Ratiometric quorum sensing governs the trade-off between bacterial vertical and horizontal antibiotic resistance propagation

Alvaro Banderas; Arthur Carcano; Elisa Sia; Shuang Li; Ariel B Lindner

doi:10.1371/journal.pbio.3000814

. 2020 Aug 14;18(8):e3000814. doi: 10.1371/journal.pbio.3000814

Ratiometric quorum sensing governs the trade-off between bacterial vertical and horizontal antibiotic resistance propagation

Alvaro Banderas ^1,^2,^¤a,^¤b,^*, Arthur Carcano ^3,⁴, Elisa Sia ^1,², Shuang Li ^1,², Ariel B Lindner ^1,^2,^*

Editor: Nathalie Balaban⁵

PMCID: PMC7449403 PMID: 32797039

Abstract

Plasmid-mediated horizontal gene transfer of antibiotic resistance and virulence in pathogenic bacteria underlies a major public health issue. Understanding how, in the absence of antibiotic-mediated selection, plasmid-bearing cells avoid being outnumbered by plasmid-free cells is key to developing counterstrategies. Here, we quantified the induction of the plasmidial sex pheromone pathway of Enterococcus faecalis to show that the integration of the stimulatory (mate-sensing) and inhibitory (self-sensing) signaling modules from the pCF10 conjugative plasmid provides a precise measure of the recipient-to-donor ratio, agnostic to variations in population size. Such ratiometric control of conjugation favors vertical plasmid transfer under low mating likelihood and allows activation of conjugation functions only under high mating likelihood. We further show that this strategy constitutes a cost-effective investment into mating effort because overstimulation produces unproductive self-aggregation and growth rate reduction. A mathematical model suggests that ratiometric control of conjugation increases plasmid fitness and predicts a robust long-term, stable coexistence of donors and recipients. Our results demonstrate how population-level parameters can control transfer of antibiotic resistance in bacteria, opening the door for biotic control strategies.

The pathogenic bacterium Enterococcus faecalis controls the horizontal gene transfer of antibiotic resistance according to the ratio of non-resistant to resistant bacteria, while remaining agnostic to variation in the total culture density. This ratiometric twist on the well-known phenomenon of quorum-sensing allows investment in expensive conjugative transfer of plasmids only when chances are high, akin to sexual systems in more complex organisms.

Introduction

Horizontal gene transfer mediated by conjugative plasmids and integrative conjugative elements (ICEs) is a major cause of the rapid spread of bacterial antibiotic resistance [1]. The process starts with a “mating” stage, which depends on contact through sexual pili or cell–cell aggregation proteins followed via a type IV secretion system [2]. Importantly, expression of these conjugation determinants bears a significant fitness cost, reducing the host’s growth rate, implying the existence of a trade-off between the horizontal and vertical modes of plasmid transfer [3]. Indeed, across gram-negative and gram-positive bacteria, plasmid donor populations are generally in a constitutive “off-state,” and only after induction by environmental factors or signaling molecules is conjugation activated [4]. Also, a general feature of these systems is that only a few members of the population activate the response [4]. This property suggests that antibiotic-resistant plasmids and ICEs naturally maintain a high proportion of vertical rather than horizontal transfer. On an evolutionary timescale, under constant low availability of plasmid recipients, plasmid variants with lower conjugation rates are expected to gain a fitness advantage by increasing their vertical inheritance through host proliferation. Contrary to that, evolution is expected to favor plasmid variants with increased conjugation rates under conditions of constant high recipient availability [5]. Therefore, the optimal effort that a plasmid invests on horizontal spread depends principally on the social environment under which conjugation control evolved. However, the primary population parameters determining the potential donor–recipient encounter rates, i.e., the densities of donors and recipients, can vary dynamically at faster timescales through growth, dilution, and migration processes. Intuitively, then, plasmid variants able to dynamically monitor mating likelihood and regulate conjugation effort accordingly could evolve.

Antibiotic-resistant plasmids from Enterococcus faecalis are a major threat to public health [6] because of the efficiency of their pheromone-sensitive conjugation systems [7] and the multiplicity of resistances they can transfer, including those against the last-line antibiotic vancomycin [8]. Plasmids from E. faecalis exhibit the capacity to sense recipient densities [9]. In the pCF10 plasmid and its family members, this mate sensing is achieved with unidirectional signaling based on conserved peptide import–export systems [10–13]. This system is also no exception in terms of the cellular cost of conjugation, given the strong protein expression up-regulation that ensues. Indeed, efficient cell–cell aggregation in this system relies on the high abundance of the aggregation substance from pCF10 (Asc10) protein [14,15], as well as on the expression of >20 pheromone-regulated genes, including the ATP-dependent type IV conjugation machinery [16,17]. Donor populations exposed to a given level of inducer also have the capacity to sort into responding and nonresponding cells—in this case, through a pheromone-dependent bistable switch at the transcriptional level [18].

From an evolutionary perspective, sensing the presence of mates through selection for specificity (Fig 1A, compare left and center panels) is straightforward to interpret because it avoids unproductive donor–donor interactions. Intriguingly, however, in the E. faecalis system, two antagonistic plasmid-encoded, pheromone-sensing systems control conjugation. These integrate information about the presence of potential plasmid recipients (mate-sensing) and about plasmid donors (self-sensing) (Fig 1A, right; [16]). Such integration is antagonistic, with recipient-produced cCF10 and donor-produced iCF10 pheromones causing activation and repression of conjugation functions, respectively (Fig 1A, right). Such repressive self-sensing could effectively prevent self-aggregation and unproductive homophilic interactions at high donor densities, in which donor-produced leaky cCF10 (Fig 1A) could accumulate to significant levels. Self-sensing is mediated by basal production of iCF10 during the growth of uninduced donors. This pheromone is essential for keeping the conjugation pathway inactive in donor populations, even if leaky cCF10 accumulates due to growth, as shown by saturated constitutive pathway expression in plasmids carrying a nonfunctional version of iCF10 [19]. We rationalized that the combination of self-sensing and mate sensing could serve another function—namely, conferring to donor cells the capacity to perceive mate availability (ratio sensing) rather than mate concentration only (quorum sensing, [20]). This could enable cells to respond specifically to the population composition (Fig 1B). Intuitively, growth in liquid may increase the rate of accumulation of both cCF10 and iCF10 (Fig 1B, right), but the effects produced by each could balance each other out, producing a response that remains insensitive to fluctuations in the degree of cellular crowding.

Here, we demonstrate that density-robust ratiometric control over horizontal plasmid transfer allows antibiotic-resistant Enterococcus plasmid donors to estimate the conjugation likelihood in a cost-effective manner, maximizing their fitness. We further suggest that this mechanism robustly stabilizes the population composition in the long term in the face of variation in resource availability.

Results

Ratiometric sensing of population composition

To test whether cells are indeed capable of distinguishing population ratio from recipient density (Fig 1B), we quantified pheromone-mediated induction of the pCF10 plasmid during sexual aggregation by performing donor–recipient coincubation experiments and measuring the gene expression response of the pCF10 mating–pheromone pathway using flow cytometry (Fig 1C, S1 Fig) in plasmid donors. For this, we used OG1RF(pCF10-GFP) donors [23]—a bright green fluorescent protein (GFP) reporter strain of pheromone induction (S2 Fig)—to both distinguish fluorescent donors from autofluorescent recipients and to quantify the pheromone responsive gene (prg) prgB (encoding Asc10) expression in flow cytometry experiments (S1 Fig). We resolved the dependency of the response on population parameters (composition and total density) across a wide range of values (Fig 2A and 2B). We observed that ratio but not density causes activation of conjugation. This analysis showed that donor cells commit to strong Asc10 expression only when populations are recipient biased, and conversely, they suppress the response in donor-biased populations. Crucially, the fraction of donor cells that show detectable response levels (Fig 2C) correlates with the result of mating assays (Fig 2D) in the wild-type strain in which on the one hand, the mating efficiency reaches 100% (all donors mate once) at highly recipient-biased ratios and, on the other, remains constant within a 5-fold difference in the total population cell density, and sensitive within two orders of magnitude of population composition values.

Fig 2 — (A, B) Response (mean GFP fluorescence intensity per cell) of plasmid pCF10 in populations with varying concentrations of emitter and donor cells at 2 hours after mixing and measured by flow cytometry (see *Flow cytometry*). The response of OG1RF(pCF10-GFP) donors is plotted as a function of the two essential population parameters, the [R+D] (A) and R:D ratio (B), each colored according to their counterpart parameter: green for the R:D ratio levels (A) and red for the total density levels (B) (data set shows seven independent experiments). (C, D) The fraction of early (1 hour) activated donors as a function of the R:D ratio from the data set in (A) and (B). Data were regularly binned (10), and the SEM was calculated (error bars). The dashed line is the basal value at R:D ratio = 0 (C). Mating efficiency (see *Mating experiment*) as a function of the R:D ratio at 2 (circles) and 3 (triangles) hours with total densities (OD600) of 0.1 (red) and 0.5 (blue). The underlying numerical data are shown in S1, S2 and S3 Data. GFP, green fluorescent protein; R:D, recipient-to-donor; [R+D], total population concentration; OD, optical density.

Our results show that the fraction of donors that mate increases at recipient-biased ratios but remains roughly constant when the total density is varied. This result implies that when populations are donor biased, the activation of the system occurs in only a subset of cells during aggregation, consistent with the bistable model of pheromone induction [18], and further shows that the donor population commits to conjugation only when in the minority and not at any specific recipient density, i.e., partner sensing is ratiometric and not densitometric.

Asc10 expression reduces vertical transfer of conjugative plasmids

Ratiometric sensing is reminiscent of the mating system of S. cerevisiae, in which the population’s sex ratio allows cells to sense the degree of mate competition in the population [24]. In yeast, A-type cells use sex ratio sensing to balance a trade-off existing between commitment to mating and clonal haploid proliferation. However, whether ratiometric sensing relates to the existence of a similar trade-off in E. faecalis is unclear. To test whether a trade-off between investment in conjugation and vertical plasmid proliferation exists in E. faecalis, we first estimated the initial pheromone concentrations that correspond to the stimulation range observed in the coincubation experiments. Shaken cultures of donors exposed to sufficient cCF10 concentrations tend to self-aggregate, i.e., [cCF10] > 10 nM (S3 Fig). Although a reduction in growth below 10 nM is detectable by optical density measurements (S4 Fig), growth is difficult to measure precisely under shaking conditions, either with optical density or colony-forming unit (CFU) counts, as microaggregates are difficult to disperse. For this reason, we developed an assay to quantify growth in static cultures, in which self-aggregation is not aided by orbital shaking and the pheromone response is detected as Asc10-dependent surface attachment at cCF10 concentrations below pathway saturation (Fig 3A). By quantifying growth under such conditions, we show that much of the growth impairment occurs at pheromone concentrations marking the transition point to surface adhesion and that such impairment increases further only at high, nonphysiological pheromone concentrations (Fig 3B). At concentrations in the micromolar range, we observed a time-dependent decrease in the optical density at the highest cCF10 concentrations, consistent with pheromone-induced toxicity [25]. Importantly, at concentrations lower than approximately 1 pM, cells accumulate as an easily visible dense precipitate that can be easily resuspended, unlike Asc10-expressing cells, which distributed homogeneously as a film in the bottom and remained adhered after resuspension (Fig 3A and 3B). This allowed us to determine that the physiological range of sensing, defined as the pheromone concentrations within which cells can activate Asc10 and avoid self-aggregation (S3 Fig), is roughly two orders of magnitude, consistent with the dynamic range of sensing on the recipient-to-donor ratio scale in coincubation experiments (Fig 2B).

Fig 3 — (A) A phenotypic assay for Asc10 activity (see *Adherence/growth assay*) showing the macroscopic appearance of static cultures of OG1RF donors carrying wild-type pCF10 or the pCF10-GFP Asc10 expression reporter [23] after an overnight incubation with varying concentrations of purified cCF10. Unstimulated cells appear as a precipitate in the center of the microwell. Adhered cells appear as a film. (B) Time-resolved microwell horizontal spatial scanning (11 positions) of optical density in OG1RF(pCF10). The underlying numerical data are shown in S4 Data. GFP, green fluorescent protein.

Our results show that a trade-off between vertical and horizontal plasmid transfer exists in E. faecalis. This suggests that, similar to budding yeast [24], specifically controlling Asc10 expression according to the recipient-to-donor ratio (a proxy for the mating likelihood) could be selected by evolution. Such pheromone-induced fitness loss is likely due to mild pheromone-induced prgB-dependent toxicity, similar to previous observations in prgU knockouts [25]. Using our assay, we show that wild-type pCF10 exhibits an approximately 20% maximal growth reduction, within the sensitive range of the pathway (S5 Fig), whereas a prgU knockout is more severe at higher pheromone concentrations (S6 Fig). Balancing the observed trade-off to maximize fitness might therefore be the primary role of ratio sensing.

Ratio sensing can increase plasmid fitness and facilitates donor–recipient coexistence

To understand the role that ratiometric regulation of conjugation has on plasmid fitness, we built a simplified dynamic model consisting of donor and recipient populations described by two coupled ordinary differential equations (ODEs).

Donor population (d) is governed by three dynamics: first, an irreversible second-order mass-action–like process that depicts horizontal plasmid transfer (conjugation), transforming recipients (r) to donors with a rate constant (λ_conj), weighed by function h(r,d), which takes values between 0 and 1 and whose form depends on the strategy analyzed; second, a logistic growth law (i.e., vertical transfer), with maximal rate λ_d limited by the carrying capacity K and hindered by the cost of mating activation up to a constant c, as estimated from our experiments (c = 0.2; S5 Fig); and third, an imposed constant dilution rate (μ):

d^{'} = λ_{c o n j} \cdot h (r, d) \cdot d \cdot r + (λ_{d} \cdot (1 - c \cdot h (r, d)) \cdot (1 - \frac{r + d}{K}) - μ) \cdot d

(1)

Likewise, recipients grow logistically with a maximal rate λ_r, yet they are removed from the pool by dilution and upon conjugation:

r' = - λ_{c o n j} \cdot h (r, d) \cdot d \cdot r + (λ_{r} \cdot (1 - \frac{r + d}{K}) - μ) \cdot r

(2)

We assume that the presence of the plasmid burdens donor proliferation, i.e., λ_d < λ_R. Therefore, plasmid transfer decreases the fraction of recipients, and growth competition increases it. Using this approach, we compared four different strategies (h(r,d)): a strategy with constitutive activation (a constant value), a recipient-sensing strategy (a sigmoidal, Hill-type function of the recipient density), a population-sensing strategy (a Hill function of the total density), and a ratio-sensing strategy (a Hill function of the population ratio) (see Mathematical modeling).

In principle, such strategies may result in recipient or donor takeover or, alternatively, reach a nontrivial coexistence steady state (see example in S7 Fig). We find that ratio sensing outperforms all other strategies, maximizing the donor population size at any carrying capacity (K) value (Fig 4A). Furthermore, the estimated response sensitivity to recipient-to-donor ratio (θ, see Mathematical modeling; Fig 2C), shows that the ratio-sensing strategy achieves the highest predicted plasmid fitness relative to other strategies with their own varying sensitivities (S8 Fig). Interestingly, ratio sensing additionally prevails as the only strategy allowing coexistence to be maintained across a wide range of carrying capacity (K) values (Fig 4B, S8 Fig).

Fig 4 — (A, B) Steady-state donor density (A) and donor fraction (B) for different sensing strategies as a function of the carrying capacity (K). The underlying numerical data are shown in S5 Data. Python code with model and simulations can be found in https://gitlab.inria.fr/InBio/Public/efaecalis-ratio.

Discussion

Here, we demonstrated that E. faecalis pCF10 plasmid donors perform ratiometric sensing, largely agnostic to changes in total density at the tested ranges of population parameters. In very diluted cultures below the tested density range, we expect ratiometric sensing to be unlikely because pheromone concentrations fall below the absolute threshold for mating activation. Additionally, although ratios could be recipient biased at these densities, the overall encounter probability is rather low, making ratiometric sensing unnecessary.

Ratiometric sensing was recently described for other binary populations, such as those composed of signal producers and signal “cheaters” in the PhrA-RapA-Spo0F signaling pathway in Bacillus subtilis [26] and yeast mating types [24]. In both cases, ratiometric sensing is achieved by cell density–dependent reductions in extracellular signal availability. In B. subtilis, the ratio of signal producers to nonproducers is sensed as a result of the role of population-wide signal internalization or “pumping in” (through the oligopeptide permease [Opp]) in depleting the available extracellular signal. The E. faecalis system uses molecular components homologous to those used in B. subtilis (Fig 1C). However, two important differences between these systems suggest that the dominant mechanism for ratio sensing in E. faecalis relies on signaling rather than pumping alone. First, in the absence of functional iCF10, mating activation is constitutively high in E. faecalis [19], making pheromone sensing impossible. Second, signal uptake is donor specific, which can reduce the capacity for ratio sensing by poor global uptake [26], especially in recipient-biased populations. Ratio sensing through iCF10 could have become necessary as PrgZ-dependent, cCF10-specific uptake evolved, both sensitizing the pathway and making recipients comparatively weaker pumps. Despite this, pumping itself can indeed affect extracellular concentrations of both signals. We speculate that even though pumping can modify extracellular signal availability, its effects remain equal for both pheromones, leaving the pheromone ratio unaltered. Nevertheless, the exact relative contributions of signal depletion and iCF10 signaling in shaping the ratiometric response function remain to be uncovered.

Why would such ratiometric sensing evolve? Overall, our modeling results shows that ratiometric sensing is superior to competing strategies because it maximizes plasmid fitness. Our simulations show that ratio sensing maximizes donor concentrations at steady state by letting faster-growing recipients reach a certain fraction of the population before transferring the plasmid (S7 Fig). Therefore, maintaining recipients as a substrate for spread seems more efficient than simply taking over and relying on a comparatively slower vertical spread, maximizing plasmid proliferation in the absence of antibiotics.

Overall, our results suggest that conjugation could in principle be altered externally as a possible therapeutic to artificially drive antibiotic-resistant cells to extinction.

Materials and methods

Bacterial strains and culture conditions

Bacterial strains used in this study are listed in Table 1. For gene expression quantification in coincubation (mixed-population) experiments, the OG1RF(pCF10-GFP) reporter strain was used as the donor [23], and OG1RFSSp was used as the recipient cell. For mating experiments, strain OG1RF-GFP (expressing GFP constitutively, [27]) was used as recipient, and the wild-type OG1RF-pCF10 was used as donor. In general, overnight cultures were grown in brain heart infusion (BHI) media and appropriate antibiotics, whereas MM9YEG [28] semisynthetic media was used for day cultures, inductions, and coincubation experiments. The characterization of OG1RF(pCF10-GFP) as a useful reporter for coincubation experiments (see Coincubation experiments below) was done by comparing it to a 2-fold-dimmer wild-type GFP reporter strain OG1RF(pCF10-prgC-GFP) carrying a GFP downstream of prgC (a kind gift from Gary Dunny and Wei-Shou Hu).

Table 1. Strains used in this study.

E. faecalis strain	Description	Reference
OG1RF(pCF10)	Wild-type OG1RF carrying wild-type pCF10 plasmid.	[29]
OG1RF(pCF10-GFP)	pCF10 with a, RBSprgB-GFP construct located between prgB and prgC. The cassette disrupts the small prgU ORF¹. Alternative name: OG1RF(pCF10-LC1).	[23] and sequencing results
OG1SSp	Wild-type recipient.	[30]
OG1RF-GFP	Wild-type expressing GFP constitutively. Alternative name is SD234.	[27]
OG1RF(pCF10-prgC-GFP)	Wild-type pCF10 GFP construct located downstream of prgC.	Gift from Gary Dunny and Wei-Shou Hu

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¹This ORF was unknown at the time of publication of the referred work.

Abbreviations: GFP, green fluorescent protein; ORF, open reading frame; prg, pheromone responding gene; RBS, ribosome binding site

Pheromone stimulation

The cCF10 (synthesized by Biocat) pheromone was dissolved in 100% DMSO. Serial dilutions of cCF10 were prepared in DMSO and then added to 96- or 24-well plates (costar) containing the test cultures at an equal optical density of approximately 0.1 using an adjustable-spacer multichannel pipet or a multichannel pipet. Stimulations have a constant final concentration of 0.1% DMSO (which does not affect growth) and 1% bovine seroalbumin (BSA, Sigma) to block peptide adhesion to surfaces. Reported pheromone concentrations are initial, as donor uptake modifies the extracellular concentration with time.

Coincubation experiments

After day culture growth, donor and receiver strains were mixed at different ratios and immediately serially diluted in 24-well plates, sealed (Breath-easy seals, Diversity Biotech), and shaken at 37°C (Infors orbital incubator). Strain OG1RF(pCF10-GFP) harbors a GFP construct located downstream of prgB ([23], Fig 1C), which is inserted within the prgU gene, as determined by sequencing. This reporter shows a high output signal, which is crucial to distinguish populations from autofluorescent recipients (S1 and S2 Figs). Also, the strain does not mate efficiently, further allowing us to minimize the effects of transconjugants that may alter the recipient-to-donor ratio tested. The strain nevertheless maintains normal sexual aggregation and, importantly, remains sensitive in the same physiological range of pheromone stimulation as the wild type (S2 Fig). To calculate the fraction of stimulated donors, we counted the stimulated donors (S1 Fig, gate R1 plus gate R3) and divided it by the total number of donors, which is the total number of cells in the sample: [gate R1 + gate R2 + 2×R3 (each event in R3 is a cell pair)] multiplied by the donor fraction, which is a known variable ab initio.

Flow cytometry

Before quantification, homogenization of self-aggregates was done mechanically by pipetting up and down approximately 50 times using a multichannel pipet. Cultures were immediately diluted 4-fold, and sampling was performed quickly in a flow cytometer. Flow cytometry was performed in a Gallios instrument (Beckman Coulter) or a Fortessa HT (Becton Dickinson) with sample sizes of 10,000 to 1,000,000 cells. The fluorescence signal from GFP was acquired using a 488-nm laser for excitation and a 525/40 (Gallios) or a 530/30 filter (Fortessa) for emission.

Adherence/growth assay

Cell densities in the absence of aggregates were reconstructed from spatially resolved OD600 measurements done on glass-bottom microwell plates (Corning) in a microplate reader (Tecan-Infinity, equipped with a monochromator) with an incubation temperature of 37°C, no shaking, and the “lid on” mode activated. The lid of the plate was kept on and sealed with punctured parafilm to avoid evaporation. Eleven positions within each microwell were acquired at regular intervals. After the experiment finalized, images were acquired with a SCAN 1200 colony counter. To avoid uneven illumination, only three microwells centered per picture were acquired.

Aggregation and turbidity assay

Aggregation levels were determined in 24-well plates with shaking at 150 RPM at 37°C containing BSA 1% in MM9YEG [28]. Quantification was done by measuring the optical density decrease in time (S4A Fig). Aliquots were sampled near the liquid surface, touching the well wall with the pipet tip. The total turbidity loss (S4B Fig) was determined by sampling the culture at the last time point in S4A Fig after strong resuspension and an immediate dilution in MM9YEG.

Mating experiment

Mating assays were performed by coincubating the wild-type OG1RF(pCF10) with constitutively fluorescent strain OG1RF-GFP [28] for 2 or 3 hours under optimal conditions for aggregate formation, i.e., in 24-well plates (Costar) under orbital shaking at 150 RPM in a final volume of 1 ml in the presence of 1% BSA. Homogenization of macroscopic sexual aggregates formed during mating reactions was done mechanically by pipetting up and down approximately 50 times using an adjustable-spacer multichannel pipet (Rainin) set to half of the total reaction volume (0.5 ml) and immediately diluting the samples to the appropriate dilution. Dilutions were plated in BHI agar medium. When colonies achieved an appropriate size, a velvet colony replicator was used to stamp the colonies on BHI containing tetracycline (10 μg/ml) to select for donors and transconjugants (and, by plate comparison, identify recipients), which we further distinguished from each other by detecting GFP-positive colonies (transconjugants) in a transilluminator equipped with a camera (Biorad Chemidoc). The mating efficiency was calculated by dividing the number of transconjugants by the number of donors.

Mathematical modeling

The different donor (d) strategies to sense recipients (r) were modeled with the Hill-type function h(r,d) varying between 0 and 1 as follows. For the ratiometric-sensing strategy $h = \frac{r^{η}}{r^{η} + {(θ \cdot d)}^{η}},$ where θ is the recipient-to-donor ratio producing half-maximal activation and η is the Hill coefficient; for the mate-sensing strategy, $h = \frac{r^{η}}{r^{η} + {r_{50}}^{η}}$ , where r₅₀ is the recipient concentration producing half-maximal activation; for the total density-sensing strategy, $h = \frac{P^{η}}{P^{η} + {P_{50}}^{η}}$ , where P₅₀ is the recipient concentration producing half-maximal activation; and for constant activation, h = a, where a is a constant. Fixed parameter values for simulations (Fig 4, S8 Fig) are shown in Table 2. Coding was done on Python 3.7 with packages sympy 1.4, matplotlib 3.1.1, and scipy 1.3.1. The “BDF” method of the “solve_ivp” function was used to integrate ODEs.

Table 2. Fixed model parameter values.

Parameter	Value	Origin
λ_conj	0.012 ml·cell⁻¹·min⁻¹	From [19]
θ	0.5	Estimated from Fig 2C
η	4.3	Estimated from Fig 2C
λ_D	0.01548 min⁻¹	From [19]
λ_R	0.0201 min⁻¹	From [19]
μ	0.012 min⁻¹	Arbitrary
c	0.2	Estimated from S5 Fig

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Supporting information

S1 Fig. Identification of activated donors in mixed-population experiments.

(A, B) Scatter plot of pure donor OG1RF(pCF10-GFP) (A) and recipient OG1SSp (B) cell populations. (C) Example of a coincubation reaction of donors and recipients at a recipient-biased ratio. The mean single-cell intensity of donors was obtained from population R1. Population R2 corresponds to a mixture of autofluorescent donors and nonactivated recipients. Population R3 corresponds to donor–recipient pairs (and higher-order aggregates, i.e., events in R3 with higher fluorescence values than events in R1 [“tail”] were not considered in the analysis). To estimate the fraction of activated donors (Fig 2C), we considered the three populations and the known experimentally determined initial ratio in the calculation (see Coincubation experiments). (D) Histogram with GFP intensities from the data in (C). The underlying numerical data are shown in S6, S7 and S8 Data. GFP, green fluorescent protein.

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S2 Fig. Fluorescent Reporter comparison.

Comparison of (OG1RF(pCF10-GFP)) (ΔprgU, increased output) and wild-type (OG1RF(pCF10-prgC-GFP)) cCF10 dose-responses. Error bars are SEM (n = 4). The underlying numerical data are shown in S9 Data. GFP, green fluorescent protein. prg, pheromone responding gene.

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S3 Fig. Self-aggregate formation.

Orbital shaking–dependent macroscopic self-aggregate formation in OG1RF(pCF10) as a function of cCF10 concentration. The physiological (heterophilic) range of pheromone concentration required to induce detectable Asc10 expression lies below 1 nM cCF10.

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S4 Fig. Pheromone-dependent WT growth under shaking.

(A, B) WT (OG1RF(pCF10)) versus OG1RF(pCF10-prgC-GFP) reporter comparison in an aggregate formation assay (A) and postaggregate dispersion cell density measurement (B) (see Aggregation and turbidity assay). Note the increase in turbidity at 1 hour in (A), the time at which aggregation brings it back to the baseline. The underlying numerical data are shown in S10 Data. GFP, green fluorescent protein; prg, pheromone responding gene; WT, wild-type.

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S5 Fig. Pheromone-dependent wild-type growth in static cultures.

Growth reduction in the physiological (sub-nanomolar) response range in growth/adhesion assays (see Adherence/growth assay). Error bars are SD of n = 3 independent experiments (colors). The underlying numerical data are shown in S11 Data.

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S6 Fig. PrgU suppresses excessive fitness costs associated with unproductive activation of conjugation.

(A, B) Growth/adhesion assay showing mean growth (per well) of pCF10 (A, from Fig 3C) and pCF10-GFP (ΔprgU) (B). The underlying numerical data are shown in S12 Data. GFP, green fluorescent protein; Prg, pheromone responding gene.

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S7 Fig. Example of simulated growth dynamics.

Representative example of a simulation showing the dynamics of (in this case) ratio-sensing strategy with different starting fraction of recipients and total population sizes. The underlying numerical data are shown in S13 Data.

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S8 Fig

Ratio sensing maximizes donor fitness (top) and helps coexistence (bottom). Strategy comparison showing extended simulations varying relevant parameter values for each strategy: activation level (a) for the constitutive activation strategy and sensitivity (half-maximal activation; P₅₀, θ, and r₅₀; see Mathematical modeling) for the rest. Each value is normalized by the maximum value observed within each K. The underlying numerical data are shown in S14 Data.

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S1 Data. CSV file containing the underlying numerical data for Fig 2A and 2B.

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S2 Data. CSV file containing the underlying numerical data for Fig 2C.

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S3 Data. CSV file containing the underlying numerical data for Fig 2D.

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S4 Data. CSV file containing the underlying numerical data for Fig 3B.

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S5 Data. CSV file containing the underlying numerical data for Fig 4A and 4B.

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S6 Data. CSV file the underlying single-cell data for S1A Fig.

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S7 Data. CSV file the underlying single-cell data for S1B Fig.

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S8 Data. CSV file the underlying single-cell data for S1C and S1D Fig.

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S9 Data. CSV file containing the underlying numerical data for S2 Fig.

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S10 Data. CSV file containing the underlying numerical data for S4A and S4B Fig.

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S11 Data. CSV file containing the underlying numerical data for S5 Fig.

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S12 Data. CSV file the underlying numerical data for S6A and S6B Fig.

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S13 Data. CSV file containing numerical data for S7 Fig.

Time traces 1–8 correspond to lowest to highest initial densities in the legend of S7 Fig.

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Click here for additional data file.^{(159.6KB, csv)}

S14 Data. CSV file containing the underlying numerical data for S8 Fig.

(CSV)

Click here for additional data file.^{(93.5KB, csv)}

Acknowledgments

We thank Gary Dunny, Wei-Shou Hu, Ivan Matic, Dawn Manias, Rebecca Breue, Laura Cook, Christopher Kristich, Barbara Murray, Danielle Garsin, and Lynn Hancock for valuable advice and/or strains.

Abbreviations

ccfA: cCF10 pheromone gene
CFU: colony-forming unit
GFP: green fluorescent protein
LTA: lipoteichoic acid
ICE: integrative conjugative element
ODE: ordinary differential equation
Opp: oligopeptide permease
ORF: open reading frame
Prg: pheromone responding gene
RBS: ribosome binding site
Sec10: surface exclusion from pCF10

Data Availability

All relevant data are within the paper and its Supporting Information files. Python code with mathematical model and simulations is available at https://gitlab.inria.fr/InBio/Public/efaecalis-ratio.

Funding Statement

LabEx -Who am I?- Postdoctoral fellowship to AB (ANR-11-LABX-0071 SEXASYM)(https://www.enseignementsup-recherche.gouv.fr/cid51355/laboratoires-d-excellence.html) Permanent support. Fondation Bettencourt Schueller (https://www.fondationbs.org/) to ABL The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Biol. doi: 10.1371/journal.pbio.3000814.r001

Decision Letter 0

Roland G Roberts

21 Feb 2020

Dear Dr Banderas,

Thank you for submitting your manuscript entitled "Ratiometric quorum sensing in Enterococcus faecalis conjugation" for consideration as a Research Article by PLOS Biology.

Your manuscript has now been evaluated by the PLOS Biology editorial staff, as well as by an academic editor with relevant expertise, and I'm writing to let you know that we would like to send your submission out for external peer review. IMPORTANT: We note that you have submitted this as a full Research Article, but we think that it would be better reviewed as a Short Report. No re-formatting is required, as your paper is already quite concise, but please change the article type to "Short Reports" when you upload your additional metadata (see next paragraph).

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Roland G Roberts, PhD,

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PLOS Biology

PLoS Biol. doi: 10.1371/journal.pbio.3000814.r002

Decision Letter 1

Roland G Roberts

19 Mar 2020

Dear Dr Banderas,

Thank you very much for submitting your manuscript "Ratiometric quorum sensing in Enterococcus faecalis conjugation" for consideration as a Short Report at PLOS Biology. Your manuscript has been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by three independent reviewers.

You'll see that all three of the reviewers are broadly positive about your study. However, reviewers #2 and #3 raise a significant number of concerns, mostly involving your mathematical model and its relationship to the rest of the manuscript. These issues will need to be addressed before we can consider your manuscript further.

In light of the reviews (below), we will not be able to accept the current version of the manuscript, but we would welcome re-submission of a much-revised version that takes into account the reviewers' comments. We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent for further evaluation by the reviewers.

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Roland G Roberts, PhD,

Senior Editor

PLOS Biology

*****************************************************

REVIEWERS' COMMENTS:

Reviewer #1:

This manuscript focuses on defining the parameters and mechanism that allow plasmid-bearing antibiotic resistant cells to avoid being outcompeted in the absence of antibiotics. The authors evaluate several potential sensing mechanisms and conclude that ratiometric sensing of donor:recipient cells drives sexual behavior in bacteria. This is a well-executed study that report provocative results that should be of high interest to researchers studying sociomicrobiology and evolution. I also believe that the conclusions of this study are well-supported by the experimental data. I therefore recommend publication in PLOS Biology.

Reviewer #2:

Banderas et al. present an interesting set of data on the function of the E. faecalis extracellular signaling system in the regulation of conjugation. A key finding is that the response changes a function of the composition of the population rather than by absolute cell density. This result is very convincingly demonstrated in Fig. 2. The authors also provide evidence that there is a trade-off between investing in horizontal versus vertical plasmid transfer as the induction of the pheromone pathway reduces the growth rate (Fig. 3). Finally, the authors present a model that could explain how ratio-sensing contributes to a stable equilibrium of donor-recipient ratio in the population based on a mathematical model (Fig. 4).

I found the MS interesting and thought provoking and generally feel that it could qualify as a Short Communication. However, I have a few remarks which should be considered.

Major comments

1. Ratio-sensing and signaling architecture: The authors motivate their ratio-sensing hypothesis by highlighting specific features of regulatory architecture of the signaling system. In Figure 1 the authors propose that the ratio-sensing ability requires a) two signaling systems and they assume b) that the extracellular concentrations of both signals are proportional to the donor and recipient concentrations, respectively. These assumptions are not tested in the MS and they may not hold.

It seems important to point out that the investigated signaling system functions by means of a peptide-based export-import circuit (a wide-spread signaling architecture found in many G+ bacteria, Neiditch et al., Annu. Rev. Genet. 2017. 51:311-33, https://doi.org/10.1146/annurev-genet-120116-023507 ). The authors do not mention/consider this aspect. However, this could be important to explain the phenomena observed by the authors. In a recent publication it was shown that export-import (or „pump-probe" signaling systems) extracellular concentrations do not necessarily increase with increasing population density. Moreover, such systems are capable of ratio-sensing in mixed populations of producer and non-producer cells, when signals are taken up very efficiently compared to overall signal production in the population (Babel et al., Nat. Commun. 11, 2020, https://doi.org/10.1038/s41467-020-14840-w)

Hence, for ratio-sensing in E- faecalis conjugation, the second signaling system (assumption a) might in fact be dispensible and assumption b is by no means trivial and not necessarily to be expected. This should be considered/discussed. Please either provide additional data to support their specific model or more carefully introduce/motivate/discuss the ratio-sensing hypothesis adopting a broader mechanistic perspective of the overall signaling architecture.

2. Stimulation experiments to support tradeoff model: Again, given the network architecture, in the stimulation experiments performed in Fig. 3 it is not obvious that the extracellular concentration is indeed the relevant biophysical quantity as the signals will be imported by the cells and activate an intracellular receptor. It might be the available dose of signaling molecules not the extracellular concentrations of signaling molecules present at the start of the experiment, which matters. This point will likely not affect the conclusions of the experiment, since presumably cell densities were initially all the same in all experiments. Thus variation in concentrations are equivalent to variation in dose. Nevertheless, this point should at least be mentioned or alternatively clarified experimentally.

3. Role of prgU/mathematical model: Here I got confused how the tradeoff got linked to the model.

Looks like the authors caveat against the the tradeoff which they just demonstrated and then come up with an alternative hypothesis for the „function(?)" of ratio-sensing. Please clarify. Also consider to replace the title „Mathematical model for conjugation dynamics" by a statement that states the findings derived from the model. I was also confused by the intended message, is the coexistence a „function" of ratio-sensing or a „consequence"? The discussion then brings up the antibiotics for this part. The presentation of this part is sub-optimal and needs improvement for clarity.

Minor comments

4. Check for typos in the text and the figures! e.g. Fig 1a. plamid/plasmid, simulation/stimulation

5. "monitoring the expression of a GFP reporter which is driven by a copy of

prgB's ribosome binding site (RBS) further downstream in the transcript". Maybe reword to facilitate easier understanding.

6. Fig. 2A/B. Please explain the color bar in the caption.

7. Mathematical model: check consistency of notation for all parameters.

8. „If 50 is however of an order of magnitude between those of (1 −/) and (1 -/), then the system behavior is not analysed here."

Please explain a bit more.

Reviewer #3:

In this manuscript, Banderas et al. study the regulation of plasmid conjugation in the pathogenic bacterium Enterococcus faecalis. They show experimentally that conjugation is induced when the bacterial population is composed mostly of recipient (plasmid-free) cells, rather than when the overall population density is sufficiently high. Further experiments show that activation of conjugation is costly. The authors therefore argue that ratiometric control of conjugation mitigates this cost and ensures that donors only bear the cost of conjugation when recipients abund and the benefit of conjugation is high. Finally, the authors construct a mathematical model to study how the interplay between the costs and benefits of plasmid carriage affects the prevalence of plasmid-bearing cells in bacterial populations.

Overall, I find this work to be of high quality, novel and of broad interest. Ratiometric control of conjugation makes intuitive sense, and can significantly affect the dynamics of plasmid-born traits, such as pathogenicity and antibiotic resistance. Yet, I am not familiar with previous works discussing this mode of regulation and its implications. I do have major comments regarding the model as well as more minor ones, as detailed below.

Major comments

-------------------

As formulated, the model is inadequate for its declared purpose - comparing conjugation regulation strategies. This is because the model only accounts for the constant cost of plasmid carriage and does not consider the cost of activating conjugation - a cost demonstrated in Fig. 3. Therefore, in this model activation of conjugation at any population density or composition can only be beneficial for plasmid spread. Indeed that is the case in the results shown in Fig. 4B, where ratio-sensing results in the lowest prevalence of donors.

Moreover, I found the modeling section to be misleading, and at odds with the rest of the paper. The conclusion of this section is that ratio-sensing is "the only strategy allowing a robust co-existence of the two populations [donors and recipients]". Since in previous sections the authors argue that ratio-sensing may be optimal for the plasmid in mitigating the demonstrated cost of conjugation, I initially took that conclusion to mean that ratio sensing prevents the extinction of donor, thus allowing co-existence of recipient and donors. However, the model results in fact show the exact opposite - ratio sensing prevents the extinction of recipients.

Since in this model ratio sensing is not beneficial to the plasmid which encodes the regulatory machinery implementing this regulation it is also not clear why such regulation would evolve. In the discussion, the authors argue that maintaining coexistence between carriers and plasmid-free cells may be beneficial to the population as a whole under intermittent antibiotic exposure. This is an interesting idea, and ratio sensing may be an evolutionary stable state under specific conditions. However, a simpler, and likely more robust mechanism for the evolution of ratio sensing is that it is directly beneficial to the plasmids that implement it.

Therefore, the model needs to be revised to include a cost for conjugation. I realize this entails some arbitrary modeling decisions and the addition of parameters to the model. However, I believe that any reasonable choice can dramatically change the behavior of the model, and make it suitable to address the question of when is ratio sensing favorable to the donors. Since this manuscript is being considered as a Short Report, a simple "proof-of-concept" model showing the benefit of ratio sensing for plasmid donors would suffice. Such a model would likely motivate subsequent more comprehensive dedicated modeling efforts.

Additional comments

-------------------------

*The experiments demonstrating the cost of conjugation do not rule out the possibility of cCF10 toxicity. The claim would be significantly strengthened by additional experiments showing that plasmid-free recipient strains are unaffected by exposure to the same concentrations of cCF10.

*I find it unlikely that conjugation regulation is insensitive to population density even at very low population densities, as stated in the text and implied in Fig. 1B. At low population densities, it would be unlikely for a donor to encounter a recipient even at a high R:D ratio. The current experiments demonstrating ratiometric regulation are all done in very high population densities (OD 0.1-1), therefore it is still unknown how conjugation is regulated at low population densities. I am not advocating that the authors conduct further experiments, since the novelty is in the fact that ratiometric regulation occurs at all. But, this caveat should be stated and discussed.

*More information is required regarding the experiments shown in Fig. 3B. Data from how many replicates is shown? Or is it a single replicate per condition?

*Additionally, the histograms to the right have some white dots in them. Is that an issue with the rendering?

*I would replace Fig. 4A, with a heatmap showing the steady-state donor fraction as a function of initial total population size and donor fraction.

*While arbitrary, the choice of nM units for the total population size 'K' in panel B is unusual and confusing. Suggest replacing it with cells/ml.

PLoS Biol. 2020 Aug 14;18(8):e3000814. doi: 10.1371/journal.pbio.3000814.r003

Author response to Decision Letter 1

21 May 2020

Attachment

Submitted filename: Response to reviewers.pdf

Click here for additional data file.^{(561.5KB, pdf)}

PLoS Biol. doi: 10.1371/journal.pbio.3000814.r004

Decision Letter 2

Roland G Roberts

16 Jun 2020

Dear Dr Banderas,

Thank you for submitting your revised Short Report entitled "Ratiometric quorum sensing in Enterococcus faecalis conjugation" for publication in PLOS Biology. I have now obtained advice from two of the original reviewers and have discussed their comments with the Academic Editor.

We're delighted to let you know that we're now editorially satisfied with your manuscript. However before we can formally accept your paper and consider it "in press", we also need to ensure that your article conforms to our guidelines. A member of our team will be in touch shortly with a set of requests. As we can't proceed until these requirements are met, your swift response will help prevent delays to publication. Please also make sure to address the data and other policy-related requests noted at the end of this email.

IMPORTANT:

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Roli Roberts

Roland G Roberts, PhD,

Senior Editor

PLOS Biology

------------------------------------------------------------------------

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REVIEWERS' COMMENTS:

Reviewer #2:

The manuscript has improved in clarity and my concerns have been addressed by the authors.

Reviewer #3:

I thank the authors for their detailed response. All of my concerns have been addressed, and I am happy to recommend the this manuscript for publication.

PLoS Biol. 2020 Aug 14;18(8):e3000814. doi: 10.1371/journal.pbio.3000814.r005

Author response to Decision Letter 2

14 Jul 2020

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(11.5KB, docx)}

PLoS Biol. doi: 10.1371/journal.pbio.3000814.r006

Decision Letter 3

Roland G Roberts

20 Jul 2020

Dear Dr Banderas,

On behalf of my colleagues and the Academic Editor, Nathalie Balaban, I am pleased to inform you that we will be delighted to publish your Short Reports in PLOS Biology.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Identification of activated donors in mixed-population experiments.

(TIF)

Click here for additional data file.^{(638.1KB, tif)}

S2 Fig. Fluorescent Reporter comparison.

(TIF)

Click here for additional data file.^{(148.3KB, tif)}

S3 Fig. Self-aggregate formation.

(TIF)

Click here for additional data file.^{(230.1KB, tif)}

S4 Fig. Pheromone-dependent WT growth under shaking.

(TIF)

Click here for additional data file.^{(1.5MB, tif)}

S5 Fig. Pheromone-dependent wild-type growth in static cultures.

(TIF)

Click here for additional data file.^{(835.1KB, tif)}

S6 Fig. PrgU suppresses excessive fitness costs associated with unproductive activation of conjugation.

(TIF)

Click here for additional data file.^{(2.2MB, tif)}

S7 Fig. Example of simulated growth dynamics.

(TIF)

Click here for additional data file.^{(341.4KB, tif)}

S8 Fig

(TIF)

Click here for additional data file.^{(741.8KB, tif)}

S1 Data. CSV file containing the underlying numerical data for Fig 2A and 2B.

(CSV)

Click here for additional data file.^{(2.2KB, csv)}

S2 Data. CSV file containing the underlying numerical data for Fig 2C.

(CSV)

Click here for additional data file.^{(2.7KB, csv)}

S3 Data. CSV file containing the underlying numerical data for Fig 2D.

(CSV)

Click here for additional data file.^{(1.4KB, csv)}

S4 Data. CSV file containing the underlying numerical data for Fig 3B.

(CSV)

Click here for additional data file.^{(120.2KB, csv)}

S5 Data. CSV file containing the underlying numerical data for Fig 4A and 4B.

(CSV)

Click here for additional data file.^{(495.8KB, csv)}

S6 Data. CSV file the underlying single-cell data for S1A Fig.

(CSV)

Click here for additional data file.^{(1.5MB, csv)}

S7 Data. CSV file the underlying single-cell data for S1B Fig.

(CSV)

Click here for additional data file.^{(1.5MB, csv)}

S8 Data. CSV file the underlying single-cell data for S1C and S1D Fig.

(CSV)

Click here for additional data file.^{(1.5MB, csv)}

S9 Data. CSV file containing the underlying numerical data for S2 Fig.

(CSV)

Click here for additional data file.^{(1.6KB, csv)}

S10 Data. CSV file containing the underlying numerical data for S4A and S4B Fig.

(CSV)

Click here for additional data file.^{(4.8KB, csv)}

S11 Data. CSV file containing the underlying numerical data for S5 Fig.

(CSV)

Click here for additional data file.^{(6.2KB, csv)}

S12 Data. CSV file the underlying numerical data for S6A and S6B Fig.

(CSV)

Click here for additional data file.^{(526.6KB, csv)}

S13 Data. CSV file containing numerical data for S7 Fig.

Time traces 1–8 correspond to lowest to highest initial densities in the legend of S7 Fig.

(CSV)

Click here for additional data file.^{(159.6KB, csv)}

S14 Data. CSV file containing the underlying numerical data for S8 Fig.

(CSV)

Click here for additional data file.^{(93.5KB, csv)}

Attachment

Submitted filename: Response to reviewers.pdf

Click here for additional data file.^{(561.5KB, pdf)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(11.5KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files. Python code with mathematical model and simulations is available at https://gitlab.inria.fr/InBio/Public/efaecalis-ratio.

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PERMALINK

Ratiometric quorum sensing governs the trade-off between bacterial vertical and horizontal antibiotic resistance propagation

Alvaro Banderas

Arthur Carcano

Elisa Sia

Shuang Li

Ariel B Lindner

Roles

Abstract

Introduction

Fig 1. Population parameter sensing in E. faecalis plasmid donors.

Results

Ratiometric sensing of population composition

Fig 2. Population density robust ratiometric sensing in E. faecalis.

Asc10 expression reduces vertical transfer of conjugative plasmids

Fig 3. Response to purified cCF10 is sensitive and costly.

Ratio sensing can increase plasmid fitness and facilitates donor–recipient coexistence

Fig 4. Mathematical model of mating population dynamics.

Discussion

Materials and methods

Bacterial strains and culture conditions

Table 1. Strains used in this study.

Pheromone stimulation

Coincubation experiments

Flow cytometry

Adherence/growth assay

Aggregation and turbidity assay

Mating experiment

Mathematical modeling

Table 2. Fixed model parameter values.

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Roland G Roberts

Roles

Decision Letter 1

Roland G Roberts

Roles

Author response to Decision Letter 1

Decision Letter 2

Roland G Roberts

Roles

Author response to Decision Letter 2

Decision Letter 3

Roland G Roberts

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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