DOI: 10.5061/dryad.msbcc2g5c
title: Fight not flight: Parasites drive the bacterial evolution of resistance, not escape

description: In the face of ubiquitous threats from parasites, hosts can evolve
 strategies to resist infection or to altogether avoid parasitism, for
 instance by avoiding behavior that could expose them to parasites or by
 dispersing away from local parasite threats. At the microbial scale,
 bacteria frequently encounter viral parasites, bacteriophages. While
 bacteria are known to utilize a number of strategies to resist infection
 by phages, and can have the capacity to avoid moving towards
 phage-infected cells, it is unknown whether bacteria can evolve dispersal
 to escape from phages. In order to answer this question, we combined
 experimental evolution and mathematical modeling. Experimental evolution
 of the bacterium Pseudomonas fluorescens in environments with differing
 spatial distributions of the phage Phi2 revealed that the host bacteria
 evolved resistance depending on parasite distribution, but did not evolve
 dispersal to escape parasite infection. Simulations using parameterized
 mathematical models of bacterial growth and swimming motility showed that
 this is a general finding: while increased dispersal is adaptive in the
 absence of parasites, in the presence of parasites that fitness benefit
 disappears and resistance becomes adaptive, regardless of the spatial
 distribution of parasites. Together, these experiments suggest that
 parasites should rarely, if ever, drive the evolution of bacterial escape
 via dispersal.
description: <strong>Bacteria & Phage Strains and Culturing
 </strong> We used the widely-studied model system
 of <em>P. fluorescens </em>SBW25 and its strictly-lytic phage
 Phi2 (Brockhurst et al. 2007), which putatively binds the bacterial
 lipopolysaccharide (LPS) (Scanlan and Buckling 2012). We used variations
 of King’s B (KB) medium (the standard SBW25 bacterial media, see
 experimental evolution section below): 10 g/L LP0037 Oxoid Bacteriological
 Peptone, 15 g/L glycerol, 1.5 g/L potassium phosphate, and 0.6 g/L
 magnesium sulfate. Amplified phage stocks were created by infecting an
 actively growing SBW25 liquid culture, incubating while shaking for 24
 hours at 28°C, then adding 25 µL chloroform per mL culture and storing at
 4°C. <strong>Measuring Phage & Bacterial
 Density</strong> Phage and bacterial
 concentrations were quantified using standard plating and optical density
 measurements. Phages were quantified in plaque-forming-units per mL
 (PFU/mL) using a plaque assay: phage suspensions and 100 µL of an
 overnight bacterial culture in the appropriate KB concentration were added
 to 4 mL of 0.5% agar KB media at 45°C then poured over 1.5% agar KB
 plates. After overnight incubation at 28°C plaque forming units were
 counted. Bacteria were quantified by plating serial dilutions onto 1.5%
 agar KB plates and counting the number of colony-forming-units (CFU). In
 liquid culture, bacterial density was quantified by measuring the optical
 density at 600 nanometers (OD600) with a spectrophotometer, then
 translating that to CFU/mL using a previously-established standard
 curve. <strong>Experimental
 Evolution</strong> Experimental evolution was
 conducted in plates containing media with 3 g/L agar. Approximately 22
 hours before inoculation or passage, 25 mL of media was poured into each
 plate with an internal diameter of 90 mm. For the global parasite
 treatment, media was cooled to 50°C and Phi2 was added before pouring to
 reach a final phage concentration of 10<sup>6</sup> PFU/mL.
 For the control and local parasite treatments, no phage was added before
 pouring. Initial inoculations for timepoint 0 were
 taken from an exponentially growing culture of ancestral <em>P.
 fluorescens</em> that had been shaken at 250 rpm. For the control
 and global treatments, 5 µL of a bacterial suspension with 5 x
 10<sup>7</sup> CFU/mL was spotted onto the center of the
 plate. For the local treatment, phages were added to the bacteria to
 achieve 5 µL of a mixture containing 5 x 10<sup>7</sup> CFU/mL
 bacteria and 5 x 10<sup>6</sup> PFU/mL phages, then spotted
 onto the center of the plate. These concentrations were chosen so that the
 initial inoculum droplet of bacteria would be exposed to approximately the
 same number of phages between the local and global treatments.
 After bacteria are inoculated in the center of the plate,
 nutrient consumption creates a spatial gradient in chemoattractants. This
 gradient induces bacterial dispersal from the center of the plate towards
 the periphery, primarily via flagella-driven swimming. Thus,
 population-level dispersal is the result of both bacterial growth and
 motility (Fraebel et al. 2017). In these conditions, phages like Phi2 only
 passively diffuse (Sampedro et al. 2015). After initial
 inoculation, populations were then propagated to a new set of plates daily
 for 14 transfers, while re-creating the same phage spatial distribution
 each time. After ~24 hours incubation, a 300 µL pipette tip set to 5 µL
 was stabbed into the agar immediately past the visible cell front and
 drawn up slowly, to ensure no bubbles entered the tip. This 5 µL sample
 was then spotted onto the center of a new plate. For the global treatment,
 plates were poured in the same manner as initial inoculations,
 incorporating phages throughout the media. For the local treatment, 2.5 µL
 of a phage stock with 10<sup>7</sup> PFU/mL was added onto the
 5 µL droplet on the new plate, yielding the same area-density as the
 initial inoculations. For the control treatment, no phages were added.
 After 14 transfers, evolved isolates were taken by spreading diluted
 suspensions of the 5 µL sample onto 1.5% agar KB plates and incubating for
 48 hours. By sampling from the cell front, our experimental design
 inherently selects for increased dispersal in all treatments. We chose
 this sampling method because we expected it would maximize the
 evolutionary benefits of increased dispersal, reducing the chance that
 parasite selection for escape would be obscured by sampling that selected
 against increased dispersal. This experiment, with five
 replicate populations in each of the three parasite distribution
 treatments (Fig 1), was carried out twice under different media and
 incubation conditions. In the first experiment, bacteria and phages were
 incubated at 30°C; in the second experiment, bacteria and phages were
 incubated at 29°C. Previous work in other labs found that Phi2 does not
 grow at 30°C but can grow at 29°C (Padfield et al. 2019). Similarly, we
 found that Phi2 formed plaques on ancestral bacteria at 29°C but not at
 30°C. Accordingly, we label the experiment at 29°C as “more favorable” for
 parasite growth, and label the experiment at 30°C as “less favorable” for
 parasite growth. The number of generations was estimated by dividing the
 total duration of the experiment by the average ancestral growth rate (Fig
 S1) from each experiment. Nutrient conditions also
 differed between the two experiments. Bacteria growing in media with 3 g/L
 agar can disperse via swarming or swimming motility (Kohler et al. 2000;
 Harshey 2003). For experimental tractability, nutrient concentrations in
 the media were altered to prevent the formation of swarms. At each
 temperature, we tested KB formulations with 3 g/L agar, either 25%, 50%,
 75%, or 100% concentrations of glycerol and peptone (the carbon and
 nitrogen sources), and 50% concentrations of potassium phosphate and
 magnesium sulfate. For each experiment, we then selected the
 highest-nutrient formulation that did not induce bacterial swarming. For
 the first experiment, in the conditions relatively less favorable for
 phage growth, the chosen media was “50%/50% KB”; for the second
 experiment, relatively more favorable for phage growth, the chosen media
 was “25%/50% KB”. Note that, because the two experiments were not
 conducted concurrently, and because of the media and temperature
 differences between the two experiments, comparisons should only be made
 within each experiment. Experimental evolution
 populations were blocked such that each block contained one population in
 each of the control, local, and global treatments. In some cases, plates
 showed no growth after 24 hours of incubation. In these cases, all three
 populations in the block were re-inoculated from the previous transfer,
 which had been stored at 4°C. This occurred seven times (Table S1),
 predominantly in the first two transfers, when phage killing was most
 frequent. Additionally, one global and one local population, both in the
 experiment with less favorable conditions for phage growth, became
 contaminated during the experiment and were excluded from
 analysis. <strong>Quantifying Bacterial
 Dispersal/Growth in Soft Agar</strong> Bacterial
 growth during experimental evolution and dispersal (sometimes called
 ‘migration rate’ in the literature) of evolved bacterial isolates were
 measured by photographing plates at 300 dpi. For experimental evolution,
 plates were photographed immediately before passaging. For evolved
 isolates, 5 µL of a bacterial suspension at 5x10<sup>7
 </sup>CFU/mL was spotted onto the center of a 25mL 0.3% agar plate
 of the appropriate media, then incubated for approximately 24 hours before
 photographing. To reduce batch effects, each block was poured from the
 same bottle of media the day before inoculation, although the drying time
 was not precisely standardized. After photographing, the area of the
 circular bacterial growth in these images was measured manually using Fiji
 (Schindelin et al. 2012). This area was normalized for the exact time of
 incubation under the assumption that radius increases linearly with time
 (Croze et al. 2011). For evolved isolates, the batch-corrected dispersal
 rate is reported. <strong>Quantifying Bacterial
 Resistance to Phage</strong> Bacterial resistance
 to phage was quantified by tittering a stock of the phage on lawns of the
 ancestral bacteria and evolved isolates, then reporting the
 batch-corrected number of plaques observed on each isolate.
 <strong>Statistical Analysis</strong>
 All analyses and visualization were carried out in R 4.2.2 (R
 Core Team 2022) and RStudio 2022.12.0+353 using packages reshape (Wickham
 2007), tidyr (Wickham et al. 2023<em>b</em>), dplyr (Wickham
 et al. 2023<em>a</em>), ggplot2 (Wickham 2016), ggh4x (Brand
 2022), lme4 (Bates et al. 2015), pbkrtest (Halekoh and Højsgaard 2014),
 gcplyr (Blazanin 2023), cowplot (Wilke 2020), ggrepel (Slowikowski 2022),
 ggtext (Wilke and Wiernik 2022), magrittr (Bache and Wickham 2022), and
 purr (Wickham and Henry 2023), along with the Okabe and Ito
 colorblind-friendly palette (Okabe and Ito 2008). Any other statistical
 functions, like prcomp, were built-in to R. See https://github.com/mikeblazanin/trav-phage for all analysis code.<strong> </strong>A permanent record of all data and code is available from Dryad. <strong>Simulations</strong> To simulate bacterial dispersal with different parasite spatial distributions, we modified the widely-used Patlak-Keller-Segel model of bacterial chemotaxis and growth (Patlak 1953; Keller and Segel 1970) to include phage populations (Eqs. 3 – 6). These models have been previously validated to recreate <em>in vitro</em> bacterial population behavior with great precision (Cremer et al. 2019; Li et al. 2020; Ping et al. 2020; Mattingly and Emonet 2022). In this model we track the population density of hosts (N), parasites (P), resources (R), and attractants (A) through both space and time, using parameters as defined in Table 1. Simulations were initiated with parasites excluded (Control), gaussian distributed centered on the origin (peak height =  parasites/ , sd = ) (Local), or uniformly distributed (2.5 parasites/ ) (Global). Across all treatments, hosts were initiated gaussian distributed centered on the origin (peak height 10<sup>10</sup> cells/<em> </em> <em>, </em>sd = 20<em> </em> ), resources were initiated uniformly at 50 mM, and attractants were initiated uniformly at 2 mM. To assess the fitness landscape between resistance (i) and dispersal (χ), growth rate (c<sub>R</sub>), attractant consumption (c<sub>A</sub>), or yield (Y), the host population was split into two equally-sized sub-populations that shared the same initial distribution. One sub-population (the resident) had the parameter values listed in Table 1, while the other (the invader) had one or two parameter values which differed from the resident. See Table S3 and the Supplemental Materials for how parameter values were determined. Following this setup, the simulation was run for 20 simulated hours. At the end of the simulation, the fitness of the invader was calculated as log<sub>10</sub>(invader frequency/resident frequency). All models were implemented in Matlab (Mattingly and Emonet 2022) and all code is available at https://github.com/jeremymoore558/ks_phage. Visualization code is available at https://github.com/mikeblazanin/trav-phage. A permanent record of all data and code is available from Dryad.
description: # Fight not flight: Parasites drive the bacterial evolution of resistance,
 not escape
 [https://doi.org/10.5061/dryad.msbcc2g5c](https://doi.org/10.5061/dryad.msbcc2g5c) ## Abstract In the face of ubiquitous threats from parasites, hosts can evolve strategies to resist infection or to altogether avoid parasitism, for instance by avoiding behavior that could expose them to parasites or by dispersing away from local parasite threats. At the microbial scale, bacteria frequently encounter viral parasites, bacteriophages. While bacteria are known to utilize a number of strategies to resist infection by phages, and can have the capacity to avoid moving towards phage-infected cells, it is unknown whether bacteria can evolve dispersal to escape from phages. In order to answer this question, we combined experimental evolution and mathematical modeling. Experimental evolution of the bacterium *Pseudomonas fluorescens* in environments with differing spatial distributions of the phage Phi2 revealed that the host bacteria evolved resistance depending on parasite distribution, but did not evolve dispersal to escape parasite infection. Simulations using parameterized mathematical models of bacterial growth and swimming motility showed that this is a general finding: while increased dispersal is adaptive in the absence of parasites, in the presence of parasites that fitness benefit disappears and resistance becomes adaptive, regardless of the spatial distribution of parasites. Together, these experiments suggest that parasites should rarely, if ever, drive the evolution of bacterial escape via dispersal. ## Manuscript metadata **Fight not flight: parasites drive the bacterial evolution of resistance, not escape** Michael Blazanin, Jeremy Moore, Sydney Olsen, and Michael Travisano *Corresponding author, email: [mikeblazanin@gmail.com](mailto:mikeblazanin@gmail.com) Published in The American Naturalist ## Description of the data and file structure ### Experimental data All experimental data files are contained in the Clean_Data subdirectory. Analyze_Data.R is an R script that reads those files and analyzes and visualizes the experimental data. Experimental_evolution_growth.csv measured the area of bacterial growth each day during experimental evolution (Fig 2). Proj denotes whether data are from the experiments done under conditions less favorable to the parasite (coded "7x") or more favorable to the parasite (coded "125"). Pop is simply lettered for each replicate. Treat is "C" for control, "L" for local, or "G" for global. Timepoint is the day of experimental evolution. start\_timestamp is the exact date-time when the plate was inoculated, end\_timestamp is the exact date-time when the plate was scanned, time\_since\_inoc is the difference between start time and end time. Width\_cm and Height\_cm are the areas of visible bacterial growth, measured in cm. Isolate_resistance.csv measured the susceptibility of evolved bacterial isolates to phage infection (Fig 3). Date denotes the date (batch) when data was collected. Proj denotes whether isolates are from the experiments done under conditions less favorable to the parasite (coded "7x") or more favorable to the parasite (coded "125"). Pop is simply lettered for each replicate. Treat is "C" for control, "L" for local, or "G" for global. Timepoint is the day of experimental evolution. Isol is simply lettered for each replicate. PFU is the number of plaque-forming units observed, and dilution is the effective dilution of those PFU's to the phage stock, such that PFU times dilution gives the estimated plaque-forming units per mL of phage stock (the pfu_ml column). Isolate_migration.csv measured the area of overnight bacterial growth in soft agar for evolved bacterial isolates (Fig 4). Proj denotes whether isolates are from the experiments done under conditions less favorable to the parasite (coded "7x") or more favorable to the parasite (coded "125"). Pop is simply lettered for each replicate. Treat is "C" for control, "L" for local, or "G" for global. Timepoint is the day of experimental evolution. Isol is simply lettered for each replicate. start\_timestamp is the exact date-time when the plate was inoculated, end\_timestamp is the exact date-time when the plate was scanned, time\_since\_inoc is the difference between start time and end time. Width\_cm and Height\_cm are the areas of visible bacterial growth, measured in cm. Isolate_growth_curves.csv measured the optical density of overnight bacterial growth in liquid culture in 96 well plates for evolved bacterial isolates (Figs S1 - S5). Proj denotes whether isolates are from the experiments done under conditions less favorable to the parasite (coded "7x") or more favorable to the parasite (coded "125"). Pop is simply lettered for each replicate. Treat is "C" for control, "L" for local, or "G" for global. Timepoint is the day of experimental evolution. Isol is simply lettered for each replicate. Rep_Well is simply numbered for each replicate well in the plate (technical replicate). Media are listed as the percentage concentration relative to full strength King's B media. When carbon sources and buffer components differed in their concentration, this is denoted with a hyphen of the carbon source concentration first followed by the buffer concentration (e.g. 25-50 media is 25% carbon source and 50% buffer concentrations relative to King's B). Time_s is the time in seconds since the growth curve began. Temp_C recorded the temperature of the growth curve chamber at each timepoint. OD600 is the absorbance at 600 nm of each well. cfu_ml is the estimated cell density (calculated by converting OD600 with a standard curve). ### Modeling To simulate bacterial dispersal with different parasite spatial distributions, we modified the widely-used Patlak-Keller-Segel model of bacterial chemotaxis and growth. To assess the fitness landscape between resistance (i) and dispersal (χ), growth rate (cR), attractant consumption (cA), or yield (Y), the host population was split into two equally-sized sub-populations that shared the same initial distribution. One sub-population (the resident) had constant parameter values, while the other (the invader) had one or two parameter values which differed from the resident. Following this setup, the simulation was run for 20 simulated hours. At the end of the simulation, the fitness of the invader was calculated as log10(invader frequency/resident frequency). Modeling data was generated by running each of the Main_*.m files in phaged_0 and phaged_40 subdirectories with Matlab R2023b. Each of these wrappers scans over multiple combinations of two parameters at a time and simulates two populations of bacteria migrating on a single shared resource pool. The actual simulations are performed by the 'simulateWave.m' scripts. Parameters for cell and phage behavior are in 'parameters.m' The wrapper then generates output files in newly created Outputs subdirectories. These results can be summarized by running the AnalyzeWave\_\*.m files located in the Analysis\_\* subdirectories. For convenience, we have compiled all of the resulting data into a single file, Model_output.csv. vars_manip_1 and vars_manip_2 list the bacterial traits that were varied in each set of simulations, distrib lists the initial distribution of phages (local, global, global_gauss, or no_parasites), phage_disp lists the rate of diffusion of the phages, and Cell_population and Cell2_population list the final population sizes of Cell1 (the resident) and Cell2 (the invader). Then a series of columns list the resident value for each parameter (cA, irate, Chi, cR, Y, b), and the relative value of each parameter for the invader, such that the relative value times the resident value gives the invader's value (relativecA, relativeI, relativeR, relativeChi, relativecR, relativeY, relativeb). Finally Cell2_Cell1 lists the ratio of Cell2 to Cell1 at the end of the simulation. This tidy version of the modeling data is visualized by Visualize_ModelOutput.R to generate the figures in the manuscript. ## Sharing/Access information All of the data and analysis scripts are retained in their original form in a github repository [https://github.com/mikeblazanin/trav-phage](https://github.com/mikeblazanin/trav-phage). Modeling data was generated by Matlab scripts located in the repo at [https://github.com/jeremymoore558/ks_phage](https://github.com/jeremymoore558/ks_phage). ## Code/Software The uploaded renv.lock file was generated using the R package renv and lists all software and packages, as well as their versions, used to analyze and visualize the data. This file can also be used with the R package renv to easily re-create the computational environment all R code was run in.