1
|
Nyhoegen C, Bonhoeffer S, Uecker H. The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution. Evol Appl 2024; 17:e13764. [PMID: 39100751 PMCID: PMC11297101 DOI: 10.1111/eva.13764] [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: 11/19/2023] [Revised: 05/30/2024] [Accepted: 07/14/2024] [Indexed: 08/06/2024] Open
Abstract
In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs-those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence-Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio-even if not always optimal-is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.
Collapse
Affiliation(s)
- Christin Nyhoegen
- Research Group Stochastic Evolutionary Dynamics, Department of Theoretical BiologyMax Planck Institute for Evolutionary BiologyPlonGermany
| | - Sebastian Bonhoeffer
- Department of Environmental Systems Science, Institute of Integrative BiologyETH ZurichZurichSwitzerland
| | - Hildegard Uecker
- Research Group Stochastic Evolutionary Dynamics, Department of Theoretical BiologyMax Planck Institute for Evolutionary BiologyPlonGermany
| |
Collapse
|
2
|
Saha R, Vázquez-Salazar A, Nandy A, Chen IA. Fitness Landscapes and Evolution of Catalytic RNA. Annu Rev Biophys 2024; 53:109-125. [PMID: 39013026 DOI: 10.1146/annurev-biophys-030822-025038] [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: 07/18/2024]
Abstract
The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future.
Collapse
Affiliation(s)
- Ranajay Saha
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California, USA; ,
| | - Alberto Vázquez-Salazar
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California, USA; ,
| | - Aditya Nandy
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California, USA; ,
- Department of Chemistry, The University of Chicago, Chicago, Illinois, USA
- The James Franck Institute, The University of Chicago, Chicago, Illinois, USA
| | - Irene A Chen
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California, USA; ,
- Department of Chemistry and Biochemistry, University of California, Los Angeles, California, USA
| |
Collapse
|
3
|
Jiménez-Castellanos JC, Waclaw B, Meynert A, McAteer SP, Schneiders T. Rapid evolution of colistin resistance in a bioreactor model of infection of Klebsiella pneumoniae. Commun Biol 2024; 7:794. [PMID: 38951173 PMCID: PMC11217424 DOI: 10.1038/s42003-024-06378-0] [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: 03/16/2023] [Accepted: 05/23/2024] [Indexed: 07/03/2024] Open
Abstract
Colistin remains an important antibiotic for the therapeutic management of drug-resistant Klebsiella pneumoniae. Despite the numerous reports of colistin resistance in clinical strains, it remains unclear exactly when and how different mutational events arise resulting in reduced colistin susceptibility. Using a bioreactor model of infection, we modelled the emergence of colistin resistance in a susceptible isolate of K. pneumoniae. Genotypic, phenotypic and mathematical analyses of the antibiotic-challenged and un-challenged population indicates that after an initial decline, the population recovers within 24 h due to a small number of "founder cells" which have single point mutations mainly in the regulatory genes encoding crrB and pmrB that when mutated results in up to 100-fold reduction in colistin susceptibility. Our work underlines the rapid development of colistin resistance during treatment or exposure of susceptible K. pneumoniae infections having implications for the use of cationic antimicrobial peptides as a monotherapy.
Collapse
Affiliation(s)
- Juan-Carlos Jiménez-Castellanos
- Chemical Biology of Antibiotics, Centre for Infection & Immunity (CIIL), Pasteur Institute, INSERM U1019-CNRS UMR 9017, Lille, France
| | - Bartlomiej Waclaw
- School of Physics and Astronomy, The University of Edinburgh, JCMB, Edinburgh, UK.
- Dioscuri Centre for Physics and Chemistry of Bacteria, Institute of Physical Chemistry, Warsaw, Poland.
| | - Alison Meynert
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, The University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Sean P McAteer
- Department of Bacteriology, The Roslin Institute and R(D) SVS, The University of Edinburgh, Easter Bush Campus, Midlothian, Edinburgh, UK
| | - Thamarai Schneiders
- Centre for Inflammation Research, Institute of Regeneration and Repair, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK.
| |
Collapse
|
4
|
Hinz A, Amado A, Kassen R, Bank C, Wong A. Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli. Mol Biol Evol 2024; 41:msae086. [PMID: 38709811 PMCID: PMC11110942 DOI: 10.1093/molbev/msae086] [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: 11/08/2023] [Revised: 04/11/2024] [Accepted: 04/30/2024] [Indexed: 05/08/2024] Open
Abstract
The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.
Collapse
Affiliation(s)
- Aaron Hinz
- Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada
| | - André Amado
- Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
- Division of Theoretical Ecology and Evolution, Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Evolutionary Dynamics Group, Gulbenkian Science Institute, Oeiras, Portugal
| | - Rees Kassen
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada
| | - Claudia Bank
- Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
- Division of Theoretical Ecology and Evolution, Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Evolutionary Dynamics Group, Gulbenkian Science Institute, Oeiras, Portugal
| | - Alex Wong
- Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| |
Collapse
|
5
|
King ES, Stacy AE, Scott JG. A low-footprint, fluorescence-based bacterial time-kill assay for estimating dose-dependent cell death dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584154. [PMID: 38562844 PMCID: PMC10983867 DOI: 10.1101/2024.03.08.584154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Dose-response curves that describe the relationship between antibiotic dose and growth rate in bacteria are commonly measured with optical density (OD) based assays. While being simple and high-throughput, any dose-dependent cell death dynamics are obscured, as OD assays in batch culture can only quantify a positive net change in cells. Time-kill experiments can be used to quantify cell death rates, but current techniques are extremely resource-intensive and may be biased by residual drug carried over into the quantification assay. Here, we report a novel, fluorescence-based time-kill assay leveraging resazurin as a viable cell count indicator. Our method improves upon previous techniques by greatly reducing the material cost and being robust to residual drug carry-over. We demonstrate our technique by quantifying a dose-response curve in Escherichia coli subject to cefotaxime, revealing dose-dependent death rates. We also show that our method is robust to extracellular debris and cell aggregation. Dose-response curves quantified with our method may provide a more accurate description of pathogen response to therapy, paving the way for more accurate integrated pharmacodynamic-pharmacokinetic studies.
Collapse
Affiliation(s)
- Eshan S. King
- Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Anna E. Stacy
- 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 Physics, Case Western Reserve University, Cleveland, Ohio, United States of America
- Department of Translational Hematology and Oncology Research and Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, United States of America
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Papkou A, Garcia-Pastor L, Escudero JA, Wagner A. A rugged yet easily navigable fitness landscape. Science 2023; 382:eadh3860. [PMID: 37995212 DOI: 10.1126/science.adh3860] [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: 03/01/2023] [Accepted: 09/29/2023] [Indexed: 11/25/2023]
Abstract
Fitness landscape theory predicts that rugged landscapes with multiple peaks impair Darwinian evolution, but experimental evidence is limited. In this study, we used genome editing to map the fitness of >260,000 genotypes of the key metabolic enzyme dihydrofolate reductase in the presence of the antibiotic trimethoprim, which targets this enzyme. The resulting landscape is highly rugged and harbors 514 fitness peaks. However, its highest peaks are accessible to evolving populations via abundant fitness-increasing paths. Different peaks share large basins of attraction that render the outcome of adaptive evolution highly contingent on chance events. Our work shows that ruggedness need not be an obstacle to Darwinian evolution but can reduce its predictability. If true in general, the complexity of optimization problems on realistic landscapes may require reappraisal.
Collapse
Affiliation(s)
- Andrei Papkou
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Lucia Garcia-Pastor
- Departamento de Sanidad Animal and VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid, Spain
| | - José Antonio Escudero
- Departamento de Sanidad Animal and VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid, Spain
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- The Santa Fe Institute, Santa Fe, NM, USA
| |
Collapse
|
9
|
Kosterlitz O, Grassi N, Werner B, McGee RS, Top EM, Kerr B. Evolutionary "Crowdsourcing": Alignment of Fitness Landscapes Allows for Cross-species Adaptation of a Horizontally Transferred Gene. Mol Biol Evol 2023; 40:msad237. [PMID: 37931146 PMCID: PMC10657783 DOI: 10.1093/molbev/msad237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/15/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023] Open
Abstract
Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds. Despite the ubiquity of cross-species HGT, the effects of switching hosts on gene evolution remains understudied. Here, we present a framework to examine the evolutionary consequences of host-switching and apply this framework to an antibiotic resistance gene commonly found on conjugative plasmids. Specifically, we determined the adaptive landscape of this gene for a small set of mutationally connected genotypes in 3 enteric species. We uncovered that the landscape topographies were largely aligned with minimal host-dependent mutational effects. By simulating gene evolution over the experimentally gauged landscapes, we found that the adaptive evolution of the mobile gene in one species translated to adaptation in another. By simulating gene evolution over artificial landscapes, we found that sufficient alignment between landscapes ensures such "adaptive equivalency" across species. Thus, given adequate landscape alignment within a bacterial community, vehicles of HGT such as plasmids may enable a distributed form of genetic evolution across community members, where species can "crowdsource" adaptation.
Collapse
Affiliation(s)
- Olivia Kosterlitz
- Biology Department, University of Washington, Seattle, WA 98195, USA
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
| | - Nathan Grassi
- Biology Department, University of Washington, Seattle, WA 98195, USA
| | - Bailey Werner
- Biology Department, University of Washington, Seattle, WA 98195, USA
| | - Ryan Seamus McGee
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
- Department of Neuroscience, Washington University, St.Louis, MO 63110, USA
| | - Eva M Top
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
- Department of Biological Sciences and Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID 83844, USA
| | - Benjamin Kerr
- Biology Department, University of Washington, Seattle, WA 98195, USA
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
| |
Collapse
|
10
|
Wahl LM, Campos PRA. Evolutionary rescue on genotypic fitness landscapes. J R Soc Interface 2023; 20:20230424. [PMID: 37963553 PMCID: PMC10645506 DOI: 10.1098/rsif.2023.0424] [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: 07/25/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023] Open
Abstract
Populations facing adverse environments, novel pathogens or invasive competitors may be destined to extinction if they are unable to adapt rapidly. Quantitative predictions of the probability of survival through adaptation, evolutionary rescue, have been previously developed for one of the most natural and well-studied mappings from an organism's traits to its fitness, Fisher's geometric model (FGM). While FGM assumes that all possible trait values are accessible via mutation, in many applications only a finite set of rescue mutations will be available, such as mutations conferring resistance to a parasite, predator or toxin. We predict the probability of evolutionary rescue, via de novo mutation, when this underlying genetic structure is included. We find that rescue probability is always reduced when its genetic basis is taken into account. Unlike other known features of the genotypic FGM, however, the probability of rescue increases monotonically with the number of available mutations and approaches the behaviour of the classical FGM as the number of available mutations approaches infinity.
Collapse
Affiliation(s)
- L. M. Wahl
- Department of Mathematics, Western University, London, Ontario, Canada N6A 5B7
- Departamento de Física, Centro de Ciências Exatas e da Natureza, Universidade Federal de Pernambuco, Recife-PE 50670-901, Brazil
| | - Paulo R. A. Campos
- Departamento de Física, Centro de Ciências Exatas e da Natureza, Universidade Federal de Pernambuco, Recife-PE 50670-901, Brazil
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Cetin E, Guclu TF, Kantarcioglu I, Gaszek IK, Toprak E, Atilgan AR, Dedeoglu B, Atilgan C. Kinetic Barrier to Enzyme Inhibition Is Manipulated by Dynamical Local Interactions in E. coli DHFR. J Chem Inf Model 2023; 63:4839-4849. [PMID: 37491825 PMCID: PMC10428214 DOI: 10.1021/acs.jcim.3c00818] [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: 05/29/2023] [Indexed: 07/27/2023]
Abstract
Dihydrofolate reductase (DHFR) is an important drug target and a highly studied model protein for understanding enzyme dynamics. DHFR's crucial role in folate synthesis renders it an ideal candidate to understand protein function and protein evolution mechanisms. In this study, to understand how a newly proposed DHFR inhibitor, 4'-deoxy methyl trimethoprim (4'-DTMP), alters evolutionary trajectories, we studied interactions that lead to its superior performance over that of trimethoprim (TMP). To elucidate the inhibition mechanism of 4'-DTMP, we first confirmed, both computationally and experimentally, that the relative binding free energy cost for the mutation of TMP and 4'-DTMP is the same, pointing the origin of the characteristic differences to be kinetic rather than thermodynamic. We then employed an interaction-based analysis by focusing first on the active site and then on the whole enzyme. We confirmed that the polar modification in 4'-DTMP induces additional local interactions with the enzyme, particularly, the M20 loop. These changes are propagated to the whole enzyme as shifts in the hydrogen bond networks. To shed light on the allosteric interactions, we support our analysis with network-based community analysis and show that segmentation of the loop domain of inhibitor-bound DHFR must be avoided by a successful inhibitor.
Collapse
Affiliation(s)
- Ebru Cetin
- Faculty
of Engineering and Natural Sciences, Sabanci
University, Tuzla 34956, Istanbul, Turkey
| | - Tandac F. Guclu
- Faculty
of Engineering and Natural Sciences, Sabanci
University, Tuzla 34956, Istanbul, Turkey
| | - Isik Kantarcioglu
- Faculty
of Engineering and Natural Sciences, Sabanci
University, Tuzla 34956, Istanbul, Turkey
- Department
of Pharmacology, University of Texas Southwestern
Medical Center, Dallas 75390, Texas, United States
| | - Ilona K. Gaszek
- Department
of Pharmacology, University of Texas Southwestern
Medical Center, Dallas 75390, Texas, United States
| | - Erdal Toprak
- Department
of Pharmacology, University of Texas Southwestern
Medical Center, Dallas 75390, Texas, United States
| | - Ali Rana Atilgan
- Faculty
of Engineering and Natural Sciences, Sabanci
University, Tuzla 34956, Istanbul, Turkey
| | - Burcu Dedeoglu
- Department
of Chemistry, Gebze Technical University, Gebze 41400, Kocaeli, Turkey
| | - Canan Atilgan
- Faculty
of Engineering and Natural Sciences, Sabanci
University, Tuzla 34956, Istanbul, Turkey
| |
Collapse
|
13
|
Ghenu AH, Amado A, Gordo I, Bank C. Epistasis decreases with increasing antibiotic pressure but not temperature. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220058. [PMID: 37004727 PMCID: PMC10067269 DOI: 10.1098/rstb.2022.0058] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
Collapse
Affiliation(s)
- Ana-Hermina Ghenu
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - André Amado
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Isabel Gordo
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
| | - Claudia Bank
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| |
Collapse
|
14
|
Servajean R, Bitbol AF. Impact of population size on early adaptation in rugged fitness landscapes. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220045. [PMID: 37004726 PMCID: PMC10067268 DOI: 10.1098/rstb.2022.0045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Owing to stochastic fluctuations arising from finite population size, known as genetic drift, the ability of a population to explore a rugged fitness landscape depends on its size. In the weak mutation regime, while the mean steady-state fitness increases with population size, we find that the height of the first fitness peak encountered when starting from a random genotype displays various behaviours versus population size, even among small and simple rugged landscapes. We show that the accessibility of the different fitness peaks is key to determining whether this height overall increases or decreases with population size. Furthermore, there is often a finite population size that maximizes the height of the first fitness peak encountered when starting from a random genotype. This holds across various classes of model rugged landscapes with sparse peaks, and in some experimental and experimentally inspired ones. Thus, early adaptation in rugged fitness landscapes can be more efficient and predictable for relatively small population sizes than in the large-size limit. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
Collapse
Affiliation(s)
- Richard Servajean
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| |
Collapse
|
15
|
Farr AD, Pesce D, Das SG, Zwart MP, de Visser JAGM. The Fitness of Beta-Lactamase Mutants Depends Nonlinearly on Resistance Level at Sublethal Antibiotic Concentrations. mBio 2023:e0009823. [PMID: 37129484 DOI: 10.1128/mbio.00098-23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Abstract
Adaptive evolutionary processes are constrained by the availability of mutations which cause a fitness benefit and together make up the fitness landscape, which maps genotype space onto fitness under specified conditions. Experimentally derived fitness landscapes have demonstrated a predictability to evolution by identifying limited "mutational routes" that evolution by natural selection may take between low and high-fitness genotypes. However, such studies often utilize indirect measures to determine fitness. We estimated the competitive fitness of mutants relative to all single-mutation neighbors to describe the fitness landscape of three mutations in a β-lactamase enzyme. Fitness assays were performed at sublethal concentrations of the antibiotic cefotaxime in a structured and unstructured environment. In the unstructured environment, the antibiotic selected for higher-resistance types-but with an equivalent fitness for a subset of mutants, despite substantial variation in resistance-resulting in a stratified fitness landscape. In contrast, in a structured environment with a low antibiotic concentration, antibiotic-susceptible genotypes had a relative fitness advantage, which was associated with antibiotic-induced filamentation. These results cast doubt that highly resistant genotypes have a unique selective advantage in environments with subinhibitory concentrations of antibiotics and demonstrate that direct fitness measures are required for meaningful predictions of the accessibility of evolutionary routes. IMPORTANCE The evolution of antibiotic-resistant bacterial populations underpins the ongoing antibiotic resistance crisis. We aim to understand how antibiotic-degrading enzymes can evolve to cause increased resistance, how this process is constrained, and whether it can be predictable. To this end, competition experiments were performed with a combinatorially complete set of mutants of a β-lactamase gene subject to subinhibitory concentrations of the antibiotic cefotaxime. While some mutations confer on their hosts high resistance to cefotaxime, in competition these mutations do not always confer a selective advantage. Specifically, high-resistance mutants had equivalent fitnesses despite different resistance levels and even had selective disadvantages under conditions involving spatial structure. Together, our findings suggest that the relationship between resistance level and fitness at subinhibitory concentrations is complex; predicting the evolution of antibiotic resistance requires knowledge of the conditions that select for resistant genotypes and the selective advantage evolved types have over their predecessors.
Collapse
Affiliation(s)
- Andrew D Farr
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Diego Pesce
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
| | - Suman G Das
- Institute for Biological Physics, University of Cologne, Cologne, Germany
| | - Mark P Zwart
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - J Arjan G M de Visser
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
| |
Collapse
|
16
|
Crona K, Krug J, Srivastava M. Geometry of fitness landscapes: peaks, shapes and universal positive epistasis. J Math Biol 2023; 86:62. [PMID: 36976406 DOI: 10.1007/s00285-023-01889-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 03/29/2023]
Abstract
Darwinian evolution is driven by random mutations, genetic recombination (gene shuffling) and selection that favors genotypes with high fitness. For systems where each genotype can be represented as a bitstring of length L, an overview of possible evolutionary trajectories is provided by the L-cube graph with nodes labeled by genotypes and edges directed toward the genotype with higher fitness. Peaks (sinks in the graphs) are important since a population can get stranded at a suboptimal peak. The fitness landscape is defined by the fitness values of all genotypes in the system. Some notion of curvature is necessary for a more complete analysis of the landscapes, including the effect of recombination. The shape approach uses triangulations (shapes) induced by fitness landscapes. The main topic for this work is the interplay between peak patterns and shapes. Because of constraints on the shapes for [Formula: see text] imposed by peaks, there are in total 25 possible combinations of peak patterns and shapes. Similar constraints exist for higher L. Specifically, we show that the constraints induced by the staircase triangulation can be formulated as a condition of universal positive epistasis, an order relation on the fitness effects of arbitrary sets of mutations that respects the inclusion relation between the corresponding genetic backgrounds. We apply the concept to a large protein fitness landscape for an immunoglobulin-binding protein expressed in Streptococcal bacteria.
Collapse
Affiliation(s)
- Kristina Crona
- Department of Mathematics and Statistics, American University, Washington, DC, USA.
| | - Joachim Krug
- Institute for Biological Physics, University of Cologne, Cologne, Germany
| | - Malvika Srivastava
- Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| |
Collapse
|
17
|
Nyhoegen C, Uecker H. Sequential antibiotic therapy in the laboratory and in the patient. J R Soc Interface 2023; 20:20220793. [PMID: 36596451 PMCID: PMC9810433 DOI: 10.1098/rsif.2022.0793] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/30/2022] [Indexed: 01/05/2023] Open
Abstract
Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions in vivo differ from those in vitro. One key difference is that drugs can be switched abruptly in the laboratory, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug-drug interactions may affect the evolutionary dynamics. To address the gap between the laboratory and potential clinical applications, we set up two models for comparison-a 'laboratory model' and a pharmacokinetic-pharmacodynamic 'patient model'. The analysis shows that in the laboratory, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug-drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs.
Collapse
Affiliation(s)
- Christin Nyhoegen
- Department of Evolutionary Theory, Research Group Stochastic Evolutionary Dynamics, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Hildegard Uecker
- Department of Evolutionary Theory, Research Group Stochastic Evolutionary Dynamics, Max Planck Institute for Evolutionary Biology, Plön, Germany
| |
Collapse
|
18
|
Draghi JA, Ogbunugafor CB. Exploring the expanse between theoretical questions and experimental approaches in the modern study of evolvability. JOURNAL OF EXPERIMENTAL ZOOLOGY. PART B, MOLECULAR AND DEVELOPMENTAL EVOLUTION 2023; 340:8-17. [PMID: 35451559 PMCID: PMC10083935 DOI: 10.1002/jez.b.23134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/04/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022]
Abstract
Despite several decades of computational and experimental work across many systems, evolvability remains on the periphery with regards to its status as a widely accepted and regularly applied theoretical concept. Here we propose that its marginal status is partly a result of large gaps between the diverse but disconnected theoretical treatments of evolvability and the relatively narrower range of studies that have tested it empirically. To make this case, we draw on a range of examples-from experimental evolution in microbes, to molecular evolution in proteins-where attempts have been made to mend this disconnect. We highlight some examples of progress that has been made and point to areas where synthesis and translation of existing theory can lead to further progress in the still-new field of empirical measurements of evolvability.
Collapse
Affiliation(s)
- Jeremy A Draghi
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - C Brandon Ogbunugafor
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, Connecticut, USA
| |
Collapse
|
19
|
Srivastava M, Payne JL. On the incongruence of genotype-phenotype and fitness landscapes. PLoS Comput Biol 2022; 18:e1010524. [PMID: 36121840 PMCID: PMC9521842 DOI: 10.1371/journal.pcbi.1010524] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 09/29/2022] [Accepted: 08/30/2022] [Indexed: 11/22/2022] Open
Abstract
The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, implying that genotype-phenotype landscapes with only simple sign epistasis motifs will always give rise to single-peaked fitness landscapes under such selection. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation. How do mutations change phenotypic traits and organismal fitness? This question is often addressed in the context of a classic metaphor of evolutionary theory—the fitness landscape. A fitness landscape is akin to a physical landscape, in which genotypes define spatial coordinates, and fitness defines the elevation of each coordinate. Evolution then acts like a hill-climbing process, in which populations ascend fitness peaks as a consequence of mutation and selection. It is becoming increasingly common to construct such landscapes using experimental data from high-throughput sequencing technologies and phenotypic assays, in systems such as macromolecules and gene regulatory circuits. Although these landscapes are typically defined by molecular phenotypes, and are therefore more appropriately referred to as genotype-phenotype landscapes, they are often used to study evolutionary dynamics. This requires the assumption that the molecular phenotype is a reasonable proxy for fitness, which need not be the case. For example, selection may favor a low or intermediate phenotypic value, causing incongruence between a fitness landscape and its underlying genotype-phenotype landscape. Here, we study such incongruence using a diversity of theoretical models and experimental data from gene regulatory systems. We regularly find incongruence, in that fitness landscapes tend to comprise more peaks than their underlying genotype-phenotype landscapes. However, using evolutionary simulations, we show that this increased ruggedness need not impede adaptation.
Collapse
Affiliation(s)
- Malvika Srivastava
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joshua L. Payne
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- * E-mail:
| |
Collapse
|
20
|
Hemez C, Clarelli F, Palmer AC, Bleis C, Abel S, Chindelevitch L, Cohen T, Abel zur Wiesch P. Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations. Comput Struct Biotechnol J 2022; 20:4688-4703. [PMID: 36147681 PMCID: PMC9463365 DOI: 10.1016/j.csbj.2022.08.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 11/15/2022] Open
Abstract
Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.
Collapse
Affiliation(s)
- Colin Hemez
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Graduate Program in Biophysics, Harvard University, Boston, MA 02115, USA
- Corresponding authors at: Broad Institute, 75 Ames St, Room 3035, Cambridge, MA 02412, USA (C. Hemez). Department of Pharmacy, UiT – The Arctic University of Norway, 9019 Tromsø, Norway (P. Abel zur Wiesch).
| | - Fabrizio Clarelli
- Department of Pharmacy, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Adam C. Palmer
- Department of Pharmacology, Computational Medicine Program, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christina Bleis
- Department of Pharmacy, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Sören Abel
- Department of Pharmacy, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
- Division of Infection Control, Norwegian Institute of Public Health, Oslo 0318, Norway
| | - Leonid Chindelevitch
- Department of Infectious Disease Epidemiology, Imperial College, London SW7 2AZ, UK
| | - Theodore Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06520, USA
| | - Pia Abel zur Wiesch
- Department of Pharmacy, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
- Division of Infection Control, Norwegian Institute of Public Health, Oslo 0318, Norway
- Corresponding authors at: Broad Institute, 75 Ames St, Room 3035, Cambridge, MA 02412, USA (C. Hemez). Department of Pharmacy, UiT – The Arctic University of Norway, 9019 Tromsø, Norway (P. Abel zur Wiesch).
| |
Collapse
|
21
|
Trubenová B, Roizman D, Rolff J, Regoes RR. Modeling Polygenic Antibiotic Resistance Evolution in Biofilms. Front Microbiol 2022; 13:916035. [PMID: 35875522 PMCID: PMC9301000 DOI: 10.3389/fmicb.2022.916035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
The recalcitrance of biofilms to antimicrobials is a multi-factorial phenomenon, including genetic, physical, and physiological changes. Individually, they often cannot account for biofilm recalcitrance. However, their combination can increase the minimal inhibitory concentration of antibiotics needed to kill bacterial cells by three orders of magnitude, explaining bacterial survival under otherwise lethal drug treatment. The relative contributions of these factors depend on the specific antibiotics, bacterial strain, as well as environmental and growth conditions. An emerging population genetic property—increased biofilm genetic diversity—further enhances biofilm recalcitrance. Here, we develop a polygenic model of biofilm recalcitrance accounting for multiple phenotypic mechanisms proposed to explain biofilm recalcitrance. The model can be used to generate predictions about the emergence of resistance—its timing and population genetic consequences. We use the model to simulate various treatments and experimental setups. Our simulations predict that the evolution of resistance is impaired in biofilms at low antimicrobial concentrations while it is facilitated at higher concentrations. In scenarios that allow bacteria exchange between planktonic and biofilm compartments, the evolution of resistance is further facilitated compared to scenarios without exchange. We compare these predictions to published experimental observations.
Collapse
Affiliation(s)
- Barbora Trubenová
- Institute of Integrative Biology, ETH Zürich, Zurich, Switzerland
- *Correspondence: Barbora Trubenová
| | - Dan Roizman
- Institute of Biology – Evolutionary Biology, Freie Universität Berlin, Berlin, Germany
| | - Jens Rolff
- Institute of Biology – Evolutionary Biology, Freie Universität Berlin, Berlin, Germany
| | - Roland R. Regoes
- Institute of Integrative Biology, ETH Zürich, Zurich, Switzerland
| |
Collapse
|
22
|
Yang CH, Scarpino SV. A Family of Fitness Landscapes Modeled through Gene Regulatory Networks. ENTROPY (BASEL, SWITZERLAND) 2022; 24:622. [PMID: 35626507 PMCID: PMC9141513 DOI: 10.3390/e24050622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/11/2022] [Accepted: 04/26/2022] [Indexed: 02/01/2023]
Abstract
Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and "ruggedness". As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population's evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies.
Collapse
Affiliation(s)
- Chia-Hung Yang
- Network Science Institute, Northeastern University, Boston, MA 02115, USA
| | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, MA 02115, USA
- Physics Department, Northeastern University, Boston, MA 02115, USA
- Roux Institute, Northeastern University, Boston, MA 02115, USA
- Institute for Experiential AI, Northeastern University, Boston, MA 02115, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
| |
Collapse
|
23
|
Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape. Nat Ecol Evol 2022; 6:427-438. [PMID: 35210579 DOI: 10.1038/s41559-022-01675-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022]
Abstract
Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC50) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.
Collapse
|
24
|
Trubenová B, Roizman D, Moter A, Rolff J, Regoes RR. Population genetics, biofilm recalcitrance, and antibiotic resistance evolution. Trends Microbiol 2022; 30:841-852. [PMID: 35337697 DOI: 10.1016/j.tim.2022.02.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/11/2022]
Abstract
Biofilms are communities of bacteria forming high-density sessile colonies. Such a lifestyle comes associated with costs and benefits: while the growth rate of biofilms is often lower than that of their free-living counterparts, this cost is readily repaid once the colony is subjected to antibiotics. Biofilms can grow in antibiotic concentrations a thousand times higher than planktonic bacteria. While numerous mechanisms have been proposed to explain biofilm recalcitrance towards antibiotics, little is yet known about their effect on the evolution of resistance. We synthesize the current understanding of biofilm recalcitrance from a pharmacodynamic and a population genetics perspective. Using the pharmacodynamic framework, we discuss the effects of various mechanisms and show that biofilms can either promote or impede resistance evolution.
Collapse
Affiliation(s)
| | - Dan Roizman
- Institute of Biology, Evolutionary Biology, Freie Universität Berlin, Germany
| | - Annette Moter
- Charité, Universitätsmedizin Berlin Biofilmcenter, Berlin, Germany
| | - Jens Rolff
- Institute of Biology, Evolutionary Biology, Freie Universität Berlin, Germany
| | | |
Collapse
|
25
|
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: 8] [Impact Index Per Article: 2.7] [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.
Collapse
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
| |
Collapse
|
26
|
de Visser JAGM. Genotype-phenotype maps and the predictability of evolution: Comment on "From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics" by Susanna Manrubia et al. Phys Life Rev 2021; 39:79-81. [PMID: 34462227 DOI: 10.1016/j.plrev.2021.08.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 08/19/2021] [Indexed: 10/20/2022]
|
27
|
Gjini E, Wood KB. Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance. eLife 2021; 10:e64851. [PMID: 34289932 PMCID: PMC8331190 DOI: 10.7554/elife.64851] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 07/08/2021] [Indexed: 01/03/2023] Open
Abstract
Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.
Collapse
Affiliation(s)
- Erida Gjini
- Center for Computational and Stochastic Mathematics, Instituto Superior Tecnico, University of Lisbon, PortugalLisbonPortugal
| | - Kevin B Wood
- Departments of Biophysics and Physics, University of MichiganAnn ArborUnited States
| |
Collapse
|
28
|
Routh S, Acharyya A, Dhar R. A two-step PCR assembly for construction of gene variants across large mutational distances. Biol Methods Protoc 2021; 6:bpab007. [PMID: 33928191 PMCID: PMC8062255 DOI: 10.1093/biomethods/bpab007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/09/2021] [Accepted: 04/01/2021] [Indexed: 11/14/2022] Open
Abstract
Construction of empirical fitness landscapes has transformed our understanding of genotype–phenotype relationships across genes. However, most empirical fitness landscapes have been constrained to the local genotype neighbourhood of a gene primarily due to our limited ability to systematically construct genotypes that differ by a large number of mutations. Although a few methods have been proposed in the literature, these techniques are complex owing to several steps of construction or contain a large number of amplification cycles that increase chances of non-specific mutations. A few other described methods require amplification of the whole vector, thereby increasing the chances of vector backbone mutations that can have unintended consequences for study of fitness landscapes. Thus, this has substantially constrained us from traversing large mutational distances in the genotype network, thereby limiting our understanding of the interactions between multiple mutations and the role these interactions play in evolution of novel phenotypes. In the current work, we present a simple but powerful approach that allows us to systematically and accurately construct gene variants at large mutational distances. Our approach relies on building-up small fragments containing targeted mutations in the first step followed by assembly of these fragments into the complete gene fragment by polymerase chain reaction (PCR). We demonstrate the utility of our approach by constructing variants that differ by up to 11 mutations in a model gene. Our work thus provides an accurate method for construction of multi-mutant variants of genes and therefore will transform the studies of empirical fitness landscapes by enabling exploration of genotypes that are far away from a starting genotype.
Collapse
Affiliation(s)
- Shreya Routh
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Anamika Acharyya
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Riddhiman Dhar
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
- Correspondence address. Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India. Tel. +91-3222-304562; E-mail:
| |
Collapse
|
29
|
Kryazhimskiy S. Emergence and propagation of epistasis in metabolic networks. eLife 2021; 10:e60200. [PMID: 33527897 PMCID: PMC7924954 DOI: 10.7554/elife.60200] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.
Collapse
Affiliation(s)
- Sergey Kryazhimskiy
- Division of Biological Sciences, University of California, San DiegoLa JollaUnited States
| |
Collapse
|
30
|
Das SG, Direito SOL, Waclaw B, Allen RJ, Krug J. Predictable properties of fitness landscapes induced by adaptational tradeoffs. eLife 2020; 9:e55155. [PMID: 32423531 PMCID: PMC7297540 DOI: 10.7554/elife.55155] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/05/2020] [Indexed: 02/06/2023] Open
Abstract
Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations. Our model generates a succession of landscapes with predictable properties as antibiotic concentration is varied. The landscape is nearly smooth at low and high concentrations, but the tradeoff induces a high ruggedness at intermediate antibiotic concentrations. Despite this high ruggedness, however, all the fitness maxima in the landscapes are evolutionarily accessible from the wild type. This implies that selection for antibiotic resistance in multiple mutational steps is relatively facile despite the complexity of the underlying landscape.
Collapse
Affiliation(s)
- Suman G Das
- Institute for Biological Physics, University of CologneCologneGermany
| | - Susana OL Direito
- School of Physics and Astronomy, University of EdinburghEdinburghUnited Kingdom
| | - Bartlomiej Waclaw
- School of Physics and Astronomy, University of EdinburghEdinburghUnited Kingdom
| | - Rosalind J Allen
- School of Physics and Astronomy, University of EdinburghEdinburghUnited Kingdom
| | - Joachim Krug
- Institute for Biological Physics, University of CologneCologneGermany
| |
Collapse
|