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Supporting Information
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Data analysis
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Testing for genetic variance in resistance and tolerance
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We tested for genetic variance for resistance for both MA and non-MA lines by looking for an
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effect of genotype on bacterial load. Genetic variance for resistance was assessed in each sex
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separately using linear mixed-effect models of d, excluding the sham treatment, with initial load
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as a fixed effect and genotype and block as random effects. We assessed the significance of fixed
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effects using likelihood ratio tests, and the significance of random effects using the RLRsim
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package [S1] which provides simulation-based exact restricted likelihood ratio tests for random
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effects. Block was determined to be non-significant, and removed from the model.
Genetic variance for tolerance would lead to an effect of genotype on the slope of the
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relationship between relative fitness and bacterial load. We have two replicate measures of
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tolerance within each genotype represented by the estimates of fitness and load at two infection
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levels. We estimated genetic variance for tolerance in the presence of this residual variance in
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each sex separately using linear mixed-effect models of w, with d as a fixed effect and genotype
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as a random effect on the coefficient for d, with a fixed intercept of (0,1). We tested for
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significance of random effects as for resistance, above.
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Testing the resistance-tolerance correlation
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The way in which we measured resistance and tolerance will lead to the appearance of a negative
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genetic correlation between these estimates due to chance alone, in the absence of any true
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underlying correlation. This spurious correlation arises because our resistance and tolerance
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measures both involve bacterial load, d. To examine the true correlation between resistance and
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tolerance we considered the traits that we measured independently: fitness and bacterial load.
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Conceptually, in the absence of any true correlation between resistance and tolerance, excluding
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the uninfected treatment, fitness and bacterial load will tend to be negatively correlated,
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assuming that bacteria reduce fitness. A positive correlation between resistance and tolerance
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will result in a correlation between fitness and load in infected flies that is more negative, since
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genotypes with relatively high load will also tend to have relatively low fitness. The converse is
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true when there is a negative correlation between resistance and tolerance: genotypes with
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relatively high load will tend to have higher fitness than expected in the absence of any true
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resistance-tolerance correlation, leading to a more positive correlation between fitness and load.
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Using simulation, we were able to confirm these verbal arguments, and determine the
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null distribution for the correlation between fitness and load in the absence of any true
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correlation between resistance and tolerance. We determined the correlation between fitness and
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load in our data, averaging across infection levels, and excluding the uninfected (sham)
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treatment, by determining the expected values of w and d at a single arbitrary infection level, and
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taking their rank correlation. We observed a slightly positive correlation between fitness and load
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of 0.024 in males and 0.076 in females, suggesting a negative correlation between resistance and
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tolerance in both sexes. We combined the evidence from both sexes by considering the average
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fitness-load correlation across sexes, 0.050. (Note that this positive correlation does not imply
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that bacteria increase fitness: uninfected values, which would make the fitness-load correlation
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strongly negative, are excluded from this procedure). To determine whether this value is
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expected by chance alone, given the distributions of resistance and tolerance that we observed,
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we first determined the best-fit gamma distribution for our estimates of resistance and tolerance
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in each sex using maximum likelihood (see details below). We then assigned random resistance
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and tolerance values to 50 genotypes by simulating random values from these gamma
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distributions. We then determined the resulting expected values of fitness and load for each
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genotype and sex given these resistance and tolerance values, and determined the fitness-load
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correlation, averaged across sexes. This procedure was repeated 10000 times. We confirmed that
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the average simulated correlation between resistance and tolerance was approximately zero. We
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found that the expected fitness-load correlation given random, uncorrelated values of resistance
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and tolerance was approximately –0.156. The correlation we observed (0.050) was found to be
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significantly more positive than expected given this null distribution (two-tailed P = 0.0340),
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indicating that our data most likely reflect an underlying negative correlation between resistance
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and tolerance (see also Fig. S3). In other words, the association we observed between fitness and
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load is unlikely to have occurred in the absence of a true negative resistance-tolerance
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correlation.
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In addition to the procedure described above, we repeated this approach using
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randomization of the association between the observed values of resistance and tolerance, rather
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than simulating values from gamma distributions. The result was essentially the same: the
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expected fitness-load correlation in the absence of a true resistance-tolerance correlation was -
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0.171, and the correlation we observed was significantly more positive than this expected value
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(two-tailed P = 0.0264).
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Maximum likelihood details
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We determined the most likely displaced gamma distribution (three parameters) for resistance
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and tolerance in each sex, where the log likelihood was given by
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log L(k,  ,  | data)   log DG(x i   | k,  )
i

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where k and θ are shape and scale parameters, δ is the displacement, xi is a given data point
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(line mean), and DG is the gamma density function. For each group we performed
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optimization using the Nelder-Mead algorithm with the R function optim, replicated 20 times
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with different random starting parameters, and determined the best parameter values. Most runs
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converged on similar values, but for male resistance we performed an additional 20 optimization
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runs to ensure we found the best parameters. The best-fit parameters are shown in Table S1, and
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were found to be a good fit to the actual data by visual inspection.
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Supplemental References
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S1. Scheipl, F., Greven, S., and Kuechenhoff, H. (2008). Size and power of tests for a zero
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random effect variance or polynomial regression in additive and linear mixed models.
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Computational Statistics & Data Analysis. 52, 3283-3299.
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Table S1. Estimates of genetic variance (VG) for resistance and tolerance in mutant and non-mutant males and
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females; residual variance (Vresid); number of replicates (N); number of genotypes (n); restricted likelihood ratio
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(RLR); p-value (p).
Trait
Sex
Group
N
n
VG
Vresid
RLR
p
Resistance
Male
Non-mutant
200
25
0.1481
1.8127
3.0267
0.0349
Resistance
Male
Mutant
200
25
0.2086
2.2132
3.8305
0.0223
Resistance
Female
Non-mutant
200
25
0.1235
1.2094
4.3647
0.0152
Resistance
Female
Mutant
201
25
0.3040
1.6933
10.4528
0.0004
Tolerance
Male
Non-mutant
72
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0.0066
0.7805
2.1386
0.0662
Tolerance
Male
Mutant
69
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0.0237
0.7047
8.4176
0.0019
Tolerance
Female
Non-mutant
69
23
0.0021
0.0781
9.2959
0.0012
Tolerance
Female
Mutant
66
22
0.0011
0.0858
3.4652
0.0292
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Table S2. Best-fit parameters from maximum likelihood models of resistance and tolerance in each sex.
Sex
Trait
Shape
Scale
Displacement
Male
Resistance
1.0368
0.0525
-0.3625
Female
Resistance
2.4168
0.0244
-0.3383
Male
Tolerance
1.2846
0.1109
0.1931
Female
Tolerance
4.4249
0.0249
0.2206
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Figure S1. Details of fly crosses performed. The first three chromosomes are shown for each
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genotype; the tiny fourth chromosome was not manipulated. Males lack recombination, and are
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identified here by the presence of a Y chromosome. Crosses took place using virgin females
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where appropriate. Chromosomes were identified using recessive phenotypic markers (bw, vg,
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se), dominant phenotypic markers (L, Ki), and a balancer chromosome (CyO), which suppresses
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recombination on the second chromosome. Mutation accumulation (MA) details are given in ref.
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[25]. Briefly, a single second chromosome marked with bw was used to initiate three control
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populations and numerous MA lines. These focal chromosomes are shown in red. (A) Control
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populations homozygous for the focal chromosome were maintained at a moderate size (450
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adults) to prevent mutation accumulation. (B) MA chromosomes were propagated by
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bottlenecking to a single heterozygous male each generation, allowing new mutations to
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accumulate. (C) Following 62 generations of MA, crosses were performed to replace all non-
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focal chromosomes with an isogenic background. Within-line variation on the focal chromosome
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was eliminated by bottlenecking (square brackets). Each cross included 1-4 males and 4 females
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per line. These crosses involved several marker stocks, which were created using standard
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crossing methods (not shown). An isogenic stock with vgL/CyO was created as shown in (D),
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after creating a completely isogenic genotype using standard balancer chromosome methods (not
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shown). We obtained focal males (E) and females (F) from 25 control lines and 25 MA lines.
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Focal flies were inoculated with sterile MgSO4 or one of two doses of P. aeruginosa. One day
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following inoculation flies were either homogenized and plated to assess bacterial load or placed
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in mating groups to assess fitness. Each replicate mating assay consisted of one focal fly, one
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wild-type competitor of the same sex, and two outbred bw/bw flies of the opposite sex. Flies
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were allowed to interact and oviposit for three days, and then discarded. Offspring phenotypes
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were scored 12 and 15 days after the start of the mating trial. We measured absolute fitness as
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the number of bw/bw offspring relative to the total number of offspring produced over three
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days.
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Figure S2. Approach to estimating resistance and tolerance. Example data are shown to
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illustrate how resistance and tolerance were estimated. (A) We operationally defined resistance
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as the ability to limit the growth of bacteria. For each combination of genotype, sex, and dose we
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first calculated mean bacterial load across i replicates. Mean bacterial load was calculated as d =
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E[log10(coli + 1)], where coli is the number of colonies scored in replicate i. Resistance was
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calculated as 1/mR, where mR is the linear slope of d on initial dose (dashed line), with a fixed
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intercept of (0,0) (i.e., when initial load is zero, bacterial load is always zero). Transforming the
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slope as –mR instead of 1/mR produced the same results. Initial loads were constant across
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groups, represented by 0, 1.85, and 2.75. (B) We operationally defined tolerance as the ability to
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maintain fitness in the presence of bacterial load, relative to fitness in the absence of bacteria.
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For each combination of genotype, sex, and infection level we first calculated mean fitness
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across replicates as W = Σbwi/ Σtotali, where bwi and totali are the number of brown-eyed
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offspring and the total number of offspring scored in replicate i, respectively. Relative fitness at
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infection level k was then calculated as wk = Wk/W0, where W0 is fitness in the absence of
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infection (sham treatment, i.e. injection with sterile MgSO4). Tolerance was calculated as the
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linear slope of w on d (dashed line, mT), with a fixed intercept of (1,0) (i.e., sham-treated flies,
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with d = 0, have relative fitness of 1), where d is our estimate of mean bacterial load for that
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combination of genotype, sex, and infection level, as described in (A).
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Figure S3. Results of simulations to test the resistance-tolerance correlation. Each point
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represents the correlation between simulated resistance and tolerance values, averaged across
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sexes (x), and the resulting fitness-load correlation, averaged across sexes (y). The average
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simulated resistance-tolerance correlation was approximately zero (vertical gray line). The
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fitness-load correlation expected given this null distribution was approximately -0.156
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(horizontal dashed line). The fitness-load correlation we observed (0.050, horizontal solid line),
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was significantly more positive than expected under the null distribution (P = 0.0340). This
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figure also illustrates that lower resistance-tolerance correlations are associated with higher
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fitness-load correlations.