Antibiotic resistance: Insights from evolution experiments and mathematical modeling

Antibiotic resistance is a growing public health problem. To gain a fundamental understanding of resistance evolution, a combination of systematic experimental and theoretical approaches is required. Evolution experiments combined with next-generation sequencing techniques, laboratory automation, and mathematical modeling are enabling the investigation of resistance development at an unprecedented level of detail. Recent work has directly tracked the intricate stochastic dynamics of bacterial populations in which resistant mutants emerge and compete. In addition, new approaches have enabled measuring how prone a large number of genetically perturbed strains are to evolve resistance. Based on advances in quantitative cell physiology, predictive theoretical models of resistance are increasingly being developed. Taken together, a new strategy for observing, predicting, and ultimately controlling resistance evolution is emerging.

[1]  S. Partridge,et al.  Mobile Genetic Elements Associated with Antimicrobial Resistance , 2018, Clinical Microbiology Reviews.

[2]  A. Wong.  Epistasis and the Evolution of Antimicrobial Resistance , 2017, Front. Microbiol..

[3]  D. J. Kiviet,et al.  Effective polyploidy causes phenotypic delay and influences bacterial evolvability , 2017, bioRxiv.

[4]  U. Alon,et al.  Optimality and sub-optimality in a bacterial growth law , 2017, Nature Communications.

[5]  Gavin Sherlock,et al.  Quantitative evolutionary dynamics using high-resolution lineage tracking , 2015, Nature.

[6]  Floyd E Romesberg,et al.  Open access, freely available online PLoS BIOLOGY Inhibition of Mutation and Combating the Evolution of Antibiotic Resistance , 2022 .

[7]  Diarmaid Hughes,et al.  Antibiotic resistance and its cost: is it possible to reverse resistance? , 2010, Nature Reviews Microbiology.

[8]  Christian Munck,et al.  Prediction of antibiotic resistance: time for a new preclinical paradigm? , 2017, Nature Reviews Microbiology.

[9]  P. Rosenstiel,et al.  Alternative Evolutionary Paths to Bacterial Antibiotic Resistance Cause Distinct Collateral Effects , 2017, Molecular biology and evolution.

[10]  U. Gophna,et al.  Antibiotic resistance: turning evolutionary principles into clinical reality. , 2020, FEMS microbiology reviews.

[11]  Elin E. Lilja,et al.  A Roadblock-and-Kill Mechanism of Action Model for the DNA-Targeting Antibiotic Ciprofloxacin , 2020, Antimicrobial Agents and Chemotherapy.

[12]  T. Bollenbach,et al.  Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections , 2017, Proceedings of the National Academy of Sciences.

[13]  Benjamin H. Good,et al.  The Dynamics of Molecular Evolution Over 60,000 Generations , 2017, Nature.

[14]  Philip Greulich,et al.  Growth‐dependent bacterial susceptibility to ribosome‐targeting antibiotics , 2014, Molecular systems biology.

[15]  Remy Chait,et al.  Evolutionary paths to antibiotic resistance under dynamically sustained drug selection , 2011, Nature Genetics.

[16]  M. Tyers,et al.  Drug combinations: a strategy to extend the life of antibiotics in the 21st century , 2019, Nature Reviews Microbiology.

[17]  Adrian W. R. Serohijos,et al.  Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution , 2019, Nature Ecology & Evolution.

[18]  H. Bremer.  Modulation of Chemical Composition and Other Parameters of the Cell by Growth Rate , 1999 .

[19]  Christian Munck,et al.  Chromosomal barcoding as a tool for multiplexed phenotypic characterization of laboratory evolved lineages , 2018, Scientific Reports.

[20]  P. Yeh,et al.  Strength of Selection Pressure Is an Important Parameter Contributing to the Complexity of Antibiotic Resistance Evolution , 2014, Molecular biology and evolution.

[21]  S. Molin,et al.  Drug-Driven Phenotypic Convergence Supports Rational Treatment Strategies of Chronic Infections , 2018, Cell.

[22]  G. Tkačik,et al.  Mechanisms of drug interactions between translation-inhibiting antibiotics , 2020, Nature Communications.

[23]  Chikara Furusawa,et al.  Development of an Automated Culture System for Laboratory Evolution , 2014, Journal of laboratory automation.

[24]  R. Kishony,et al.  Multidrug evolutionary strategies to reverse antibiotic resistance , 2016, Science.

[25]  Michael M. Desai,et al.  Global epistasis makes adaptation predictable despite sequence-level stochasticity , 2014, Science.

[26]  Floyd E Romesberg,et al.  Combating bacteria and drug resistance by inhibiting mechanisms of persistence and adaptation , 2007, Nature Chemical Biology.

[27]  D. Andersson,et al.  Mechanisms and clinical relevance of bacterial heteroresistance , 2019, Nature Reviews Microbiology.

[28]  Dilay Hazal Ayhan,et al.  Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance , 2015, PLoS biology.

[29]  Danna R. Gifford,et al.  Identifying and exploiting genes that potentiate the evolution of antibiotic resistance , 2018, Nature Ecology & Evolution.

[30]  Kathryn E Holt,et al.  Genomic insights into the emergence and spread of antimicrobial-resistant bacterial pathogens , 2018, Science.

[31]  Martín Carballo-Pacheco,et al.  Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications , 2019, bioRxiv.

[32]  G. Dantas,et al.  Next-generation approaches to understand and combat the antibiotic resistome , 2017, Nature Reviews Microbiology.

[33]  Minsu Kim,et al.  Antibiotic-induced population fluctuations and stochastic clearance of bacteria , 2018, eLife.

[34]  T. Bollenbach,et al.  Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance , 2020, Nature Communications.

[35]  R. Kishony,et al.  Personal clinical history predicts antibiotic resistance in urinary tract infections , 2018, bioRxiv.

[36]  Richard E. Lenski,et al.  Historical contingency in the evolution of antibiotic resistance after decades of relaxed selection , 2019, bioRxiv.

[37]  C. Pál,et al.  Genomewide Screen for Modulators of Evolvability under Toxic Antibiotic Exposure , 2013, Antimicrobial Agents and Chemotherapy.

[38]  C. Furusawa,et al.  Suppression of antibiotic resistance evolution by single-gene deletion , 2020, Scientific Reports.

[39]  N. Krogan,et al.  Phenotypic Landscape of a Bacterial Cell , 2011, Cell.

[40]  Richard E. Lenski,et al.  Tempo and mode of genome evolution in a 50,000-generation experiment , 2016, Nature.

[41]  C. Shee,et al.  Stress-induced mutation via DNA breaks in Escherichia coli: A molecular mechanism with implications for evolution and medicine , 2012, BioEssays : news and reviews in molecular, cellular and developmental biology.

[42]  R. MacLean,et al.  Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells , 2020, Proceedings of the National Academy of Sciences.

[43]  Jeffrey E. Barrick,et al.  Genomic evolution of antibiotic resistance is contingent on genetic background following a long-term experiment with Escherichia coli , 2020, Proceedings of the National Academy of Sciences.

[44]  S. Bonhoeffer,et al.  Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting , 2021, Proceedings of the National Academy of Sciences.

[45]  T. Hwa,et al.  Emergence of robust growth laws from optimal regulation of ribosome synthesis , 2014, Molecular systems biology.

[46]  M. Lässig,et al.  Metabolic fitness landscapes predict the evolution of antibiotic resistance , 2021, Nature Ecology & Evolution.

[47]  A. Buckling,et al.  The Distribution of Fitness Effects of Beneficial Mutations in Pseudomonas aeruginosa , 2009, PLoS genetics.

[48]  Suman G. Das,et al.  Stochastic effects on the establishment of mutants resistant to β-lactam antibiotics , 2021 .

[49]  J. Pitchford,et al.  Ecology and evolution of antimicrobial resistance in bacterial communities , 2020, The ISME Journal.

[50]  Jonathan M Stokes,et al.  Clinically relevant mutations in core metabolic genes confer antibiotic resistance , 2021, Science.

[51]  Sasha F. Levy,et al.  High-resolution lineage tracking reveals traveling wave of adaptation in laboratory yeast , 2019, Nature.