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• W2782393048 abstract "•Collateral sensitivity can evolve from diverse genetic and phenotypic starting points•Collateral effects of resistance evolution converges to distinct phenotypic states•Genetic markers associated with convergent states were linked to nfxB mutations•nfxB mutants were eradicated in vivo from the lung of a CF patient during treatment Chronic Pseudomonas aeruginosa infections evade antibiotic therapy and are associated with mortality in cystic fibrosis (CF) patients. We find that in vitro resistance evolution of P. aeruginosa toward clinically relevant antibiotics leads to phenotypic convergence toward distinct states. These states are associated with collateral sensitivity toward several antibiotic classes and encoded by mutations in antibiotic resistance genes, including transcriptional regulator nfxB. Longitudinal analysis of isolates from CF patients reveals similar and defined phenotypic states, which are associated with extinction of specific sub-lineages in patients. In-depth investigation of chronic P. aeruginosa populations in a CF patient during antibiotic therapy revealed dramatic genotypic and phenotypic convergence. Notably, fluoroquinolone-resistant subpopulations harboring nfxB mutations were eradicated by antibiotic therapy as predicted by our in vitro data. This study supports the hypothesis that antibiotic treatment of chronic infections can be optimized by targeting phenotypic states associated with specific mutations to improve treatment success in chronic infections. Chronic Pseudomonas aeruginosa infections evade antibiotic therapy and are associated with mortality in cystic fibrosis (CF) patients. We find that in vitro resistance evolution of P. aeruginosa toward clinically relevant antibiotics leads to phenotypic convergence toward distinct states. These states are associated with collateral sensitivity toward several antibiotic classes and encoded by mutations in antibiotic resistance genes, including transcriptional regulator nfxB. Longitudinal analysis of isolates from CF patients reveals similar and defined phenotypic states, which are associated with extinction of specific sub-lineages in patients. In-depth investigation of chronic P. aeruginosa populations in a CF patient during antibiotic therapy revealed dramatic genotypic and phenotypic convergence. Notably, fluoroquinolone-resistant subpopulations harboring nfxB mutations were eradicated by antibiotic therapy as predicted by our in vitro data. This study supports the hypothesis that antibiotic treatment of chronic infections can be optimized by targeting phenotypic states associated with specific mutations to improve treatment success in chronic infections. The emergence of drug-resistant bacteria coupled with a lack of novel structural classes of antibiotics have made antibiotic resistance one of the most eminent threats to global health (May, 2014May M. Drug development: Time for teamwork.Nature. 2014; 509: S4-S5Crossref PubMed Scopus (32) Google Scholar, O’Neill, 2016O’Neill (2016). Tackling drug-resistant infections globally: Final report and recommendations. https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf.Google Scholar). Therapeutic options and strategies are especially scarce for Gram-negative pathogens such as Pseudomonas aeruginosa (Boucher et al., 2013Boucher H.W. Talbot G.H. Benjamin Jr., D.K. Bradley J. Guidos R.J. Jones R.N. Murray B.E. Bonomo R.A. Gilbert D. Infectious Diseases Society of America10 x ’20 Progress—Development of new drugs active against gram-negative bacilli: An update from the Infectious Diseases Society of America.Clin. Infect. Dis. 2013; 56: 1685-1694Crossref PubMed Scopus (581) Google Scholar, Cabot et al., 2012Cabot G. Ocampo-Sosa A.A. Domínguez M.A. Gago J.F. Juan C. Tubau F. Rodríguez C. Moyà B. Peña C. Martínez-Martínez L. Oliver A. Spanish Network for Research in Infectious Diseases (REIPI)Genetic markers of widespread extensively drug-resistant Pseudomonas aeruginosa high-risk clones.Antimicrob. Agents Chemother. 2012; 56: 6349-6357Crossref PubMed Scopus (166) Google Scholar). This versatile, opportunistic pathogen is a frequent cause of acute nosocomial infections as well as chronic infections in high-risk patient groups, such as those suffering from cystic fibrosis (CF) (Mesaros et al., 2007Mesaros N. Nordmann P. Plésiat P. Roussel-Delvallez M. Van Eldere J. Glupczynski Y. Van Laethem Y. Jacobs F. Lebecque P. Malfroot A. et al.Pseudomonas aeruginosa: resistance and therapeutic options at the turn of the new millennium.Clin. Microbiol. Infect. 2007; 13: 560-578Abstract Full Text Full Text PDF PubMed Scopus (447) Google Scholar). CF is a recessive lethal genetic disorder among the Caucasian population that is caused by mutations in the CF transmembrane conductance regulator (CFTR) gene (Elborn et al., 2016Elborn J.S. Ramsey B.W. Boyle M.P. Konstan M.W. Huang X. Marigowda G. Waltz D. Wainwright C.E. VX-809 TRAFFIC and TRANSPORT study groupsEfficacy and safety of lumacaftor/ivacaftor combination therapy in patients with cystic fibrosis homozygous for Phe508del CFTR by pulmonary function subgroup: a pooled analysis.Lancet Respir. Med. 2016; 4: 617-626Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar). While intensive antibiotic treatment for the eradication of P. aeruginosa infections has been successful in young patients, eradication ultimately fails, leading to the chronic infections experienced by most adult CF patients (Folkesson et al., 2012Folkesson A. Jelsbak L. Yang L. Johansen H.K. Ciofu O. Høiby N. Molin S. Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective.Nat. Rev. Microbiol. 2012; 10: 841-851Crossref PubMed Scopus (480) Google Scholar, Gibson et al., 2003Gibson R.L. Burns J.L. Ramsey B.W. Pathophysiology and management of pulmonary infections in cystic fibrosis.Am. J. Respir. Crit. Care Med. 2003; 168: 918-951Crossref PubMed Scopus (1312) Google Scholar, Johansen et al., 2004Johansen H.K. Nørregaard L. Gøtzsche P.C. Pressler T. Koch C. Høiby N. Antibody response to Pseudomonas aeruginosa in cystic fibrosis patients: A marker of therapeutic success?--A 30-year cohort study of survival in Danish CF patients after onset of chronic P. aeruginosa lung infection.Pediatr. Pulmonol. 2004; 37: 427-432Crossref PubMed Scopus (71) Google Scholar). During chronic infection, antibiotic treatments can temporarily reduce airway infection and inflammation, thus extending the periods of stable disease status and maintained lung function (Fodor et al., 2012Fodor A.A. Klem E.R. Gilpin D.F. Elborn J.S. Boucher R.C. Tunney M.M. Wolfgang M.C. The adult cystic fibrosis airway microbiota is stable over time and infection type, and highly resilient to antibiotic treatment of exacerbations.PLoS ONE. 2012; 7: e45001Crossref PubMed Scopus (278) Google Scholar). Nevertheless, the ability of P. aeruginosa to sustain chronic infection and resist antibiotic treatment is associated with decline in lung function, respiratory failure, and death in CF patients (Hauser et al., 2011Hauser A.R. Jain M. Bar-Meir M. McColley S.A. Clinical significance of microbial infection and adaptation in cystic fibrosis.Clin. Microbiol. Rev. 2011; 24: 29-70Crossref PubMed Scopus (283) Google Scholar, Pittman et al., 2011Pittman J.E. Calloway E.H. Kiser M. Yeatts J. Davis S.D. Drumm M.L. Schechter M.S. Leigh M.W. Emond M. Van Rie A. Knowles M.R. Age of Pseudomonas aeruginosa acquisition and subsequent severity of cystic fibrosis lung disease.Pediatr. Pulmonol. 2011; 46: 497-504Crossref PubMed Scopus (36) Google Scholar, Taylor-Robinson et al., 2012Taylor-Robinson D. Whitehead M. Diderichsen F. Olesen H.V. Pressler T. Smyth R.L. Diggle P. Understanding the natural progression in %FEV1 decline in patients with cystic fibrosis: a longitudinal study.Thorax. 2012; 67: 860-866Crossref PubMed Scopus (122) Google Scholar). The antibiotic resistance of P. aeruginosa is driven by several factors in CF patients, including the activation of chromosomally encoded resistance mechanisms, such as decreased production of the outer membrane porin, inducible chromosomal β-lactamase AmpC, and overexpression of several efflux systems (Lister et al., 2009Lister P.D. Wolter D.J. Hanson N.D. Antibacterial-resistant Pseudomonas aeruginosa: clinical impact and complex regulation of chromosomally encoded resistance mechanisms.Clin. Microbiol. Rev. 2009; 22: 582-610Crossref PubMed Scopus (1187) Google Scholar, Marvig et al., 2015aMarvig R.L. Sommer L.M. Molin S. Johansen H.K. Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis.Nat. Genet. 2015; 47: 57-64Crossref PubMed Scopus (342) Google Scholar). The main efflux pumps are tripartite systems consisting of a resistance nodulation cell division (RND) transporter, a membrane fusion protein (MFP), and an outer membrane factor (OMF). MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MexXY-OprM are the main efflux pumps that expel functionally and structurally dissimilar antibiotics (Li et al., 2015Li X.-Z. Plésiat P. Nikaido H. The challenge of efflux-mediated antibiotic resistance in Gram-negative bacteria.Clin. Microbiol. Rev. 2015; 28: 337-418Crossref PubMed Scopus (809) Google Scholar). When Escherichia coli and Staphylococcus aureus evolve resistance toward specific antibiotics, they also develop sensitivity toward other antibiotics (Baym et al., 2016Baym M. Stone L.K. Kishony R. Multidrug evolutionary strategies to reverse antibiotic resistance.Science. 2016; 351: aad3292Crossref PubMed Scopus (368) Google Scholar, Imamovic and Sommer, 2013Imamovic L. Sommer M.O.A. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development.Sci. Transl. Med. 2013; 5: 204ra132Crossref PubMed Scopus (258) Google Scholar, Lázár et al., 2013Lázár V. Pal Singh G. Spohn R. Nagy I. Horváth B. Hrtyan M. Busa-Fekete R. Bogos B. Méhi O. Csörgő B. et al.Bacterial evolution of antibiotic hypersensitivity.Mol. Syst. Biol. 2013; 9: 700Crossref PubMed Scopus (203) Google Scholar, Munck et al., 2014Munck C. Gumpert H.K. Wallin A.I.N. Wang H.H. Sommer M.O.A. Prediction of resistance development against drug combinations by collateral responses to component drugs.Sci. Transl. Med. 2014; 6: 262ra156Crossref PubMed Scopus (109) Google Scholar, Rodriguez de Evgrafov et al., 2015Rodriguez de Evgrafov M. Gumpert H. Munck C. Thomsen T.T. Sommer M.O.A. Collateral resistance and sensitivity modulate evolution of high-level resistance to drug combination treatment in Staphylococcus aureus.Mol. Biol. Evol. 2015; 32: 1175-1185Crossref PubMed Scopus (65) Google Scholar). This observation led to the proposal of a new, rational drug treatment paradigm termed collateral sensitivity cycling, in which sequential drug treatments are designed to exploit collateral sensitivity resulting from resistance evolution (Imamovic and Sommer, 2013Imamovic L. Sommer M.O.A. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development.Sci. Transl. Med. 2013; 5: 204ra132Crossref PubMed Scopus (258) Google Scholar). Collateral sensitivity has also been demonstrated in cancer cell lines (Hall et al., 2009Hall M.D. Handley M.D. Gottesman M.M. Is resistance useless? Multidrug resistance and collateral sensitivity.Trends Pharmacol. Sci. 2009; 30: 546-556Abstract Full Text Full Text PDF PubMed Scopus (209) Google Scholar) and was recently successfully deployed for treatment of Ph(+) acute lymphoblastic leukemia in an animal model (Zhao et al., 2016Zhao B. Sedlak J.C. Srinivas R. Creixell P. Pritchard J.R. Tidor B. Lauffenburger D.A. Hemann M.T. Exploiting temporal collateral sensitivity in tumor clonal evolution.Cell. 2016; 165: 234-246Abstract Full Text Full Text PDF PubMed Scopus (72) Google Scholar). Collateral sensitivity may be particularly useful for optimizing treatments of chronic infections since their nature and severity warrants and requires tailored treatment strategies. Chronic lung infections of CF patients caused by P. aeruginosa may be a useful clinical model to study the evolution of collateral sensitivity in response to antibiotic therapy. While a recent study reported a lack of collateral sensitivity in clinical isolates from CF patients (Jansen et al., 2016Jansen G. Mahrt N. Tueffers L. Barbosa C. Harjes M. Adolph G. Friedrichs A. Kreinz-Weinreich A. Rosenstiel P. Schulenburg H. Association between clinical antibiotic resistance and susceptibility of Pseudomonas in the cystic fibrosis lung.Evol. Med. Public Heal. 2016; 2016: 182-194Crossref PubMed Google Scholar), the study did not investigate relative changes in strain susceptibility, and, thus, collateral sensitivity might be missed due to the lack of appropriate baseline controls. Moreover, if evolutionary tradeoffs, such as collateral sensitivity, do not occur in vivo, then ever-increasing resistance would be the consequence of decades of antibiotic exposure. Yet, previous phenotypic characterization of CF isolates did not observe such monotonic increase in antibiotic resistance over time (López-Causapé et al., 2013López-Causapé C. Rojo-Molinero E. Mulet X. Cabot G. Moyà B. Figuerola J. Togores B. Pérez J.L. Oliver A. Clonal dissemination, emergence of mutator lineages and antibiotic resistance evolution in Pseudomonas aeruginosa cystic fibrosis chronic lung infection.PLoS ONE. 2013; 8: e71001Crossref PubMed Scopus (58) Google Scholar). Accordingly, we hypothesized that P. aeruginosa might evolve collateral sensitivity in response to antibiotic exposure both in vitro and in patients and that these vulnerabilities modulate population dynamics during antibiotic treatment. To elucidate the collateral sensitivity network of drug-resistant strains of P. aeruginosa, PAO1 were experimentally evolved in media that resembled the chemical composition encountered in the lungs of CF patients (SCFM) (Palmer et al., 2007Palmer K.L. Aye L.M. Whiteley M. Nutritional cues control Pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum.J. Bacteriol. 2007; 189: 8079-8087Crossref PubMed Scopus (398) Google Scholar). Twenty-four clinically relevant antibiotics that included anti-pseudomonal antibiotics and other drugs were chosen from eight chemical classes affecting different targets in P. aeruginosa (Table 1). To exclude possible effects on resistance phenotypes from adaptation to novel growth conditions, we adapted the ancestral PA01 strain to SCFM for 10 days as a media control (WTE). At the last day of the adaptive evolution experiment, all lineages could grow in the media with antibiotic concentrations exceeding the clinical breakpoint defined by EUCAST for P. aeruginosa (Table 1; Figures S1A–S1F) (EUCAST, 2016EUCAST 2016 The European Committee on Antimicrobial Susceptibility Testing. MIC Clinical breakpoints version 6.0. http://www.eucast.org/.Google Scholar). Collateral sensitivity or collateral resistance was defined as a decrease or increase in the MIC (minimal inhibitory concentration) of the antibiotic-resistant strain relative to the wild-type adapted to SCFM (WTE) (Figures S1G–S1I) (Imamovic and Sommer, 2013Imamovic L. Sommer M.O.A. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development.Sci. Transl. Med. 2013; 5: 204ra132Crossref PubMed Scopus (258) Google Scholar). To confirm the robustness of our susceptibility tests, we measured the significance of the fold increase or decrease in resistance relative to the WTE (see STAR Methods). We observed that collateral sensitivity toward ampicillin decreased by 8.5-fold for ciprofloxacin-resistant strain (p value 3.42e−21, t test). Increase in susceptibility for other antibiotics such as amikacin and colistin was 2.5- and 1.5-fold relative to the WTE; yet, in both cases, we observed that collateral sensitivity observed was statistically significant (p value 7.87e−55 and 2.07e−290, t test, respectively) (Figure S1J).Table 1List of Antibiotics Used in the StudyAntibioticAntibiotic AbbreviationClass (sub-class)Class AbbreviationTargetEUCAST BreakpointsAmikacinAMIaminoglycosideAprotein synthesis, 30S16GentamicinGENaminoglycosideAprotein synthesis, 30S4TobramycinTOBaminoglycosideAprotein synthesis, 30S4CiprofloxacinCIPquinoloneQDNA gyrase1LevofloxacinLEVquinoloneQDNA gyrase2AmpicillinAMPβ-lactam (penicillin)Bcell walln.a.PiperacillinPIPβ-lactam (penicillin)Bcell wall16CarbenicillinCARβ-lactam (penicillin)Bcell walln.a.TicarcillinTICβ-lactam (penicillin)Bcell wall16AztreonamAZEβ-lactam (monobactam)Bcell wall16CefepimeCFPβ-lactam (cephalosporin)Bcell wall8CefuroximeCFXβ-lactam (cephalosporin)Bcell walln.a.CeftazidimeCFZβ-lactam (cephalosporin)Bcell wall8MeropenemMERβ-lactam (carbapenem)Bcell wall8ImipenemIMIβ-lactam (carbapenem)Bcell wall8MinocyclineMINtetracyclineTprotein synthesis, 30Sn.a.DoxycyclineDOXtetracyclineTprotein synthesis, 30Sn.a.AzithromycinAZYmacrolideMprotein synthesis, 50Sn.a.ErythromycinERImacrolideMprotein synthesis, 50Sn.a.ClarithromycinCLAmacrolideMprotein synthesis, 50Sn.a.ColistinCOLpolymyxinPlipopolysaccharide4FosfomycinFOSfosfomycinFcell wall biogenesisn.a.RifampicinRIFrifamycinRRNA synthesisn.a.Trimethoprim/SulfamethoxazoleTMSantifolateCcombination folic acid pathway/ synthesis of dihydrofolic acidn.a.n.a., no EUCAST breakpoints listed (EUCAST, 2016EUCAST 2016 The European Committee on Antimicrobial Susceptibility Testing. MIC Clinical breakpoints version 6.0. http://www.eucast.org/.Google Scholar). Open table in a new tab n.a., no EUCAST breakpoints listed (EUCAST, 2016EUCAST 2016 The European Committee on Antimicrobial Susceptibility Testing. MIC Clinical breakpoints version 6.0. http://www.eucast.org/.Google Scholar). Given that the majority of resistant strains (75%) were collaterally sensitive to at least one antibiotic (Figure 1A; Table S1), we were able to construct a collateral sensitivity network for P. aeruginosa (Figures 1B and S2A). We simulated the number of collateral sensitivity cycles comprising: (1) all antibiotics in the study and (2) anti-pseudomonal antibiotics that have EUCAST-defined resistance breakpoints for P. aeruginosa (Table 1). For EUCAST-defined anti-pseudomonal antibiotics, we detected five collateral sensitivity cycles including two and three drugs (Figure 1C; Table S2). However, expanding the simulation for collateral sensitivity cycles to all antibiotics tested, the majority of antibiotics exhibiting collateral sensitivity (78%) could be employed in collateral sensitivity cycling. The number of simulated collateral sensitivity cycles including two and three drugs reached 18 and 51 cycles, respectively (Figures 1C and S2B).Figure S2Complex Networks of Interactions Based on the Collateral Susceptibility Profiles, Related to Figure 1Show full caption(A) Collateral sensitivity network. For collateral susceptibility networks, the directed path of each arrow represents the collateral sensitivity (blue) or collateral resistance (red) of an affected variable (drug-resistant strain) on the causal variable (drug). Antibiotic abbreviations are listed in Table 1.(B) Number of collateral sensitivity cycles simulated for all drugs employed in the study. The cycles are based on PAO1 susceptibility profiles (Figure 1A).View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) Collateral sensitivity network. For collateral susceptibility networks, the directed path of each arrow represents the collateral sensitivity (blue) or collateral resistance (red) of an affected variable (drug-resistant strain) on the causal variable (drug). Antibiotic abbreviations are listed in Table 1. (B) Number of collateral sensitivity cycles simulated for all drugs employed in the study. The cycles are based on PAO1 susceptibility profiles (Figure 1A). Exploring the effects of exposure of drugs beyond typical anti-pseudomonal range is relevant for designing treatment strategies since the CF airways are frequently infected by complex microbiota, including potentially pathogenic bacteria such as Staphylococcus aureus, Haemophilus influenza, Burkholderia cepacia, or Stenotrophomonas maltophilia (Parkins and Floto, 2015Parkins M.D. Floto R.A. Emerging bacterial pathogens and changing concepts of bacterial pathogenesis in cystic fibrosis.J. Cyst. Fibros. 2015; 14: 293-304Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar, Willner et al., 2012Willner D. Haynes M.R. Furlan M. Schmieder R. Lim Y.W. Rainey P.B. Rohwer F. Conrad D. Spatial distribution of microbial communities in the cystic fibrosis lung.ISME J. 2012; 6: 471-474Crossref PubMed Scopus (137) Google Scholar). These bacteria may be treated with drugs toward which P. aeruginosa is considered intrinsically resistant or not used in treatment due to quick resistance development. Intriguingly, we observed changes in collateral susceptibility profiles for P. aeruginosa strains exposed to such drugs (Figure 1A), including trimethoprim-sulfamethoxazole (TMS) (Table 1), which was used in some centers for the treatment of S. aureus infections in CF patients (Gibson et al., 2003Gibson R.L. Burns J.L. Ramsey B.W. Pathophysiology and management of pulmonary infections in cystic fibrosis.Am. J. Respir. Crit. Care Med. 2003; 168: 918-951Crossref PubMed Scopus (1312) Google Scholar). The TMS-exposed PAO1 strain became collaterally sensitive toward aminoglycosides, polymyxin, and several β-lactam antibiotics. Simultaneously, the TMS-exposed PAO1 strain conferred resistance toward quinolone and tetracycline drugs (Figure 1A). These results suggest that, following treatment of S. aureus using TMS, P. aeruginosa treatment with aminoglycosides would be more effective than quinolones. This finding supports the hypothesis that the patient’s treatment history should be considered in order to exploit specific vulnerabilities of P. aeruginosa that result even from management of other pathogens. To systematically elucidate the similarity of susceptibility phenotypes between the evolved strains, we computed Spearman correlation coefficients (ρ) for each pairwise comparison of their normalized susceptibility profiles (see STAR Methods). We detected 107 significant correlations (p < 0.05, a two-tailed significance test) between resistant strains. Interestingly, 67 (60%) pairwise comparisons had high positive correlation coefficients for strains resistant to drugs from different chemical classes suggesting convergent phenotypes (Figure S3A; Table S3). Among those strains were tetracycline- and macrolide- and quinolone-resistant strains. In contrast, 40 pairwise comparisons had a negative correlation between their susceptibility profiles (p < 0.05) suggesting orthogonal phenotypic states. Notably, negative correlations of susceptibility profiles were observed in 90% of cases between polymyxin- and β-lactam-resistant strains (Figure S3A). For instance, colistin- and ceftazidime-resistant strains had strong negatively correlated susceptibility (ρ = −0.74; p < 0.0001) (Figure S3A; Table S3). Interestingly, several strains resistant to β-lactams (aztreonam, carbenicillin, and ampicillin) also displayed reciprocal collateral sensitivity with colistin (Figure 1A). To further examine the phenotypic states of the resistant strains, we reduced the dimensionality of the data using principal component analysis (PCA). This analysis revealed that resistant strains are divided into four groups that are positioned in different regions of PCA (Figure 2A), highlighting the convergence toward specific phenotypic states in response to antibiotic exposure. β-lactams, aminoglycoside, quinolone, and polymyxin antibiotics are commonly applied for the treatment of lung infections in CF patients. Notably, strains resistant to these antibiotic classes were positioned in different regions of PCA indicating that understanding the convergence toward drug-specific phenotypes for these drugs could inform treatment strategies. Interestingly, region II in the PCA plot included strains resistant to quinolones, macrolides, and tetracyclines (Figure 2A), highlighting that these different drug classes select for similar phenotypic states with strongly correlated susceptibility profiles (ρ = 0.84 – 0.97, p < 0.0001, Spearman correlation) (Figure S3A; Table S3). To explore further the phenotypic convergence in response to antibiotic exposure, we investigated whether antibiotic resistance evolution from different genetic starting points would lead to convergent evolution of their susceptibility profiles. We selected five clinical CF isolates from the DK2 clone type that share a common ancestor but have diverged during years of isolation in three different hosts (Marvig et al., 2013Marvig R.L. Johansen H.K. Molin S. Jelsbak L. Genome analysis of a transmissible lineage of pseudomonas aeruginosa reveals pathoadaptive mutations and distinct evolutionary paths of hypermutators.PLoS Genet. 2013; 9: e1003741Crossref PubMed Scopus (139) Google Scholar). We observed changes in susceptibility for all adapted clinical isolates, indicating that evolutionary trajectories toward collateral sensitivity can occur in divergent lineages (with diverse phenotypic and genotypic starting point) (Figure S3B). Importantly, several collateral sensitivity interactions remained preserved in the majority of strains tested (Figure S3C). For instance, resistance development for ciprofloxacin was consistently associated with collateral sensitivity toward aminoglycoside antibiotics in all different genetic backgrounds. In addition, the fold change relative to the ancestral strains was higher in clinical isolates than observed in PAO1 evolution. Notably, the clinical isolates evolved to azithromycin and ciprofloxacin resistance (173-1991-CIP and 173-1991-AZY) were 32-fold more sensitive to colistin antibiotics then the WT (Figure S3C; Table S1). Profound collateral sensitivity was also previously observed for E. coli clinical isolates (Imamovic and Sommer, 2013Imamovic L. Sommer M.O.A. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development.Sci. Transl. Med. 2013; 5: 204ra132Crossref PubMed Scopus (258) Google Scholar), indicating the potential for exploiting collateral sensitivity to optimize antibiotic regimens. To explore the link between the phenotypic changes of laboratory-evolved resistant PAO1 and clinical isolates, we determined the Spearman correlation coefficients for their resistance profiles. Overall, this analysis shows that exposure of different P. aeruginosa strains to a particular drug tends to increase the correlation between them (Figure 2B; Table S4). When considering selective antibiotic pressure in clinical isolates, all clinical isolates exposed to aztreonam and ciprofloxacin as well as 80% of azithromycin- and tobramycin-resistant DK2 strains increased the correlation of their susceptibility profiles to the respective antibiotic-resistant PAO1 strains (Figure 2B). For instance, the correlation coefficient for clinical isolate 173 evolved to ciprofloxacin changed from a negative ρ = −0.67 (p < 0.01) to a positive ρ = 0.65 (p < 0.01) (Table S4). The exception was the colistin-resistant strain, for which no changes in correlation ρ were linked to the colistin-resistant PAO1 strain, indicating that the difference with PAO1 might be reflected in the different genetic background between DK2 and PAO1 (Gutu et al., 2015Gutu A.D. Rodgers N.S. Park J. Moskowitz S.M. Pseudomonas aeruginosa high-level resistance to polymyxins and other antimicrobial peptides requires cprA, a gene that is disrupted in the PAO1 strain.Antimicrob. Agents Chemother. 2015; 59: 5377-5387Crossref PubMed Scopus (20) Google Scholar). Using PCA we observed that the phenotypic states of the ciprofloxacin and aztreonam evolved clinical isolates shifted their corresponding resistant PAO1 strain in response to antibiotic resistance evolution (Figures 2C and 2D). Indeed, this analysis indicates that for some antibiotics, exposure and subsequent resistance evolution leads to convergence toward specific phenotypic states in diverse phenotypic and genotypic backgrounds. To explore the genetic basis of phenotypic changes in susceptibility profiles, we sequenced the genomes of experimentally evolved strains. We observed that the impact of drug exposure on genome-wide evolutionary paths causing collateral resistance and sensitivity were associated with drug resistance genes also found to be undergoing selection in chronically infected CF patients (Marvig et al., 2013Marvig R.L. Johansen H.K. Molin S. Jelsbak L. Genome analysis of a transmissible lineage of pseudomonas aeruginosa reveals pathoadaptive mutations and distinct evolutionary paths of hypermutators.PLoS Genet. 2013; 9: e1003741Crossref PubMed Scopus (139) Google Scholar). Mutations in seven out of nine pathoadaptive, antibiotic resistance genes (fusA1, ampC, ampD, gyrA, gyrB, mexB, and pmrB) were observed in our adaptive evolution experiment (Figure S4A; Table S5). Uniformly, PAO1 strains resistant to quinolone, macrolide, and tetracyclines had mutations in the pathoadaptive gene nfxB (Marvig et al., 2015aMarvig R.L. Sommer L.M. Molin S. Johansen H.K. Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis.Nat. Genet. 2015; 47: 57-64Crossref PubMed Scopus (342) Google Scholar, Marvig et al., 2015bMarvig R.L. Dolce D. Sommer L.M. Petersen B. Ciofu O. Campana S. Molin S. Taccetti G. Johansen H.K. Within-host microevolution of Pseudomonas aeruginosa in Italian cystic fibrosis patients.BMC Microbiol. 2015; 15: 218Crossref PubMed Scopus (42) Google Sch" @default.
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• W2782393048 title "Drug-Driven Phenotypic Convergence Supports Rational Treatment Strategies of Chronic Infections" @default.
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• W2782393048 doi "https://doi.org/10.1016/j.cell.2017.12.012" @default.
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