample (input and ChIP), 0.5 ng of DNA was diluted to 50 μL and used as input material for NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB) with NEBNext® Multiplex Oligos for Illumina Dual Index Primers Sets 1 and 2 (NEB). Manufacturer’s instructions were followed except the post-ligation bead clean-up, which was replaced with Oligo Clean & Concentrator kit (Zymo Research Corporation; performed per manufacturer’s protocol except DNA was eluted with 16.2 μL of 0.1X TE).

NGS data analysis

Read pre-processing

Illumina sequencing adapters were trimmed from reads using cutadapt v4.0 with the following command line arguments: –quality-base=33 -a AGATCGGAAGAGC -A AGATCGGAAGAGC -n 3 –match-read-wildcards [43]. Trimmomatic v0.39 was then used to remove low-quality bases from reads with the following command line arguments: -phred33 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:10 [44].

RNAP ChIP-seq analysis

Read alignment, fragment counting, and enrichment inference

To align pre-processed reads, we first created a custom reference sequence comprising GenBank CP064350.1 (V. cholerae C6706 chromosome I), GenBank CP064351.1 (V. cholerae C6706 chromosome II), and the sequence of the plasmid pBRP02, which was used to overexpress QrgB. Alignment of pre-processed reads to the bowtie2 index created from this reference sequence was performed using bowtie2 v2.4.4 with the following command line arguments: -q –end-to-end –very-sensitive –phred33 –fr -I 0 -X 2000 –dovetail [45]. Position-sorted bam files were prepared using samtools v1.9 [46]. Aligned fragments were counted at 5-bp resolution and the resulting bedgraph files were used as inputs to Enricherator to infer RNAP ChIP-seq enrichment [47]. Arguments of note to our call of “cmdstan_fit_enrichment_model.R” were --ext_subsample_dist 75 --ext_fragment_length 150 --input_subsample_dist 70 --input_fragment_length 140 --frac_genome_enriched 0.2 and --grad_samps 2.

We used Enricherator v0.3.0 [47] to infer RNAP occupancy and to estimate the contrast between occupancy in QrgB vs. QrgB*. Example code used for fragment counting and Enricherator analysis can be found at https://github.com/jwschroeder3/cdgc_code.

RNA-seq analysis

Our analysis of RNA-seq data is described in detail below, but briefly, we assembled the V. cholerae C6706 transcriptome using Rockhopper [48], and quantified transcript abundance and differential expression in QrgB vs. QrgB* using a combination of kallisto [49] and the R package sleuth [50].

Preparation of kallisto index

A kallisto index was prepared by first performing a reference-guided assembly of the transcriptome in our RNA-seq samples using Rockhopper [48]. Rockhopper was run using each chromosome, CP064350.1 (V. cholerae C6706 chromosome I) and CP064351.1 (V. cholerae C6706 chromosome II), as the reference separately, after which we pooled the two chromosomes’ Rockhopper assembled transcripts and operons. Some annotated transcripts present in the GenBank gff files for CP064350.1 and CP064351.1 were not assembled by Rockhopper, so we supplemented the transcript and operon assemblies from Rockhopper with any annotations in the GenBank gff files that were absent from the Rockhopper results, resulting in a final set of annotated and newly assembled transcripts and operons. In order to accurately quantify transcript abundance in antisense RNAs, for each CDS or operon of CDSs, we inserted a line into the final annotations representing a full-length antisense transcript for the given CDS or operon of CDSs. This procedure allowed kallisto to properly allocate antisense reads between short antisense RNAs and background fragments pseudoaligning to the full length antisense annotations we inserted. Our manual inspection of alignments of RNA-seq data revealed the snrC locus to be on the minus strand of ChrII, from nucleotides 796,238 to 796,567, inclusive, and we inserted the snrC annotation into our annotations file accordingly. Finally, we extracted the sequence of each transcript using bedtools getfasta with the -s flag set to enforce stranded retrieval of each transcript’s sequence. The resulting fasta file was used to prepare a kallisto index for pseudoalignment.

RNA-seq quantification

Transcript abundance was estimated using kallisto v0.50.1 via the “kallisto quant” command with the --bootstrap_samples argument set to 100. The R package sleuth v0.30.1 was used to estimate differential expression of QrgB vs. QrgB*. Example code used for transcript assembly and differential expression analysis can be found at https://github.com/jwschroeder3/cdgc_code.

Supplementary Material

Supplement 1

Acknowledgements

We thank Jasper Gomez for the construction of C6706 lac− strain. We thank Lijun Chen and Anthony Schilmiller of MSU Mass Spectrometry core for their technical support. The research was supported by NIH grant R35 GM128637 (to L.F.), NSF grant AWD2025426 (to L.F.), and NIH grants GM139537 and AI158433 and a MSU Tetrad award to C.M.W.

Data availability

RNAP ChIP-seq data and enrichment scores are available under GEO accession GSE274215, and RNA-seq data and differential expression analyses are available under GEO accession GSE274216.

Figure 1. VpsR is involved in negative feedback regulation of cdG levels.

In all cases QrgB and QrgB* were induced with 1mM IPTG beginning at 0 min. A) Intracellular concentrations of cdG measured by UPLC-MS/MS in WT, ΔvpsR, and ΔvpsT mutants at different timepoints after QrgB induction. Points and error bars in panels A-C represent the mean ± SEM of 3 biological replicates B) Similar conditions to panel A, but QrgB* was induced. C) Data in panels A and B are represented as a ratio of cdG levels in QrgB/QrgB* over time. D) RNA-seq results for biofilm-related genes and operons. Color scale denotes the log2-scaled ratio between expression in QrgB induction versus QrgB* induction, and the size of each point indicates the false discovery rate, with the largest points denoting a q-value <= 0.1 and the smallest points denoting a q-value = 1.0. E) Regulatory network implied by our RNA-seq results. F) RNA-seq results for flagella-related genes and operons. Color scale and size of points are the same as panel C, except the color scale limits differ between panels C and D. “Class” indicates the step in the flagellum synthesis cascade in which the indicated operon has been assigned by [41].

Figure 2. CdgC is responsible for reduction in cdG levels during cdG response.

A) Effects of cdG induction on transcript abundance for all genes with EAL, GGDEF, or HD-GYP domains. Point size indicates statistical significance, with larger points denoting lower FDR, and the color scale denotes the log2-scaled fold difference between gene expression in QrgB vs. QrgB*. Heatmap rows were ordered by increasing effect of cdG in WT cells at 60 minutes. B) Expression of PcdgC-gfp promoter fusion in WT and ΔvpsR with QrgB or QrgB* overexpression at different timepoints after normalization with OD600. Bar height represents the mean of three biological replicates; overlaid points represent the value for each of three biological replicates. C) Intracellular concentrations of cdG measured by UPLC-MS/MS in ΔcdgC, ΔvpsR ΔcdgC mutants and cdgC overexpression (p-cdgC) in WT and ΔvpsR at different time points after QrgB induction. Lines are the mean value and overlaid points are the values from each of three biological replicates.

Figure 3. An antisense transcript regulates cdG levels through cdgC.

A) Effect of cdG induction on RNAP occupancy at the cdgC locus is plotted as the mean (solid line) and 90% credible interval (shaded region). Potential evidence of RNAP occupancy at promoters internal to cdgC is exhibited by peaks, indicated by arrows in the top-left subplot, at early timepoints in WT cells. The vertical gray rectangle indicates the approximate location of the cdgC promoter. Purple arrows in panels A and B indicate our manual approximations of the cdgC promoter (PcdgC) and the promoter that may drive expression of snrC (PsnrC). B) Antisense (relative to cdgC) RNA-seq fragment abundance is plotted for WT (left) and ΔvpsR (right), for each of QrgB (Active) and QrgB* (Inactive) induction at each timepoint. C) Effect of cdG induction on expression of snrC (measure via RNA-seq) is plotted at each timepoint. D-E) Intracellular concentrations of cdG measured by UPLC-MS/MS in D) WT and E) ΔcdgC mutant with ectopic overexpression of snrC, and control transcripts RpoA and the reverse complement of snrC (sense-snrC). Data in D and E represent the mean ± SEM. from 3 biological replicates.

Figure 4. CdgC promotes balanced biofilm formation and dispersal.

A) Growth curves of WT, ΔvpsR, ΔcdgC, and ΔvpsR ΔcdgC strains containing QrgB induced with 0, 0.15, 0.625 and 1.25 mM IPTG. Data represent the mean ± SD of 3 biological replicates. B) Schematic demonstrating competition assay. Biofilm-coated beads are either used to count blue/white colonies or to inoculate sterile medium containing a fresh sterile bead every 24 hours. Static growth requires cells in the biofilm to disperse from the left bead and form a new biofilm on the right bead to be competitive in the assay. C) Data showing ratio of colony forming units (cfu) between WT (lac−) and ΔcdgC (lac+) in the presence (left plot) and absence (middle plot) of beads and WT (lac+); or ΔcdgC lacZ::PcdgC:cdgC (lac−) in the presence of beads (right plot). Thin lines represent independent biological replicates and thick lines represent the mean ratio of three biological replicates for each starting ratio. The ratios were clipped to a maximum value of 1,000.

Figure 5. Model showing feedback regulation of cyclic di-GMP levels by CdgC via VpsR.

During increased intracellular levels of cyclic di-GMP, VpsR in the presence of cyclic di-GMP activates expression of CdgC which decreases cyclic di-GMP levels via its PDE activity. Additionally, through an unknown mechanism, very high cyclic di-GMP levels induce SnrC expression (dashed arrow) and SnrC counteracts cdgC expression. This feedback enables V. cholerae to quickly switch between motile and sessile lifestyles without becoming over-committed to a biofilm lifestyle.

1 https://www.who.int/emergencies/situations/cholera-upsurge
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