1
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Lozano-Huntelman NA, Cook E, Bullivant A, Ida N, Zhou A, Boyd S, Yeh PJ. Interactions within higher-order antibiotic combinations do not influence the rate of adaptation in bacteria. Evolution 2025; 79:875-882. [PMID: 39918979 PMCID: PMC12081359 DOI: 10.1093/evolut/qpaf023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 12/20/2024] [Accepted: 02/04/2025] [Indexed: 02/09/2025]
Abstract
The prevalence and strength of antibiotic resistance has led to an ongoing battle between the development of new treatments and the evolution of resistance. Combining multiple drugs simultaneously is a potential solution for combating antibiotic resistance. However, this approach introduces new factors that must be considered, including the influence of drug interactions on the rate of resistance evolution. When antibiotics are used in combination, their effects can be additive, synergistic, or antagonistic. In this study, we investigated the effect of higher-order interactions involving 3 drugs on resistance evolution in Staphylococcus epidermidis. Previous studies have shown that synergistic interactions can increase the adaptation rate. However, the effects of higher-order interactions on rates of adaptation are unclear. We investigated the adaptation of Staphylococcus epidermidis to single-, 2-, and 3-drug environments to assess how interactions within drug combinations influence the rate of adaptation. We analyzed both the overall interaction and emergent interaction, the latter being a unique interaction that occurs in 3-drug combinations due to the presence of all three drugs, rather than simply strong pairwise interactions. Our results show that neither the overall interactions nor the emergent interactions affect adaptation rates.
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Affiliation(s)
- Natalie Ann Lozano-Huntelman
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emoni Cook
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Austin Bullivant
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nick Ida
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States
| | - April Zhou
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sada Boyd
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Pamela J Yeh
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States
- Santa Fe Institute, Santa Fe, NM, United States
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2
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Bognár B, Spohn R, Lázár V. Drug combinations targeting antibiotic resistance. NPJ ANTIMICROBIALS AND RESISTANCE 2024; 2:29. [PMID: 39843924 PMCID: PMC11721080 DOI: 10.1038/s44259-024-00047-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 09/02/2024] [Indexed: 01/24/2025]
Abstract
While the rise of antibiotic resistance poses a global health challenge, the development of new antibiotics has slowed down over the past decades. This turned the attention of researchers towards the rational design of drug combination therapies to combat antibiotic resistance. In this review we discuss how drug combinations can exploit the deleterious pleiotropic effects of antibiotic resistance and conclude that each drug interaction has its prospective therapeutic application.
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Affiliation(s)
- Bence Bognár
- HCEMM-BRC Pharmacodynamic Drug Interaction Research Group, Szeged, Hungary
- Synthetic and Systems Biology Unit, Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Réka Spohn
- Synthetic and Systems Biology Unit, Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Viktória Lázár
- HCEMM-BRC Pharmacodynamic Drug Interaction Research Group, Szeged, Hungary.
- Synthetic and Systems Biology Unit, Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary.
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3
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Orr JA, Macaulay SJ, Mordente A, Burgess B, Albini D, Hunn JG, Restrepo-Sulez K, Wilson R, Schechner A, Robertson AM, Lee B, Stuparyk BR, Singh D, O'Loughlin I, Piggott JJ, Zhu J, Dinh KV, Archer LC, Penk M, Vu MTT, Juvigny-Khenafou NPD, Zhang P, Sanders P, Schäfer RB, Vinebrooke RD, Hilt S, Reed T, Jackson MC. Studying interactions among anthropogenic stressors in freshwater ecosystems: A systematic review of 2396 multiple-stressor experiments. Ecol Lett 2024; 27:e14463. [PMID: 38924275 DOI: 10.1111/ele.14463] [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: 01/18/2024] [Revised: 05/26/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024]
Abstract
Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple-stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co-occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open-source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple-stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.
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Affiliation(s)
- James A Orr
- Department of Biology, University of Oxford, Oxford, UK
- School of the Environment, University of Queensland, Brisbane, Queensland, Australia
| | | | | | - Benjamin Burgess
- Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Dania Albini
- Department of Biology, University of Oxford, Oxford, UK
| | - Julia G Hunn
- Department of Zoology, University of Otago, Dunedin, New Zealand
| | | | - Ramesh Wilson
- Department of Biology, University of Oxford, Oxford, UK
| | - Anne Schechner
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
- Ruumi ApS, Svendborg, Denmark
| | - Aoife M Robertson
- Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland
| | - Bethany Lee
- Department of Biology, University of Oxford, Oxford, UK
| | - Blake R Stuparyk
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Delezia Singh
- Natural Resources Institute, University of Manitoba, Winnipeg, Canada
| | | | - Jeremy J Piggott
- Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland
| | - Jiangqiu Zhu
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Khuong V Dinh
- Section for Aquatic Biology and Toxicology, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Louise C Archer
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - Marcin Penk
- Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Minh Thi Thuy Vu
- Section for Aquatic Biology and Toxicology, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Noël P D Juvigny-Khenafou
- Institute of Aquaculture, University of Stirling, Scotland, UK
- Institute of Environmental Sciences, RPTU Kaiserslautern-Landau, Germany
| | - Peiyu Zhang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | | | - Ralf B Schäfer
- Research Center One Health Ruhr, University Alliance Ruhr
- Faculty of Biology, University Duisburg-Essen, Essen, Germany
| | - Rolf D Vinebrooke
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Sabine Hilt
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Thomas Reed
- School of Biological, Earth & Environmental Sciences, University College Cork, Cork, Ireland
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4
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Lyons MA, Obregon-Henao A, Ramey ME, Bauman AA, Pauly S, Rossmassler K, Reid J, Karger B, Walter ND, Robertson GT. Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis. Antimicrob Agents Chemother 2024; 68:e0101023. [PMID: 38501805 PMCID: PMC11064538 DOI: 10.1128/aac.01010-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen [bedaquiline-pretomanid-linezolid (BPaL)] during the first 3 weeks of treatment at human equivalent doses. The rRNA synthesis (RS) ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, together with solid culture CFU counts and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time-course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.
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Affiliation(s)
- M. A. Lyons
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - A. Obregon-Henao
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - M. E. Ramey
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - A. A. Bauman
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - S. Pauly
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - K. Rossmassler
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - J. Reid
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - B. Karger
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - N. D. Walter
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Consortium for Applied Microbial Metrics, Aurora, Colorado, USA
- Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
| | - G. T. Robertson
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
- Consortium for Applied Microbial Metrics, Aurora, Colorado, USA
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5
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Lyons MA, Obregon-Henao A, Ramey ME, Bauman AA, Pauly S, Rossmassler K, Reid J, Karger B, Walter ND, Robertson GT. Use of Multiple Pharmacodynamic Measures to Deconstruct the Nix-TB Regimen in a Short-Course Murine Model of Tuberculosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.08.566205. [PMID: 37986955 PMCID: PMC10659381 DOI: 10.1101/2023.11.08.566205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen (bedaquiline-pretomanid-linezolid [BPaL]) during the first three weeks of treatment at human equivalent doses. RS ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, to-gether with solid culture CFU and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.
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6
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Diamant ES, Boyd S, Lozano-Huntelman NA, Enriquez V, Kim AR, Savage VM, Yeh PJ. Meta-analysis of three-stressor combinations on population-level fitness reveal substantial higher-order interactions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 864:161163. [PMID: 36572303 DOI: 10.1016/j.scitotenv.2022.161163] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Although natural populations are typically subjected to multiple stressors, most past research has focused on single-stressor and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 142 ecological three-stressor interactions on species' populations from 38 published papers; the vast majority of these studies were from the past 10 years. We found that 95.8 % (n = 136) of the three-stressor combinations had either not been categorized before or resulted in different interactions than previously reported. We also found substantial levels of emergent properties-interactions that are not due to strong pairwise interactions within the combination but rather uniquely due to all three stressors being combined. Calculating net interactions-the overall accounting for all possible interactions within a combination including the emergent and all pairwise interactions-we found that the most prevalent interaction type is antagonism, corresponding to a smaller than expected effect based on single stressor effects. In contrast, for emergent interactions, the most prevalent interaction type is synergistic, resulting in a larger than expected effect based on single stressor effects. Additionally, we found that hidden suppressive interactions-where a pairwise interaction is suppressed by a third stressor-are found in the majority of combinations (74 %). Collectively, understanding multiple stressor interactions through applying an appropriate framework is crucial for answering fundamental questions in ecology and has implications for conservation biology and population management. Crucially, identifying emergent properties can reveal hidden suppressive interactions that could be particularly important for the ecological management of at-risk populations.
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Affiliation(s)
- Eleanor S Diamant
- Ecology and Evolutionary Biology, University of California, Los Angeles, USA
| | - Sada Boyd
- Ecology and Evolutionary Biology, University of California, Los Angeles, USA
| | | | - Vivien Enriquez
- Ecology and Evolutionary Biology, University of California, Los Angeles, USA
| | - Alexis R Kim
- Ecology and Evolutionary Biology, University of California, Los Angeles, USA
| | - Van M Savage
- Ecology and Evolutionary Biology, University of California, Los Angeles, USA; Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, USA; Santa Fe Institute, Santa Fe, NM, USA
| | - Pamela J Yeh
- Ecology and Evolutionary Biology, University of California, Los Angeles, USA; Santa Fe Institute, Santa Fe, NM, USA.
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7
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Anderson E, Nair B, Nizet V, Kumar G. Man vs Microbes - The Race of the Century. J Med Microbiol 2023; 72. [PMID: 36748622 DOI: 10.1099/jmm.0.001646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The complexity of the antimicrobial resistance (AMR) crisis and its global impact on healthcare invokes an urgent need to understand the underlying forces and to conceive and implement innovative solutions. Beyond focusing on a traditional pathogen-centric approach to antibiotic discovery yielding diminishing returns, future therapeutic interventions can expand to focus more comprehensively on host-pathogen interactions. In this manner, increasing the resiliency of our innate immune system or attenuating the virulence mechanisms of the pathogens can be explored to improve therapeutic outcomes. Key pathogen survival strategies such as tolerance, persistence, aggregation, and biofilm formation can be considered and interrupted to sensitize pathogens for more efficient immune clearance. Understanding the evolution and emergence of so-called 'super clones' that drive AMR spread with rapid clonotyping assays may guide more precise antibiotic regimens. Innovative alternatives to classical antibiotics such as bacteriophage therapy, novel engineered peptide antibiotics, ionophores, nanomedicines, and repurposing drugs from other domains of medicine to boost innate immunity are beginning to be successfully implemented to combat AMR. Policy changes supporting shorter durations of antibiotic treatment, greater antibiotic stewardship, and increased surveillance measures can enhance patient safety and enable implementation of the next generation of targeted prevention and control programmes at a global level.
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Affiliation(s)
- Ericka Anderson
- Collaborative to Halt Antibiotic Resistant Microbes (CHARM), Department of Pediatrics University of California San Diego, La Jolla, CA, USA
| | - Bipin Nair
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
| | - Victor Nizet
- Collaborative to Halt Antibiotic Resistant Microbes (CHARM), Department of Pediatrics University of California San Diego, La Jolla, CA, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego, La Jolla, CA, USA
| | - Geetha Kumar
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
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8
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Antibiotic combinations reduce Staphylococcus aureus clearance. Nature 2022; 610:540-546. [PMID: 36198788 PMCID: PMC9533972 DOI: 10.1038/s41586-022-05260-5] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 08/22/2022] [Indexed: 12/17/2022]
Abstract
The spread of antibiotic resistance is attracting increased attention to combination-based treatments. Although drug combinations have been studied extensively for their effects on bacterial growth1–11, much less is known about their effects on bacterial long-term clearance, especially at cidal, clinically relevant concentrations12–14. Here, using en masse microplating and automated image analysis, we systematically quantify Staphylococcus aureus survival during prolonged exposure to pairwise and higher-order cidal drug combinations. By quantifying growth inhibition, early killing and longer-term population clearance by all pairs of 14 antibiotics, we find that clearance interactions are qualitatively different, often showing reciprocal suppression whereby the efficacy of the drug mixture is weaker than any of the individual drugs alone. Furthermore, in contrast to growth inhibition6–10 and early killing, clearance efficacy decreases rather than increases as more drugs are added. However, specific drugs targeting non-growing persisters15–17 circumvent these suppressive effects. Competition experiments show that reciprocal suppressive drug combinations select against resistance to any of the individual drugs, even counteracting methicillin-resistant Staphylococcus aureus both in vitro and in a Galleria mellonella larva model. As a consequence, adding a β-lactamase inhibitor that is commonly used to potentiate treatment against β-lactam-resistant strains can reduce rather than increase treatment efficacy. Together, these results underscore the importance of systematic mapping the long-term clearance efficacy of drug combinations for designing more-effective, resistance-proof multidrug regimes. Different pairs of antibiotics show qualitatively different bacterial clearance interactions—some pairs show reciprocal suppression whereby the drug mixture efficacy is weaker than the individual drugs alone, and the clearance efficacy decreases as more drugs are added.
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9
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Brennan J, Jain L, Garman S, Donnelly AE, Wright ES, Jamieson K. Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. PLoS Comput Biol 2022; 18:e1010311. [PMID: 35849634 PMCID: PMC9333450 DOI: 10.1371/journal.pcbi.1010311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 07/28/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022] Open
Abstract
Antibiotic resistance is an important public health problem. One potential solution is the development of synergistic antibiotic combinations, in which the combination is more effective than the component drugs. However, experimental progress in this direction is severely limited by the number of samples required to exhaustively test for synergy, which grows exponentially with the number of drugs combined. We introduce a new metric for antibiotic synergy, motivated by the popular Fractional Inhibitory Concentration Index and the Highest Single Agent model. We also propose a new experimental design that samples along all appropriately normalized diagonals in concentration space, and prove that this design identifies all synergies among a set of drugs while only sampling a small fraction of the possible combinations. We applied our method to screen two- through eight-way combinations of eight antibiotics at 10 concentrations each, which requires sampling only 2,560 unique combinations of antibiotic concentrations. Antibiotic resistance is a growing public health concern, and there is an increasing need for methods to combat it. One potential approach is the development of synergistic antibiotic combinations, in which a mixture of drugs is more effective than any individual component. Unfortunately, the search for clinically beneficial drug combinations is severely restricted by the pace at which drugs can be screened. To date, most studies of combination therapies have been limited to testing only pairs or triples of drugs. These studies have identified primarily antagonistic drug interactions, in which the combination is less effective than the individual components. There is an acute need for methodologies that enable screening of higher-order drug combinations, both to identify synergies among many drugs and to understand the behavior of higher-order combinations. In this work we introduce a new paradigm for combination testing, the normalized diagonal sampling design, that makes identifying interactions among eight or more drugs feasible for the first time. Screening d drugs at m different combinations requires m ⋅ 2d samples under our design as opposed to md under exhaustive screening, while provably identifying all synergies under mild assumptions about antibiotic behavior. Scientists can use our design to quickly screen for antibiotic interactions, accelerating the pace of combination therapy development.
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Affiliation(s)
- Jennifer Brennan
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, United States of America
| | - Lalit Jain
- Foster School of Business, University of Washington, Seattle, Washington, United States of America
| | - Sofia Garman
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ann E. Donnelly
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Erik Scott Wright
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Pittsburgh Center for Evolutionary Biology and Medicine, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
| | - Kevin Jamieson
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, United States of America
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10
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Zeng W, Chen P, Li S, Sha Q, Li P, Zeng X, Feng X, Du W, Liu BF. Hand-powered vacuum-driven microfluidic gradient generator for high-throughput antimicrobial susceptibility testing. Biosens Bioelectron 2022; 205:114100. [PMID: 35219023 DOI: 10.1016/j.bios.2022.114100] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 02/05/2023]
Abstract
The growth of bacterial resistance to antimicrobials is a serious problem attracting much attention nowadays. To prevent the misuse and abuse of antimicrobials, it is important to carry out antibiotic susceptibility testing (AST) before clinical use. However, conventional AST methods are relatively laborious and time-consuming (18-24 h). Here, we present a hand-powered vacuum-driven microfluidic (HVM) device, in which a syringe is used as the only vacuum source for rapid generating concentration gradient of antibiotics in different chambers. The HVM device can be preassembled with various amounts of antibiotics, lyophilized, and stored for ready-to-use. Bacterial samples can be loaded into the HVM device through a simple suction step. With the assistance of Alamar Blue, the AST assay and the minimum inhibitory concentration (MIC) of different antibiotics can be investigated by comparing the growth results of bacteria in different culture chambers. In addition, a parallel HVM device was proposed, in which eight AST assays can be performed simultaneously. The results of MIC of three commonly used antibiotics against E. coli K-12 in our HVM device were consistent with those obtained by traditional method while the detection time was shortened to less than 8 h. We believe that our platform is high-throughput, cost-efficient, easy to use, and suitable for POCT applications.
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Affiliation(s)
- Wenyi Zeng
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Peng Chen
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Shunji Li
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Qiuyue Sha
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Pengjie Li
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xuemei Zeng
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xiaojun Feng
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wei Du
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Bi-Feng Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
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11
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Danner MC, Azams SO, Robertson A, Perkins D, Behrends V, Reiss J. It More than Adds Up: Interaction of Antibiotic Mixing and Temperature. Life (Basel) 2021; 11:life11121435. [PMID: 34947966 PMCID: PMC8703992 DOI: 10.3390/life11121435] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/10/2021] [Accepted: 12/12/2021] [Indexed: 11/16/2022] Open
Abstract
Use of antibiotics for the treatment and prevention of bacterial infections in humans, agri- and aquaculture as well as livestock rearing leads to antibiotic pollution of fresh water and these antibiotics have an impact on free-living bacteria. While we know which antibiotics are most common in natural environments such as rivers and streams, there is considerable uncertainty regarding antibiotics’ interactions with one another and the effect of abiotic factors such as temperature. Here, we used an experimental approach to explore the effects of antibiotic identity, concentration, mixing and water temperature on the growth of Pseudomonas fluorescens, a common, ubiquitous bacterium. We exposed P. fluorescens to the four antibiotics most commonly found in surface waters (ciprofloxacin, ofloxacin, sulfamethoxazole and sulfapyridine) and investigated antibiotic interactions for single and mixed treatments at different, field-realistic temperatures. We observed an overall dependence of antibiotic potency on temperature, as temperature increased efficacy of ciprofloxacin and ofloxacin with their EC50 lowered by >75% with a 10 °C temperature increase. Further, we show that mixtures of ciprofloxacin and ofloxacin, despite both belonging to the fluoroquinolone class, exhibit low-temperature-dependent synergistic effects in inhibiting bacterial growth. These findings highlight the context dependency of antibiotic efficacy. They further suggest antibiotic-specific off-target effects that only affect the bacteria once they enter a certain temperature range. This has important implications as freshwater systems already contain multi-drug antibiotic cocktails and are changing temperature due to environmental warming. These factors will interact and affect aquatic food webs, and hence this creates an urgent need to adapt and improve laboratory testing conditions to closer reflect natural environments.
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Affiliation(s)
- Marie-Claire Danner
- School of Life and Health Sciences, Whitelands College, University of Roehampton, London SW15 4JD, UK; (M.-C.D.); (S.O.A.); (A.R.); (D.P.); (V.B.)
- FRB—CESAB, Institut Bouisson Bertrand, 34070 Montpellier, France
| | - Sharon Omonor Azams
- School of Life and Health Sciences, Whitelands College, University of Roehampton, London SW15 4JD, UK; (M.-C.D.); (S.O.A.); (A.R.); (D.P.); (V.B.)
| | - Anne Robertson
- School of Life and Health Sciences, Whitelands College, University of Roehampton, London SW15 4JD, UK; (M.-C.D.); (S.O.A.); (A.R.); (D.P.); (V.B.)
| | - Daniel Perkins
- School of Life and Health Sciences, Whitelands College, University of Roehampton, London SW15 4JD, UK; (M.-C.D.); (S.O.A.); (A.R.); (D.P.); (V.B.)
| | - Volker Behrends
- School of Life and Health Sciences, Whitelands College, University of Roehampton, London SW15 4JD, UK; (M.-C.D.); (S.O.A.); (A.R.); (D.P.); (V.B.)
| | - Julia Reiss
- School of Life and Health Sciences, Whitelands College, University of Roehampton, London SW15 4JD, UK; (M.-C.D.); (S.O.A.); (A.R.); (D.P.); (V.B.)
- Correspondence:
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12
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Mann A, Nehra K, Rana J, Dahiya T. Antibiotic resistance in agriculture: Perspectives on upcoming strategies to overcome upsurge in resistance. CURRENT RESEARCH IN MICROBIAL SCIENCES 2021; 2:100030. [PMID: 34841321 PMCID: PMC8610298 DOI: 10.1016/j.crmicr.2021.100030] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/24/2021] [Accepted: 03/28/2021] [Indexed: 12/12/2022] Open
Abstract
Antibiotic resistance is a massive problem rising constantly and spreading rapidly since the past decade. The major underlying mechanism responsible for this problem is an overuse or severe misuse of antibiotics. Regardless of this emerging global threat, antibiotics are still being widely used, not only for treatment of human infections, but also to a great extent in agriculture, livestock and animal husbandry. If the current scenario persists, we might enter into a post-antibiotic era where drugs might not be able to treat even the simplest of infections. This review discusses the current status of antibiotic utilization and molecular basis of antibiotic resistance mechanisms acquired by bacteria, along with the modes of transmittance of the resultant resistant genes into human pathogens through their cycling among different ecosystems. The main focus of the article is to provide an insight into the different molecular and other strategies currently being studied worldwide for their use as an alternate to antibiotics with an overall aim to overcome or minimize the global problem of antibiotic resistance.
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13
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Lozano-Huntelman NA, Zhou A, Tekin E, Cruz-Loya M, Østman B, Boyd S, Savage VM, Yeh P. Hidden suppressive interactions are common in higher-order drug combinations. iScience 2021; 24:102355. [PMID: 33870144 PMCID: PMC8044428 DOI: 10.1016/j.isci.2021.102355] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 01/26/2021] [Accepted: 03/22/2021] [Indexed: 11/25/2022] Open
Abstract
The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments. Most instances of suppressive interactions are missed by standard methods A majority (54%) of all antibiotic combinations tested contain hidden suppression Identifying hidden suppression can affect what combinations should be used
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Affiliation(s)
| | - April Zhou
- Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA.,Computational and Systems Biology, University of California, Los Angeles, 90095, USA
| | - Elif Tekin
- Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA
| | - Mauricio Cruz-Loya
- Computational and Systems Biology, University of California, Los Angeles, 90095, USA
| | - Bjørn Østman
- Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA
| | - Sada Boyd
- Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA
| | - Van M Savage
- Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA.,Computational and Systems Biology, University of California, Los Angeles, 90095, USA.,Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Pamela Yeh
- Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA.,Santa Fe Institute, Santa Fe, NM 87501, USA
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14
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Tekin E, Diamant ES, Cruz‐Loya M, Enriquez V, Singh N, Savage VM, Yeh PJ. Using a newly introduced framework to measure ecological stressor interactions. Ecol Lett 2020; 23:1391-1403. [DOI: 10.1111/ele.13533] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 02/13/2020] [Accepted: 04/16/2020] [Indexed: 12/30/2022]
Affiliation(s)
- Elif Tekin
- Department of Ecology and Evolutionary Biology University of California Los Angeles CA90095USA
- Department of Computational Medicine the David Geffen School of Medicine University of California Los Angeles CA USA
| | - Eleanor S. Diamant
- Department of Ecology and Evolutionary Biology University of California Los Angeles CA90095USA
| | - Mauricio Cruz‐Loya
- Department of Computational Medicine the David Geffen School of Medicine University of California Los Angeles CA USA
| | - Vivien Enriquez
- Department of Ecology and Evolutionary Biology University of California Los Angeles CA90095USA
| | - Nina Singh
- Department of Ecology and Evolutionary Biology University of California Los Angeles CA90095USA
| | - Van M. Savage
- Department of Ecology and Evolutionary Biology University of California Los Angeles CA90095USA
- Department of Computational Medicine the David Geffen School of Medicine University of California Los Angeles CA USA
- Santa Fe Institute Santa Fe NM87501USA
| | - Pamela J. Yeh
- Department of Ecology and Evolutionary Biology University of California Los Angeles CA90095USA
- Santa Fe Institute Santa Fe NM87501USA
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15
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Yu T, Jiang G, Gao R, Chen G, Ren Y, Liu J, van der Mei HC, Busscher HJ. Circumventing antimicrobial-resistance and preventing its development in novel, bacterial infection-control strategies. Expert Opin Drug Deliv 2020; 17:1151-1164. [PMID: 32510243 DOI: 10.1080/17425247.2020.1779697] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Development of new antimicrobials with ever 'better' bacterial killing has long been considered the appropriate response to the growing threat of antimicrobial-resistant infections. However, the time-period between the introduction of a new antibiotic and the appearance of resistance amongst bacterial pathogens is getting shorter and shorter. This suggests that alternative pathways than making ever 'better' antimicrobials should be taken. AREAS COVERED This review aims to answer the questions (1) whether we have means to circumvent existing antibiotic-resistance mechanisms, (2) whether we can revert existing antibiotic-resistance, (3) how we can prevent the development of antimicrobial-resistance against novel infection-control strategies, including nano-antimicrobials. EXPERT OPINION Relying on relieving antibiotic-pressure and natural outcompeting of antimicrobial-resistant bacteria seems an uncertain way out of the antibiotic-crisis facing us. Novel, non-antibiotic, nanotechnology-based infection control-strategies are promising. At the same time, rapid development of new resistance mechanisms once novel strategies is taken into global clinical use, may not be ruled out and must be closely monitored. This suggests focusing research and development on designing suitable combinations of existing antibiotics with new nano-antimicrobials in a way that induction of new antimicrobial-resistance mechanisms is avoided. The latter suggestion, however, requires a change of focus in research and development.
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Affiliation(s)
- Tianrong Yu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University , Jiangsu, P. R. China.,Department of Biomedical Engineering, University of Groningen and University Medical Center , Groningen, The Netherlands
| | - Guimei Jiang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University , Jiangsu, P. R. China.,Department of Biomedical Engineering, University of Groningen and University Medical Center , Groningen, The Netherlands
| | - Ruifang Gao
- Department of Biomedical Engineering, University of Groningen and University Medical Center , Groningen, The Netherlands.,College of Chemistry, Chemical Engineering and Materials Science, Soochow University , Suzhou, P.R. China
| | - Gaojian Chen
- College of Chemistry, Chemical Engineering and Materials Science, Soochow University , Suzhou, P.R. China
| | - Yijin Ren
- Department of Orthodontics, University of Groningen and University Medical Center of Groningen , Groningen, The Netherlands
| | - Jian Liu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University , Jiangsu, P. R. China
| | - Henny C van der Mei
- Department of Biomedical Engineering, University of Groningen and University Medical Center , Groningen, The Netherlands
| | - Henk J Busscher
- Department of Biomedical Engineering, University of Groningen and University Medical Center , Groningen, The Netherlands
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16
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Aranda-Díaz A, Obadia B, Dodge R, Thomsen T, Hallberg ZF, Güvener ZT, Ludington WB, Huang KC. Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance. eLife 2020; 9:51493. [PMID: 31995029 PMCID: PMC7025823 DOI: 10.7554/elife.51493] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 01/28/2020] [Indexed: 12/11/2022] Open
Abstract
Predicting antibiotic efficacy within microbial communities remains highly challenging. Interspecies interactions can impact antibiotic activity through many mechanisms, including alterations to bacterial physiology. Here, we studied synthetic communities constructed from the core members of the fruit fly gut microbiota. Co-culturing of Lactobacillus plantarum with Acetobacter species altered its tolerance to the transcriptional inhibitor rifampin. By measuring key metabolites and environmental pH, we determined that Acetobacter species counter the acidification driven by L. plantarum production of lactate. Shifts in pH were sufficient to modulate L. plantarum tolerance to rifampin and the translational inhibitor erythromycin. A reduction in lag time exiting stationary phase was linked to L. plantarum tolerance to rifampicin, opposite to a previously identified mode of tolerance to ampicillin in E. coli. This mechanistic understanding of the coupling among interspecies interactions, environmental pH, and antibiotic tolerance enables future predictions of growth and the effects of antibiotics in more complex communities.
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Affiliation(s)
- Andrés Aranda-Díaz
- Department of Bioengineering, Stanford University, Stanford, United States
| | - Benjamin Obadia
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Ren Dodge
- Department of Embryology, Carnegie Institution of Washington, Baltimore, United States
| | - Tani Thomsen
- Department of Bioengineering, Stanford University, Stanford, United States
| | - Zachary F Hallberg
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, United States
| | - Zehra Tüzün Güvener
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - William B Ludington
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,Department of Embryology, Carnegie Institution of Washington, Baltimore, United States
| | - Kerwyn Casey Huang
- Department of Bioengineering, Stanford University, Stanford, United States.,Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, United States.,Chan Zuckerberg Biohub, San Francisco, United States
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17
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Dall GF, Tsang STJ, Gwynne PJ, MacKenzie SP, Simpson AHRW, Breusch SJ, Gallagher MP. Unexpected synergistic and antagonistic antibiotic activity against Staphylococcus biofilms. J Antimicrob Chemother 2019; 73:1830-1840. [PMID: 29554250 DOI: 10.1093/jac/dky087] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 02/15/2018] [Indexed: 11/13/2022] Open
Abstract
Objectives To evaluate putative anti-staphylococcal biofilm antibiotic combinations used in the management of periprosthetic joint infections (PJIs). Methods Using the dissolvable bead biofilm assay, the minimum biofilm eradication concentration (MBEC) was determined for the most commonly used antimicrobial agents and combination regimens against staphylococcal PJIs. The established fractional inhibitory concentration (FIC) index was modified to create the fractional biofilm eradication concentration (FBEC) index to evaluate synergism or antagonism between antibiotics. Results Only gentamicin (MBEC 64 mg/L) and daptomycin (MBEC 64 mg/L) were observed to be effective antistaphylococcal agents at clinically achievable concentrations. Supplementation of gentamicin with daptomycin, vancomycin or ciprofloxacin resulted in a similar or lower MBEC than gentamicin alone (FBEC index 0.25-2). Conversely, when rifampicin, clindamycin or linezolid was added to gentamicin, there was an increase in the MBEC of gentamicin relative to its use as a monotherapy (FBEC index 8-32). Conclusions This study found that gentamicin and daptomycin were the only effective single-agent antibiotics against established Staphylococcus biofilms. Interestingly the addition of a bacteriostatic antibiotic was found to antagonize the ability of gentamicin to eradicate Staphylococcus biofilms.
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Affiliation(s)
- G F Dall
- Department of Orthopaedic Surgery, Borders General Hospital, Huntlyburn, Melrose TD6 9BS, UK.,School of Biological Sciences, University of Edinburgh, Darwin Building, King's Buildings, Mayfield Road, Edinburgh EH9 3JR, UK.,Department of Orthopaedic Surgery, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Old Dalkeith Road, Edinburgh EH16 4SB, UK
| | - S-T J Tsang
- School of Biological Sciences, University of Edinburgh, Darwin Building, King's Buildings, Mayfield Road, Edinburgh EH9 3JR, UK.,Department of Orthopaedic Surgery, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Old Dalkeith Road, Edinburgh EH16 4SB, UK.,Department of Orthopaedic Surgery, Royal Infirmary of Edinburgh, 51 Little France Crescent, Old Dalkeith Road, Edinburgh EH16 4SA, UK
| | - P J Gwynne
- School of Biological Sciences, University of Edinburgh, Darwin Building, King's Buildings, Mayfield Road, Edinburgh EH9 3JR, UK
| | - S P MacKenzie
- Department of Orthopaedic Surgery, Royal Infirmary of Edinburgh, 51 Little France Crescent, Old Dalkeith Road, Edinburgh EH16 4SA, UK
| | - A H R W Simpson
- Department of Orthopaedic Surgery, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Old Dalkeith Road, Edinburgh EH16 4SB, UK
| | - S J Breusch
- Department of Orthopaedic Surgery, Royal Infirmary of Edinburgh, 51 Little France Crescent, Old Dalkeith Road, Edinburgh EH16 4SA, UK
| | - M P Gallagher
- School of Biological Sciences, University of Edinburgh, Darwin Building, King's Buildings, Mayfield Road, Edinburgh EH9 3JR, UK
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18
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Kim S, Masum F, Kim JK, Chung HJ, Jeon JS. On-chip phenotypic investigation of combinatory antibiotic effects by generating orthogonal concentration gradients. LAB ON A CHIP 2019; 19:959-973. [PMID: 30768106 DOI: 10.1039/c8lc01406j] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Combinatory therapy using two or more kinds of antibiotics is attracting considerable attention for inhibiting multi-drug resistant pathogenic bacteria. Although the therapy mostly leads to more powerful antimicrobial effects than using a single antibiotic (synergy), interference may arise from certain antibiotic combinations, resulting in the antimicrobial effect being suppressed (antagonism). Here, we present a microfluidic-based phenotypic screening chip to investigate combinatory antibiotic effects by automatically generating two orthogonal concentration gradients on a bacteria-trapping agarose gel. Computational simulations and fluorescence experiments together verify the simultaneous establishment of 121 concentration combinations, facilitating on-chip drug testing with stability and efficiency. Against Gram-negative bacteria, Pseudomonas aeruginosa, our chip allows the measurement of phenotypic growth levels, and enables various types of analyses for all antibiotic pairs to be conducted in 7 h. Furthermore, by providing a specific amount of susceptibility data, our chip enables the two reference models, Loewe additivity and Bliss independence, to be implemented, which classify the antibiotic interaction types into synergy or antagonism. These results suggest the efficacy of our chip as a cell-based drug screening platform for exploring the underlying pharmacological patterns of antibiotic interactions, with potential applications in guidance in clinical therapies and in screening other cell-type agents.
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Affiliation(s)
- Seunggyu Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
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19
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Du D, Chang CH, Wang Y, Tong P, Chan WK, Chiu Y, Peng B, Tan L, Weinstein JN, Lorenzi PL. Response envelope analysis for quantitative evaluation of drug combinations. Bioinformatics 2019; 35:3761-3770. [PMID: 30851108 PMCID: PMC7963081 DOI: 10.1093/bioinformatics/btz091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 01/21/2019] [Accepted: 03/07/2019] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations. RESULTS We present here a computational framework, termed response envelope analysis (REA), that makes use of 3D response surfaces formed by generalized Loewe Additivity and Bliss Independence models of interaction to evaluate drug combination effects. Because the two models imply two extreme limits of drug interaction (mutually exclusive and mutually non-exclusive), a response envelope defined by them provides a quantitatively stringent additivity model for identifying combination effects without knowing the inhibition mechanism. As a demonstration, we apply REA to representative published data from large screens of anticancer and antibiotic combinations. We show that REA is more accurate than existing methods and provides more consistent results in the context of cross-experiment evaluation. AVAILABILITY AND IMPLEMENTATION The open-source software package associated with REA is available at: https://github.com/4dsoftware/rea. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Di Du
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chia-Hua Chang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yumeng Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pan Tong
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wai Kin Chan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yulun Chiu
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bo Peng
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lin Tan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John N Weinstein
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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20
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Katzir I, Cokol M, Aldridge BB, Alon U. Prediction of ultra-high-order antibiotic combinations based on pairwise interactions. PLoS Comput Biol 2019; 15:e1006774. [PMID: 30699106 PMCID: PMC6370231 DOI: 10.1371/journal.pcbi.1006774] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 02/11/2019] [Accepted: 01/10/2019] [Indexed: 11/19/2022] Open
Abstract
Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.
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Affiliation(s)
- Itay Katzir
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Murat Cokol
- Axcella Health, Cambridge, MA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA
| | - Bree B. Aldridge
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA
- Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA
- * E-mail: (BBA); (UA)
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail: (BBA); (UA)
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21
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Drug combinations: a strategy to extend the life of antibiotics in the 21st century. Nat Rev Microbiol 2019; 17:141-155. [PMID: 30683887 DOI: 10.1038/s41579-018-0141-x] [Citation(s) in RCA: 526] [Impact Index Per Article: 87.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 11/22/2018] [Indexed: 01/03/2023]
Abstract
Antimicrobial resistance threatens a resurgence of life-threatening bacterial infections and the potential demise of many aspects of modern medicine. Despite intensive drug discovery efforts, no new classes of antibiotics have been developed into new medicines for decades, in large part owing to the stringent chemical, biological and pharmacological requisites for effective antibiotic drugs. Combinations of antibiotics and of antibiotics with non-antibiotic activity-enhancing compounds offer a productive strategy to address the widespread emergence of antibiotic-resistant strains. In this Review, we outline a theoretical and practical framework for the development of effective antibiotic combinations.
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22
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Tekin E, White C, Kang TM, Singh N, Cruz-Loya M, Damoiseaux R, Savage VM, Yeh PJ. Prevalence and patterns of higher-order drug interactions in Escherichia coli. NPJ Syst Biol Appl 2018; 4:31. [PMID: 30181902 PMCID: PMC6119685 DOI: 10.1038/s41540-018-0069-9] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 07/02/2018] [Accepted: 07/03/2018] [Indexed: 01/23/2023] Open
Abstract
Interactions and emergent processes are essential for research on complex systems involving many components. Most studies focus solely on pairwise interactions and ignore higher-order interactions among three or more components. To gain deeper insights into higher-order interactions and complex environments, we study antibiotic combinations applied to pathogenic Escherichia coli and obtain unprecedented amounts of detailed data (251 two-drug combinations, 1512 three-drug combinations, 5670 four-drug combinations, and 13608 five-drug combinations). Directly opposite to previous assumptions and reports, we find higher-order interactions increase in frequency with the number of drugs in the bacteria’s environment. Specifically, as more drugs are added, we observe an elevated frequency of net synergy (effect greater than expected based on independent individual effects) and also increased instances of emergent antagonism (effect less than expected based on lower-order interaction effects). These findings have implications for the potential efficacy of drug combinations and are crucial for better navigating problems associated with the combinatorial complexity of multi-component systems. Interactions play an important role in determining the dynamics of complex systems yet higher-order interactions that involve more than two components are poorly understood. A research team from University of California, Los Angeles led by Pamela Yeh, use a bacteria system to show that higher-order interactions among antibiotics are prevalent and also that there are systematic patterns in how they occur: the frequency of higher-order interactions increases with the number of components, net interactions tend to be more synergistic, and emergent interactions—arising at specific higher-order levels—tend toward antagonism. By detecting patterns in interactions as the number of drugs increases, they provide a method to handle the combinatorial complexity that results from higher-order interactions, yielding a solid foundation for exploring the patterns and consequences of emergent phenomena in other research areas.
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Affiliation(s)
- Elif Tekin
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA.,2Department of Biomathematics, University of California, David Geffen School of Medicine, Los Angeles, CA 90095 USA
| | - Cynthia White
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA
| | - Tina Manzhu Kang
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA
| | - Nina Singh
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA
| | - Mauricio Cruz-Loya
- 2Department of Biomathematics, University of California, David Geffen School of Medicine, Los Angeles, CA 90095 USA
| | - Robert Damoiseaux
- 3California NanoSystems Institute, University of California, Los Angeles, 570 Westwood Plaza, Los Angeles, CA 90095 USA
| | - Van M Savage
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA.,2Department of Biomathematics, University of California, David Geffen School of Medicine, Los Angeles, CA 90095 USA.,4Santa Fe Institute, Santa Fe, NM 87501 USA
| | - Pamela J Yeh
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA.,4Santa Fe Institute, Santa Fe, NM 87501 USA
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Wicha SG, Chen C, Clewe O, Simonsson USH. A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions. Nat Commun 2017; 8:2129. [PMID: 29242552 PMCID: PMC5730559 DOI: 10.1038/s41467-017-01929-y] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 10/25/2017] [Indexed: 12/20/2022] Open
Abstract
Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs’ role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments in Saccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process. Assessment of pharmacodynamic interactions is at the heart of combination therapy development. Here the authors introduce a general drug interaction scoring model that enables quantification of synergistic and antagonistic interactions and determination of the directionality of the interactions.
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Affiliation(s)
- Sebastian G Wicha
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, 75124, Sweden.
| | - Chunli Chen
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, 75124, Sweden
| | - Oskar Clewe
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, 75124, Sweden
| | - Ulrika S H Simonsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, 75124, Sweden
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Suppressive drug combinations and their potential to combat antibiotic resistance. J Antibiot (Tokyo) 2017; 70:1033-1042. [PMID: 28874848 DOI: 10.1038/ja.2017.102] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 07/26/2017] [Accepted: 07/28/2017] [Indexed: 12/25/2022]
Abstract
Antibiotic effectiveness often changes when two or more such drugs are administered simultaneously and unearthing antibiotic combinations with enhanced efficacy (synergy) has been a longstanding clinical goal. However, antibiotic resistance, which undermines individual drugs, threatens such combined treatments. Remarkably, it has emerged that antibiotic combinations whose combined effect is lower than that of at least one of the individual drugs can slow or even reverse the evolution of resistance. We synthesize and review studies of such so-called 'suppressive interactions' in the literature. We examine why these interactions have been largely disregarded in the past, the strategies used to identify them, their mechanistic basis, demonstrations of their potential to reverse the evolution of resistance and arguments for and against using them in clinical treatment. We suggest future directions for research on these interactions, aiming to expand the basic body of knowledge on suppression and to determine the applicability of suppressive interactions in the clinic.
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