1
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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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2
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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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3
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Ardell S, Martsul A, Johnson MS, Kryazhimskiy S. Environment-independent distribution of mutational effects emerges from microscopic epistasis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.18.567655. [PMID: 38014325 PMCID: PMC10680819 DOI: 10.1101/2023.11.18.567655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, where the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global-epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.
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Affiliation(s)
- Sarah Ardell
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Alena Martsul
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Milo S. Johnson
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA 94720
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
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4
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Freire TFA, Hu Z, Wood KB, Gjini E. Modeling spatial evolution of multi-drug resistance under drug environmental gradients. PLoS Comput Biol 2024; 20:e1012098. [PMID: 38820350 PMCID: PMC11142541 DOI: 10.1371/journal.pcbi.1012098] [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: 11/17/2023] [Accepted: 04/23/2024] [Indexed: 06/02/2024] Open
Abstract
Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.
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Affiliation(s)
- Tomas Ferreira Amaro Freire
- Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Zhijian Hu
- Departments of Biophysics and Physics, University of Michigan, United States of America
| | - Kevin B. Wood
- Departments of Biophysics and Physics, University of Michigan, United States of America
| | - Erida Gjini
- Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
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5
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Wong DPGH, Good BH. Quantifying the adaptive landscape of commensal gut bacteria using high-resolution lineage tracking. Nat Commun 2024; 15:1605. [PMID: 38383538 PMCID: PMC10881964 DOI: 10.1038/s41467-024-45792-0] [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: 09/21/2022] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
Gut microbiota can adapt to their host environment by rapidly acquiring new mutations. However, the dynamics of this process are difficult to characterize in dominant gut species in their complex in vivo environment. Here we show that the fine-scale dynamics of genome-wide transposon libraries can enable quantitative inferences of these in vivo evolutionary forces. By analyzing >400,000 lineages across four human Bacteroides strains in gnotobiotic mice, we observed positive selection on thousands of cryptic variants - most of which were unrelated to their original gene knockouts. The spectrum of fitness benefits varied between species, and displayed diverse tradeoffs over time and in different dietary conditions, enabling inferences of their underlying function. These results suggest that within-host adaptations arise from an intense competition between numerous contending variants, which can strongly influence their emergent evolutionary tradeoffs.
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Affiliation(s)
- Daniel P G H Wong
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA.
- Department of Biology, Stanford University, Stanford, CA, 94305, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, 94158, USA.
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6
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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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7
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Freire T, Hu Z, Wood KB, Gjini E. Modeling spatial evolution of multi-drug resistance under drug environmental gradients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.567447. [PMID: 38014279 PMCID: PMC10680811 DOI: 10.1101/2023.11.16.567447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.
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Affiliation(s)
- Tomas Freire
- Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Zhijian Hu
- Departments of Biophysics and Physics, University of Michigan, USA
| | - Kevin B. Wood
- Departments of Biophysics and Physics, University of Michigan, USA
| | - Erida Gjini
- Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
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8
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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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9
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Dapa T, Wong DP, Vasquez KS, Xavier KB, Huang KC, Good BH. Within-host evolution of the gut microbiome. Curr Opin Microbiol 2023; 71:102258. [PMID: 36608574 PMCID: PMC9993085 DOI: 10.1016/j.mib.2022.102258] [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: 11/06/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 01/06/2023]
Abstract
Gut bacteria inhabit a complex environment that is shaped by interactions with their host and the other members of the community. While these ecological interactions have evolved over millions of years, mounting evidence suggests that gut commensals can evolve on much shorter timescales as well, by acquiring new mutations within individual hosts. In this review, we highlight recent progress in understanding the causes and consequences of short-term evolution in the mammalian gut, from experimental evolution in murine hosts to longitudinal tracking of human cohorts. We also discuss new opportunities for future progress by expanding the repertoire of focal species, hosts, and surrounding communities, and by combining deep-sequencing technologies with quantitative frameworks from population genetics.
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Affiliation(s)
- Tanja Dapa
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
| | - Daniel Pgh Wong
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Kimberly S Vasquez
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Kerwyn Casey Huang
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
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10
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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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11
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Jagdish T, Nguyen Ba AN. Microbial experimental evolution in a massively multiplexed and high-throughput era. Curr Opin Genet Dev 2022; 75:101943. [PMID: 35752001 DOI: 10.1016/j.gde.2022.101943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022]
Abstract
Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.
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Affiliation(s)
- Tanush Jagdish
- Department of Molecular and Cellular Biology and The Program for Systems Synthetic and Quantitative Biology, Harvard University, Cambridge, United States.
| | - Alex N Nguyen Ba
- Department of Biology, University of Toronto at Mississauga, Mississauga, Canada; Department of Cell and Systems Biology, University of Toronto, Toronto, Canada.
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