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Laborda P, Gil‐Gil T, Martínez JL, Hernando‐Amado S. Preserving the efficacy of antibiotics to tackle antibiotic resistance. Microb Biotechnol 2024; 17:e14528. [PMID: 39016996 PMCID: PMC11253305 DOI: 10.1111/1751-7915.14528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024] Open
Abstract
Different international agencies recognize that antibiotic resistance is one of the most severe human health problems that humankind is facing. Traditionally, the introduction of new antibiotics solved this problem but various scientific and economic reasons have led to a shortage of novel antibiotics at the pipeline. This situation makes mandatory the implementation of approaches to preserve the efficacy of current antibiotics. The concept is not novel, but the only action taken for such preservation had been the 'prudent' use of antibiotics, trying to reduce the selection pressure by reducing the amount of antibiotics. However, even if antibiotics are used only when needed, this will be insufficient because resistance is the inescapable outcome of antibiotics' use. A deeper understanding of the alterations in the bacterial physiology upon acquisition of resistance and during infection will help to design improved strategies to treat bacterial infections. In this article, we discuss the interconnection between antibiotic resistance (and antibiotic activity) and bacterial metabolism, particularly in vivo, when bacteria are causing infection. We discuss as well how understanding evolutionary trade-offs, as collateral sensitivity, associated with the acquisition of resistance may help to define evolution-based therapeutic strategies to fight antibiotic resistance and to preserve currently used antibiotics.
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Affiliation(s)
- Pablo Laborda
- Department of Clinical MicrobiologyRigshospitaletCopenhagenDenmark
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2
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Schmidlin, Apodaca, Newell, Sastokas, Kinsler, Geiler-Samerotte. Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.17.562616. [PMID: 37905147 PMCID: PMC10614906 DOI: 10.1101/2023.10.17.562616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.
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Affiliation(s)
- Schmidlin
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Apodaca
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Newell
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Sastokas
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Kinsler
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Geiler-Samerotte
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
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3
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Guerrero RF, Dorji T, Harris RM, Shoulders MD, Ogbunugafor CB. Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications. eLife 2024; 12:RP88480. [PMID: 38833384 PMCID: PMC11149929 DOI: 10.7554/elife.88480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024] Open
Abstract
The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and 7 β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ('variant vulnerability'), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ('drug applicability'). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).
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Affiliation(s)
- Rafael F Guerrero
- Department of Biological Sciences, North Carolina State UniversityRaleighUnited States
| | - Tandin Dorji
- Department of Mathematics and Statistics, University of VermontBurlingtonUnited States
| | - Ra'Mal M Harris
- Department of Chemistry, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Matthew D Shoulders
- Department of Chemistry, Massachusetts Institute of TechnologyCambridgeUnited States
| | - C Brandon Ogbunugafor
- Department of Chemistry, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Ecology and Evolutionary Biology, Yale UniversityNew HavenUnited States
- Santa Fe InstituteSanta FeUnited States
- Public Health Modeling Unit, Yale School of Public HealthNew HavenUnited States
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4
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Wang M, Scott JG, Vladimirsky A. Threshold-awareness in adaptive cancer therapy. PLoS Comput Biol 2024; 20:e1012165. [PMID: 38875286 PMCID: PMC11210878 DOI: 10.1371/journal.pcbi.1012165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/27/2024] [Accepted: 05/09/2024] [Indexed: 06/16/2024] Open
Abstract
Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models.
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Affiliation(s)
- MingYi Wang
- Center for Applied Mathematics, Cornell University, Ithaca, New York, United States of America
| | - Jacob G. Scott
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Alexander Vladimirsky
- Department of Mathematics and Center for Applied Mathematics, Cornell University, Ithaca, New York, United States of America
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5
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Mesüm O, Atilgan AR, Kocuk B. A stochastic programming approach to the antibiotics time machine problem. Math Biosci 2024; 372:109191. [PMID: 38604597 DOI: 10.1016/j.mbs.2024.109191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 02/26/2024] [Accepted: 04/06/2024] [Indexed: 04/13/2024]
Abstract
Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modeled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics is given. The transition probabilities of a 'realization' of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizations. The aim is to maximize the expected probability of reaching to the genotype with all unmutated genes given the initial genotype in a predetermined number of transitions, considering the following two sources of uncertainties: (i) the randomness in growth rates, (ii) the randomness in transition probabilities, which are functions of growth rates. We develop stochastic mixed-integer linear programming and dynamic programming approaches to solve static and dynamic versions of the Antibiotics Time Machine Problem under the aforementioned uncertainties. We adapt a Sample Average Approximation approach that exploits the special structure of the problem and provide accurate solutions that perform very well in an out-of-sample analysis.
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Affiliation(s)
- Oğuz Mesüm
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.
| | - Ali Rana Atilgan
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.
| | - Burak Kocuk
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.
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Weaver DT, King ES, Maltas J, Scott JG. Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance. Proc Natl Acad Sci U S A 2024; 121:e2303165121. [PMID: 38607932 PMCID: PMC11032439 DOI: 10.1073/pnas.2303165121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 02/23/2024] [Indexed: 04/14/2024] Open
Abstract
Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.
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Affiliation(s)
- Davis T. Weaver
- Case Western Reserve University School of Medicine, Cleveland, OH44106
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH44106
| | - Eshan S. King
- Case Western Reserve University School of Medicine, Cleveland, OH44106
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH44106
| | - Jeff Maltas
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH44106
| | - Jacob G. Scott
- Case Western Reserve University School of Medicine, Cleveland, OH44106
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH44106
- Department of Physics, Case Western Reserve University, Cleveland, OH44106
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7
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Kerek Á, Török B, Laczkó L, Somogyi Z, Kardos G, Bányai K, Kaszab E, Bali K, Jerzsele Á. In Vitro Microevolution and Co-Selection Assessment of Amoxicillin and Cefotaxime Impact on Escherichia coli Resistance Development. Antibiotics (Basel) 2024; 13:247. [PMID: 38534682 DOI: 10.3390/antibiotics13030247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 03/28/2024] Open
Abstract
The global spread of antimicrobial resistance has become a prominent issue in both veterinary and public health in the 21st century. The extensive use of amoxicillin, a beta-lactam antibiotic, and consequent resistance development are particularly alarming in food-producing animals, with a focus on the swine and poultry sectors. Another beta-lactam, cefotaxime, is widely utilized in human medicine, where the escalating resistance to third- and fourth-generation cephalosporins is a major concern. The aim of this study was to simulate the development of phenotypic and genotypic resistance to beta-lactam antibiotics, focusing on amoxicillin and cefotaxime. The investigation of the minimal inhibitory concentrations (MIC) of antibiotics was performed at 1×, 10×, 100×, and 1000× concentrations using the modified microbial evolution and growth arena (MEGA-plate) method. Our results indicate that amoxicillin significantly increased the MIC values of several tested antibiotics, except for oxytetracycline and florfenicol. In the case of cefotaxime, this increase was observed in all classes. A total of 44 antimicrobial resistance genes were identified in all samples. Chromosomal point mutations, particularly concerning cefotaxime, revealed numerous complex mutations, deletions, insertions, and single nucleotide polymorphisms (SNPs) that were not experienced in the case of amoxicillin. The findings suggest that, regarding amoxicillin, the point mutation of the acrB gene could explain the observed MIC value increases due to the heightened activity of the acrAB-tolC efflux pump system. However, under the influence of cefotaxime, more intricate processes occurred, including complex amino acid substitutions in the ampC gene promoter region, increased enzyme production induced by amino acid substitutions and SNPs, as well as mutations in the acrR and robA repressor genes that heightened the activity of the acrAB-tolC efflux pump system. These changes may contribute to the significant MIC increases observed for all tested antibiotics. The results underscore the importance of understanding cross-resistance development between individual drugs when choosing clinical alternative drugs. The point mutations in the mdtB and emrR genes may also contribute to the increased activity of the mdtABC-tolC and emrAB-tolC pump systems against all tested antibiotics. The exceptionally high mutation rate induced by cephalosporins justifies further investigations to clarify the exact mechanism behind.
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Affiliation(s)
- Ádám Kerek
- Department of Pharmacology and Toxicology, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
| | - Bence Török
- Department of Pharmacology and Toxicology, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
| | - Levente Laczkó
- One Health Institute, University of Debrecen, Nagyerdei krt. 98, H-4032 Debrecen, Hungary
- HUN-REN-UD Conservation Biology Research Group, Egyetem tér 1, H-4032 Debrecen, Hungary
| | - Zoltán Somogyi
- Department of Pharmacology and Toxicology, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
| | - Gábor Kardos
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- One Health Institute, University of Debrecen, Nagyerdei krt. 98, H-4032 Debrecen, Hungary
- National Public Health Center, Albert Flórián út 2-6, H-1097 Budapest, Hungary
- Department of Gerontology, Faculty of Health Sciences, University of Debrecen, Sóstói út 2-4, H-4400 Nyíregyháza, Hungary
| | - Krisztián Bányai
- Department of Pharmacology and Toxicology, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- Veterinary Medical Research Institute, H-1143 Budapest, Hungary
| | - Eszter Kaszab
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- One Health Institute, University of Debrecen, Nagyerdei krt. 98, H-4032 Debrecen, Hungary
- Department of Microbiology and Infectious Diseases, University of Veterinary Medicine, István u 2, H-1078 Budapest, Hungary
| | - Krisztina Bali
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- Department of Microbiology and Infectious Diseases, University of Veterinary Medicine, István u 2, H-1078 Budapest, Hungary
| | - Ákos Jerzsele
- Department of Pharmacology and Toxicology, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, H-1078 Budapest, Hungary
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8
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Crozier D, Gray JM, Maltas JA, Bonomo RA, Burke ZDC, Card KJ, Scott JG. The evolution of diverse antimicrobial responses in vancomycin-intermediate Staphylococcus aureus and its therapeutic implications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.30.569373. [PMID: 38077036 PMCID: PMC10705500 DOI: 10.1101/2023.11.30.569373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Staphylococcus aureus causes endocarditis, osteomyelitis, and bacteremia. Clinicians often prescribe vancomycin as an empiric therapy to account for methicillin-resistant S. aureus (MRSA) and narrow treatment based on culture susceptibility results. However, these results reflect a single time point before empiric treatment and represent a limited subset of the total bacterial population within the patient. Thus, while they may indicate that the infection is susceptible to a particular drug, this recommendation may no longer be accurate during therapy. Here, we addressed how antibiotic susceptibility changes over time by accounting for evolution. We evolved 18 methicillin-susceptible S. aureus (MSSA) populations under increasing vancomycin concentrations until they reached intermediate resistance levels. Sequencing revealed parallel mutations that affect cell membrane stress response and cell-wall biosynthesis. The populations exhibited repeated cross-resistance to daptomycin and varied responses to meropenem, gentamicin, and nafcillin. We accounted for this variability by deriving likelihood estimates that express a population's probability of exhibiting a drug response following vancomycin treatment. Our results suggest antistaphylococcal penicillins are preferable first-line treatments for MSSA infections but also highlight the inherent uncertainty that evolution poses to effective therapies. Infections may take varied evolutionary paths; therefore, considering evolution as a probabilistic process should inform our therapeutic choices.
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Aduru SV, Szenkiel K, Rahman A, Ahmad M, Fabozzi M, Smith RP, Lopatkin AJ. Sub-inhibitory antibiotic treatment selects for enhanced metabolic efficiency. Microbiol Spectr 2024; 12:e0324123. [PMID: 38226801 PMCID: PMC10846238 DOI: 10.1128/spectrum.03241-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/05/2023] [Indexed: 01/17/2024] Open
Abstract
Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions of Escherichia coli under carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches.IMPORTANCEThe sustained emergence of antibiotic-resistant pathogens combined with the stalled drug discovery pipelines highlights the critical need to better understand the underlying evolution mechanisms of antibiotic resistance. To this end, bacterial growth and metabolic rates are often closely related, and resistant cells have historically been characterized exclusively in the context of growth. However, under antibiotic selection, antibiotics counterintuitively favor cells with fast growth, and slow metabolism. Through an integrated approach of mathematical modeling and experiments, this study thereby addresses the significant knowledge gap of whether antibiotic selection drives changes in metabolism that complement, and/or act independently, of antibiotic resistance phenotypes.
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Affiliation(s)
- Sai Varun Aduru
- Department of Chemical Engineering, University of Rochester, Rochester, New York, USA
| | | | - Anika Rahman
- Department of Biology, Barnard College, New York, New York, USA
| | - Mehrose Ahmad
- Department of Biology, Barnard College, New York, New York, USA
| | - Maya Fabozzi
- Department of Biology, Barnard College, New York, New York, USA
| | - Robert P. Smith
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Allison J. Lopatkin
- Department of Chemical Engineering, University of Rochester, Rochester, New York, USA
- Department of Biology, Barnard College, New York, New York, USA
- Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, New York, USA
- Data Science Institute, Columbia University, New York, New York, USA
- Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, New York, USA
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10
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King ES, Tadele DS, Pierce B, Hinczewski M, Scott JG. Diverse mutant selection windows shape spatial heterogeneity in evolving populations. PLoS Comput Biol 2024; 20:e1011878. [PMID: 38386690 PMCID: PMC10914271 DOI: 10.1371/journal.pcbi.1011878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 03/05/2024] [Accepted: 01/31/2024] [Indexed: 02/24/2024] Open
Abstract
Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.
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Affiliation(s)
- Eshan S. King
- Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Dagim S. Tadele
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, Ohio, United States of America
- Oslo University Hospital, Ullevål, Department of Medical Genetics, Oslo, Norway
| | - Beck Pierce
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Jacob G. Scott
- Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Physics, Case Western Reserve University, Cleveland, Ohio, United States of America
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, United States of America
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11
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Zhao C, Wang Y, Mulchandani R, Van Boeckel TP. Global surveillance of antimicrobial resistance in food animals using priority drugs maps. Nat Commun 2024; 15:763. [PMID: 38278814 PMCID: PMC10817973 DOI: 10.1038/s41467-024-45111-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
Antimicrobial resistance (AMR) in food animals is a growing threat to animal health and potentially to human health. In resource-limited settings, allocating resources to address AMR can be guided with maps. Here, we mapped AMR prevalence in 7 antimicrobials in Escherichia coli and nontyphoidal Salmonella species across low- and middle-income countries (LIMCs), using 1088 point-prevalence surveys in combination with a geospatial model. Hotspots of AMR were predicted in China, India, Brazil, Chile, and part of central Asia and southeastern Africa. The highest resistance prevalence was for tetracycline (59% for E. coli and 54% for nontyphoidal Salmonella, average across LMICs) and lowest for cefotaxime (33% and 19%). We also identified the antimicrobial with the highest probability of resistance exceeding critical levels (50%) in the future (1.7-12.4 years) for each 10 × 10 km pixel on the map. In Africa and South America, 78% locations were associated with penicillins or tetracyclines crossing 50% resistance in the future. In contrast, in Asia, 77% locations were associated with penicillins or sulphonamides. Our maps highlight diverging geographic trends of AMR prevalence across antimicrobial classes, and can be used to target AMR surveillance in AMR hotspots for priority antimicrobial classes.
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Affiliation(s)
- Cheng Zhao
- Health Geography and Policy Group, ETH Zürich, Zürich, Switzerland
| | - Yu Wang
- Health Geography and Policy Group, ETH Zürich, Zürich, Switzerland
| | | | - Thomas P Van Boeckel
- Health Geography and Policy Group, ETH Zürich, Zürich, Switzerland.
- One Health Trust, Washington DC, USA.
- Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium.
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12
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Maeda T, Furusawa C. Laboratory Evolution of Antimicrobial Resistance in Bacteria to Develop Rational Treatment Strategies. Antibiotics (Basel) 2024; 13:94. [PMID: 38247653 PMCID: PMC10812413 DOI: 10.3390/antibiotics13010094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024] Open
Abstract
Laboratory evolution studies, particularly with Escherichia coli, have yielded invaluable insights into the mechanisms of antimicrobial resistance (AMR). Recent investigations have illuminated that, with repetitive antibiotic exposures, bacterial populations will adapt and eventually become tolerant and resistant to the drugs. Through intensive analyses, these inquiries have unveiled instances of convergent evolution across diverse antibiotics, the pleiotropic effects of resistance mutations, and the role played by loss-of-function mutations in the evolutionary landscape. Moreover, a quantitative analysis of multidrug combinations has shed light on collateral sensitivity, revealing specific drug combinations capable of suppressing the acquisition of resistance. This review article introduces the methodologies employed in the laboratory evolution of AMR in bacteria and presents recent discoveries concerning AMR mechanisms derived from laboratory evolution. Additionally, the review outlines the application of laboratory evolution in endeavors to formulate rational treatment strategies.
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Affiliation(s)
- Tomoya Maeda
- Laboratory of Microbial Physiology, Research Faculty of Agriculture, Hokkaido University, Kita 9, Nishi 9, Kita-ku, Sapporo 060-8589, Japan
- Center for Biosystems Dynamics Research, RIKEN, 6-2-3 Furuedai, Suita 565-0874, Japan;
| | - Chikara Furusawa
- Center for Biosystems Dynamics Research, RIKEN, 6-2-3 Furuedai, Suita 565-0874, Japan;
- Universal Biology Institute, The University of Tokyo, 7-3-1 Hongo, Tokyo 113-0033, Japan
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13
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Sanz-García F, Laborda P, Ochoa-Sánchez LE, Martínez JL, Hernando-Amado S. The Pseudomonas aeruginosa Resistome: Permanent and Transient Antibiotic Resistance, an Overview. Methods Mol Biol 2024; 2721:85-102. [PMID: 37819517 DOI: 10.1007/978-1-0716-3473-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
One of the most concerning characteristics of Pseudomonas aeruginosa is its low susceptibility to several antibiotics of common use in clinics, as well as its facility to acquire increased resistance levels. Consequently, the study of the antibiotic resistance mechanisms of this bacterium is of relevance for human health. For such a study, different types of resistance should be distinguished. The intrinsic resistome is composed of a set of genes, present in the core genome of P. aeruginosa, which contributes to its characteristic, species-specific, phenotype of susceptibility to antibiotics. Acquired resistance refers to those genetic events, such as the acquisition of mutations or antibiotic resistance genes that reduce antibiotic susceptibility. Finally, antibiotic resistance can be transiently acquired in the presence of specific compounds or under some growing conditions. The current article provides information on methods currently used to analyze intrinsic, mutation-driven, and transient antibiotic resistance in P. aeruginosa.
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Affiliation(s)
| | - Pablo Laborda
- Centro Nacional de Biotecnología, CSIC, Madrid, Spain
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14
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Lozano‐Huntelman NA, Bullivant A, Chacon‐Barahona J, Valencia A, Ida N, Zhou A, Kalhori P, Bello G, Xue C, Boyd S, Kremer C, Yeh PJ. The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component. Evol Appl 2023; 16:1901-1920. [PMID: 38143903 PMCID: PMC10739078 DOI: 10.1111/eva.13608] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 06/26/2023] [Accepted: 10/04/2023] [Indexed: 12/26/2023] Open
Abstract
Multidrug antibiotic resistance is an urgent public health concern. Multiple strategies have been suggested to alleviate this problem, including the use of antibiotic combinations and cyclic therapies. We examine how adaptation to (1) combinations of drugs affects resistance to individual drugs, and to (2) individual drugs alters responses to drug combinations. To evaluate this, we evolved multiple strains of drug resistant Staphylococcus epidermidis in the lab. We show that evolving resistance to four highly synergistic combinations does not result in cross-resistance to all of their components. Likewise, prior resistance to one antibiotic in a combination does not guarantee survival when exposed to the combination. We also identify four 3-step and four 2-step treatments that inhibit bacterial growth and confer collateral sensitivity with each step, impeding the development of multidrug resistance. This study highlights the importance of considering higher-order drug combinations in sequential therapies and how antibiotic interactions can influence the evolutionary trajectory of bacterial populations.
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Affiliation(s)
| | - Austin Bullivant
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Jonathan Chacon‐Barahona
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Alondra Valencia
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Nick Ida
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - April Zhou
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Pooneh Kalhori
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Gladys Bello
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Carolyn Xue
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Sada Boyd
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Colin Kremer
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Pamela J. Yeh
- Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesCaliforniaUSA
- Santa Fe InstituteSanta FeNew MexicoUSA
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15
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Miele L, Evans RML, Cunniffe NJ, Torres-Barceló C, Bevacqua D. Evolutionary Epidemiology Consequences of Trait-Dependent Control of Heterogeneous Parasites. Am Nat 2023; 202:E130-E146. [PMID: 37963120 DOI: 10.1086/726062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
AbstractDisease control can induce both demographic and evolutionary responses in host-parasite systems. Foreseeing the outcome of control therefore requires knowledge of the eco-evolutionary feedback between control and system. Previous work has assumed that control strategies have a homogeneous effect on the parasite population. However, this is not true when control targets those traits that confer to the parasite heterogeneous levels of resistance, which can additionally be related to other key parasite traits through evolutionary trade-offs. In this work, we develop a minimal model coupling epidemiological and evolutionary dynamics to explore possible trait-dependent effects of control strategies. In particular, we consider a parasite expressing continuous levels of a trait-determining resource exploitation and a control treatment that can be either positively or negatively correlated with that trait. We demonstrate the potential of trait-dependent control by considering that the decision maker may want to minimize both the damage caused by the disease and the use of treatment, due to possible environmental or economic costs. We identify efficient strategies showing that the optimal type of treatment depends on the amount applied. Our results pave the way for the study of control strategies based on evolutionary constraints, such as collateral sensitivity and resistance costs, which are receiving increasing attention for both public health and agricultural purposes.
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16
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Chowdhury F, Findlay BL. Fitness Costs of Antibiotic Resistance Impede the Evolution of Resistance to Other Antibiotics. ACS Infect Dis 2023; 9:1834-1845. [PMID: 37726252 PMCID: PMC10581211 DOI: 10.1021/acsinfecdis.3c00156] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Indexed: 09/21/2023]
Abstract
Antibiotic resistance is a major threat to global health, claiming the lives of millions every year. With a nearly dry antibiotic development pipeline, novel strategies are urgently needed to combat resistant pathogens. One emerging strategy is the use of sequential antibiotic therapy, postulated to reduce the rate at which antibiotic resistance evolves. Here, we use the soft agar gradient evolution (SAGE) system to carry out high-throughput in vitro bacterial evolution against antibiotic pressure. We find that evolution of resistance to the antibiotic chloramphenicol (CHL) severely affects bacterial fitness, slowing the rate at which resistance to the antibiotics nitrofurantoin and streptomycin emerges. In vitro acquisition of compensatory mutations in the CHL-resistant cells markedly improves fitness and nitrofurantoin adaptation rates but fails to restore rates to wild-type levels against streptomycin. Genome sequencing reveals distinct evolutionary paths to resistance in fitness-impaired populations, suggesting resistance trade-offs in favor of mitigation of fitness costs. We show that the speed of bacterial fronts in SAGE plates is a reliable indicator of adaptation rates and evolutionary trajectories to resistance. Identification of antibiotics whose mutational resistance mechanisms confer stable impairments may help clinicians prescribe sequential antibiotic therapies that are less prone to resistance evolution.
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Affiliation(s)
- Farhan
R. Chowdhury
- Department
of Biology, Concordia University, Montréal, Québec H4B 1R6, Canada
| | - Brandon L. Findlay
- Department
of Biology, Concordia University, Montréal, Québec H4B 1R6, Canada
- Department
of Chemistry and Biochemistry, Concordia
University, Montréal, Québec H4B 1R6, Canada
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17
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Strobl MAR, Gallaher J, Robertson-Tessi M, West J, Anderson ARA. Treatment of evolving cancers will require dynamic decision support. Ann Oncol 2023; 34:867-884. [PMID: 37777307 PMCID: PMC10688269 DOI: 10.1016/j.annonc.2023.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 10/02/2023] Open
Abstract
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
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Affiliation(s)
- M A R Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA
| | - J Gallaher
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - M Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - J West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - A R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa.
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18
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Sanz-García F, Gil-Gil T, Laborda P, Blanco P, Ochoa-Sánchez LE, Baquero F, Martínez JL, Hernando-Amado S. Translating eco-evolutionary biology into therapy to tackle antibiotic resistance. Nat Rev Microbiol 2023; 21:671-685. [PMID: 37208461 DOI: 10.1038/s41579-023-00902-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 05/21/2023]
Abstract
Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.
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Affiliation(s)
- Fernando Sanz-García
- Departamento de Microbiología, Medicina Preventiva y Salud Pública, Universidad de Zaragoza, Zaragoza, Spain
| | - Teresa Gil-Gil
- Centro Nacional de Biotecnología, CSIC, Darwin 3, Madrid, Spain
- Programa de Doctorado en Biociencias Moleculares, Universidad Autónoma de Madrid, Madrid, Spain
| | - Pablo Laborda
- Centro Nacional de Biotecnología, CSIC, Darwin 3, Madrid, Spain
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
- Department of Clinical Microbiology, 9301, Rigshospitalet, Copenhagen, Denmark
| | - Paula Blanco
- Molecular Basis of Adaptation, Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid, Spain
- VISAVET Health Surveillance Centre, Universidad Complutense Madrid, Madrid, Spain
| | | | - Fernando Baquero
- Department of Microbiology, Hospital Universitario Ramón y Cajal (IRYCIS), CIBER en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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19
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Gabaldón T. Nothing makes sense in drug resistance except in the light of evolution. Curr Opin Microbiol 2023; 75:102350. [PMID: 37348192 DOI: 10.1016/j.mib.2023.102350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/29/2023] [Accepted: 05/29/2023] [Indexed: 06/24/2023]
Abstract
Our ability to fight infectious diseases is being increasingly compromised due to the emergence and spread of pathogens that become resistant to one or several drugs. This phenomenon is ubiquitous among pathogens and has parallels in cancer treatment. Given the urgency of the problem, there is a need for a paradigm shift in drug therapy toward one in which the objective to prevent the evolution of drug resistance is considered alongside the main objective of eliminating the infection or tumor. Here, I stress the importance of considering an evolutionary perspective to achieve this goal, and review recent advances in this direction, including therapies that exploit the fitness trade-offs of resistance.
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Affiliation(s)
- Toni Gabaldón
- Barcelona Supercomputing Centre (BSC-CNS), Plaça Eusebi Güell, 1-3, 08034 Barcelona, Spain; Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain; Centro de Investigación Biomédica En Red de Enfermedades Infecciosas (CIBERINFEC), Barcelona, Spain.
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20
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Mandt REK, Luth MR, Tye MA, Mazitschek R, Ottilie S, Winzeler EA, Lafuente-Monasterio MJ, Gamo FJ, Wirth DF, Lukens AK. Diverse evolutionary pathways challenge the use of collateral sensitivity as a strategy to suppress resistance. eLife 2023; 12:e85023. [PMID: 37737220 PMCID: PMC10695565 DOI: 10.7554/elife.85023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 09/21/2023] [Indexed: 09/23/2023] Open
Abstract
Drug resistance remains a major obstacle to malaria control and eradication efforts, necessitating the development of novel therapeutic strategies to treat this disease. Drug combinations based on collateral sensitivity, wherein resistance to one drug causes increased sensitivity to the partner drug, have been proposed as an evolutionary strategy to suppress the emergence of resistance in pathogen populations. In this study, we explore collateral sensitivity between compounds targeting the Plasmodium dihydroorotate dehydrogenase (DHODH). We profiled the cross-resistance and collateral sensitivity phenotypes of several DHODH mutant lines to a diverse panel of DHODH inhibitors. We focus on one compound, TCMDC-125334, which was active against all mutant lines tested, including the DHODH C276Y line, which arose in selections with the clinical candidate DSM265. In six selections with TCMDC-125334, the most common mechanism of resistance to this compound was copy number variation of the dhodh locus, although we did identify one mutation, DHODH I263S, which conferred resistance to TCMDC-125334 but not DSM265. We found that selection of the DHODH C276Y mutant with TCMDC-125334 yielded additional genetic changes in the dhodh locus. These double mutant parasites exhibited decreased sensitivity to TCMDC-125334 and were highly resistant to DSM265. Finally, we tested whether collateral sensitivity could be exploited to suppress the emergence of resistance in the context of combination treatment by exposing wildtype parasites to both DSM265 and TCMDC-125334 simultaneously. This selected for parasites with a DHODH V532A mutation which were cross-resistant to both compounds and were as fit as the wildtype parent in vitro. The emergence of these cross-resistant, evolutionarily fit parasites highlights the mutational flexibility of the DHODH enzyme.
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Affiliation(s)
- Rebecca EK Mandt
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public HealthBostonUnited States
| | - Madeline R Luth
- Division of Host Pathogen Systems and Therapeutics, Department of Pediatrics, University of California, San DiegoSan DiegoUnited States
| | - Mark A Tye
- Center for Systems Biology, Massachusetts General HospitalBostonUnited States
- Harvard Graduate School of Arts and SciencesCambridgeUnited States
| | - Ralph Mazitschek
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public HealthBostonUnited States
- Center for Systems Biology, Massachusetts General HospitalBostonUnited States
| | - Sabine Ottilie
- Division of Host Pathogen Systems and Therapeutics, Department of Pediatrics, University of California, San DiegoSan DiegoUnited States
| | - Elizabeth A Winzeler
- Division of Host Pathogen Systems and Therapeutics, Department of Pediatrics, University of California, San DiegoSan DiegoUnited States
- Skaggs School of Pharmaceutical Sciences, University of California, San DiegoLa JollaUnited States
| | | | - Francisco Javier Gamo
- Tres Cantos Medicines Development Campus, Diseases of the Developing World, GlaxoSmithKlineMadridSpain
| | - Dyann F Wirth
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public HealthBostonUnited States
- Infectious Disease and Microbiome Program, The Broad InstituteCambridgeUnited States
| | - Amanda K Lukens
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public HealthBostonUnited States
- Infectious Disease and Microbiome Program, The Broad InstituteCambridgeUnited States
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21
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King ES, Pierce B, Hinczewski M, Scott JG. Diverse mutant selection windows shape spatial heterogeneity in evolving populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.09.531899. [PMID: 37732215 PMCID: PMC10508720 DOI: 10.1101/2023.03.09.531899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N*2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.
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Affiliation(s)
- Eshan S. King
- Systems Biology and Bioinformatics Program, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Beck Pierce
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
| | - Jacob G. Scott
- Systems Biology and Bioinformatics Program, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
- Department of Translational Hematology and Oncology Research and Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA
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22
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Guerrero RF, Dorji T, Harris RM, Shoulders MD, Ogbunugafor CB. Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.08.536116. [PMID: 37066376 PMCID: PMC10104179 DOI: 10.1101/2023.04.08.536116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and seven β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).
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Affiliation(s)
| | - Tandin Dorji
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT
| | - Ra’Mal M. Harris
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA
| | | | - C. Brandon Ogbunugafor
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA
- DDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT
- Santa Fe Institute, Santa Fe, NM
- Public Health Modeling Unit, Yale School of Public Health, New Haven, CT
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23
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Nesse LL, Osland AM, Asal B, Mo SS. Evolution of antimicrobial resistance in E. coli biofilm treated with high doses of ciprofloxacin. Front Microbiol 2023; 14:1246895. [PMID: 37731931 PMCID: PMC10509014 DOI: 10.3389/fmicb.2023.1246895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 08/07/2023] [Indexed: 09/22/2023] Open
Abstract
The evolution of antimicrobial resistance (AMR) has mainly been studied in planktonic bacteria exposed to sub-inhibitory antimicrobial (AM) concentrations. However, in a number of infections that are treated with AMs the bacteria are located in biofilms where they tolerate high doses of AM. In the present study, we continuously exposed biofilm residing E. coli at body temperature to high ciprofloxacin (CIP) concentrations increasing from 4 to 130 times the minimal inhibitory concentration (MIC), i.e., from 0.06 to 2.0 mg/L. After 1 week, the biofilms were full of CIP resistant bacteria. The evolutionary trajectory observed was the same as described in the literature for planktonic bacteria, i.e., starting with a single mutation in the target gene gyrA followed by mutations in parC, gyrB, and parE, as well as in genes for regulation of multidrug efflux pump systems and outer membrane porins. Strains with higher numbers of these mutations also displayed higher MIC values. Furthermore, the evolution of CIP resistance was more rapid, and resulted in strains with higher MIC values, when the bacteria were biofilm residing than when they were in a planktonic suspension. These results may indicate that extensive clinical AM treatment of biofilm-residing bacteria may not only fail to eradicate the infection but also pose an increased risk of AMR development.
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Affiliation(s)
- Live L. Nesse
- Department of Food Safety and Animal Health Research, Norwegian Veterinary Institute, Ås Municipality, Norway
| | - Ane Mohr Osland
- Department of Microbiology, Norwegian Veterinary Institute, Ås Municipality, Norway
| | - Basma Asal
- Department of Bacteriology, Norwegian Veterinary Institute, Ås Municipality, Norway
| | - Solveig Sølverød Mo
- Department of Bacteriology, Norwegian Veterinary Institute, Ås Municipality, Norway
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24
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Soley JK, Jago M, Walsh CJ, Khomarbaghi Z, Howden BP, Lagator M. Pervasive genotype-by-environment interactions shape the fitness effects of antibiotic resistance mutations. Proc Biol Sci 2023; 290:20231030. [PMID: 37583318 PMCID: PMC10427823 DOI: 10.1098/rspb.2023.1030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/21/2023] [Indexed: 08/17/2023] Open
Abstract
The fitness effects of antibiotic resistance mutations are a major driver of resistance evolution. While the nutrient environment affects bacterial fitness, experimental studies of resistance typically measure fitness of mutants in a single environment only. We explored how the nutrient environment affected the fitness effects of rifampicin-resistant rpoB mutations in Escherichia coli under several conditions critical for the emergence and spread of resistance-the presence of primary or secondary antibiotic, or the absence of any antibiotic. Pervasive genotype-by-environment (GxE) interactions determined fitness in all experimental conditions, with rank order of fitness in the presence and absence of antibiotics being strongly dependent on the nutrient environment. GxE interactions also affected the magnitude and direction of collateral effects of secondary antibiotics, in some cases so drastically that a mutant that was highly sensitive in one nutrient environment exhibited cross-resistance to the same antibiotic in another. It is likely that the mutant-specific impact of rpoB mutations on the global transcriptome underpins the observed GxE interactions. The pervasive, mutant-specific GxE interactions highlight the importance of doing what is rarely done when studying the evolution and spread of resistance in experimental and clinical work: assessing fitness of antibiotic-resistant mutants across a range of relevant environments.
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Affiliation(s)
- Jake K. Soley
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria 3000, Australia
| | - Matthew Jago
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Calum J. Walsh
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria 3000, Australia
| | - Zahra Khomarbaghi
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Benjamin P. Howden
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria 3000, Australia
- Centre for Pathogen Genomics, University of Melbourne, Melbourne, Victoria 3000, Australia
| | - Mato Lagator
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
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25
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Barker-Clarke R, Weaver DT, Scott JG. Graph 'texture' features as novel metrics that can summarize complex biological graphs. Phys Med Biol 2023; 68:174001. [PMID: 37385267 PMCID: PMC10598684 DOI: 10.1088/1361-6560/ace305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/08/2023] [Accepted: 06/29/2023] [Indexed: 07/01/2023]
Abstract
Objective.Image texture features, such as those derived by Haralicket al, are a powerful metric for image classification and are used across fields including cancer research. Our aim is to demonstrate how analogous texture features can be derived for graphs and networks. We also aim to illustrate how these new metrics summarize graphs, may aid comparative graph studies, may help classify biological graphs, and might assist in detecting dysregulation in cancer.Approach.We generate the first analogies of image texture for graphs and networks. Co-occurrence matrices for graphs are generated by summing over all pairs of neighboring nodes in the graph. We generate metrics for fitness landscapes, gene co-expression and regulatory networks, and protein interaction networks. To assess metric sensitivity we varied discretization parameters and noise. To examine these metrics in the cancer context we compare metrics for both simulated and publicly available experimental gene expression and build random forest classifiers for cancer cell lineage.Main results.Our novel graph 'texture' features are shown to be informative of graph structure and node label distributions. The metrics are sensitive to discretization parameters and noise in node labels. We demonstrate that graph texture features vary across different biological graph topologies and node labelings. We show how our texture metrics can be used to classify cell line expression by lineage, demonstrating classifiers with 82% and 89% accuracy.Significance.New metrics provide opportunities for better comparative analyzes and new models for classification. Our texture features are novel second-order graph features for networks or graphs with ordered node labels. In the complex cancer informatics setting, evolutionary analyses and drug response prediction are two examples where new network science approaches like this may prove fruitful.
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Affiliation(s)
- R Barker-Clarke
- Department of Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland, OH 44195, United States of America
| | - D T Weaver
- Department of Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland, OH 44195, United States of America
- School of Medicine, Case Western Reserve University, Cleveland, OH 44195, United States of America
| | - J G Scott
- Department of Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland, OH 44195, United States of America
- School of Medicine, Case Western Reserve University, Cleveland, OH 44195, United States of America
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26
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Yekani M, Azargun R, Sharifi S, Nabizadeh E, Nahand JS, Ansari NK, Memar MY, Soki J. Collateral sensitivity: An evolutionary trade-off between antibiotic resistance mechanisms, attractive for dealing with drug-resistance crisis. Health Sci Rep 2023; 6:e1418. [PMID: 37448730 PMCID: PMC10336338 DOI: 10.1002/hsr2.1418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/29/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
Background The discovery and development of antimicrobial drugs were one of the most significant advances in medicine, but the evolution of microbial resistance limited the efficiency of these drugs. Aim This paper reviews the collateral sensitivity in bacteria and its potential and limitation as a new target for treating infections. Results and Discussion Knowledge mechanisms of resistance to antimicrobial agents are useful to trace a practical approach to treat and control of resistant pathogens. The effect of a resistance mechanism to certain antibiotics on the susceptibility or resistance to other drugs is a key point that may be helpful for applying a strategy to control resistance challenges. In an evolutionary trade-off known as collateral sensitivity, the resistance mechanism to a certain drug may be mediated by the hypersensitivity to other drugs. Collateral sensitivity has been described for different drugs in various bacteria, but the molecular mechanisms affecting susceptibility are not well demonstrated. Collateral sensitivity could be studied to detect its potential in the battle against resistance crisis as well as in the treatment of pathogens adapting to antibiotics. Collateral sensitivity-based antimicrobial therapy may have the potential to limit the emergence of antibiotic resistance.
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Affiliation(s)
- Mina Yekani
- Department of Microbiology, Faculty of MedicineKashan University of Medical SciencesKashanIran
- Infectious and Tropical Diseases Research CenterTabriz University of Medical SciencesTabrizIran
- Student Research CommitteeKashan University of Medical SciencesKashanIran
| | - Robab Azargun
- Department of Microbiology, Faculty of MedicineMaragheh University of Medical ScienceMaraghehIran
| | - Simin Sharifi
- Dental and Periodontal Research CenterTabriz University of Medical SciencesTabrizIran
| | - Edris Nabizadeh
- Infectious and Tropical Diseases Research CenterTabriz University of Medical SciencesTabrizIran
| | - Javid Sadri Nahand
- Infectious and Tropical Diseases Research CenterTabriz University of Medical SciencesTabrizIran
| | - Navideh Karimi Ansari
- Department of Microbiology, Faculty of MedicineTabriz University of Medical SciencesTabrizIran
| | - Mohammad Yousef Memar
- Infectious and Tropical Diseases Research CenterTabriz University of Medical SciencesTabrizIran
| | - Jozsef' Soki
- Institute of Medical Microbiology, Albert Szent‐Györgyi Faculty of MedicineUniversity of SzegedSzegedHungary
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Kinsler G, Schmidlin K, Newell D, Eder R, Apodaca S, Lam G, Petrov D, Geiler-Samerotte K. Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch. J Mol Evol 2023; 91:293-310. [PMID: 37237236 PMCID: PMC10276131 DOI: 10.1007/s00239-023-10114-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/30/2023] [Indexed: 05/28/2023]
Abstract
The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment dependence. This work was inspired by the scientific community who followed us and gave us tips as we live tweeted a high-replicate fitness measurement experiment at #1BigBatch.
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Affiliation(s)
| | - Kara Schmidlin
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
| | - Daphne Newell
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Rachel Eder
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Sam Apodaca
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | | | | | - Kerry Geiler-Samerotte
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA.
- School of Life Sciences, Arizona State University, Tempe, USA.
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28
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Scarborough JA, Eschrich SA, Torres-Roca J, Dhawan A, Scott JG. Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature. NPJ Precis Oncol 2023; 7:38. [PMID: 37076665 PMCID: PMC10115855 DOI: 10.1038/s41698-023-00375-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 03/21/2023] [Indexed: 04/21/2023] Open
Abstract
Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.
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Affiliation(s)
- Jessica A Scarborough
- Systems Biology and Bioinformatics Department, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Department of Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Steven A Eschrich
- Biostatistics and Bioinformatics Program, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Andrew Dhawan
- Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - Jacob G Scott
- Systems Biology and Bioinformatics Department, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
- Department of Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA.
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29
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Liu DY, Phillips L, Wilson DM, Fulton KM, Twine SM, Wong A, Linington RG. Collateral sensitivity profiling in drug-resistant Escherichia coli identifies natural products suppressing cephalosporin resistance. Nat Commun 2023; 14:1976. [PMID: 37031190 PMCID: PMC10082850 DOI: 10.1038/s41467-023-37624-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/22/2023] [Indexed: 04/10/2023] Open
Abstract
The rapid emergence of antimicrobial resistance presents serious health challenges to the management of infectious diseases, a problem that is further exacerbated by slowing rates of antimicrobial drug discovery in recent years. The phenomenon of collateral sensitivity (CS), whereby resistance to one drug is accompanied by increased sensitivity to another, provides new opportunities to address both these challenges. Here, we present a high-throughput screening platform termed Collateral Sensitivity Profiling (CSP) to map the difference in bioactivity of large chemical libraries across 29 drug-resistant strains of E. coli. CSP screening of 80 commercial antimicrobials demonstrated multiple CS interactions. Further screening of a 6195-member natural product library revealed extensive CS relationships in nature. In particular, we report the isolation of known and new analogues of borrelidin A with potent CS activities against cephalosporin-resistant strains. Co-dosing ceftazidime with borrelidin A slows broader cephalosporin resistance with no recognizable resistance to borrelidin A itself.
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Affiliation(s)
- Dennis Y Liu
- Department of Chemistry, Simon Fraser University, 8888 University Dr., V5A 1S6, Burnaby, BC, Canada
| | - Laura Phillips
- Department of Biology, Carleton University, 1125 Colonel By Dr., K1S 5B6, Ottawa, ON, Canada
| | - Darryl M Wilson
- Department of Chemistry, Simon Fraser University, 8888 University Dr., V5A 1S6, Burnaby, BC, Canada
| | - Kelly M Fulton
- Human Health Therapeutics Research Center, National Research Council Canada, 100 Sussex Dr., K1N 5A2, Ottawa, ON, Canada
| | - Susan M Twine
- Department of Biology, Carleton University, 1125 Colonel By Dr., K1S 5B6, Ottawa, ON, Canada
- Human Health Therapeutics Research Center, National Research Council Canada, 100 Sussex Dr., K1N 5A2, Ottawa, ON, Canada
| | - Alex Wong
- Department of Biology, Carleton University, 1125 Colonel By Dr., K1S 5B6, Ottawa, ON, Canada
- Institute for Advancing Health Through Agriculture, Texas A&M AgriLife, 1500 Research Parkway, 77845, College Station, TX, USA
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, 8888 University Dr., V5A 1S6, Burnaby, BC, Canada.
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30
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Hernando-Amado S, Laborda P, Martínez JL. Tackling antibiotic resistance by inducing transient and robust collateral sensitivity. Nat Commun 2023; 14:1723. [PMID: 36997518 PMCID: PMC10063638 DOI: 10.1038/s41467-023-37357-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
Collateral sensitivity (CS) is an evolutionary trade-off traditionally linked to the mutational acquisition of antibiotic resistance (AR). However, AR can be temporally induced, and the possibility that this causes transient, non-inherited CS, has not been addressed. Mutational acquisition of ciprofloxacin resistance leads to robust CS to tobramycin in pre-existing antibiotic-resistant mutants of Pseudomonas aeruginosa. Further, the strength of this phenotype is higher when nfxB mutants, over-producing the efflux pump MexCD-OprJ, are selected. Here, we induce transient nfxB-mediated ciprofloxacin resistance by using the antiseptic dequalinium chloride. Notably, non-inherited induction of AR renders transient tobramycin CS in the analyzed antibiotic-resistant mutants and clinical isolates, including tobramycin-resistant isolates. Further, by combining tobramycin with dequalinium chloride we drive these strains to extinction. Our results support that transient CS could allow the design of new evolutionary strategies to tackle antibiotic-resistant infections, avoiding the acquisition of AR mutations on which inherited CS depends.
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Affiliation(s)
| | - Pablo Laborda
- Centro Nacional de Biotecnología, CSIC, 28049, Madrid, Spain
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
- Department of Clinical Microbiology 9301, Rigshospitalet, 2100, Copenhagen, Denmark
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31
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Waller NJE, Cheung CY, Cook GM, McNeil MB. The evolution of antibiotic resistance is associated with collateral drug phenotypes in Mycobacterium tuberculosis. Nat Commun 2023; 14:1517. [PMID: 36934122 PMCID: PMC10024696 DOI: 10.1038/s41467-023-37184-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/06/2023] [Indexed: 03/20/2023] Open
Abstract
The increasing incidence of drug resistance in Mycobacterium tuberculosis has diminished the efficacy of almost all available antibiotics, complicating efforts to combat the spread of this global health burden. Alongside the development of new drugs, optimised drug combinations are needed to improve treatment success and prevent the further spread of antibiotic resistance. Typically, antibiotic resistance leads to reduced sensitivity, yet in some cases the evolution of drug resistance can lead to enhanced sensitivity to unrelated drugs. This phenomenon of collateral sensitivity is largely unexplored in M. tuberculosis but has the potential to identify alternative therapeutic strategies to combat drug-resistant strains that are unresponsive to current treatments. Here, by using drug susceptibility profiling, genomics and evolutionary studies we provide evidence for the existence of collateral drug sensitivities in an isogenic collection M. tuberculosis drug-resistant strains. Furthermore, in proof-of-concept studies, we demonstrate how collateral drug phenotypes can be exploited to select against and prevent the emergence of drug-resistant strains. This study highlights that the evolution of drug resistance in M. tuberculosis leads to collateral drug responses that can be exploited to design improved drug regimens.
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Affiliation(s)
- Natalie J E Waller
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Chen-Yi Cheung
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - Gregory M Cook
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Matthew B McNeil
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand.
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand.
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32
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Hernando-Amado S, López-Causapé C, Laborda P, Sanz-García F, Oliver A, Martínez JL. Rapid Phenotypic Convergence towards Collateral Sensitivity in Clinical Isolates of Pseudomonas aeruginosa Presenting Different Genomic Backgrounds. Microbiol Spectr 2023; 11:e0227622. [PMID: 36533961 PMCID: PMC9927454 DOI: 10.1128/spectrum.02276-22] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Collateral sensitivity (CS) is an evolutionary trade-off by which acquisition of resistance to an antibiotic leads to increased susceptibility to another. This Achilles' heel of antibiotic resistance could be exploited to design evolution-based strategies for treating bacterial infections. To date, most studies in the field have focused on the identification of CS patterns in model strains. However, one of the main requirements for the clinical application of this trade-off is that it must be robust and has to emerge in different genomic backgrounds, including preexisting drug-resistant isolates, since infections are frequently caused by pathogens already resistant to antibiotics. Here, we report the first analysis of CS robustness in clinical strains of Pseudomonas aeruginosa presenting different ab initio mutational resistomes. We identified a robust CS pattern associated with short-term evolution in the presence of ciprofloxacin of clinical P. aeruginosa isolates, including representatives of high-risk epidemic clones belonging to sequence type (ST) 111, ST175, and ST244. We observed the acquisition of different ciprofloxacin resistance mutations in strains presenting varied STs and different preexisting mutational resistomes. Importantly, despite these genetic differences, the use of ciprofloxacin led to a robust CS to aztreonam and tobramycin. In addition, we describe the possible application of this evolutionary trade-off to drive P. aeruginosa infections to extinction by using the combination of ciprofloxacin-tobramycin or ciprofloxacin-aztreonam. Our results support the notion that the identification of robust patterns of CS may establish the basis for developing evolution-informed treatment strategies to tackle bacterial infections, including those due to antibiotic-resistant pathogens. IMPORTANCE Collateral sensitivity (CS) is a trade-off of antibiotic resistance evolution that could be exploited to design strategies for treating bacterial infections. Clinical application of CS requires it to robustly emerge in different genomic backgrounds. In this study, we performed an analysis to identify robust patterns of CS associated with the use of ciprofloxacin in clinical isolates of P. aeruginosa presenting different mutational resistomes and including high-risk epidemic clones (ST111, ST175, and ST244). We demonstrate the robustness of CS to tobramycin and aztreonam and the potential application of this evolutionary observation to drive P. aeruginosa infections to extinction. Our results support the notion that the identification of robust CS patterns may establish the basis for developing evolutionary strategies to tackle bacterial infections, including those due to antibiotic-resistant pathogens.
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Affiliation(s)
| | - Carla López-Causapé
- Servicio de Microbiología, Hospital Universitario Son Espases, Instituto de Investigación Sanitaria Illes Balears, CIBERINFEC, Palma de Mallorca, Spain
| | - Pablo Laborda
- Centro Nacional de Biotecnología, CSIC, Madrid, Spain
| | - Fernando Sanz-García
- Centro Nacional de Biotecnología, CSIC, Madrid, Spain
- Departamento de Microbiología, Medicina Preventiva y Salud Pública, Universidad de Zaragoza, Zaragoza, Spain
| | - Antonio Oliver
- Servicio de Microbiología, Hospital Universitario Son Espases, Instituto de Investigación Sanitaria Illes Balears, CIBERINFEC, Palma de Mallorca, Spain
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33
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Batchelder JI, Hare PJ, Mok WWK. Resistance-resistant antibacterial treatment strategies. FRONTIERS IN ANTIBIOTICS 2023; 2:1093156. [PMID: 36845830 PMCID: PMC9954795 DOI: 10.3389/frabi.2023.1093156] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Antibiotic resistance is a major danger to public health that threatens to claim the lives of millions of people per year within the next few decades. Years of necessary administration and excessive application of antibiotics have selected for strains that are resistant to many of our currently available treatments. Due to the high costs and difficulty of developing new antibiotics, the emergence of resistant bacteria is outpacing the introduction of new drugs to fight them. To overcome this problem, many researchers are focusing on developing antibacterial therapeutic strategies that are "resistance-resistant"-regimens that slow or stall resistance development in the targeted pathogens. In this mini review, we outline major examples of novel resistance-resistant therapeutic strategies. We discuss the use of compounds that reduce mutagenesis and thereby decrease the likelihood of resistance emergence. Then, we examine the effectiveness of antibiotic cycling and evolutionary steering, in which a bacterial population is forced by one antibiotic toward susceptibility to another antibiotic. We also consider combination therapies that aim to sabotage defensive mechanisms and eliminate potentially resistant pathogens by combining two antibiotics or combining an antibiotic with other therapeutics, such as antibodies or phages. Finally, we highlight promising future directions in this field, including the potential of applying machine learning and personalized medicine to fight antibiotic resistance emergence and out-maneuver adaptive pathogens.
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Affiliation(s)
- Jonathan I Batchelder
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, United States
| | - Patricia J Hare
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, United States.,School of Dental Medicine, University of Connecticut, Farmington, CT, United States
| | - Wendy W K Mok
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, United States
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34
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Brepoels P, Appermans K, Pérez-Romero CA, Lories B, Marchal K, Steenackers HP. Antibiotic Cycling Affects Resistance Evolution Independently of Collateral Sensitivity. Mol Biol Evol 2022; 39:6884036. [PMID: 36480297 PMCID: PMC9778841 DOI: 10.1093/molbev/msac257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/13/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022] Open
Abstract
Antibiotic cycling has been proposed as a promising approach to slow down resistance evolution against currently employed antibiotics. It remains unclear, however, to which extent the decreased resistance evolution is the result of collateral sensitivity, an evolutionary trade-off where resistance to one antibiotic enhances the sensitivity to the second, or due to additional effects of the evolved genetic background, in which mutations accumulated during treatment with a first antibiotic alter the emergence and spread of resistance against a second antibiotic via other mechanisms. Also, the influence of antibiotic exposure patterns on the outcome of drug cycling is unknown. Here, we systematically assessed the effects of the evolved genetic background by focusing on the first switch between two antibiotics against Salmonella Typhimurium, with cefotaxime fixed as the first and a broad variety of other drugs as the second antibiotic. By normalizing the antibiotic concentrations to eliminate the effects of collateral sensitivity, we demonstrated a clear contribution of the evolved genetic background beyond collateral sensitivity, which either enhanced or reduced the adaptive potential depending on the specific drug combination. We further demonstrated that the gradient strength with which cefotaxime was applied affected both cefotaxime resistance evolution and adaptation to second antibiotics, an effect that was associated with higher levels of clonal interference and reduced cost of resistance in populations evolved under weaker cefotaxime gradients. Overall, our work highlights that drug cycling can affect resistance evolution independently of collateral sensitivity, in a manner that is contingent on the antibiotic exposure pattern.
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Affiliation(s)
| | | | - Camilo Andres Pérez-Romero
- Department of Information Technology and the Department of Plant Biotechnology, Biochemistry and Bioinformatics, Ghent University, Ghent, Belgium
| | - Bram Lories
- Department of Microbial and Molecular Systems, Centre of Microbial and Plant Genetics (CMPG), KU Leuven, Leuven, Belgium
| | - Kathleen Marchal
- Department of Information Technology and the Department of Plant Biotechnology, Biochemistry and Bioinformatics, Ghent University, Ghent, Belgium
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35
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Maron B, Rolff J, Friedman J, Hayouka Z. Antimicrobial Peptide Combination Can Hinder Resistance Evolution. Microbiol Spectr 2022; 10:e0097322. [PMID: 35862981 PMCID: PMC9430149 DOI: 10.1128/spectrum.00973-22] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 06/24/2022] [Indexed: 12/29/2022] Open
Abstract
Antibiotic-resistant microbial pathogens are becoming a major threat to human health. Therefore, there is an urgent need to develop new alternatives to conventional antibiotics. One such promising alternative is antimicrobial peptides (AMPs), which are produced by virtually all organisms and typically inhibit bacteria via membrane disruption. However, previous studies demonstrated that bacteria can rapidly develop AMP resistance. Here, we study whether combination therapy, known to be able to inhibit the evolution of resistance to conventional antibiotics, can also hinder the evolution of AMP resistance. To do so, we evolved the opportunistic pathogen Staphylococcus aureus in the presence of individual AMPs, AMP pairs, and a combinatorial antimicrobial peptide library. Treatment with some AMP pairs indeed hindered the evolution of resistance compared with individual AMPs. In particular, resistance to pairs was delayed when resistance to the individual AMPs came at a cost of impaired bacterial growth and did not confer cross-resistance to other tested AMPs. The lowest level of resistance evolved during treatment with the combinatorial antimicrobial peptide library termed random antimicrobial peptide mixture, which contains more than a million different peptides. A better understanding of how AMP combinations affect the evolution of resistance is a crucial step in order to design "resistant proof" AMP cocktails that will offer a sustainable treatment option for antibiotic-resistant pathogens. IMPORTANCE The main insights gleaned from this study are the following. (i) AMP combination treatment can delay the evolution of resistance in S. aureus. Treatment with some AMP pairs resulted in significantly lower resistance then treatment with either of the individual AMPs. Treatment with a random AMP library resulted in no detectable resistance. (ii) The rate at which resistance to combination arises correlates with the cost of resistance to individual AMPs and their cross-resistance. In particular, combinations to which the least resistance arose involved AMPs with high fitness cost of resistance and low cross-resistance. (iii) No broad-range AMP resistance evolved. Strains that evolved resistance to some AMPs typically remained sensitive to other AMPs, alleviating concerns regarding the evolution of resistance to immune system AMPs in response to AMP treatment.
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Affiliation(s)
- Bar Maron
- Institute of Biochemistry, Food Science and Nutrition, The Hebrew University of Jerusalem, Rehovot, Israel
- Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Jens Rolff
- Institute of Biology, Evolutionary Biology, Freie University, Berlin, Germany
| | - Jonathan Friedman
- Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Zvi Hayouka
- Institute of Biochemistry, Food Science and Nutrition, The Hebrew University of Jerusalem, Rehovot, Israel
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36
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Brettner L, Ho WC, Schmidlin K, Apodaca S, Eder R, Geiler-Samerotte K. Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes. Curr Opin Genet Dev 2022; 75:101951. [PMID: 35797741 DOI: 10.1016/j.gde.2022.101951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/01/2022] [Accepted: 06/14/2022] [Indexed: 11/29/2022]
Abstract
All organisms are defined by the makeup of their DNA. Over billions of years, the structure and information contained in that DNA, often referred to as genetic architecture, have been honed by a multitude of evolutionary processes. Mutations that cause genetic elements to change in a way that results in beneficial phenotypic change are more likely to survive and propagate through the population in a process known as adaptation. Recent work reveals that the genetic targets of adaptation are varied and can change with genetic background. Further, seemingly similar adaptive mutations, even within the same gene, can have diverse and unpredictable effects on phenotype. These challenges represent major obstacles in predicting adaptation and evolution. In this review, we cover these concepts in detail and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models. Our review largely focuses on recent literature in yeast, and the field seems to be on the cusp of a new era with regard to studying the predictability of evolution.
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37
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Fait A, Seif Y, Mikkelsen K, Poudel S, Wells JM, Palsson BO, Ingmer H. Adaptive laboratory evolution and independent component analysis disentangle complex vancomycin adaptation trajectories. Proc Natl Acad Sci U S A 2022; 119:e2118262119. [PMID: 35858453 PMCID: PMC9335240 DOI: 10.1073/pnas.2118262119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 05/11/2022] [Indexed: 11/27/2022] Open
Abstract
Human infections with methicillin-resistant Staphylococcus aureus (MRSA) are commonly treated with vancomycin, and strains with decreased susceptibility, designated as vancomycin-intermediate S. aureus (VISA), are associated with treatment failure. Here, we profiled the phenotypic, mutational, and transcriptional landscape of 10 VISA strains adapted by laboratory evolution from one common MRSA ancestor, the USA300 strain JE2. Using functional and independent component analysis, we found that: 1) despite the common genetic background and environmental conditions, the mutational landscape diverged between evolved strains and included mutations previously associated with vancomycin resistance (in vraT, graS, vraFG, walKR, and rpoBCD) as well as novel adaptive mutations (SAUSA300_RS04225, ssaA, pitAR, and sagB); 2) the first wave of mutations affected transcriptional regulators and the second affected genes involved in membrane biosynthesis; 3) expression profiles were predominantly strain-specific except for sceD and lukG, which were the only two genes significantly differentially expressed in all clones; 4) three independent virulence systems (φSa3, SaeR, and T7SS) featured as the most transcriptionally perturbed gene sets across clones; 5) there was a striking variation in oxacillin susceptibility across the evolved lineages (from a 10-fold increase to a 63-fold decrease) that also arose in clinical MRSA isolates exposed to vancomycin and correlated with susceptibility to teichoic acid inhibitors; and 6) constitutive expression of the VraR regulon explained cross-susceptibility, while mutations in walK were associated with cross-resistance. Our results show that adaptation to vancomycin involves a surprising breadth of mutational and transcriptional pathways that affect antibiotic susceptibility and possibly the clinical outcome of infections.
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Affiliation(s)
- Anaëlle Fait
- Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, 1870 Denmark
| | - Yara Seif
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093
- Merck & Co., Inc., South San Francisco, CA 94080
| | - Kasper Mikkelsen
- Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, 1870 Denmark
| | - Saugat Poudel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093
| | - Jerry M. Wells
- Host-Microbe Interactomics, Animal Sciences Group, Wageningen University, Wageningen, The Netherlands
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093
| | - Hanne Ingmer
- Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, 1870 Denmark
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Abstract
Pseudomonas aeruginosa is an opportunistic human pathogen that usually causes difficult-to-treat infections due to its low intrinsic antibiotic susceptibility and outstanding capacity for becoming resistant to antibiotics. In addition, it has a remarkable metabolic versatility, being able to grow in different habitats, from natural niches to different and changing inpatient environments. Study of the environmental conditions that shape genetic and phenotypic changes of P. aeruginosa toward antibiotic resistance supposes a novelty, since experimental evolution assays are usually performed with well-defined antibiotics in regular laboratory growth media. Therefore, in this work we address the extent to which the nutrients’ availability may constrain the evolution of antibiotic resistance. We determined that P. aeruginosa genetic trajectories toward resistance to tobramycin, ceftazidime, and ceftazidime-avibactam are different when evolving in laboratory rich medium, urine, or synthetic sputum. Furthermore, our study, linking genotype with phenotype, showed a clear impact of each analyzed environment on both the fitness and resistance level associated with particular resistance mutations. This indicates that the phenotype associated with specific resistance mutations is variable and dependent on the bacterial metabolic state in each particular habitat. Our results support that the design of evolution-based strategies to tackle P. aeruginosa infections should be based on robust patterns of evolution identified within each particular infection and body location. IMPORTANCE Predicting evolution toward antibiotic resistance (AR) and its associated trade-offs, such as collateral sensitivity, is important to design evolution-based strategies to tackle AR. However, the effect of nutrients' availability on such evolution, particularly those that can be found under in vivo infection conditions, has been barely addressed. We analyzed the evolutionary patterns of P. aeruginosa in the presence of antibiotics in different media, including urine and synthetic sputum, whose compositions are similar to the ones in infections, finding that AR evolution differs, depending on growth conditions. Furthermore, the representative mutants isolated under each condition tested render different AR levels and fitness costs, depending on nutrients’ availability, supporting the idea that environmental constraints shape the phenotypes associated with specific AR mutations. Consequently, the selection of AR mutations that render similar phenotypes is environment dependent. The analysis of evolution patterns toward AR requires studying growth conditions mimicking those that bacteria face during in vivo evolution.
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Feng S, Wu Z, Liang W, Zhang X, Cai X, Li J, Liang L, Lin D, Stoesser N, Doi Y, Zhong LL, Liu Y, Xia Y, Dai M, Zhang L, Chen X, Yang JR, Tian GB. Prediction of Antibiotic Resistance Evolution by Growth Measurement of All Proximal Mutants of Beta-Lactamase. Mol Biol Evol 2022; 39:msac086. [PMID: 35485492 PMCID: PMC9087888 DOI: 10.1093/molbev/msac086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
The antibiotic resistance crisis continues to threaten human health. Better predictions of the evolution of antibiotic resistance genes could contribute to the design of more sustainable treatment strategies. However, comprehensive prediction of antibiotic resistance gene evolution via laboratory approaches remains challenging. By combining site-specific integration and high-throughput sequencing, we quantified relative growth under the respective selection of cefotaxime or ceftazidime selection in ∼23,000 Escherichia coli MG1655 strains that each carried a unique, single-copy variant of the extended-spectrum β-lactamase gene blaCTX-M-14 at the chromosomal att HK022 site. Significant synergistic pleiotropy was observed within four subgenic regions, suggesting key regions for the evolution of resistance to both antibiotics. Moreover, we propose PEARP and PEARR, two deep-learning models with strong clinical correlations, for the prospective and retrospective prediction of blaCTX-M-14 evolution, respectively. Single to quintuple mutations of blaCTX-M-14 predicted to confer resistance by PEARP were significantly enriched among the clinical isolates harboring blaCTX-M-14 variants, and the PEARR scores matched the minimal inhibitory concentrations obtained for the 31 intermediates in all hypothetical trajectories. Altogether, we conclude that the measurement of local fitness landscape enables prediction of the evolutionary trajectories of antibiotic resistance genes, which could be useful for a broad range of clinical applications, from resistance prediction to designing novel treatment strategies.
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Affiliation(s)
- Siyuan Feng
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
| | - Zhuoxing Wu
- Department of Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Wanfei Liang
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
| | - Xin Zhang
- Department of Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiujuan Cai
- Department of Genetics and Cellular Biology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Jiachen Li
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
| | - Lujie Liang
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
| | - Daixi Lin
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
| | - Nicole Stoesser
- Modernising Medical Microbiology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Yohei Doi
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh 15261, PA, USA
- Department of Microbiology, Fujita Health University School of Medicine, Aichi 470-1192, Japan
- Department of Infectious Diseases, Fujita Health University School of Medicine, Aichi 470-1192, Japan
| | - Lan-lan Zhong
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
| | - Yan Liu
- Clinical Laboratory, Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Yong Xia
- Department of Clinical Laboratory Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Min Dai
- School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
| | - Liyan Zhang
- Department of Clinical Laboratory, Guangdong Provincial People’s Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
| | - Xiaoshu Chen
- Department of Genetics and Cellular Biology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Jian-Rong Yang
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
- Department of Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Guo-bao Tian
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-sen University, Ministry of Education, Guangzhou 510080, China
- School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China
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Sepich-Poore GD, Guccione C, Laplane L, Pradeu T, Curtius K, Knight R. Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution. Bioessays 2022; 44:e2100252. [PMID: 35253252 PMCID: PMC10506734 DOI: 10.1002/bies.202100252] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/31/2022] [Accepted: 02/16/2022] [Indexed: 12/13/2022]
Abstract
The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.
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Affiliation(s)
| | - Caitlin Guccione
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Lucie Laplane
- Institut d’histoire et de philosophie des sciences et des techniques (UMR8590), CNRS & Panthéon-Sorbonne University, 75006 Paris, France
- Hematopoietic stem cells and the development of myeloid malignancies (UMR1287), Gustave Roussy Cancer Campus, 94800 Villejuif, France
| | - Thomas Pradeu
- ImmunoConcept (UMR5164), CNRS & University of Bordeaux, 33076 Bordeaux Cedex, France
| | - Kit Curtius
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
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41
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Li Q, Chen S, Zhu K, Huang X, Huang Y, Shen Z, Ding S, Gu D, Yang Q, Sun H, Hu F, Wang H, Cai J, Ma B, Zhang R, Shen J. Collateral sensitivity to pleuromutilins in vancomycin-resistant Enterococcus faecium. Nat Commun 2022; 13:1888. [PMID: 35393429 PMCID: PMC8990069 DOI: 10.1038/s41467-022-29493-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 03/17/2022] [Indexed: 01/24/2023] Open
Abstract
The acquisition of resistance to one antibiotic sometimes leads to collateral sensitivity to a second antibiotic. Here, we show that vancomycin resistance in Enterococcus faecium is associated with a remarkable increase in susceptibility to pleuromutilin antibiotics (such as lefamulin), which target the bacterial ribosome. The trade-off between vancomycin and pleuromutilins is mediated by epistasis between the van gene cluster and msrC, encoding an ABC-F protein that protects bacterial ribosomes from antibiotic targeting. In mouse models of vancomycin-resistant E. faecium colonization and septicemia, pleuromutilin treatment reduces colonization and improves survival more effectively than standard therapy (linezolid). Our findings suggest that pleuromutilins may be useful for the treatment of vancomycin-resistant E. faecium infections.
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Affiliation(s)
- Qian Li
- National Center for Veterinary Drug Safety Evaluation, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Shang Chen
- National Center for Veterinary Drug Safety Evaluation, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Kui Zhu
- National Center for Veterinary Drug Safety Evaluation, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China.
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs, Beijing, 100193, China.
| | - Xiaoluo Huang
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Yucheng Huang
- National Center for Veterinary Drug Safety Evaluation, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Zhangqi Shen
- National Center for Veterinary Drug Safety Evaluation, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Shuangyang Ding
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs, Beijing, 100193, China
| | - Danxia Gu
- Centre of Laboratory Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, 310014, China
| | - Qiwen Yang
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Hongli Sun
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Fupin Hu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Hui Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, 100044, China
| | - Jiachang Cai
- Department of Clinical Laboratory, Second Affiliated Hospital of Zhejiang University, School of Medicine, Zhejiang, Hangzhou, 310009, China
| | - Bing Ma
- Clinical Laboratory, Medicine Department, Henan Provincial People's Hospital, Zhengzhou, 450003, China
| | - Rong Zhang
- Department of Clinical Laboratory, Second Affiliated Hospital of Zhejiang University, School of Medicine, Zhejiang, Hangzhou, 310009, China.
| | - Jianzhong Shen
- National Center for Veterinary Drug Safety Evaluation, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China.
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs, Beijing, 100193, China.
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42
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Rojas LJ, Yasmin M, Benjamino J, Marshall SM, DeRonde KJ, Krishnan NP, Perez F, Colin AA, Cardenas M, Martinez O, Pérez-Cardona A, Rhoads DD, Jacobs MR, LiPuma JJ, Konstan MW, Vila AJ, Smania A, Mack AR, Scott JG, Adams MD, Abbo LM, Bonomo RA. Genomic heterogeneity underlies multidrug resistance in Pseudomonas aeruginosa: A population-level analysis beyond susceptibility testing. PLoS One 2022; 17:e0265129. [PMID: 35358221 PMCID: PMC8970513 DOI: 10.1371/journal.pone.0265129] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 02/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Pseudomonas aeruginosa is a persistent and difficult-to-treat pathogen in many patients, especially those with Cystic Fibrosis (CF). Herein, we describe a longitudinal analysis of a series of multidrug resistant (MDR) P. aeruginosa isolates recovered in a 17-month period, from a young female CF patient who underwent double lung transplantation. Our goal was to understand the genetic basis of the observed resistance phenotypes, establish the genomic population diversity, and define the nature of sequence evolution over time. METHODS Twenty-two sequential P. aeruginosa isolates were obtained within a 17-month period, before and after a double-lung transplant. At the end of the study period, antimicrobial susceptibility testing, whole genome sequencing (WGS), phylogenetic analyses and RNAseq were performed in order to understand the genetic basis of the observed resistance phenotypes, establish the genomic population diversity, and define the nature of sequence changes over time. RESULTS The majority of isolates were resistant to almost all tested antibiotics. A phylogenetic reconstruction revealed 3 major clades representing a genotypically and phenotypically heterogeneous population. The pattern of mutation accumulation and variation of gene expression suggested that a group of closely related strains was present in the patient prior to transplantation and continued to change throughout the course of treatment. A trend toward accumulation of mutations over time was observed. Different mutations in the DNA mismatch repair gene mutL consistent with a hypermutator phenotype were observed in two clades. RNAseq performed on 12 representative isolates revealed substantial differences in the expression of genes associated with antibiotic resistance and virulence traits. CONCLUSIONS The overwhelming current practice in the clinical laboratories setting relies on obtaining a pure culture and reporting the antibiogram from a few isolated colonies to inform therapy decisions. Our analyses revealed significant underlying genomic heterogeneity and unpredictable evolutionary patterns that were independent of prior antibiotic treatment, highlighting the need for comprehensive sampling and population-level analysis when gathering microbiological data in the context of CF P. aeruginosa chronic infection. Our findings challenge the applicability of antimicrobial stewardship programs based on single-isolate resistance profiles for the selection of antibiotic regimens in chronic infections such as CF.
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Affiliation(s)
- Laura J. Rojas
- Department of Molecular Biology and Microbiology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Research Service, Louis Stokes Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES), Cleveland, Ohio, United States of America
| | - Mohamad Yasmin
- Research Service, Louis Stokes Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
| | - Jacquelynn Benjamino
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Steven M. Marshall
- Research Service, Louis Stokes Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
| | - Kailynn J. DeRonde
- Jackson Memorial Hospital, Jackson Health System, Miami, Florida, United States of America
| | - Nikhil P. Krishnan
- Center for Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Departments of Translational Hematology and Oncology Research and Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Federico Perez
- Medical Service, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, United States of America
- CONICET, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC), Córdoba, Argentina
- Division of Infectious Diseases and HIV Medicine, Cleveland, Ohio, United States of America
- GRECC Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
| | - Andrew A. Colin
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Monica Cardenas
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Octavio Martinez
- Jackson Memorial Hospital, Jackson Health System, Miami, Florida, United States of America
- Division of Pulmonology, Department of Pathology University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Armando Pérez-Cardona
- Jackson Memorial Hospital, Jackson Health System, Miami, Florida, United States of America
| | - Daniel D. Rhoads
- Department of Laboratory Medicine and Infection Biology Program, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Pathology, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University Cleveland, Ohio, United States of America
| | - Michael R. Jacobs
- Department of Pathology, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University Cleveland, Ohio, United States of America
| | - John J. LiPuma
- Division of Pediatric Infectious Diseases, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Michael W. Konstan
- Department of Pediatrics, Case Western Reserve University School of Medicine and Rainbow Babies and Children’s Hospital, Cleveland, Ohio, United States of America
| | - Alejandro J. Vila
- Instituto de Biología Molecular y Celular de Rosario (IBR, CONICET-UNR), Rosario, Argentina
| | - Andrea Smania
- CONICET, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC), Córdoba, Argentina
- Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Biológica, Córdoba, Argentina
| | - Andrew R. Mack
- Department of Molecular Biology and Microbiology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Research Service, Louis Stokes Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
| | - Jacob G. Scott
- Center for Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Departments of Translational Hematology and Oncology Research and Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Mark D. Adams
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Lilian M. Abbo
- Jackson Memorial Hospital, Jackson Health System, Miami, Florida, United States of America
- Division of Infectious Diseases Department of Medicine University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Robert A. Bonomo
- Department of Molecular Biology and Microbiology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Research Service, Louis Stokes Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES), Cleveland, Ohio, United States of America
- Center for Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Medical Service, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, United States of America
- Division of Infectious Diseases and HIV Medicine, Cleveland, Ohio, United States of America
- GRECC Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
- Department of Pharmacology, Cleveland, Ohio, United States of America
- Department of Biochemistry Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
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43
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Dalin S, Grauman-Boss B, Lauffenburger DA, Hemann MT. Collateral responses to classical cytotoxic chemotherapies are heterogeneous and sensitivities are sparse. Sci Rep 2022; 12:5453. [PMID: 35361803 PMCID: PMC8971507 DOI: 10.1038/s41598-022-09319-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/14/2022] [Indexed: 11/09/2022] Open
Abstract
Chemotherapy resistance is a major obstacle to curing cancer patients. Combination drug regimens have shown promise as a method to overcome resistance; however, to date only some cancers have been cured with this method. Collateral sensitivity-the phenomenon whereby resistance to one drug is co-occurrent with sensitivity to a second drug-has been gaining traction as a promising new concept to guide rational design of combination regimens. Here we evolved over 100 subclones of the Eµ-Myc; p19ARF-/- cell line to be resistant to one of four classical chemotherapy agents: doxorubicin, vincristine, paclitaxel, and cisplatin. We then surveyed collateral responses to acquisition of resistance to these agents. Although numerous collateral sensitivities have been documented for antibiotics and targeted cancer therapies, we observed only one collateral sensitivity: half of cell lines that acquired resistance to paclitaxel also acquired a collateral sensitivity to verapamil. However, we found that the mechanism of this collateral sensitivity was unrelated to the mechanism of paclitaxel resistance. Interestingly, we observed heterogeneity in the phenotypic response to acquisition of resistance to most of the drugs we tested, most notably for paclitaxel, suggesting the existence of multiple different states of resistance. Surprisingly, this phenotypic heterogeneity in paclitaxel resistant cell lines was unrelated to transcriptomic heterogeneity among those cell lines. These features of phenotypic and transcriptomic heterogeneity must be taken into account in future studies of treated tumor subclones and in design of chemotherapy combinations.
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Affiliation(s)
- Simona Dalin
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.,Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Beatrice Grauman-Boss
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.,Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas A Lauffenburger
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Biological Engineering, Massachusetts Institute of Technology, Room: 16-343, Cambridge, MA, 02139, USA.
| | - Michael T Hemann
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Hernando-Amado S, Laborda P, Valverde JR, Martínez JL. Rapid decline of ceftazidime resistance in antibiotic-free and sub-lethal environments is contingent on genetic background. Mol Biol Evol 2022; 39:6543660. [PMID: 35291010 PMCID: PMC8935207 DOI: 10.1093/molbev/msac049] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Trade-offs of antibiotic resistance evolution, such as fitness cost and collateral sensitivity (CS), could be exploited to drive evolution toward antibiotic susceptibility. Decline of resistance may occur when resistance to other drug leads to CS to the first one and when compensatory mutations, or genetic reversion of the original ones, reduce fitness cost. Here we describe the impact of antibiotic-free and sublethal environments on declining ceftazidime resistance in different Pseudomonas aeruginosa resistant mutants. We determined that decline of ceftazidime resistance occurs within 450 generations, which is caused by newly acquired mutations and not by reversion of the original ones, and that the original CS of these mutants is preserved. In addition, we observed that the frequency and degree of this decline is contingent on genetic background. Our results are relevant to implement evolution-based therapeutic approaches, as well as to redefine global policies of antibiotic use, such as drug cycling.
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Affiliation(s)
| | - Pablo Laborda
- Centro Nacional de Biotecnología. CSIC, Madrid, 28049, Spain
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Howard GR, Jost TA, Yankeelov TE, Brock A. Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules. PLoS Comput Biol 2022; 18:e1009104. [PMID: 35358172 PMCID: PMC9004764 DOI: 10.1371/journal.pcbi.1009104] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 04/12/2022] [Accepted: 02/07/2022] [Indexed: 01/05/2023] Open
Abstract
While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval. Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.
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Affiliation(s)
- Grant R. Howard
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Tyler A. Jost
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Oncology, The University of Texas at Austin, Austin, Texas, United States of America
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Cell and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America
- * E-mail:
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Laborda P, Martínez JL, Hernando‐Amado S. Convergent phenotypic evolution towards fosfomycin collateral sensitivity of Pseudomonas aeruginosa antibiotic-resistant mutants. Microb Biotechnol 2022; 15:613-629. [PMID: 33960651 PMCID: PMC8867969 DOI: 10.1111/1751-7915.13817] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 12/19/2022] Open
Abstract
The rise of antibiotic resistance and the reduced amount of novel antibiotics support the need of developing novel strategies to fight infections, based on improving the use of the antibiotics we already have. Collateral sensitivity is an evolutionary trade-off associated with the acquisition of antibiotic resistance that can be exploited to tackle this relevant health problem. However, different works have shown that patterns of collateral sensitivity are not always conserved, thus precluding the exploitation of this evolutionary trade-off to fight infections. In this work, we identify a robust pattern of collateral sensitivity to fosfomycin in Pseudomonas aeruginosa antibiotic-resistant mutants, selected by antibiotics belonging to different structural families. We characterize the underlying mechanism of the collateral sensitivity observed, which is a reduced expression of the genes encoding the peptidoglycan-recycling pathway, which preserves the peptidoglycan synthesis in situations where its de novo synthesis is blocked, and a reduced expression of fosA, encoding a fosfomycin-inactivating enzyme. We propose that the identification of robust collateral sensitivity patterns, as well as the understanding of the molecular mechanisms behind these phenotypes, would provide valuable information to design evolution-based strategies to treat bacterial infections.
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Affiliation(s)
- Pablo Laborda
- Centro Nacional de BiotecnologíaCSICMadrid28049Spain
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Durmaz A, Scott JG. Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches. Evol Bioinform Online 2022; 18:11769343221123050. [PMID: 36199555 PMCID: PMC9527995 DOI: 10.1177/11769343221123050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 07/18/2022] [Indexed: 11/04/2022] Open
Abstract
Background: Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference. Methods: We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6 k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off. Results: Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.
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Affiliation(s)
- Arda Durmaz
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
- Systems Biology and Bioinformatics Graduate Program, Case Western Reserve University, Cleveland, OH, USA
| | - Jacob G Scott
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
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48
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Hasan CM, Dutta D, Nguyen ANT. Revisiting Antibiotic Resistance: Mechanistic Foundations to Evolutionary Outlook. Antibiotics (Basel) 2021; 11:antibiotics11010040. [PMID: 35052917 PMCID: PMC8773413 DOI: 10.3390/antibiotics11010040] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/12/2022] Open
Abstract
Antibiotics are the pivotal pillar of contemporary healthcare and have contributed towards its advancement over the decades. Antibiotic resistance emerged as a critical warning to public wellbeing because of unsuccessful management efforts. Resistance is a natural adaptive tool that offers selection pressure to bacteria, and hence cannot be stopped entirely but rather be slowed down. Antibiotic resistance mutations mostly diminish bacterial reproductive fitness in an environment without antibiotics; however, a fraction of resistant populations 'accidentally' emerge as the fittest and thrive in a specific environmental condition, thus favouring the origin of a successful resistant clone. Therefore, despite the time-to-time amendment of treatment regimens, antibiotic resistance has evolved relentlessly. According to the World Health Organization (WHO), we are rapidly approaching a 'post-antibiotic' era. The knowledge gap about antibiotic resistance and room for progress is evident and unified combating strategies to mitigate the inadvertent trends of resistance seem to be lacking. Hence, a comprehensive understanding of the genetic and evolutionary foundations of antibiotic resistance will be efficacious to implement policies to force-stop the emergence of resistant bacteria and treat already emerged ones. Prediction of possible evolutionary lineages of resistant bacteria could offer an unswerving impact in precision medicine. In this review, we will discuss the key molecular mechanisms of resistance development in clinical settings and their spontaneous evolution.
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Affiliation(s)
- Chowdhury M. Hasan
- School of Biological Sciences, University of Queensland, Brisbane 4072, Australia
- Department of Clinical Infection, Microbiology and Immunology, Institute of Infection, Veterinary & Ecological Sciences (IVES), University of Liverpool, Liverpool L7 3EA, UK;
- School of Biological Sciences, Monash University, Melbourne 3800, Australia;
- Correspondence:
| | - Debprasad Dutta
- Department of Clinical Infection, Microbiology and Immunology, Institute of Infection, Veterinary & Ecological Sciences (IVES), University of Liverpool, Liverpool L7 3EA, UK;
- Department of Human Genetics, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore 560029, India
| | - An N. T. Nguyen
- School of Biological Sciences, Monash University, Melbourne 3800, Australia;
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Abstract
Mutations conferring resistance to one antibiotic can increase (cross-resistance) or decrease (collateral sensitivity) resistance to others. Antibiotic combinations displaying collateral sensitivity could be used in treatments that slow resistance evolution. However, lab-to-clinic translation requires understanding whether collateral effects are robust across different environmental conditions. Here, we isolated and characterized resistant mutants of Escherichia coli using five antibiotics, before measuring collateral effects on resistance to other paired antibiotics. During both isolation and phenotyping, we varied conditions in ways relevant in nature (pH, temperature, and bile). This revealed that local abiotic conditions modified expression of resistance against both the antibiotic used during isolation and other antibiotics. Consequently, local conditions influenced collateral sensitivity in two ways: by favoring different sets of mutants (with different collateral sensitivities) and by modifying expression of collateral effects for individual mutants. These results place collateral sensitivity in the context of environmental variation, with important implications for translation to real-world applications. IMPORTANCE When bacteria become resistant to an antibiotic, the genetic changes involved sometimes increase (cross-resistance) or decrease (collateral sensitivity) their resistance to other antibiotics. Antibiotic combinations showing repeatable collateral sensitivity could be used in treatment to slow resistance evolution. However, collateral sensitivity interactions may depend on the local environmental conditions that bacteria experience, potentially reducing repeatability and clinical application. Here, we show that variation in local conditions (pH, temperature, and bile salts) can influence collateral sensitivity in two ways: by favoring different sets of mutants during bacterial resistance evolution (with different collateral sensitivities to other antibiotics) and by modifying expression of collateral effects for individual mutants. This suggests that translation from the lab to the clinic of new approaches exploiting collateral sensitivity will be influenced by local abiotic conditions.
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Ardell SM, Kryazhimskiy S. The population genetics of collateral resistance and sensitivity. eLife 2021; 10:73250. [PMID: 34889185 PMCID: PMC8765753 DOI: 10.7554/elife.73250] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 12/06/2021] [Indexed: 12/05/2022] Open
Abstract
Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments. Drugs known as antibiotics are the main treatment for most serious infections caused by bacteria. However, many bacteria are acquiring genetic mutations that make them resistant to the effects of one or more types of antibiotics, making them harder to eliminate. One way to tackle drug-resistant bacteria is to develop new types of antibiotics; however, in recent years, the rate at which new antibiotics have become available has been dwindling. Using two or more existing drugs, one after another, can also be an effective way to eliminate resistant bacteria. The success of any such ‘multi-drug’ treatment lies in being able to predict whether mutations that make the bacteria resistant to one drug simultaneously make it sensitive to another, a phenomenon known as collateral sensitivity. Different resistance mutations may have different collateral effects: some may increase the bacteria’s sensitivity to the second drug, while others might make the bacteria more resistant. However, it is currently unclear how to design robust multi-drug treatments that take this diversity of collateral effects into account. Here, Ardell and Kryazhimskiy used a concept called JDFE (short for the joint distribution of fitness effects) to describe the diversity of collateral effects in a population of bacteria exposed to a single drug. This information was then used to mathematically model how collateral effects evolved in the population over time. Ardell and Kryazhimskiy showed that this approach can predict how likely a population is to become collaterally sensitive or collaterally resistant to a second antibiotic. Drug pairs can then be ranked according to the risk of collateral resistance emerging, so long as information on the variety of resistance mutations available to the bacteria are included in the model. Each year, more than 700,000 people die from infections caused by bacteria that are resistant to one or more antibiotics. The findings of Ardell and Kryazhimskiy may eventually help clinicians design multi-drug treatments that effectively eliminate bacterial infections and help to prevent more bacteria from evolving resistance to antibiotics. However, to achieve this goal, more research is needed to fully understand the range collateral effects caused by resistance mutations.
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Affiliation(s)
- Sarah M Ardell
- Division of Biological Sciences, University of California, San Diego, La Jolla, United States
| | - Sergey Kryazhimskiy
- Division of Biological Sciences, University of California, San Diego, La Jolla, United States
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