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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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2
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Mira P, Guzman-Cole C, Meza JC. Understanding the effects of sub-inhibitory antibiotic concentrations on the development of β-lactamase resistance based on quantile regression analysis. J Appl Microbiol 2024; 135:lxae084. [PMID: 38544328 DOI: 10.1093/jambio/lxae084] [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: 10/08/2023] [Revised: 02/29/2024] [Accepted: 03/26/2024] [Indexed: 04/13/2024]
Abstract
AIMS Quantile regression is an alternate type of regression analysis that has been shown to have numerous advantages over standard linear regression. Unlike linear regression, which uses the mean to fit a linear model, quantile regression uses a data set's quantiles (or percentiles), which leads to a more comprehensive analysis of the data. However, while relatively common in other scientific fields such as economic and environmental modeling, it is infrequently used to understand biological and microbiological systems. METHODS AND RESULTS We analyzed a set of bacterial growth rates using quantile regression analysis to better understand the effects of antibiotics on bacterial fitness. Using a bacterial model system containing 16 variant genotypes of the TEM β-lactamase enzyme, we compared our quantile regression analysis to a previously published study that uses the Tukey's range test, or Tukey honestly significantly difference (HSD) test. We find that trends in the distribution of bacterial growth rate data, as viewed through the lens of quantile regression, can distinguish between novel genotypes and ones that have been clinically isolated from patients. Quantile regression also identified certain combinations of genotypes and antibiotics that resulted in bacterial populations growing faster as the antibiotic concentration increased-the opposite of what was expected. These analyses can provide new insights into the relationships between enzymatic efficacy and antibiotic concentration. CONCLUSIONS Quantile regression analysis enhances our understanding of the impacts of sublethal antibiotic concentrations on enzymatic (TEM β-lactamase) efficacy and bacterial fitness. We illustrate that quantile regression analysis can link patterns in growth rates with clinically relevant mutations and provides an understanding of how increasing sub-lethal antibiotic concentrations, like those found in our modern environment, can affect bacterial growth rates, and provide insight into the genetic basis for varied resistance.
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Affiliation(s)
- Portia Mira
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, 90095, United States
| | - Candace Guzman-Cole
- Department of Cell and Molecular Biology, University of California, Merced, 95343, United States
| | - Juan C Meza
- Department of Applied Mathematics, University of California, Merced, 95343, United States
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3
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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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4
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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'.
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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
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5
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Mira P, Lozano‐Huntelman N, Johnson A, Savage VM, Yeh P. Evolution of antibiotic resistance impacts optimal temperature and growth rate in
Escherichia coli
and
Staphylococcus epidermidis. J Appl Microbiol 2022; 133:2655-2667. [DOI: 10.1111/jam.15736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Portia Mira
- Department of Ecology and Evolutionary Biology University of California Los Angeles U.S.A
| | | | - Adrienne Johnson
- Department of Ecology and Evolutionary Biology University of California Los Angeles U.S.A
| | - Van M. Savage
- Department of Ecology and Evolutionary Biology University of California Los Angeles U.S.A
- Department of Computational Medicine, David Geffen School of Medicine University of California Los Angeles U.S.A
- Santa Fe Institute Santa Fe New Mexico U.S.A
| | - Pamela Yeh
- Department of Ecology and Evolutionary Biology University of California Los Angeles U.S.A
- Santa Fe Institute Santa Fe New Mexico U.S.A
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Baquero F, Martínez JL, Novais Â, Rodríguez-Beltrán J, Martínez-García L, Coque TM, Galán JC. Allogenous Selection of Mutational Collateral Resistance: Old Drugs Select for New Resistance Within Antibiotic Families. Front Microbiol 2021; 12:757833. [PMID: 34745065 PMCID: PMC8569428 DOI: 10.3389/fmicb.2021.757833] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/05/2021] [Indexed: 11/22/2022] Open
Abstract
Allogeneous selection occurs when an antibiotic selects for resistance to more advanced members of the same family. The mechanisms of allogenous selection are (a) collateral expansion, when the antibiotic expands the gene and gene-containing bacterial populations favoring the emergence of other mutations, inactivating the more advanced antibiotics; (b) collateral selection, when the old antibiotic selects its own resistance but also resistance to more modern drugs; (c) collateral hyper-resistance, when resistance to the old antibiotic selects in higher degree for populations resistant to other antibiotics of the family than to itself; and (d) collateral evolution, when the simultaneous or sequential use of antibiotics of the same family selects for new mutational combinations with novel phenotypes in this family, generally with higher activity (higher inactivation of the antibiotic substrates) or broader spectrum (more antibiotics of the family are inactivated). Note that in some cases, collateral selection derives from collateral evolution. In this article, examples of allogenous selection are provided for the major families of antibiotics. Improvements in minimal inhibitory concentrations with the newest drugs do not necessarily exclude “old” antibiotics of the same family of retaining some selective power for resistance to the newest agents. If this were true, the use of older members of the same drug family would facilitate the emergence of mutational resistance to the younger drugs of the family, which is frequently based on previously established resistance traits. The extensive use of old drugs (particularly in low-income countries and in farming) might be significant for the emergence and selection of resistance to the novel members of the family, becoming a growing source of variation and selection of resistance to the whole family. In terms of future research, it could be advisable to focus antimicrobial drug discovery more on the identification of new targets and new (unique) classes of antimicrobial agents, than on the perpetual chemical exploitation of classic existing ones.
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Affiliation(s)
- Fernando Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - José L Martínez
- Department of Microbial Biotechnology, National Center for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Ângela Novais
- UCIBIO - Applied Molecular Biosciences Unit, Laboratory of Microbiology, Department of Biological Sciences, REQUIMTE, Faculty of Pharmacy, University of Porto, Porto, Portugal.,Associate Laboratory i4HB - Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, Porto, Portugal
| | - Jerónimo Rodríguez-Beltrán
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Laura Martínez-García
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Teresa M Coque
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Juan Carlos Galán
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
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