Downloaded by guest on September 28, 2021 ramn mlmnigraitceieilgclpopulation epidemiological under vitro realistic in evolution implementing platform. resistance liquid-handling study alternative treatment we of robotic platform, effect a this the With using compare strategies to approach treatment we alternative, an possible compare here a to present As ability humans. limited to the spread and epidemiological con- costs, experimental prohibitive controlling the of ditions, epi- difficulty the realistic to under due are impractical however, scenarios models, treatment Animal dynamics. of prac- population demiological effect a allows of the which lack of the epidemiology, testing is experimental sce- field for of the framework set in ticable limited progress a impedes only study What com- to narios. of realism. one superiority allow their show but in not therapy, limited bination do ther- often naturally however, combination are studies, but that Clinical beneficial, show range be often broad can and a apy explore strategies to treatment (15– one (RCTs) of allow trials models clinical randomized Computational (4–14) by 17). extent, models lesser computational a inves- to using been and, by have extensively mixing, bacterial quite or cycling tigated hospital-acquired as such For multidrug strategies, Other treatment (3). common. less is become practice therapy has combination infections, of and resistance standard to due the successful failure highly treatment been reducing has in develop therapy to combination falciparum, potential ium high with HIV, antibiotics as pathogens such with resistance, of treat treatment resistance. best the antibiotic to of For risk how the minimizing of simultaneously required, question while is the treatment however, raises health situations, public which many a In is find (2). antimicrobials to of priority unnec- use need reducing inappropriate Clearly, we and use. essary sustainable antibiotics, to more available a threatens for preserve currently approaches antibiotics, To of (1). new efficacy therapy antibiotic of the of discovery options future of undermine rate slowing the M therapy combination into resistance double of community. influx treated maximizing no focal is and the there evolution as long resistance as cycling minimizing uninfecteds, as in well underlying mixing, as and the comple- monotherapies, infer outperforms and com- study, therapy to our d bination in model that 40 find computational We to parameters. a population-dynamical up with this over ment evolution massively perform experimental We hospitals. parallel in observed and be admission may as patient resistance discharge, and population transmission, study treatment, epidemiological liquid- to for implementing dynamics robotic framework alternative spread this a of and use using evolution effect We vitro the platform. in compare handling strategies to test- treatment approach allow multidrug Here, an conditions. that by present epidemiological approaches realistic impeded we under experimental is effects suitable their however, for ing of strategies, calls lack such antibiotics, the of new use. antibiotic development develop sustainable more The to enable that difficulties strategies increas- 2020) treatment and 23, the November with cost review combined for resistance, (received ing antibiotic 2021 of 23, February rise approved rapid and The GA, Atlanta, University, Emory Levin, R. Bruce by Edited a setting epidemiological Angst C. vitro Daniel in an in antibiotic resistance reduce to strategies treatment Comparing PNAS nttt fItgaieBooy eatetfrEvrnetlSse cec,EHZrc,89 uih Switzerland Zurich, 8092 Zurich, ETH Science, System Environmental for Department Biology, Integrative of Institute 01Vl 1 o 3e2023467118 13 No. 118 Vol. 2021 niitc.Terpdrs frssac,tgte with together resistance, of rise rapid The potent of availability antibiotics. the on relies critically medicine odern a,1,2 | niitcresistance antibiotic uc Tepekule Burcu , or tuberculosis, Mycobacterium | xeietlepidemiology experimental a,1,3 e Sun Lei , Plasmod- a,4 Bal , z Bogos azs ´ doi:10.1073/pnas.2023467118/-/DCSupplemental hwsnritco naoitcitrcin n hwsimilar, not show do and Furthermore, interactions antibiotics (19). antagonistic two or resistance these synergistic collateral show concentrations, show subinhibitory of not at strains did and that (18), each mutations drugs epistasis point for single no because inhibi- well-described show and synthesis are respectively) there (protein inhibition, because antibiotic, action gyrase chosen of and were modes tion antibiotics different These have (B). they acid (A) nalidixic streptomycin and antibiotics, two used We patient/well). (treated type. wild the resis- than novo rate a mutation de higher observe used 100-fold to used we likelihood we the tance, organism, increase (infected To model strains MG1655. which (uninfected a resistant plate, medium or As microtiter sensitive growth patient/well). a contain bacterial in or only well patient/well), contain a is either patient can individual 1 an (Fig. ing patients of discharge and epidemiological an using processes 1A). complement same (Fig. model We the migration. simulating patient by this and infection for dynamics hsatcecnan uprigifrainoln at online information supporting contains article This 5 4 3 2 1 under distributed BY).y is (CC article access open This Submission.y Direct PNAS a is article This interest.y competing no declare authors The ulse ac 5 2021. 25, March Published tools; supervised. S.B. reagents/analytic and paper; new the wrote contributed S.B. and B.B. B.T., D.C.A., and data; per- analyzed B.B. L.S., B.T. and and B.T., B.T., D.C.A. D.C.A., D.C.A., research; research; designed S.B. formed and B.T., D.C.A., contributions: Author rsn drs:RditcA,85 clee,Switzerland.y Schlieren, 8952 AG, Redbiotec address: Present MA Boston, Epidemiology, School, Medical Hospital Harvard Biology, 02115.y Systems and of Department Diseases address: Present Infectious Switzerland.y Zurich, of 8091 Zurich, Hospital Department University address: [email protected] y Email: addressed. be may correspondence whom To work.y this to equally contributed B.T. and D.C.A. taeis vna ocnrtoslwrta hs sdfor used into those resistance than double lower monotherapy. of concentrations at influx tested even other no all strategies, outperforms is therapy there combination system, as the long setting as hospital show that We a populations. bacterial in evolving experimentally expected using dynamics epidemi- population a the Using approximate ological narrow we studies. system, to clinical automation order and laboratory computational in effects between vitro the gap in test the strategies to treatment approach different an epidemi- of realistic present a We in experimental context. strategies an ological of of testing lack allows the that develop- to by system However, hindered strategy is resistance. strategies of promising such ing problem a rising is the combat therapy combination Antibiotic Significance ramn a iuae yadn niitc otewells the to antibiotics adding by simulated was Treatment admission continuous with hospital a mimics approach Our a,5 n eata Bonhoeffer Sebastian and , mutS https://doi.org/10.1073/pnas.2023467118 ncotwihehbt naround an exhibits which knockout .Tecnrlui represent- unit central The B). raieCmosAtiuinLcne4.0 License Attribution Commons Creative . y https://www.pnas.org/lookup/suppl/ .coli E. a dpe othese to adapted shrci coli Escherichia | f7 of 1 y

EVOLUTION ABepidemiological model experimental framework Community admission & incubation discharge Hospital infection & day clearance S infection & admission superinfection superinfection n+1 mutation & selection clearance transfer AUB day day clearance n+1 n treatment

discharge day phenotyping AB n

Community Hospital CDno treatment mono A E mono B 1.0 Phenotype U A A/B 0.5 S B AB

0.0 FGcombination cycling H mixing 1.0

population phenotype frequency 0.5

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Fig. 1. (A) Schematic diagram of the dynamical epidemiological model used (Materials and Methods and SI Appendix). Colors correspond to processes in B and phenotypes in C–H.(B) Schematic representation of one treatment arm in the experimental framework. Rounded rectangles represent a hos- pital (microtiter plate) containing uninfected (wells containing only bacterial growth medium) and infected (growth medium with sensitive or resistant strains) patients. Plates for day n + 1 are prepared with medium and antibiotics of the corresponding treatment arm and are then inoculated from the community (rectangle) and/or from the previous day’s plate according to the chosen scenario (see Results). Infection and superinfection are modeled by additional transfers from the previous day’s (n) plate. After incubation, cultures are used for the next day’s inoculation, and all cultures are spot- ted on agar plates for the determination of population phenotypes. Colors correspond for processes in A; purple is used for procedures with no direct equivalent in the epidemiological model. (C–H) Population phenotype frequency during experimental evolution in the absence of preexisting resistance (scenario ∅). Ribbons indicate the observed range in four replicate populations. Lines denote the frequency of population phenotypes after each transfer based on a model fit to all data simultaneously using the mean of the posterior distribution for each parameter. A, resistant to nalidixic acid; B, resis- tant to streptomycin, A/B, mixed population of single resistants; AB, resistant to both drugs; S, sensitive; U, uninfected patients. (C) No treatment. The gap at transfer 13 is due to missing data resulting from a mechanical malfunction of the setup. (D) Monotherapy A (nalidixic acid). (E) Monotherapy B (streptomycin). (F) Combination therapy. (G) Cycling: Treatment is switched every two transfers between nalidixic acid and streptomycin. (H) Mixing: Treatment with nalidixic acid or streptomycin is randomly assigned to each well for each transfer. In all treatment strategies, each antibiotic is used at 2× MIC.

bactericidal, time-kill kinetics (20). While nalidixic acid is no Each treatment strategy was implemented in four replicates longer in clinical use, newer quinolones (such as ciprofloxacin) on a 384-well plate, thus simulating four replicate hospitals, with the same mode of action are still widely used. Streptomycin each with 94 patients (plus eight control wells to monitor unin- is widely used and is considered a critically important antibiotic, tended cross-contamination). Bacterial populations were grown according to the World Health Organization (21). for 24 h at 37 ◦C, representing 1 d in the hospital. Then, a frac- We considered three different combination strategies: combi- tion of wells on the incubated plate were chosen randomly to nation therapy, where patients are treated with both antibiotics represent turnover due to discharge and admission of patients. simultaneously; cycling, meaning temporal rotation of treatment The average stay of a patient was thus independent of the with either antibiotic; and mixing, meaning the random treat- disease status. From all other wells, a small volume was trans- ment of half the population with antibiotic A and the other half ferred to new microtiter plates containing growth medium and with antibiotic B at the same time. In addition, we considered the antibiotics corresponding to the treatment regimen. For all treat- two monotherapy strategies with either antibiotic alone, and we ment regimens, including combination therapy, each antibiotic also included no treatment as a control. was used at twice the minimal inhibitory concentration (MIC).

2 of 7 | PNAS Angst et al. https://doi.org/10.1073/pnas.2023467118 Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting Downloaded by guest on September 28, 2021 Downloaded by guest on September 28, 2021 oprn ramn taeist eueatboi eitnei ni ir pdmooia setting epidemiological vitro in an in resistance antibiotic reduce to strategies treatment Comparing al. et Angst resistance-determinant the Sequencing of (18). regions cost fitness antibiotics low both to at resistance high-level conferred that antibiotic, was (i.e., there and mutations that single-point L fact two S83 of the combination despite one spread, least and at emerge not antibi- than both did lower to otics and resistance Surprisingly, similar, monotherapies. was the mixing under and cycling resistance under combi- The again, frequency strategies. other resistance, the on outperformed effect experiment. therapy treatment nation the the in to maintained regard be With to expected not dilu- are (a persisters, transfer every at of bottleneck tion strong monother- the both of than Because higher apies. level substantially lower still a but to uninfecteds, lead mixing of and Cycling the experiment. throughout the cleared of was course 1 effec- population infected most Fig. entire was the in therapy as tive, combination shown that is found arms successive we 40 uninfecteds, treatment of six course all time C–H for The treatment 0.3. of day. was per days infection 0.2 of of rate rate a hospital. resis- with The the admitted all in and Hence, treatment discharged to were only). Patients response bacteria in outside evolved contained sensitive observed from patients or tance admitted bacteria infections newly no antibiotic-resistant representing either wells of the influx (i.e., no was nm 595 at density assessed optical not we (i.e., but [OD Additionally, growth plate, plates. by A-containing AB-containing phenotype the resistance and and B- no-drug would the phenotype the on A-resistant antibiotic, on An no both. grow or either thus B, well containing or the A plates antibiotic from agar only aliquots different small four transferring a onto in by population measured bacterial was the well of resis- phenotype of no resistance evolution using The the prevents tance. control trivially with control the comparison as allow The antibiotics, second not The strain. arms. does treatment resistant however, six all a one, to applied by be can infected criterion individuals first of minimizing frequency 2) maxi- and the individuals 1) uninfected on of effect frequency their the mizing on based regimens treatment Strategies. alternative Other Outperforms Therapy Combination see Results level the details, epidemiological further to the For refer Methods. to dynamics. and will population referring we the when resistance of readability, patients antibiotic better as of be wells For treatment spread may alternative and compared. as of evolution were dynamics, effects the patient the on simulate and regimens hospitals, to the in days iter- of observed was many effect procedure for above the The ated on densities. but bacterial depended arms, on another. treatment transmission treatment to all of across well probability identical one the was simu- from step were volumes infection small superinfection This transferring and by community Infection lated the hospital. in the individuals outside resistant-infected and uninfected, of sensitive-, prevalence from the inoculum of small representative a populations received stock instead but transferred, not were S2B). to Fig. resistance Appendix, existing (SI of drug strep- irrespective other same and the the acid these were nalidixic but to in for tomycin, probabilities different used Mutation were strain resistance S2A). high-level sensitive Fig. rela- Appendix , the (SI our for experiments resis- in frequencies similar mutant observed resistance we tant novo concentrations, these to de At enough [CFU]/mL). of low (approximately but emergence populations small MIC, tively the the for above concentration allow a chose We o h rtseai (scenario scenario first the For admission and discharge patient represent to chosen wells The 595 ihrgr otetetetefc ntefeunyof frequency the on effect treatment the to regard With . ] ,lwfeuny ogoigpeoye,sc as such phenotypes, nongrowing low-frequency, 1/166), > .)i h xeietlcultures. experimental the in 0.1) rpsL gyrA 4 ) ahgvn ihlvlrssac oone to resistance high-level giving each R), K43 and rpsL eeldmsl ifrn mutations different mostly revealed ,w sue htthere that assumed we ∅), 10 7 ooyfrigunits colony-forming ecompare We Materials gyrA upromdteohrtetetrgmn,wt eadto regard with regimens, treatment in other shown the are of 12 outperformed series frequencies to scenarios time Under 9 the S8). transfers full of over 3; basis respectively, (Fig. the resistants, and on treatment different uninfecteds compared the were scenarios, resistance three regimens double the carrying For patients patients admitted. also, antibiotic. were additionally, single I, a II, to scenario scenario resistant In In bacteria carry of hospital. who levels admitted the progressive were into with address scenarios inflow To further resistant two admitted? studied also therapy we are combination this, patients all does resistant how outperforms when But perform therapy strategies. combination treatment other only), patients infected Ther- Combination of apy. the Benefit Negates of Resistance effect Double Preexisting the of basis the S3). Fig. on Appendix, anticipated bet- (SI perform monotherapies be mixing would and than cycling both ter frequency that the implying of the infecteds, underestimation of of an overestimation and an the resistants to of leads model frequency mixing to and monotherapies cycling the of outcome from using estimates Importantly, parameter only. mea- monotherapies the from the accurately indi- on predicted such based 2) be strategies, surements cannot (Fig. multidrug mixing, parameters of and cycling dynamics key as population underestimates in the differences and that fre- The A cate on the cycling. monotherapy overestimates Based for in systematically it uninfecteds Predicted it of arms, than quency treatment Better all to Perform Mixing Monotherapies. and Cycling certain a to processes. treatment-specific up excluding outcomes 1 treatment (Fig. explaining different level in of succeeds different model dynamics general the the a using from of estab- approach the in that obtained lished implies cycling This resistance substantially. estimates and differed of arms monotherapies parameter treatment the evolution mixing, in novo occurred or de drugs) (e.g., to Although response 2). processes (Fig. same treat- regimens parameters each the treatment key to alternative some Appendix, the separately for (SI between differences model diagram substantial the Venn revealed fitting a ment However, in S4). shown differ- is Fig. the for arms parameters set treatment the same ent of the identifiability using The by processes. explained of be can of arms epi- dynamics treatment an the different that building assumes since fundamentally estimated, model be arms demiological can treatment parameter those the across which parameter for given posterior a similar for expect would distributions dif- we for the Ideally, distributions arms. permitting posterior treatment by ferent different fit have to the parameter improved (12) same treatment al. each et Tepekule to by pendently used model Methods the The and arms. of (Materials treatment extension all an of is course model time the to simultaneously ted Arms. Treatment Different C–H across Vary Estimates Parameter recent a of outcomes the (17). with RCT have agreement multicenter mixing in and also et cycling is that effects Tepekule similar observation of the simulations Moreover, computer (12). in al. the are effects, with similar agreement have mixing good and cycling that and our strategies, in resistance double of evolution experiment. these the that possibility prevent the raises interactions (18) between This al. positions. interactions these et epistatic at negative Trindade mutations and S2 ). positive Table both , Appendix report (SI positions those at h nig,ta obnto hrp uprom h other the outperforms therapy combination that findings, The o scenario For losostepplto yaiso oe hti fit- is that model a of dynamics population the shows also A–F lhuhtemdlgvsa vrl odfit good overall an gives model the Although ,btde ersn noesmlfiainby oversimplification an represent does but ), ∅ ∅ amsino nnetdadsensitive and uninfected of (admission and n ,cmiainteaysignificantly therapy combination I, and .Ftigtemdlinde- model the Fitting Appendix). SI https://doi.org/10.1073/pnas.2023467118 IAppendix SI PNAS is S7– Figs. , | i.1 Fig. f7 of 3

EVOLUTION A of multiple mutations that underlies the success of combination therapy. For lower concentrations, low-level, but not high-level, resistance evolves (lower right triangles, Fig. 4). Note, how- ever, that the concentrations used in liquid are below MIC for at least one antibiotic, where even bacteria sensitive to these antibiotics are expected to grow. Given that at 1× MIC for both antibiotics, combination therapy prevents the emergence of resistance; this indicates that the benefit of combination ther- apy does not arise only from the higher combined concentration B compared to the other treatment strategies. This is another possi- ble benefit of combination therapy: Combining antibiotics might enable the reduction of the concentration of potentially toxic antibiotics, while still minimizing the likelihood of resistance evolution. Discussion In summary, we find that, as long as there is no influx of dou- C ble resistance into the focal population, combination therapy outperforms all other strategies considered here, both in terms of maximizing frequency of uninfecteds and minimizing the fre- quency of resistants. Note that the superiority of combination therapy is based on criteria that focus only on the evolution of resistance and the number of uninfecteds, but disregard other aspects that are highly relevant in clinical practice, such as side effects. The advantage of combination therapy, however, does not appear to result from the higher total antibiotic concentra- tion when combining antibiotics. When there is influx of double Fig. 2. Posterior distributions (estimated probability distribution for a given parameter [Materials and Methods ]) obtained from fitting the model resistance, all strategies fail, as they all result in the increase of to different parts of the experimental data. Mono A, mono B, combina- double resistance. In other words, combination therapy is best at tion, cycling, and mixing refer to fitting the model to data only from these preventing the evolution of resistance, and all strategies are poor treatment arms, whereas simultaneous refers to the overall fit to the time at managing preexisting resistance. course of all treatment arms simultaneously. A, B, and C show, respectively, the posterior distributions of the parameters τA, τB, and νB describing the clearance rate after treatment with antibiotic A, clearance rate after treat- ment with antibiotic B, and the rate of de novo emergence of B resistance for the sensitive strain. Posterior distributions of the parameters that cannot be identified from a given treatment arm are shown as thin box plots and equal to their prior distribution, which is uniform between zero and one. Posterior distributions, including all model parameters, are presented in SI Appendix, Fig. S5.

both maximizing the frequency of uninfecteds and minimizing the frequency of resistants. In scenario II, combination therapy was no longer significantly different from cycling and mixing, and all strategies selected for high levels of double resistance. In summary, combination therapy offers a benefit, as long as there is no influx of double resistance, and all strategies fail otherwise.

Benefits of Combination Therapy Do Not Arise only from Higher Dosage. Does the advantage of combination therapy stem from increased drug dosage? More specifically, does combination therapy outperform the other strategies because it is based on using each antibiotic at the respective concentration used in the single drug therapies? To address this question, we performed another series of analogous experiments for combination ther- apy, lowering the drug concentration from 2× MIC, as used in scenarios ∅, I, and II, to 1× and 0.5× MIC for both drugs (plus a control of no drugs). We measured the effect on the emergence of double resistance in two ways: 1) growth on solid Fig. 3. Frequencies of uninfected and resistant populations for different medium containing 10× MIC, indicating high-level resistance; treatment strategies in different resistance inflow scenarios. Points show and 2) growth in liquid culture at the respective drug concen- population phenotype frequency averaged over transfers 9 to 12 for four tration used for treatment indicating low-level resistance. The biological replicates for scenarios ∅ (no inflow of resistance), I (inflow of both single resistances), and II (inflow of double resistance). Bars show the results show that combination therapy prevents the emergence median. Samples not sharing a letter are significantly different (generalized of both high- and low-level double resistance, as long as the con- linear hypothesis test across treatment strategies, P < 0.05; ANOVA tables centration of both antibiotics are 1× MIC or higher (upper left and all P values can be found in SI Appendix, Tables S4–S9). R, resistant triangles, Fig. 4). This finding argues that it is not the effect populations; U, uninfected (sum of phenotypes A, B, A/B, and AB; Fig. 1). of drug dosage, but the probability of simultaneous acquisition The full time series are shown in SI Appendix, Figs. S6–S8.

4 of 7 | PNAS Angst et al. https://doi.org/10.1073/pnas.2023467118 Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting Downloaded by guest on September 28, 2021 Downloaded by guest on September 28, 2021 oprn ramn taeist eueatboi eitnei ni ir pdmooia setting epidemiological vitro in an in resistance antibiotic reduce to strategies treatment Comparing al. et Angst praht td eitneeouinuigafaeokbased framework a using evolution resistance study to approach mutations could resistance resistance between double experiments. our interactions pos- of in a evolving negative absence (18), strong al. the et be for Trindade by explanation lethal studied found sible we be those that from to mutations find different the seemed only are of that not some our Although mutations do combined. (18) also into when al. but introduced et epistasis, Trindade we positive rarely. that found mutations were strains precise and the described cycling However, previously both the at in as mutations populations locations well revealed of as treatments, Sequencing monotherapies, mixing (18). both cost from high-level low confer selected also at they resistance resis- combined, high-level When double confer drug. II that each and mutations to I point tance scenarios single for on used the based strains for are resistant resistance The double (18). of posi- used evolution that drugs the indicating drive reports can epistasis previous tive given experiments, our in resistance antibiotic prevent to infections. fur- nosocomial strategy for deserves a also therapy as combination investigation view, ther our malaria, the in HIV, reducing tuberculosis, as such in or infections therapy other in combination resistance of of emergence success endpoint the clinical primary Given a (15). as resistance in RCTs to scarce, regard general, are with particular monotherapy In or and superinfection. therapy all-cause endpoints, combination or (either comparing clinical mortality infection), other resistance, to to of attributable was emergence regard monotherapy as with (15), such resistance noninferior of or emergence antibiotic better the of to fail- due emergence treatment advantage ure reduce the did an therapy to combination offer While regard resistance. not with does monotherapy therapy over combination that cluded monotherapy compared different) 16) single (15, a RCTs observations. how- with empirical of therapy, with meta-analyses contradiction combination mixing RCT Two in multicenter of and somewhat recent is superiority cycling a ever, observed that by The supported finding is (17). the effects Moreover, similar have 12). (4, tions in found cul- be can liquid frequencies S9 . in phenotype Fig. used population Appendix, SI of concentrations series drug time the Full ture. to con- The high resistance a streptomycin. (10× double to plate indicates and on resistance used double acid drugs of of nalidixic frequency centration indicates of triangle left concentrations upper different at fers 4. Fig. h rsne eut orbrt h esblt four of feasibility the corroborate results presented The observed resistance double of lack the by surprised were We predic- theoretical with agreement full in are findings The enfeuniso oberssatppltosatrfietrans- five after populations double-resistant of frequencies Mean β lca n naiolcsd.Teesuiscon- studies These aminoglycoside. an and -lactam β lca ocmiainteaywt tpclya (typically with therapy combination to -lactam gyrA and rpsL al S2). Table , Appendix (SI (18) I) hl h oe ih triangle right lower the while MIC), taeiso h vlto fatboi eitne estu large-scale, up set we resistance, antibiotic of evolution the on all strategies of list Experiments. A Evolution genes. in modified found the be geno- can of used genotype All their resequencing and experiment. by strains the verified in used were 25 strains types containing the plates in on resulting selected and kanamycin, strains the into transduced also the 25 supply, mutation containing increase plates on strains double-resistant selected and (cat and single- the fluorescent gene into transduced resistance a was chloramphenicol (29) methodology, MDS42(YFP) a same to the linked selected following marker were and and Additionally, (18) 40 mycin. cost strep- containing to fitness plates resistance low 28). on or relatively (27, acid at transduction nalidixic respectively, P1 to tomycin, using resistance by conferred background or mutations fresh alone The a transduced into subsequently combination and in (26) recombineering DNA a stranded or T) (C248 antibiotics. both for medium solid on assay) microdilution broth standard concentration 2× a to correspond at tions used was 40 or acid 20 Nalidixic of respectively. medium, solid and the prepared agar. (weight/volume) was 1.5% medium with Solid supplemented but contaminations. way, same external prevent to Further- source. and carbon a as added 15 was more, glucose 0.2% and thiamine], ng/L MgSO 0.8 mM 0.4 with (NH plemented g/L 1 contains [MS Media. medium and Drugs, Strains, Methods and Materials on studies clinical to and attempt computational evolution. valid between antibiotic-resistance a gap present the may 24, experimen- narrow approach (20, for our drug models the combination epidemiology, animal of and tal of impracticalities strain effects the the Given as both 25). on strains, dependent bacterial broader are and much therapy Further- a drugs of therapy. consideration of the combination array allow studies of both vitro superiority in which more, imperfect, the is affect when regimen could or prescribed plasmid-borne the is to compliance resistance when strategies treatment observed treatment. not of have absence we the in since events effects, extinction stochastic any such to would for results robust estimation especially be our strains, that believe resistant we the therapies, of multidrug frequency this the theory, affect in might Although, other events. extinction the modeling when tation from using parameters by identifiable captured fully the be arms. treatment of cannot arm estimates treatment dynam- the given population from a the in obtained that ics highlights distributions The arms strategies. posterior treatment mixing different the and cycling among the discrepancy to all regard differences, in with quantitative are especially quali- happening there model behavior, are experimental the captures our tatively selection, while However, and same arms. the epidemio- treatment mutation Fundamentally, the priori. i.e., of a known results processes, not the was fit model would logical model experimental our many in resistance 23). to (22, contribute chromoso- pathogens important also concern, mutations big However, resistance a transferred. mal is lim- horizontally chromoso- resistance a not plasmid-mediated consider here are while only investigated that we mutations have Importantly, mal scenarios. We possi- of setup. set is automated ited dynamics an in resistance population ble antibiotic epidemiological par- of massively realistic evolution that under experimental demonstrate We long-term epidemiology. allel vitro in on l tan sdwr eie from derived were used strains All 100 or 12.5 of concentration a at used was Streptomycin of effect the studying by realism increase could studies Future limi- a is which effects, stochastic consider not does model Our of behavior the extent what to that state to important is It /Lclrmhnclwsaddt ananteue mutants used the maintain to added was chloramphenicol µg/mL /Li iudadsldmdu,rsetvl.Teeconcentra- These respectively. medium, solid and liquid in µg/mL rpsL 4 A2 )mtto a de yuigsingle- using by added was mutation C) (A129 R K43 nodrt etteefc fdfeettreatment different of effect the test to order In I nlqi n 10× and liquid in MIC /Lnldxcai n/r100 and/or acid nalidixic µg/mL l tan eegoni iia at (MS) salts minimal in grown were strains All mutS 4 4 ) .3n FeSO nM 3.33 , 2 SO ncotfrom knockout 4 / KH g/L 3 , https://doi.org/10.1073/pnas.2023467118 /Lclrmhncl nodrto order In chloramphenicol. µg/mL .coli E. al S3. Table Appendix, SI -2M15.A MG1655. K-12 I a esrdb sn a using by measured (as MIC 2 4 PO . MNa mM 1.2 , 4 .coli E. n / K g/L 7 and , YP from -YFP) W73(0 was (30) JW2703 /Lstrepto- µg/mL /Li liquid in µg/mL PNAS 3 C 6 2 gyrA H HPO 5 O | µg/mL 7 .coli E. 4 8 L S83 f7 of 5 and , sup-

EVOLUTION serial passage evolution experiments using a Tecan Evo 200 automated remaining wells. Approximately 0.3 µL of the chosen source wells were liquid handling system (Tecan) with an integrated, automated incuba- then transferred from the previous plate to the chosen target well on the tor (Liconic STX100, Liconic) and a Tecan Infinite F200 spectrophotometer new plate by using the custom pintool for a dilution of approximately (Tecan). 1/166 (approx. 5 × 104 CFU). The newly prepared plates were then moved Our experiments were designed to simulate a hospital or hospital ward. to the incubator and incubated at 37◦ C and 95% relative humidity for We modeled beds as wells in a 384-well plate (Greiner catalog no. 781186) approximately 24 h. and patients as growth media in those wells. All beds were always occu- pied, i.e., filled with a total of 50 µL of growth medium, allowing for a Phenotyping. After the newly prepared plates were placed in the incuba- maximum population size of about 107 CFU. One 384-well plate contained tor, an aliquot of the cultures from the previous transfer was spotted onto four replicate units with 94 patients each. The remaining eight wells were agar plates containing no antibiotic, nalidixic acid, streptomycin, or both used as sterile controls. We serially passaged every well every day into new antibiotics. The agar plates were then also moved into the incubator. At the plates, incorporating the following processes: 1) treatment; 2) admission beginning of the next transfer, agar plates were fetched from the incubator, and discharge; 3) transfer; 4) infection and superinfection; and 5) pheno- and a picture of each plate was taken. Pictures were analyzed for growth typing. Before each transfer, a custom R script computed the necessary (growth/no growth) by using the Pickolo Software package (SciRobotics), as pipetting steps using the parameters outlined below and prepared pipetting well as manual inspection of each picture by using a custom R script. worklists. For scenario ∅, we initially planned the experimental evolution To measure growth of cultures in the 384-well plates, OD595 of each well experiment for 40 transfers. However, we decided to extend the experi- was measured at the beginning of each transfer. We defined growth in 384- ment and switch the two monotherapies and to continue the experiment well plates as reaching an OD595 > 0.1 after incubation. for another 12 transfers. We finished the experiment after 52 transfers; how- From these two measurements, we defined six possible phenotypes: U ever, we here focus on the 40 transfers before the drug switch. The complete (uninfected), for populations that showed no growth on any of the agar time series for scenario ∅ can be found in SI Appendix, Fig. S6. Scenario I plates and in liquid culture; S, (sensitive) for populations that grew only in and scenario II were run for 13 and 22 transfers, respectively. Because these the absence of antibiotics on agar or in liquid culture; A or B, for populations experiments were largely dominated by preexisting resistance, and because that grew on agar plates or in liquid culture in the presence of nalidixic acid of the trajectories observed in the scenario ∅, we decided to stop these or streptomycin, respectively; A/B, for populations that grew in the presence scenarios earlier, as the dynamics had already run its course (SI Appendix, of nalidixic acid and streptomycin (on agar plates or in liquid culture), but Figs. S7 and S8). Every 10 transfers, and at the end of each experiment, not in the presence of both drugs at the same time, indicating a mixed pop- 35 µL of 40% glycerol was added to the assay plates, and the plates were ulation; and AB, for populations that grew both in the presence of both stored at −80 ◦C. single drugs and the combination. All other populations were classified as E, for erroneous phenotypes. Treatment. First, we prepared new 384-well plates by filling all wells with 40 µL of growth medium. We then added 5 µL of diluted antibiotic stock Sequencing. To identify common resistance mutations, we picked three solution (20× final concentration, or no antibiotic for control wells and random populations from time points between 20 and 40 from all four wells without treatment) to all wells, according to the treatment strategy. replicates of the monotherapy treatments, as well as cycling and mixing. We used a separate 384-well plate for every treatment strategy. All wells, Populations were inoculated from frozen samples and grown for 24 to except the sterile controls, were treated, irrespective of their infection sta- 48 h, and DNA was isolated by using the Wizard Genomic DNA purifica- tus. We considered the following treatment strategies: 1) no treatment; tion kit (Promega). We sequenced the main resistance-determinant regions 2) monotherapy with nalidixic acid; 3) monotherapy with streptomycin; 4) for streptomycin and nalidixic acid, which are in gyrA and rpsL, respectively combination therapy with both drugs; 5) cycling, where the used antibi- (18). The regions were amplified and sequenced by using the same primers otic was changed every other transfer; and 6) mixing, where one of the as used in Trindade et al. (18). antibiotics was randomly assigned to each patient. For the initial setup of the first 384-well plate, no antibiotics were added. While all plates were Statistical Analysis. To compare the three different scenarios studied, we prepared and treated at the same time, the following steps were con- averaged the population phenotype frequency over transfers 9 to 12 for ducted in series to minimize the difference in incubation time between all four biological replicates for each scenario. Transfers 9 to 12 were cho- the plates. sen because they are the latest timepoints for which we have data for all three scenarios. The effect of different treatment strategies on the frequen- Admission and Discharge. The experimental design also reflected the influx cies of uninfected and resistant populations in the three different scenarios of patients from a community outside the hospital. This community was was tested by using an ANOVA, followed by a post hoc generalized linear assumed sufficiently large, such that it did not change during the course of hypothesis test for a model that included both treatment strategies and the experiment. Experimentally, this was implemented as follows. A certain phenotype as a main effect, as well as their interaction. ANOVA tables, as fraction of wells (0.2 for all experiments reported here) were randomly cho- well as all tested linear hypotheses and the corresponding test statistics, sen for admission and discharge. These wells were exempt from the transfer can be found in SI Appendix, Tables S4–S9. All statistical analyses were per- to the new plate (see Transfer section). Instead, these wells were inoculated formed in R 4.0.2 (31) using the packages tidyverse (32), multcomp (33), and with 5 µL from replicate overnight cultures, or sterile medium for input multcompView (34). of uninfecteds, that was prepared from frozen stocks every day for the following day. This represents patient influx from the outside community. Mathematical Modeling. Adapting the mathematical model by Tepekule The fraction of the different phenotypes (uninfecteds, sensitive infecteds, et al. (12), we used a compartmental model to describe the population single-resistant A, single-resistant B, and double-resistant AB) constituting dynamics observed in the evolution experiment. The mathematical model is the overall influx differed for the different scenarios considered. These pro- described in greater detail in SI Appendix. The model considered each well portions were (0.15, 0.85, 0, 0, 0) for scenario ∅, (0.21, 0.57, 0.11, 0.11, 0) for as a patient and ignored the population dynamics of bacteria within the scenario I, and (0.21, 0.52, 0.11, 0.11, 0.05) for scenario II, respectively. All wells. Hence, at each time point, each well belonged to one of the five com- wells that were not chosen received 5 µL of sterile medium to equalize partments: U, S, A, B, or AB. The phenotype A/B, which represents a mixed culture volumes. population of single resistants, was not considered in the model, since the frequency of A/B in the experiment was negligible compared to the other Transfer. Next, all wells that had not been chosen for admission and dis- phenotypes. The epidemiological processes included in our mathematical charge were diluted into the new plate at the same position. This was done model are analogous to those implemented during the experiments, which by using a custom pintool developed in-house that allows excluding certain were admission and discharge of the patients, infection, superinfection, positions by removing certain pins (i.e., the pins corresponding to the wells clearance due to successful treatment, and de novo emergence of resistance that are chosen for admission and discharge). The pintool transferred a fixed during incubation (Fig. 1A). Each process was associated with a rate, and volume of about 0.3 µL of the culture for a dilution of approximately 1/166 these rates were translated into model parameters of the corresponding at each transfer (about 5 × 104 CFU). For the initial setup, this step was system of ordinary differential equations (ODEs). The system of ODEs and skipped. the model parameters with their corresponding descriptions and units are provided in SI Appendix. Infection and Superinfection. To implement infection, we randomly chose a fraction of wells (0.3 in all experiments) as the sources of infection. Parameter Estimation and Model Fitting. We estimated the parameters of The targets of infection were chosen randomly with replacement from the the ODE-based model by fitting it to the time course of the frequency of

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Viechtbauer, W. Abel, S. Kouyos, R. Wiesch, zur Abel strongly P. minimizing for variation 9. switching genetic stress-induced of Informed Implications Hadany, L. Bonhoeffer, Obolski, U. S. 8. Wiesch, Zur Abel P. Kouyos, D. R. Pe 7. R. antimicrobial Beardmore, that E. suggests R. theory 6. Ecological Lipsitch, prevent M. to Lo, protocols M. treatment Bergstrom, Evaluating T. C. Levin, R. 5. B. Lipsitch, M. Bonhoeffer, S. TB, drug-resistant 4. Fighting evolution: Outwitting Jacobs, W. Siliciano, R. Goldberg, D. 3. Organization, Health World recommenda- and 2. report Final globally: infections drug-resistant “Tackling O’Neill, J. 1. ac nitniecr nt:Acutrrnoie rsoe trial. crossover cluster-randomised (2018). 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EVOLUTION