Evolution can be propelled by natural selection, it can wander with the chance effects of mutation and genetic drift, and it can be constrained by history, whereby past events limit or even potentiate the future (Travisano et al., 1995; Keller and Taylor, 2008; Meyer et al., 2012; Kryazhimskiy et al., 2014; Rebolleda-Gomez and Travisano, 2019). The relative roles of these forces has been debated, with the constraints of history the most contentious (Blount et al., 2018). A wealth of recent research has shown that evolution can be surprisingly repeatable when selection is strong even among distantly related lineages or in different environments (Lieberman et al., 2011; Lassig et al., 2017; Turner et al., 2018), but disparate outcomes become more likely as the footprint of history (i.e. differences in genetic background caused by chance and selection in different environments) increases (Blount et al., 2018; Benton et al., 2021; Mahrt et al., 2021) (For definitions of the forces and their role in the evolution of antibiotic resistance, see Box 1). In the absence of chance and history, selection will cause the most fit genotype to fix in the particular environment, and provided this variant is available, evolution will be perfectly predictable (Bailey et al., 2015; Lassig et al., 2017). However, historical and stochastic processes inevitably produce some degree of contingency, making evolution less predictable, reflecting the importance of evolutionary history (Blount et al., 2008; Meyer et al., 2012; Bajić et al., 2018; Blount et al., 2018; Card et al., 2019; Galardini et al., 2019). The evolution of a new trait, whether by horizontally acquired genes or de novo mutation, is a stochastic process that depends on available genetic variation capable of producing a new trait (Khan et al., 2011; Salverda et al., 2011). As any other evolved trait, antimicrobial resistance (AMR) is subjected to these three evolutionary forces (Box 1).

Antibiotics can impose strong selection pressure on microbial populations, leading to highly repeatable evolutionary outcomes (Vogwill et al., 2014; Lukačišinová et al., 2020; Scribner et al., 2020), with the level of parallelism predicted to depend on the strength of antibiotic pressure (Wistrand-Yuen et al., 2018). However, evolutionary history can also alter the distribution of fitness effects of AMR mutations, their mechanisms of action, or their degree of conferred resistance (Eyre-Walker and Keightley, 2007; Hall and MacLean, 2011; Yen and Papin, 2017; Barbosa et al., 2019). The phenotypic effect of any given mutation acquired is contingent on prior events and will determine the potential of further adaptations to a given environment (Travisano et al., 1995). For example, the effects of a given mutation can vary in different genetic backgrounds (epistasis) or in different environments (pleiotropy) and those mutations can constrain further adaptations (Trindade et al., 2009; Hall and MacLean, 2011; Yen and Papin, 2017; Gifford et al., 2018; Santos-Lopez et al., 2019). Additionally, chance differences in the mutations acquired, their order of occurrence, or compensatory mutations that decrease resistance costs can affect the eventual level of resistance and its evolutionary success in the population (Salverda et al., 2011; Wistrand-Yuen et al., 2018).

The study of mutational pathways to AMR has become accessible by applying population-wide whole-genome sequencing (WGS) to experimentally evolved populations (for a review, see Baquero, 2021). Growth in antibiotics will select for resistant phenotypes whose genotypes can be determined by WGS, and their frequencies and trajectories indicate relative genotype fitness. When large populations, 1 × 10⁷ CFU/mL or higher, of bacteria are propagated, the probability that every base pair is mutated at least once approaches 99% after ~80 generations (Lynch et al., 2016; Santos-Lopez et al., 2019). Yet chance still remains important because most mutations are initially rare and subject to genetic drift until they reach a critical frequency of establishment, when selection dominates their fate (Heffernan and Wahl, 2002; Good et al., 2017; Cooper, 2018). Furthermore, many mutations arise concurrently and those with higher fitness tend to exclude other alleles, known as clonal interference. Thus, the success of new mutations will be determined by their survival of drift, the chance that they co-occur with other fit mutants, and by their relative fitness, which is shaped by selection and history (Nguyen Ba et al., 2019).

The contributions of history, chance, and selection to evolution can be measured using an elegant experimental design (depicted in Figure 1A, Box 1, and described in detail in the Methods) introduced by Travisano et al., 1995, in which replicate populations are propagated from multiple ancestral strains with different evolutionary histories. This experimental design has been used to quantify effects of these forces and to predict evolution in prokaryotes, eukaryotes and even digital organisms (Travisano et al., 1995; Flores-Moya et al., 2008; Keller and Taylor, 2008; Meyer et al., 2012; Kryazhimskiy et al., 2014; Matos et al., 2015; Rebolleda-Gomez and Travisano, 2019; Bundy et al., 2021), but has not been applied to calculate effects of these forces in the evolution of AMR, one of the most critical threats in modern medicine. Here we use this framework to measure the relative roles of history, chance, and selection in the evolution of AMR phenotypes and genotypes in the ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) pathogen Acinetobacter baumannii, a leading agent of multidrug-resistant infections worldwide and named as an urgent threat by the CDC (CDC, 2019). Quantifying contributions of these evolutionary forces is essential if we are ever to predict the evolution of drug resistance of pathogens, including HIV and malaria, and of various cancers (Hughes and Andersson, 2015; Verlinden et al., 2016; MacLean and San Millan, 2019; Pokhriyal et al., 2019; Gerstung et al., 2020).

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Figure 1—source data 1

Concentrations of CAZ and IMI (mg/L) added to the broth at different intervals of the evolution experiments.

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Figure 1—source data 2

Minimum inhibitory concentration (MIC) values for all ancestors and evolved clones by treatment.

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Previously (Santos-Lopez et al., 2019), we propagated a single clone of A. baumannii (strain 17978-mff) for 12 days or 80 generations in increasing concentrations of the fluoroquinolone antibiotic ciprofloxacin (CIP). In that experiment, which established the history for the present study and is analogous to prior exposure in a clinical setting, three replicate populations each were propagated in biofilm conditions or planktonic conditions (hereafter B1–B3 and P1–P3 respectively, Figure 1B). These environments selected for different genetic pathways to CIP resistance and replicate populations also diverged by chance, which produced the genetic and phenotypic histories of the ancestral strains in the current study (Figure 1C,D, Figure 1—source data 1). Key historical differences include reduced ceftazidime (CAZ) resistance in B populations but increased CAZ resistance in P populations (Figure 1C Santos-Lopez et al., 2019).

In the current study, the ‘selection’ phase (Figure 1B) involved experimental evolution in increasing concentrations of the cephalosporin CAZ or the carbapenem imipenem (IMI) for 12 days via serial dilution of planktonic cultures. CAZ or IMI concentrations were doubled every three days (ca. 20 generations), starting with 0.5× minimum inhibitory concentration (MIC; Figure 1—source data 1) for each clone and finishing with 4× MIC, where maximum killing has been observed with β-lactams antibiotics (Nightingale, 1980). Each population was therefore exposed to the same selective pressure during evolutionary rescue. In this study design (Figure 1A, Supplementary Text), the extent of increased resistance represents selection, effects of chance are the phenotypic variation among triplicate populations propagated from the same ancestor, and differences between populations derived from different ancestors quantifies effects of history (Figure 1B).

While the scale of this experiment could seem small, it is well suited for studying the evolution of resistance as 160 generations correspond to ca. 100 days, 15 days, or 170 days of growth in patients of Escherichia coli, P. aeruginosa, or Salmonella enterica, respectively (Gibson et al., 2018). In addition, the genetic contributions of chance, history, and selection were determined by sequencing whole populations to a mean site coverage of 358 (S.D. ± 106) bases at the end of the experiment.

Contributions of evolutionary forces under antibiotic treatment

Antibiotic treatments are designed to achieve sufficient concentration in vivo to clear the infection and prevent the development of new resistant mutants. However, for several reasons including poor drug pharmacokinetics, poor drug distribution, or poor patient compliance, antibiotic concentrations are often below the MIC in body compartments (Andersson and Hughes, 2014). It is expected that as drug concentrations increase, the strength of selection relative to other forces also increases. We therefore analyzed resistance phenotypes of the whole population after 3 days of evolution under subinhibitory drug concentration and after 12 days of evolution in increasing drug levels that concluded at four times the MIC. We analyzed population-wide resistance instead of measures of single isolates because heterogeneity can determine the success or failure of an antibiotic treatment in clinical scenarios (Sánchez-Romero and Casadesús, 2014; Dewachter et al., 2019).

We estimated the role of each force as described by Travisano et al., 1995. Briefly, we estimated the effect of history as the square root of the variance among all propagated populations; the effect of chance as the square root of the variance between the replicates propagated from the same ancestor, and the effect of selection was calculated as the difference in grand mean of the propagated replicates and their ancestors (see Materials and methods for details of this calculations). We estimated effects of these forces during propagation in two antibiotics, CAZ and IMI, and present results of each treatment sequentially. First, after 3 days of growth in subinhibitory concentrations of CAZ, history explained the largest variation in resistance phenotypes (61.7% of variation, p<0.05), with 30.7% for selection and only 7.6% chance (Figure 2A,E, Materials and methods). As expected, CAZ resistance increased overall, but some individual populations did not differ significantly from their ancestor (populations P2, P3, Figure 2A). However, by day 12, following propagation in 4× MIC CAZ, the amount of variation explained by selection increased to 47.8% and effects of history dropped to 31.4% (Figure 2B,E), indicating that strong selective pressures can diminish or erase the effects of history.

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Estimated statistics for history, chance, and selection forces.

By means of a nested linear mixed model, the estimated coefficients representing the forces are shown, in addition to the confidence intervals at a α = 0.05 significance level generated by bootstrapping and the Bayes factors computed by a Bayesian analysis. BF₁₀ is the ratio between the probabilities of the alternative and null model and therefore, it measures the degree of evidence of including the force. BF₁₀ < 1 null evidence, 3 > BF₁₀ > 1 weak evidence, 20 > BF₁₀ > 3 positive evidence, 150 > BF₁₀ > 20 strong evidence, BF₁₀ > 150 very strong evidence. This is also source data for Figure 3.

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Previous studies have shown that other evolved traits such as fitness itself show declining adaptability: less fit populations adapt faster and to a greater extent than more fit populations when propagated under the same environmental conditions (Wiser et al., 2013; Kryazhimskiy et al., 2014), which would lead to reduced variance in fitness traits among populations. This homogeneity indeed emerged as prolonged CAZ selection overcame historical variation. Populations with lower initial MICs, which by necessity were exposed to lower concentrations of CAZ, increased their resistance level more than populations with higher MICs (Figure 2—figure supplement 1), implying weak selection for further resistance in populations exceeding the MIC threshold and hence declining rates of resistance gains. This finding also suggests that the level of evolved resistance converges and may be predictable (Meyer et al., 2012; Kryazhimskiy et al., 2014), but effects of genetic background remain (Figure 2). Strong antibiotic selection has the potential to overcome but do not entirely eliminate historical differences in resistance.

Evolutionary trade-offs arise from past antibiotic selection

Evolutionary trade-offs occur when changes in a given gene or trait increase fitness in one environment but reduce fitness in another. For example, a history of adaptation to one antibiotic could alter resistance and subsequent evolution in the presence of a subsequent antibiotic. The phenomena of cross-resistance and collateral sensitivity are specific examples of pleiotropy, where the mechanism of resistance to the initial drug either directly increases or decreases resistance to other drugs, respectively (Pal et al., 2015). Additionally, the resistance mechanism could interact with other genes or alleles in the genome, a form of epistasis, and also promote or impede resistance evolution. We hypothesized that resistance mechanisms arising during selection in CAZ would alter resistance to other antibiotics both by genotype-independent (pleiotropy) and genotype-dependent (epistasis) mechanisms. Recall that during the history phase of the experiment (Santos-Lopez et al., 2019), populations propagated in increasing concentrations of CIP became from 4- to 200-fold more resistant to CIP (Figure 1C, Santos-Lopez et al., 2019). Some of these strains also became more resistant to CAZ (populations P1–P3), while others became more susceptible (populations B1 and B3, for more details, see Santos-Lopez et al., 2019), and given that these populations originated from the same ancestor, this variation in collateral resistance phenotypes is best explained by pleiotropy. In the current study, after 12 days evolving in the presence of CAZ, the grand mean of CIP resistance levels did not change, so history was the dominant force shaping the MIC to CIP (Figure 3A). However, if we analyze the P and the B populations independently, B populations became significantly more sensitive to CIP but the P populations did not (Figure 3A), showing that the emergence of collateral sensitivity may depend on prior selection in different environments. These results also indicate that CAZ resistance mechanisms interact with CIP resistance in potentially useful ways.

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We also tested if evolving in the presence of CAZ-altered resistance to the carbapenem antibiotic IMI (Figure 3B). As CAZ and IMI are both β-lactam antibiotics and mutations in efflux pumps can alter resistance to both (Lee et al., 2017), we predicted selection in CAZ would also increase IMI resistance and further, that the contributions of each evolutionary force to IMI resistance would follow that measured for CAZ (Figure 2B). As expected, all 12 populations evolved in CAZ became more resistant to IMI (two-tailed nested t-test p<0.0001, t = 7.507, df = 34), and selection was the most important force (p<0.05), explaining almost 44.3% of the variation, while history contributed 23.0% and chance 32.2% (Figure 3E).

Phenotypic divergence despite genetic parallelism

When multiple lineages evolve independently in the same environment, phenotypic convergence is usually observed, but the genotypes may be more variable (Meyer et al., 2012; Bedhomme et al., 2013; Kryazhimskiy et al., 2014). In our experiment, large populations were exposed to strong antibiotic pressure, so we predicted convergence at the genetic level owing to few solutions that improve both fitness and resistance (Lenski, 2017; Cooper, 2018). We conducted whole-population genomic sequencing of all populations at the end of the experiment to identify all contending mutations above a detection threshold of 5% and analyzed the genetic contributions of history, chance, and selection using Manhattan distance estimators as a metric for the genotypic distance between populations (Figure 4). We calculated the genotypic role of chance as the mean distance between evolved populations sharing the same ancestor; history as the mean distance between evolved populations with different ancestors, after subtracting the effect of chance; and selection as the mean distance between ancestral and evolved populations, after subtracting the effects of chance and history. Using these metrics, we infer that evolution in CAZ at the genotypic level was shaped more by selection than history, but the opposite was seen in IMI, and effects of chance were similar in both experiments (Figure 4).

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Putative driver mutations and resistance levels of the replicate populations after 12 days evolving in presence of CAZ or IMI.

The average resistance levels (mg/L) and SEM are shown in the table. Replicates highlighted acquired the same mutation.

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Complete list of mutated genes from the sequenced populations and clones.

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Clinical CAZ-resistant A. baumannii isolates commonly acquire mutations that increase the activity of Acinetobacter drug efflux (ade) pumps (Lee et al., 2017). In the history phase of CIP selection, biofilm lines (Figure 1B) selected mutations in adeL, the regulator of the adeFGH pump, which produce collateral sensitivity to CAZ and other β-lactams (Figure 1D). In contrast, P lines became cross-resistant to CAZ by adeN mutations that regulate the adeIJK complex or pgpB mutations that are also regulated by adeN (Figure 1D; Santos-Lopez et al., 2019). Subsequently, evolution in increasing concentrations of CAZ selected at least one mutation in adeJ in 16/18 populations (Figure 4A); this gene encodes the permease subunit of AdeIJK that is a known cause of CAZ resistance (Lee et al., 2017). The two exception populations instead acquired mutations in adeN, in ACX60_RS2390, a gene of unknown function, and in ftsI, the target of CAZ. Evolution in IMI also selected mutations in the ftsI gene in all populations (Figure 4B); this gene encodes penicillin binding-protein 2, one of the most common causes of de novo resistance to IMI in clinical isolates (Lee et al., 2017). Therefore, evolution in β-lactam antibiotics generated convergent evolution regardless of the genetic background (Vogwill et al., 2014; Scribner et al., 2020).

Yet despite these genetic similarities, replicate populations reached different resistance levels (Figure 2B,D). As the resistant phenotype was measured in mixed populations with diverse genetic backgrounds, it is possible that even though a resistance allele is fixed, different genotypes within each population could explain the phenotypic differences. Evidence of this heterogeneity might be seen when comparing the five replicate IMI populations that acquired the same mutation in ftsI (A579V) but differ in resistance levels by up to fourfold (Figure 4 and Figure 1—source data 1). Another potential explanation for different phenotypes associated with mutations in the same gene is that different mutations may produce different resistance levels. Evidence for this possible explanation is seen when comparing replicate populations derived from ancestor P1, where different SNPs in adeJ (Figure 4, Figure 1—source data 2) produce varied resistance (Figure 2), perhaps by altering the function of this permease in different ways. Follow-up experiments with reconstructed variants in isogenic backgrounds are needed to test this hypothesis. To summarize, both varied pleiotropy of different mutations in the same drug targets and interactions between mutations in different drug targets may constrain AMR evolution.

Stephen Jay Gould famously argued that replaying the tape of life is impossible because historical contingencies are ubiquitous (Gould, 1990). The evolution and spread of AMR provide a test of this hypothesis because countless evolution experiments are initiated each day with each new prescription to combat infections caused by bacteria with different histories. Previous studies suggest that the predictability of antibiotic resistance – or the fidelity of the replay – depends on the pathogen, the antibiotic treatment, and the growth environment (Vogwill et al., 2014; Gifford et al., 2018; Wistrand-Yuen et al., 2018; Card et al., 2019; Santos-Lopez et al., 2019; Scribner et al., 2020). Here, we have quantified contributions of history, chance, and selection to AMR evolution, using six different ancestors replicated in each of two different antibiotic treatments. In the end, selection is unsurprisingly the predominant force in the evolution of AMR and produced convergent evolution even at the nucleotide level in some instances. Yet history and chance play clear and important roles in the emergence of new resistance phenotypes (Figures 3B,D,5, Vogwill et al., 2014), the extent of evolved resistance (Figures 2 and 3), the generation of collateral sensitivity networks, (Pal et al., 2015), and the predictability of the final resistance phenotype (Figures 1 and 4, Gifford et al., 2018; Scribner et al., 2020). If we consider that the established history of these experimental populations is shallow – the result of only 80 prior generations of growth in a different antibiotic that selected between one and three mutations – it is remarkable how deeply these genotypes were imprinted, resulting in divergent evolutionary trajectories under stringent selection in new drugs. Our data also suggest that, as in Drosophila (Teotonio and Rose, 2000), viruses (Bedhomme et al., 2013) and yeast (Rebolleda-Gomez and Travisano, 2019), history and chance may determine the reversibility of acquired traits (Figure 5).

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This probability of reversion is potentially clinically important because exploitable collateral sensitivity networks can arise, such as the tradeoff between CIP resistance and β-lactam resistance identified here (Pal et al., 2015). Finally, our data reveals that evolution of AMR follows a clear diminishing return pattern, where antibiotic pressure selects for mutations with progressively smaller phenotypic effects as the population is treated with higher antibiotic concentrations (Figure 2—figure supplement 1). This result mirrors findings in the original Travisano et al. paper (Travisano et al., 1995), where populations that were pre-adapted to compete well in maltose did not adapt further, but populations with major deficiencies in maltose evolved to become just as fit. This result may be instructive for AMR management: on the one hand, more resistant populations at the outset did not increase this phenotype further, but on the other hand, more susceptible lines rapidly compensated for this deficit.

Our experiment was performed in planktonic cultures and was limited to a sensitive strain of A. baumannii treated with a single fluoroquinolone followed by one of two β-lactam drugs. These were deliberate experimental design choices that allowed careful assessment of the evolutionary forces at play in a rapidly evolving population but may be considered limitations for some broader applications. Despite these limitations, our finding that history and chance are ancillary forces compared to the strength of selection imposed by antibiotics is universal and is well supported by the literature. For instance, exposure to fluroquinolones in Gram-positive or Gram-negative bacteria commonly selects for mutations in gyrA (Seward and Towner, 1998; Weigel et al., 1998; Hooper and Jacoby, 2015). However, we also observed that history and chance can play important roles in resistance evolution in certain specific environments. For example, the reversions in adeL are probably lifestyle dependent and would not be expected to occur if we replay the experiment in the biofilm lifestyle instead of planktonic.

Finally, our experiment focuses solely on de novo mutations and does not allow the opportunity for horizontal gene transfer from other species or strains, which is the principal mechanism of the emergence of AMRs in most clinical settings (MacLean and San Millan, 2019). However, genetic background also affects the fitness of transmissible elements (Alonso-del Valle et al., 2021) and epidemiological data indicate that evolutionary history constrains the persistence of resistance mediated by plasmids (Dunn et al., 2019; León-Sampedro et al., 2021). The framework defined here illustrates the potential to identify genetic and environmental conditions where selection is the most dominant evolutionary force and it predictably produces antagonism between resistance traits. With ever greater knowledge of the present state, we gain hope for guiding the future to exploit the past.

All data generated or analyzed in this study are included in the manuscript, supporting files, or at https://github.com/sirmicrobe/chance_history_selection, where raw experimental values and statistical analysis code is shared. All sequences were deposited into NCBI under the BioProject number PRJNA485123 and accession numbers can be found in Supplementary file 1.

1. 1. Santos-Lopez A
   2. Marshall CW

   (2020) NCBI Sequence Read Archive

   ID PRJNA485123. Predicting the emergence of antibiotic resistance.

   https://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=485123

1. 1. Alonso-del Valle A
   2. León-Sampedro R
   3. Rodríguez-Beltrán J
   4. DelaFuente J
   5. Hernández-García M
   6. Ruiz-Garbajosa P
   7. Cantón R
   8. Peña-Miller R
   9. San Millán A

   (2021) Variability of plasmid fitness effects contributes to plasmid persistence in bacterial communities

   Nature Communications 12:2653.

   https://doi.org/10.1038/s41467-021-22849-y

   • PubMed
   • Google Scholar
2. 1. Andersson DI
   2. Hughes D

   (2014) Microbiological effects of sublethal levels of antibiotics

   Nat Rev Microbiol 12:465–478.

   https://doi.org/10.1038/nrmicro3270

   • PubMed
   • Google Scholar
3. 1. Bailey SF
   2. Rodrigue N
   3. Kassen R

   (2015) The effect of selection environment on the probability of parallel evolution

   Molecular Biology and Evolution 32:1436–1448.

   https://doi.org/10.1093/molbev/msv033

   • PubMed
   • Google Scholar
4. 1. Bajić D
   2. Vila JCC
   3. Blount ZD
   4. Sánchez A

   (2018) On the deformability of an empirical fitness landscape by microbial evolution

   PNAS 115:11286–11291.

   https://doi.org/10.1073/pnas.1808485115

   • PubMed
   • Google Scholar
5. 1. Baquero F

   (2021) Evolutionary pathways and trajectories in antibiotic resistance

   Clinical Microbiology Reviews 10:e00050-19.

   https://doi.org/10.1128/CMR.00050-19

   • Google Scholar
6. 1. Barbosa C
   2. Römhild R
   3. Rosenstiel P
   4. Schulenburg H

   (2019) Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa

   eLife 8:e51481.

   https://doi.org/10.7554/eLife.51481

   • Google Scholar
7. 1. Barrick JE
   2. Colburn G
   3. Deatherage DE
   4. Traverse CC
   5. Strand MD
   6. Borges JJ
   7. Knoester DB
   8. Reba A
   9. Meyer AG

   (2014) Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq

   BMC Genomics 15:1039.

   https://doi.org/10.1186/1471-2164-15-1039

   • PubMed
   • Google Scholar
8. 1. Baym M
   2. Kryazhimskiy S
   3. Lieberman TD
   4. Chung H
   5. Desai MM
   6. Kishony R

   (2015) Inexpensive multiplexed library preparation for megabase-sized genomes

   PLOS ONE 10:e0128036.

   https://doi.org/10.1371/journal.pone.0128036

   • PubMed
   • Google Scholar
9. 1. Bedhomme S
   2. Lafforgue G
   3. Elena SF

   (2013) Genotypic but not phenotypic historical contingency revealed by viral experimental evolution

   BMC Evolutionary Biology 13:46.

   https://doi.org/10.1186/1471-2148-13-46

   • PubMed
   • Google Scholar
10. 1. Benton ML
    2. Abraham A
    3. LaBella AL
    4. Abbot P
    5. Rokas A
    6. Capra JA

    (2021) The influence of evolutionary history on human health and disease

    Nature Reviews. Genetics 22:269–283.

    https://doi.org/10.1038/s41576-020-00305-9

    • PubMed
    • Google Scholar
11. 1. Blount ZD
    2. Borland CZ
    3. Lenski RE

    (2008) Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli

    PNAS 105:7899–7906.

    https://doi.org/10.1073/pnas.0803151105

    • Google Scholar
12. 1. Blount ZD
    2. Lenski RE
    3. Losos JB

    (2018) Contingency and determinism in evolution: Replaying life’s tape

    Science 362:aam5979.

    https://doi.org/10.1126/science.aam5979

    • PubMed
    • Google Scholar
13. 1. Bolger AM
    2. Lohse M
    3. Usadel B

    (2014) Trimmomatic: a flexible trimmer for Illumina sequence data

    Bioinformatics 30:2114–2120.

    https://doi.org/10.1093/bioinformatics/btu170

    • PubMed
    • Google Scholar
14. Preprint

    1. Bundy JN
    2. Ofria C
    3. Lenski RE

    (2021) How the Footprint of History Shapes the Evolution of Digital Organisms

    bioRxiv.

    https://doi.org/10.1101/2021.04.29.442046

    • Google Scholar
15. 1. Card KJ
    2. LaBar T
    3. Gomez JB
    4. Lenski RE

    (2019) Historical contingency in the evolution of antibiotic resistance after decades of relaxed selection

    PLOS Biology 17:e3000397.

    https://doi.org/10.1371/journal.pbio.3000397

    • PubMed
    • Google Scholar
16. Report

    1. CDC

    (2019) The biggest antibiotic-resistant threats in the U.S

    Centers for Disease Control and Prevention.

    https://www.cdc.gov/drugresistance/biggest-threats.html

    • Google Scholar
17. 1. Cooper VS

    (2018) Experimental evolution as a high-throughput screen for genetic adaptations

    MSphere 3:e00121-18.

    https://doi.org/10.1128/mSphere.00121-18

    • PubMed
    • Google Scholar
18. 1. Dewachter L
    2. Fauvart M
    3. Michiels J

    (2019) Bacterial Heterogeneity and Antibiotic Survival: Understanding and Combatting Persistence and Heteroresistance

    Molecular Cell 76:255–267.

    https://doi.org/10.1016/j.molcel.2019.09.028

    • PubMed
    • Google Scholar
19. 1. Dunai A
    2. Spohn R
    3. Farkas Z
    4. Lázár V
    5. Á G
    6. Apjok G
    7. Boross G
    8. Szappanos B
    9. Grézal G
    10. Faragó A

    (2019) Rapid decline of bacterial drug-resistance in an antibiotic-free environment through phenotypic reversion.Landry CR, Wittkopp PJ, editors

    eLife 8:e47088.

    https://doi.org/10.7554/eLife.47088

    • PubMed
    • Google Scholar
20. 1. Dunn SJ
    2. Connor C
    3. McNally A

    (2019) The evolution and transmission of multi-drug resistant Escherichia coli and Klebsiella pneumoniae: the complexity of clones and plasmids

    Current Opinion in Microbiology 51:51–56.

    https://doi.org/10.1016/j.mib.2019.06.004

    • PubMed
    • Google Scholar
21. 1. Durão P
    2. Balbontín R
    3. Gordo I

    (2018) Evolutionary mechanisms shaping the maintenance of antibiotic resistance

    Trends in Microbiology 26:677–691.

    https://doi.org/10.1016/j.tim.2018.01.005

    • PubMed
    • Google Scholar
22. 1. Eyre-Walker A
    2. Keightley PD

    (2007) The distribution of fitness effects of new mutations

    Nat Rev Genet 8:610–618.

    https://doi.org/10.1038/nrg2146

    • PubMed
    • Google Scholar
23. 1. Flores-Moya A
    2. Costas E
    3. Lopez-Rodas V

    (2008) Roles of adaptation, chance and history in the evolution of the dinoflagellate Prorocentrum triestinum

    Die Naturwissenschaften 95:697–703.

    https://doi.org/10.1007/s00114-008-0372-1

    • PubMed
    • Google Scholar
24. 1. Galardini M
    2. Busby BP
    3. Vieitez C
    4. Dunham AS
    5. Typas A
    6. Beltrao P

    (2019) The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae

    Molecular Systems Biology 15:e8831.

    https://doi.org/10.15252/msb.20198831

    • PubMed
    • Google Scholar
25. 1. Gerstung M
    2. Jolly C
    3. Leshchiner I
    4. Dentro SC
    5. Gonzalez S
    6. Rosebrock D
    7. Mitchell TJ
    8. Rubanova Y
    9. Anur P
    10. Yu K

    (2020) The evolutionary history of 2,658 cancers

    Nature 578:122–128.

    https://doi.org/10.1038/s41586-019-1907-7

    • PubMed
    • Google Scholar
26. 1. Gibson B
    2. Wilson DJ
    3. Feil E
    4. Eyre-Walker A

    (2018) The distribution of bacterial doubling times in the wild

    Proc Biol Sci 285:789.

    https://doi.org/10.1098/rspb.2018.0789

    • PubMed
    • Google Scholar
27. 1. Gifford DR
    2. Krasovec R
    3. Aston E
    4. Belavkin RV
    5. Channon A
    6. Knight CG

    (2018) Environmental pleiotropy and demographic history direct adaptation under antibiotic selection

    Heredity 121:438–448.

    https://doi.org/10.1038/s41437-018-0137-3

    • PubMed
    • Google Scholar
28. 1. Good BH
    2. McDonald MJ
    3. Barrick JE
    4. Lenski RE
    5. Desai MM

    (2017) The dynamics of molecular evolution over 60,000 generations

    Nature 551:45–50.

    https://doi.org/10.1038/nature24287

    • PubMed
    • Google Scholar
29. Book

    1. Gould SJ

    (1990)

    Wonderful Life: The Burgess Shale and the Nature of History

    W. W. Norton & Company.

    • Google Scholar
30. 1. Hall AR
    2. MacLean RC

    (2011) Epistasis buffers the fitness effects of rifampicin- resistance mutations in Pseudomonas aeruginosa

    Evolution 65:2370–2379.

    https://doi.org/10.1111/j.1558-5646.2011.01302.x

    • PubMed
    • Google Scholar
31. 1. Heffernan JM
    2. Wahl LM

    (2002) The effects of genetic drift in experimental evolution

    Theoretical Population Biology 62:349–356.

    https://doi.org/10.1016/s0040-5809(02)00002-3

    • PubMed
    • Google Scholar
32. 1. Hooper DC
    2. Jacoby GA

    (2015) Mechanisms of drug resistance: quinolone resistance

    Ann N Y Acad Sci 1354:12–31.

    https://doi.org/10.1111/nyas.12830

    • PubMed
    • Google Scholar
33. 1. Hughes D
    2. Andersson DI

    (2015) Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms

    Nat Rev Genet 16:459–471.

    https://doi.org/10.1038/nrg3922

    • PubMed
    • Google Scholar
34. 1. Jones RM
    2. Nicas M
    3. Hubbard A
    4. Sylvester MD
    5. Reingold A

    (2016) The Infectious Dose of Francisella Tularensis (Tularemia

    Applied Biosafety 10:227–239.

    https://doi.org/10.1177/153567600501000405

    • Google Scholar
35. 1. Keller SR
    2. Taylor DR

    (2008) History, chance and adaptation during biological invasion: separating stochastic phenotypic evolution from response to selection

    Ecol Lett 11:852–866.

    https://doi.org/10.1111/j.1461-0248.2008.01188.x

    • PubMed
    • Google Scholar
36. 1. Khan AI
    2. Dinh DM
    3. Schneider D
    4. Lenski RE
    5. Cooper TF

    (2011) Negative epistasis between beneficial mutations in an evolving bacterial population

    Science 332:1193–1196.

    https://doi.org/10.1126/science.1203801

    • PubMed
    • Google Scholar
37. 1. Kryazhimskiy S
    2. Rice DP
    3. Jerison ER
    4. Desai MM

    (2014) Global epistasis makes adaptation predictable despite sequence-level stochasticity

    Science 344:1519–1522.

    https://doi.org/10.1126/science.1250939

    • PubMed
    • Google Scholar
38. 1. Kugelberg E
    2. Lofmark S
    3. Wretlind B
    4. Andersson DI

    (2005) Reduction of the fitness burden of quinolone resistance in Pseudomonas aeruginosa

    J Antimicrob Chemother 55:22–30.

    https://doi.org/10.1093/jac/dkh505

    • PubMed
    • Google Scholar
39. 1. Lassig M
    2. Mustonen V
    3. Walczak AM

    (2017) Predicting evolution

    Nature Ecology & Evolution 1:77.

    https://doi.org/10.1038/s41559-017-0077

    • Google Scholar
40. 1. Lee CR
    2. Lee JH
    3. Park M
    4. Park KS
    5. Bae IK
    6. Kim YB
    7. Cha CJ
    8. Jeong BC
    9. Lee SH

    (2017) Biology of Acinetobacter baumannii: Pathogenesis, Antibiotic Resistance Mechanisms, and Prospective Treatment Options

    Frontiers in Cellular and Infection Microbiology 7:55.

    https://doi.org/10.3389/fcimb.2017.00055

    • PubMed
    • Google Scholar
41. 1. Lenski RE

    (2017) Convergence and Divergence in a Long-Term Experiment with Bacteria

    Am Nat 190:S57–S68.

    https://doi.org/10.1086/691209

    • PubMed
    • Google Scholar
42. 1. León-Sampedro R
    2. DelaFuente J
    3. Díaz-Agero C
    4. Crellen T
    5. Musicha P
    6. Rodríguez-Beltrán J
    7. de la Vega C
    8. Hernández-García M
    9. R-GNOSIS WP5 Study Group
    10. López-Fresneña N
    11. Ruiz-Garbajosa P
    12. Cantón R
    13. Cooper BS
    14. San Millán Á

    (2021) Pervasive transmission of a carbapenem resistance plasmid in the gut microbiota of hospitalized patients

    Nature Microbiology 6:606–616.

    https://doi.org/10.1038/s41564-021-00879-y

    • PubMed
    • Google Scholar
43. 1. Lieberman TD
    2. Michel JB
    3. Aingaran M
    4. Potter-Bynoe G
    5. Roux D
    6. Davis MR
    7. Skurnik D
    8. Leiby N
    9. LiPuma JJ
    10. Goldberg JB

    (2011) Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes

    Nat Genet 43:1275–1280.

    https://doi.org/10.1038/ng.997

    • PubMed
    • Google Scholar
44. 1. Lukačišinová M
    2. Fernando B
    3. Bollenbach T

    (2020) Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance

    Nature Communications 11:3105.

    https://doi.org/10.1038/s41467-020-16932-z

    • PubMed
    • Google Scholar
45. 1. Lynch M
    2. Ackerman MS
    3. Gout JF
    4. Long H
    5. Sung W
    6. Thomas WK
    7. Foster PL

    (2016) Genetic drift, selection and the evolution of the mutation rate

    Nature Reviews Genetics 17:704–714.

    https://doi.org/10.1038/nrg.2016.104

    • PubMed
    • Google Scholar
46. 1. MacLean RC
    2. San Millan A

    (2019) The evolution of antibiotic resistance

    Science 365:1082–1083.

    https://doi.org/10.1126/science.aax3879

    • PubMed
    • Google Scholar
47. 1. Mahrt N
    2. Tietze A
    3. Künzel S
    4. Franzenburg S
    5. Barbosa C
    6. Jansen G
    7. Schulenburg H

    (2021) Bottleneck size and selection level reproducibly impact evolution of antibiotic resistance

    Nature Ecology & Evolution 5:1233–1242.

    https://doi.org/10.1038/s41559-021-01511-2

    • Google Scholar
48. 1. Matos M
    2. Simões P
    3. Santos MA
    4. Seabra SG
    5. Faria GS
    6. Vala F
    7. Santos J
    8. Fragata I

    (2015) History, chance and selection during phenotypic and genomic experimental evolution: replaying the tape of life at different levels

    Front. Genet 6:71.

    https://doi.org/10.3389/fgene.2015.00071

    • PubMed
    • Google Scholar
49. 1. Meyer JR
    2. Dobias DT
    3. Weitz JS
    4. Barrick JE
    5. Quick RT
    6. Lenski RE

    (2012) Repeatability and contingency in the evolution of a key innovation in phage lambda

    Science 335:428–432.

    https://doi.org/10.1126/science.1214449

    • PubMed
    • Google Scholar
50. 1. Nguyen Ba AN
    2. Cvijović I
    3. Rojas Echenique JI
    4. Lawrence KR
    5. Rego-Costa A
    6. Liu X
    7. Levy SF
    8. Desai MM

    (2019) High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast

    Nature 575:494–499.

    https://doi.org/10.1038/s41586-019-1749-3

    • PubMed
    • Google Scholar
51. 1. Nightingale C

    (1980)

    Pharmacokinetics of the oral cephalosporins in adults

    The Journal of International Medical Research 8:2–8.

    • PubMed
    • Google Scholar
52. 1. Pal C
    2. Papp B
    3. Lazar V

    (2015) Collateral sensitivity of antibiotic-resistant microbes

    Trends in Microbiology 23:401–407.

    https://doi.org/10.1016/j.tim.2015.02.009

    • PubMed
    • Google Scholar
53. 1. Palaci M
    2. Dietze R
    3. Hadad DJ
    4. Ribeiro FKC
    5. Peres RL
    6. Vinhas SA
    7. Maciel ELN
    8. do Valle Dettoni V
    9. Horter L
    10. Boom WH
    11. Johnson JL
    12. Eisenach KD

    (2007) Cavitary disease and quantitative sputum bacillary load in cases of pulmonary tuberculosis

    Journal of Clinical Microbiology 45:4064–4066.

    https://doi.org/10.1128/JCM.01780-07

    • PubMed
    • Google Scholar
54. 1. Paterson IK
    2. Hoyle A
    3. Ochoa G
    4. Baker-Austin C
    5. Taylor NGH

    (2016) Optimising antibiotic usage to treat bacterial infections

    Scientific Reports 6:37853.

    https://doi.org/10.1038/srep37853

    • PubMed
    • Google Scholar
55. Preprint

    1. Pennings PS
    2. Ogbunugafor CB
    3. Hershberg R

    (2021) Reversion Is Most Likely under High Mutation Supply, When Compensatory Mutations Don’t Fully Restore Fitness Costs

    bioRxiv.

    https://doi.org/10.1101/2020.12.28.424568

    • Google Scholar
56. 1. Pokhriyal R
    2. Hariprasad R
    3. Kumar L
    4. Hariprasad G

    (2019) Chemotherapy resistance in advanced ovarian cancer patients

    Biomarkers in Cancer 11:1179299X19860815.

    https://doi.org/10.1177/1179299X19860815

    • PubMed
    • Google Scholar
57. 1. Rebolleda-Gomez M
    2. Travisano M

    (2019) Adaptation, chance, and history in experimental evolution reversals to unicellularity

    Evolution 73:73–83.

    https://doi.org/10.1111/evo.13654

    • PubMed
    • Google Scholar
58. 1. Salverda ML
    2. Dellus E
    3. Gorter FA
    4. Debets AJ
    5. van der Oost J
    6. Hoekstra RF
    7. Tawfik DS
    8. de Visser JA

    (2011) Initial mutations direct alternative pathways of protein evolution

    PLOS Genetics 7:e1001321.

    https://doi.org/10.1371/journal.pgen.1001321

    • PubMed
    • Google Scholar
59. 1. Sánchez-Romero MA
    2. Casadesús J

    (2014) Contribution of phenotypic heterogeneity to adaptive antibiotic resistance

    PNAS 111:355–360.

    https://doi.org/10.1073/pnas.1316084111

    • PubMed
    • Google Scholar
60. 1. Santos-Lopez A
    2. Marshall CW
    3. Scribner MR
    4. Snyder DJ
    5. Cooper VS

    (2019) Evolutionary pathways to antibiotic resistance are dependent upon environmental structure and bacterial lifestyle.Kirkegaard K, editor

    eLife 8:e47612.

    https://doi.org/10.7554/eLife.47612

    • PubMed
    • Google Scholar
61. 1. Scribner MR
    2. Santos-Lopez A
    3. Marshall CW
    4. Deitrick C
    5. Cooper VS

    (2020) Parallel evolution of tobramycin resistance across species and environments

    MBio 11:e00932-20.

    https://doi.org/10.1128/mBio.00932-20

    • PubMed
    • Google Scholar
62. 1. Seward RJ
    2. Towner KJ

    (1998) Molecular epidemiology of quinolone resistance in Acinetobacter spp

    Clinical Microbiology and Infection 4:248–254.

    https://doi.org/10.1111/j.1469-0691.1998.tb00052.x

    • PubMed
    • Google Scholar
63. 1. Teotonio H
    2. Rose MR

    (2000) Variation in the reversibility of evolution

    Nature 408:463–466.

    https://doi.org/10.1038/35044070

    • PubMed
    • Google Scholar
64. 1. Travisano M
    2. Mongold JA
    3. Bennett AF
    4. Lenski RE

    (1995) Experimental tests of the roles of adaptation, chance, and history in evolution

    Science 267:87–90.

    https://doi.org/10.1126/science.7809610

    • PubMed
    • Google Scholar
65. 1. Trindade S
    2. Sousa A
    3. Xavier KB
    4. Dionisio F
    5. Ferreira MG
    6. Gordo I

    (2009) Positive epistasis drives the acquisition of multidrug resistance

    PLOS Genetics 5:e1000578.

    https://doi.org/10.1371/journal.pgen.1000578

    • PubMed
    • Google Scholar
66. 1. Turner CB
    2. Marshall CW
    3. Cooper VS

    (2018) Parallel genetic adaptation across environments differing in mode of growth or resource availability

    Evolution Letters 2:355–367.

    https://doi.org/10.1002/evl3.75

    • PubMed
    • Google Scholar
67. 1. Verlinden BK
    2. Louw A
    3. Birkholtz LM

    (2016) Resisting resistance: is there a solution for malaria?

    Expert Opin Drug Discov 11:395–406.

    https://doi.org/10.1517/17460441.2016.1154037

    • PubMed
    • Google Scholar
68. 1. Vogwill T
    2. Kojadinovic M
    3. Furio V
    4. MacLean RC

    (2014) Testing the role of genetic background in parallel evolution using the comparative experimental evolution of antibiotic resistance

    Molecular Biology and Evolution 31:3314–3323.

    https://doi.org/10.1093/molbev/msu262

    • PubMed
    • Google Scholar
69. 1. Wagenmakers EJ

    (2007) A practical solution to the pervasive problems ofp values

    Psychonomic Bulletin & Review 14:779–804.

    https://doi.org/10.3758/BF03194105

    • Google Scholar
70. 1. Weigel LM
    2. Steward CD
    3. Tenover FC

    (1998) gyrA mutations associated with fluoroquinolone resistance in eight species of Enterobacteriaceae

    Antimicrob Agents Chemother 42:2661–2667.

    https://doi.org/10.1128/AAC.42.10.2661

    • PubMed
    • Google Scholar
71. 1. Wiser MJ
    2. Ribeck N
    3. Lenski RE

    (2013) Long-Term Dynamics of Adaptation in Asexual Populations

    Science 342:1364–1367.

    https://doi.org/10.1126/science.1243357

    • PubMed
    • Google Scholar
72. 1. Wistrand-Yuen E
    2. Knopp M
    3. Hjort K
    4. Koskiniemi S
    5. Berg OG
    6. Andersson DI

    (2018) Evolution of high-level resistance during low-level antibiotic exposure

    Nat Commun 9:1599.

    https://doi.org/10.1038/s41467-018-04059-1

    • PubMed
    • Google Scholar
73. 1. Yen P
    2. Papin JA

    (2017) History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment

    PLOS Biol 15:e2001586.

    https://doi.org/10.1371/journal.pbio.2001586

    • PubMed
    • Google Scholar

Author details

1. Alfonso Santos-Lopez

   Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, United States

   Present address

   Servicio de Microbiología. Hospital Universitario Ramón y Cajal and Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Christopher W Marshall

   For correspondence

   alfonsosantos2@hotmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9163-9947

2. Christopher W Marshall

   Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, United States

   Present address

   Department of Biological Sciences, Marquette University, Milwaukee, United States

   Contribution

   Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Alfonso Santos-Lopez

   For correspondence

   christopher.marshall@marquette.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6669-3231

3. Allison L Haas

   Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, United States

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2154-4328

4. Caroline Turner

   Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, United States

   Present address

   Department of Biology, Loyola University Chicago, Chicago, United States

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1347-518X

5. Javier Rasero

   Department of Psychology, Carnegie Mellon University, Pittsburgh, United States

   Contribution

   Formal analysis, Methodology, Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1661-2856

6. Vaughn S Cooper

   1. Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, United States
   2. Center for Evolutionary Biology and Medicine, University of Pittsburgh, Pittsburgh, United States

   Contribution

   Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   vaughn.cooper@pitt.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7726-0765

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