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Conjugation dynamics depend on both the plasmid acquisition cost and the fitness cost

2021; Springer Nature; Volume: 17; Issue: 3 Linguagem: Inglês

10.15252/msb.20209913

1744-4292

Hannah Prensky, Angela Gomez‐Simmonds, Anne‐Catrin Uhlemann, Allison J. Lopatkin,

SARS-CoV-2 detection and testing

Article1 March 2021Open Access Transparent processSource Data Conjugation dynamics depend on both the plasmid acquisition cost and the fitness cost Hannah Prensky Hannah Prensky Department of Biology, Barnard College, New York, NY, USA Search for more papers by this author Angela Gomez-Simmonds Angela Gomez-Simmonds Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA Search for more papers by this author Anne-Catrin Uhlemann Anne-Catrin Uhlemann Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA Search for more papers by this author Allison J Lopatkin Corresponding Author Allison J Lopatkin [email protected] orcid.org/0000-0003-0018-9205 Department of Biology, Barnard College, New York, NY, USA Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY, USA Data Science Institute, Columbia University, New York, NY, USA Search for more papers by this author Hannah Prensky Hannah Prensky Department of Biology, Barnard College, New York, NY, USA Search for more papers by this author Angela Gomez-Simmonds Angela Gomez-Simmonds Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA Search for more papers by this author Anne-Catrin Uhlemann Anne-Catrin Uhlemann Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA Search for more papers by this author Allison J Lopatkin Corresponding Author Allison J Lopatkin [email protected] orcid.org/0000-0003-0018-9205 Department of Biology, Barnard College, New York, NY, USA Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY, USA Data Science Institute, Columbia University, New York, NY, USA Search for more papers by this author Author Information Hannah Prensky1, Angela Gomez-Simmonds2, Anne-Catrin Uhlemann2 and Allison J Lopatkin *,1,3,4 1Department of Biology, Barnard College, New York, NY, USA 2Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA 3Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY, USA 4Data Science Institute, Columbia University, New York, NY, USA *Corresponding author. Tel: +1 212 853 2564; E-mail: [email protected] Molecular Systems Biology (2021)17:e9913https://doi.org/10.15252/msb.20209913 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Plasmid conjugation is a major mechanism responsible for the spread of antibiotic resistance. Plasmid fitness costs are known to impact long-term growth dynamics of microbial populations by providing plasmid-carrying cells a relative (dis)advantage compared to plasmid-free counterparts. Separately, plasmid acquisition introduces an immediate, but transient, metabolic perturbation. However, the impact of these short-term effects on subsequent growth dynamics has not previously been established. Here, we observed that de novo transconjugants grew significantly slower and/or with overall prolonged lag times, compared to lineages that had been replicating for several generations, indicating the presence of a plasmid acquisition cost. These effects were general to diverse incompatibility groups, well-characterized and clinically captured plasmids, Gram-negative recipient strains and species, and experimental conditions. Modeling revealed that both fitness and acquisition costs modulate overall conjugation dynamics, validated with previously published data. These results suggest that the hours immediately following conjugation may play a critical role in both short- and long-term plasmid prevalence. This time frame is particularly relevant to microbiomes with high plasmid/strain diversity considered to be hot spots for conjugation. Synopsis Quantification of plasmid conjugation dynamics shows the presence of a plasmid acquisition cost and indicates that the hours immediately following conjugation may be critical in both short and long-term plasmid prevalence. A novel experimental framework quantifies plasmid acquisition costs independently of fitness effects. The magnitude of the acquisition costs is potentially dictated by the initial energetic burden imposed by the newly acquired plasmid, as well as the host cells' ability to accommodate that burden in a given environment. Incorporating acquisition effects into a mathematical model of conjugation improves the temporal predictions of long-term conjugation dynamics. The time window immediately following plasmid acquisition may represent a critical time interval for quantifying conjugation dynamics. Introduction Horizontal gene transfer (HGT) by conjugation, which refers to the transfer of DNA from a donor to a recipient through direct cell-to-cell contact (Frost & Koraimann, 2010) (Fig 1A), is the predominant way bacteria mobilize and exchange antibiotic resistance genes (Maiden, 1998; Barlow, 2009). Conjugation can occur via the transfer of chromosomally integrated conjugative elements or autonomously replicating plasmids. Plasmids, which often encode one or multiple antibiotic resistance genes (Holmes et al, 2016), are primarily responsible for the global dissemination of resistance since approximately half of all plasmids are conjugative (Smillie et al, 2010) and can have broad host ranges (Klumper et al, 2015). Moreover, conjugation via plasmid transfer is postulated to be prevalent in complex microbial communities (e.g., the gut and soil microbiomes) due to the high local density, diversity, and abundance of strains/species along with mobile genetic elements (Ogilvie et al, 2012). Figure 1. Transconjugants exhibit an acquisition cost following conjugation Schematic of conjugation whereby plasmid DNA from a donor (D, blue) is transferred to a recipient (R, red), generating a transconjugant (T, yellow). R and D are each resistant to an antibiotic (A1 and A2, respectively), but sensitive to the other. The transconjugant (T) is uniquely resistant to both antibiotics. Conjugation protocol involves mixing D and R, followed by one-hour incubation at 25°C. Cells are then diluted into media containing A1 and A2 and growth is captured over time in a 96-well plate. OD600 for de novo T (aqua) and adapted T (gray), (RP4 transconjugants) initiated with the same number of cells per well. Each curve is a biological replicate. Black dashed lines are best-fits. Growth rate (left) and lag time (right) for the plasmid-free recipient R, adapted T, de novo T after 1 h of conjugation, and de novo T diluted and re-grown after 24 h. De novo T growth rates (aqua) are statistically less than adapted T (gray) (P = 1.12e-08, Appendix Table S2) and plasmid-free cells (red) (P = 7.27e-09, Appendix Table S2). De novo T lag times are statistically greater than adapted T (P = 5.71e-08 Appendix Table S2) and plasmid-free cells (red) (3.77e-09, Appendix Table S2). Data represent biological replicates. All statistics were done using a one-way ANOVA with Bonferroni correction. Left: De novo T growth rates (aqua) are statistically less than adapted T growth rates (gray) (P = 7.34e-05 and 4.95e-05 for 15 and 120 min, respectively). Right: Lag times were multiplied by true T0 and divided by the mean adapted T lag (Appendix Fig S1D is non-normalized). De novo T (aqua) normalized lag times are statistically less than adapted T (gray) (P = 1.30e-09 and 1.31e-09 for 15 and 120 min, respectively). Data represent biological replicates. All statistics were done using a one-way ANOVA with Bonferroni correction. After 24 h, each condition (E) was diluted and re-grown. Left: growth rates are statistically identical (P = 1.00 for both 15 and 120 min). Right: All lag times are statistically identical (P = 0.69 and P = 0.48 for 15 and 120 min, respectively). Lag times normalized as described in (E). All data represent biological replicates. All statistics were done using a one-way ANOVA with Bonferroni correction. Source data are available online for this figure. Source Data for Figure 1 [msb20209913-sup-0002-SDataFig1.zip] Download figure Download PowerPoint Conjugation dynamics depend on the formation and proliferation of new HGT progeny (termed transconjugants) and are fundamentally governed by two kinetic processes: the rate of gene transfer (termed the conjugation efficiency) and the relative growth rate of transconjugants (termed the growth dynamics). Both the conjugation efficiency and growth dynamics may depend on a host of extrinsic and intrinsic factors. For example, the cell's physiological state can drastically alter the conjugation efficiency of certain plasmids by orders of magnitude (Lopatkin et al, 2016b). Likewise, growth dynamics are highly dependent on both the functional benefit and metabolic burden of a given plasmid, which can yield net positive or negative effects on relative growth rates. Measuring both processes has enabled accurate predictions of plasmid persistence in simple, in vitro HGT communities, often consisting of one or few plasmids and strains, thereby improving our overall understanding of plasmid dynamics (Lopatkin et al, 2017). However, native bacterial communities consist of hundreds of diverse plasmids and species that interact, grow, and compete over multiple time scales (Jorgensen et al, 2014). Predicting plasmid fate and overall conjugation dynamics in these more complex settings remains a challenge (Dunn et al, 2019; Lopatkin & Collins, 2020). It is widely known that, in the absence of antibiotic selection, plasmids can exert a fitness cost on their hosts. Fitness costs can vary widely and are often attributed to the metabolic burden of the plasmid. A plasmid's fitness cost is typically quantified using an established lineage (e.g., isolated transconjugant clone), in direct competition assays with, or as a relative growth rate compared to, the plasmid-free counterpart (Ponciano et al, 2007). Previous studies have shown that plasmid fitness costs can alter population structure and dynamics; for example, costly plasmids can be out-competed, leading to elimination, or compensated for by mutations that ameliorate the metabolic burden, prolonging plasmid persistence over time (Dahlberg & Chao, 2003; Dionisio, 2005; Harrison et al, 2015; Loftie-Eaton et al, 2017). Separate from fitness costs, acquiring a plasmid via conjugation requires immediate physiological adaptation (e.g., altered gene regulation and/or resource allocation (San Millan et al, 2018)) and therefore also impacts cellular metabolism. For example, it has been shown that plasmid-encoded stress response genes are transiently expressed in the host for 20–40 min following plasmid acquisition (Althorpe et al, 1999; Baharoglu et al, 2010); the SOS response accounts for a considerable component of bacterial maintenance metabolism (Kempes et al, 2017). Likewise, recent work demonstrated that the overshoot of plasmid-encoded gene expression only occurs in recently generated transconjugants (Fernandez-Lopez et al, 2014). The extent of metabolic disruption following conjugation suggests that plasmid acquisition may also impact growth dynamics. However, compared to the fitness cost, considerably less work has focused on these immediate effects, which we refer to as the plasmid acquisition cost. Indeed, these impacts are not captured in traditional fitness cost measurements since they occur soon after conjugation and quickly stabilize (Fernandez-Lopez & de la Cruz, 2014); they may also manifest in diverse growth effects, such as altered growth rates and/or lag time preceding exponential growth, which renders quantification challenging. Overall, the generality and magnitude of plasmid acquisition costs remain widely unknown. Given the diversity and abundance of plasmids in natural environments, quantifying plasmid acquisition costs may shed insights into plasmid fates in mixed/competing populations. Here, we discovered that newly generated transconjugants exhibited transient but overall reduced growth rates and/or prolonged lag times, indicating the presence of an increased burden immediately following conjugation—a plasmid acquisition cost. This plasmid acquisition cost occurs independently of long-term fitness effects, potentially corresponding to the initial energetic burden imposed by establishing a newly acquired plasmid, as well as the host cells' ability to efficiently fulfill that burden in a given environment. Moreover, incorporating acquisition effects into a mathematical model of conjugation improved temporal predictions of long-term conjugation dynamics across a range of plasmids. These results demonstrate the prevalence and importance of short-term metabolic effects in conjugation dynamics and have implications in understanding and predicting dominant plasmids in the environment. Results Acquisition of RP4 impacts growth dynamics To investigate how plasmid acquisition might affect transconjugant growth, we sought to compare the growth dynamics of de novo transconjugants (which have not undergone physiological adaptation) to those previously established (and therefore fully adapted). De novo transconjugants (T) can be readily generated using our previously established protocol for estimating conjugation efficiencies (Lopatkin et al, 2016a): donors (D) and recipients (R) carrying unique resistance genes are mixed under conditions that minimize growth. Since T is uniquely resistant to both antibiotics, it can then be directly selected from the population, and its growth tracked over time in a microplate reader (Fig 1B). This procedure is ideal for our purposes since it minimizes growth and adaptation during the conjugation window, ensuring that subsequent dynamic characterization of de novo T captures emergent phenotypic changes. In contrast, adapted T can be isolated by streaking conjugation mixtures onto dual antibiotic agar plates; individual clones can then be grown and stored for subsequent testing (Dahlberg & Chao, 2003; Rozwandowicz et al, 2019). These established transconjugants exhibit stably reproducible growth rates and are often used to quantify fitness costs and/or determine the timescale of compensatory mutations (Harrison et al, 2015; Hall et al, 2020). Using this approach, we first focused on the well-established, large conjugal plasmid RP4 (~60 kb). We chose RP4 since initial characterization revealed it as imposing a fitness cost on the cell, which allows us to distinguish between immediate acquisition costs and compensatory mutations thereafter (Appendix Fig S1A). Briefly, D and R were established using Escherichia coli MG1655 strains (Appendix Table S1A); R expresses spectinomycin (Spec) resistance, and D, which carries the RP4 plasmid, encodes kanamycin (Kan) resistance. To measure the growth of de novo T, D and R were mixed for one hour at 25°C, diluted 1,000× into Spec-Kan liquid media, and tracked via OD600 in a microplate reader. In parallel, adapted T clones were incubated under identical conditions (e.g., one hour, 25°C) to control for any physiological effects of the protocol itself, and subsequently diluted into Spec-Kan liquid media at a comparable initial cell density, as verified with colony forming units (CFU) (Appendix Fig S1B). Strikingly, de novo T appeared to grow overall slower than adapted T (Fig 1C). Indeed, curve-fitting using the established Baranyi equation (Baranyi & Roberts, 1994) revealed that de novo T's growth rate was significantly lower, and the lag time significantly higher, than that of adapted T (Fig 1D, Appendix Fig S1C, P = 1.12e-08 and P = 5.71e-09, respectively). These results were independent of the conjugation duration: mixtures incubated for both 15 and 120 min exhibited similar trends (Fig 1E and Appendix Fig S1C). However, when these populations were diluted and re-grown after 24 h, both the growth rate and lag time were fully restored (Fig 1F). Importantly, in all cases, the recovered growth rates remained lower than that of the plasmid-free strain, indicating the plasmid retained its fitness cost (Fig 1D). Finally, these results were independent of the method used to quantify growth parameters since three additional quantification methods resulted in qualitatively consistent trends (Appendix Fig S2, Appendix Table S2). Although these results initially suggest that RP4 acquisition is costly, we identified several protocol-related factors that could potentially account for these observations. First, the protocol requires de novo T to adjust to a new growth environment, possibly altering growth dynamics independently from conjugation-specific effects. However, adapted T was subjected to identical experimental conditions, which accounts for any effect of environmental adaptation on growth independent of conjugation. Second, competition with residual R/D cells could alter de novo T growth. To test this, we initiated growth of adapted T with R/D diluted 1,000× in the background media; this approximates the parental densities present during the conjugation experiment. Doing so did not qualitatively affect the trajectory of adapted T, nor the growth discrepancy between adapted and de novo T (Appendix Fig S1D). Moreover, there was no appreciable background conjugation of R/D at this density (Appendix Fig S1E), indicating that neither population survived long enough to conjugate during this time window. Overall, we conclude that RP4 is indeed costly to acquire, and that plasmid acquisition can modulate both the growth rate and lag time. Introducing a quantitative metric for the plasmid acquisition cost That RP4 acquisition induced transient changes in both growth rate and lag time is intuitive: The reduced growth rate is consistent with fitness cost interpretations as a metabolic burden associated with replication/protein expression. Moreover, a cell's immediate response to a metabolic perturbation is known to manifest in altered lag dynamics, such as following a nutrient shift (Madar et al, 2013). Therefore, to facilitate quantification, we sought to define a rigorous and accurate plasmid acquisition metric that would capture all effects of plasmid acquisition on growth dynamics. To this end, a minimal model of cell growth suggested that the time required to reach a predetermined threshold density is an inclusive proxy for changes in both growth rate and lag time (Fig 2A and Appendix Fig S3); this is consistent with a recent study that used an analogous "time to threshold" method to compare conjugation efficiencies in vitro (Bethke et al, 2020). Figure 2. Acquisition cost quantification for RP4 The time (t*, orange) at which a specified cell density threshold (T*, top purple) is reached uniquely depends on the initial cell density (T0, bottom purple), and the growth rate (µ, aqua) and lag times (λ, orange). Assuming background subtraction, the line can be represented by the equation that is shown. Representative standard curve generation is shown. Left: To generate the standard curve, adapted T are diluted in 10-fold increments and growth is measured over time (dark to light gray is T0). Circles indicate the t* (purple line) corresponding to T* (orange line). Aqua line represents the out-growth of T following a conjugation experiment (i.e., de novo T). Right: The initial cell density is plotting against t*; black line indicates the standard curve. Left: RP4 adapted T growth initiated with decreasing true T0 (dark to light gray); blue markers show the time to reach OD600 of 0.275 (t*). Right: Standard curve is shown in blue. Error bars are standard deviation of three biological replicates. True and predicted CFU of RP4 with the recipient E. coli strain MG1655. Scatter points represent three biological replicates, and bar height is the average. True and predicted CFU of RP4 with the recipient K. pneumoniae (KPN) strain AL2425. Scatter points represent four biological replicates, and bar height is the average. Source data are available online for this figure. Source Data for Figure 2 [msb20209913-sup-0003-SDataFig2.zip] Download figure Download PowerPoint Briefly, let T(t) describe the growth of transconjugants over time, and µ be the maximum specific growth rate. Consistent with previous literature (Métris et al, 2006), extrapolating the exponential growth region to the horizontal axis allows us to define the geometric lag time (λ), which corresponds to the observable onset of exponential growth (Fig 2A). During the exponential phase, T(t) can thus be described by the line: ln(T(t)) = ln(T0) + µ(t-λ), where T0 is the true initial population density. Under these conditions, the time (t*) it takes the population to reach a specific detection level (T*) is inversely correlated with T0. In other words, we can predict an unknown initial cell density (Tpred) from its observed time to threshold (t*) value, assuming T*, µ, and λ are constant (Fig 2B). Conversely, a discrepancy between Tpred and true T0, which CFU can determine, specifically indicates that µ and/or λ have changed. For our purposes, consider a standard curve relating t* and T0 using adapted T, and a Tpred generated from a de novo T population as described above. In that case, any discrepancy between Tpred and true T0 (e.g., Tpred/T0 < 1) can be attributed to growth-specific consequences of acquiring a plasmid. Moreover, the use of T0 as a metric of acquisition cost, rather than t*, allows us to simultaneously compare conjugation rates across diverse experimental conditions and plasmids. Additionally, generating an a priori standard curve spanning a broad range of initial densities also avoids the variability inherent in manually diluting adapted T to a specific target number, as we had done initially (Fig 2B). As such, predicted compared to true CFU represents a more robust and trustworthy quantification method. We verified the utility of this metric by confirming it could capture the observed discrepancies in RP4 growth. Specifically, we built a standard curve using adapted T (Fig 2C) and predicted the initial de novo T population densities (Tpred) based on times to threshold quantitated from observed growth curves. Having previously verified the true T0 with CFU counts, we found that Tpred was significantly less than true T0 (P = 0.0143, right-tailed t-test), indicating an acquisition cost for RP4 (Fig 2D), as expected. We note that a standard curve generated with R and D diluted 1,000× in the background media to approximate the parental densities present during the conjugation experiment does not significantly change the observed discrepancy between true T0 and Tpred (Appendix Figs S4A and B), consistent with earlier. Generality of the plasmid acquisition cost To investigate the generality of the acquisition cost, we first determined whether it was unique to our particular experimental conditions. Specifically, we compared true T0 and Tpred estimates for RP4 using different experimental parameters (e.g., dilution factor, conjugation time window, recipient strain). Results revealed that the RP4 acquisition cost was independent of the conjugation time window as shown previously (Appendix Fig S4C i), recipient strain (Appendix Fig S4C ii), and the dilution factor (Appendix Fig S4C iii-iv); indeed, dilution factors of 150×, 500×, 1,000×, and 5,000× predicted a separate but parallel standard curve (Appendix Fig S4D), indicating a systematic difference between the two estimates. Moreover, RP4 was costly to acquire for the clinically isolated recipient strain Klebsiella pneumoniae(KPN), indicating species-level generality (Fig 2E). The drastic difference in RP4 acquisition costs between E. coli and KPN recipients suggest that cost is not solely a function of particular plasmids; strain/species-level attributes are likely key as well. Next, we reasoned that the long replication time of the large RP4 plasmid (~60 kb), coupled with the significant amount of energy required to synthesize conjugation machinery upon acquisition, likely imposed an immense energetic burden that led to transient growth inhibition, and thus, a high acquisition cost (San Millan & MacLean, 2017). Conversely, we hypothesized that mobilizable plasmids, which are transferred by conjugation in trans but do not themselves encode conjugation machinery, would most likely result in a minimal acquisition cost. The absence of genes encoding for conjugation machinery reduces both the plasmid size and the burden of the machinery production. To test this, we used the FHR helper plasmid system described previously (Dimitriu et al, 2014), which is not self-transmissible but can mobilize any co-residing plasmid that encodes the recognition sequence oriT. We chose the mobilizable plasmid pR which carries chloramphenicol resistance. As with RP4, this plasmid exhibits an overall fitness cost (Appendix Fig S5). Consistent with our hypothesis, post-conjugation growth curves overlapped with those used to determine the standard curve (Fig 3A). Indeed, experiments revealed a statistically identical match between true T0 and Tpred (Fig 3B, P = 0.34 and 0.86 for two- and one-tailed t-tests, respectively). Thus, we conclude that pR does not induce a significant acquisition cost. More generally, these results confirm that observed acquisition costs, as determined by the difference in transconjugant estimates (T0 and Tpred), are not artifacts of the experimental method and can be detected using this quantification metric. Figure 3. Generality of acquisition cost OD600 for de novo (aqua) and adapted transconjugants (gray) for the plasmid pR are shown over time. Black lines are best-fits. Individual curves are biological replicates. True and predicted CFU for the plasmid pR are statistically identical (P = 0.34 and 0.86 for one and two-tailed t-tests, respectively). Scatter points represent biological replicates, and bar height is the average. Growth rates are shown for adapted T carrying RP4 under variable glucose (glu) and casamino acid (caa) concentrations. Values represent % w/v. Scatter points represent biological replicates. Acquisition costs were quantified for the same glucose and casamino acid concentrations from (C). Scatter points represent the average, and error bars represent standard deviation, of three biological replicates. Aqua and red indicate glucose at 0.4% and 0.04% w/v, respectively. Y-axis is acquisition cost (Tpred/T0) normalized to the cost in the absence of casamino acids. Tpred compared to T0 for six well-characterized plasmids. Representatives are shown of two biological replicates (see Appendix Fig S10 for day-to-day variability). R1, R1drd, and pRK100 do not show a significant acquisition cost (P = 0.93, 0.79, and 0.28, respectively), whereas RIP113, R6K, and R6Kdrd do (P = 6.79e-05, 7.57e-05, and 0.037, respectively, one-tailed t-test, Appendix Table S3A). Tpred was significantly less than T0 for clinical plasmids p41, p168, p193, and p283 at 37°C (P = 7.10e-05, 2.10e-05, 0.021, 1.90e-05, respectively, n = 4, 4, 3, 2, respectively, one-tailed t-test, Appendix Table S3A). In all cases except p283, bars represent averages and scatter points individual measurements from at least three biological replicates; p283 has two biological replicates. Tpred for two clinical plasmids, p193 and p168, at 30°C was significantly less than T0 (P = 2.80e-04, 2.72e-04, respectively, n = 3, 4, respectively, one-tailed t-test, Appendix Table S3A). All acquisition costs scattered against the fitness cost measured under the identical condition for each plasmid. Acquisition costs are measured as the ratio between Tpred/T0. Black line is the linear regression line of best fit, and R2 = 0.01 (shown in the bottom left). Error bars represent standard deviation; the type of replicates used for these error bars is listed in Appendix Table S3A. Source data are available online for this figure. Source Data for Figure 3 [msb20209913-sup-0004-SDataFig3.zip] Download figure Download PowerPoint Given that RP4 and pR-specific differences likely arise due to differences in energetic demand, we hypothesized that altering growth efficiency (e.g., the amount of substrate consumed that is converted to biomass) (Chudoba et al, 1992) would modulate acquisition costs. Intuitively, inefficiently growing cells generate excess available energy (Russell & Cook, 1995; Russell, 2007) that may be readily applied to plasmid-related metabolic demands, potentially resulting in a lower acquisition cost. In contrast, efficiently growing cells devote the bulk of available energy to biomass production (Low & Chase, 1999), and thus, reallocating that energy to plasmid demands may increase acquisition costs. To test this hypothesis, we focused on modulating growth conditions. It is well-established that excess glucose yields highly inefficient E. coli growth (Liu, 1998; Basan et al, 2015), but that efficiency is restored with exogenous amino acid supplementation (Akashi & Gojobori, 2002; Waschina et al, 2016). Adapting a recent approach that leveraged this trade-off (Lopatkin et al, 2019), we quantified plasmid acquisition costs under three casamino acid (CAA) concentrations (0, 0.01, and 0.1% w/v) and excess glucose (0.4% w/v). Since higher CAA increases both efficiency and growth rate, we included a fourth combination (0.0