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Thao Le TM, Madec S, Gjini E. Disentangling how multiple traits drive 2 strain frequencies in SIS dynamics with coinfection. J Theor Biol 2022; 538:111041. [DOI: 10.1016/j.jtbi.2022.111041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 12/01/2021] [Accepted: 01/18/2022] [Indexed: 10/19/2022]
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2
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Wale N, Duffy MA. The Use and Underuse of Model Systems in Infectious Disease Ecology and Evolutionary Biology. Am Nat 2021; 198:69-92. [PMID: 34143716 DOI: 10.1086/714595] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
AbstractEver since biologists began studying the ecology and evolution of infectious diseases (EEID), laboratory-based model systems have been important for developing and testing theory. Yet what EEID researchers mean by the term "model systems" and what they want from them is unclear. This uncertainty hinders our ability to maximally exploit these systems, identify knowledge gaps, and establish effective new model systems. Here, we borrow a definition of model systems from the biomolecular sciences to assess how EEID researchers are (and are not) using 10 key model systems. According to this definition, model systems in EEID are not being used to their fullest and, in fact, cannot even be considered model systems. Research using these systems consistently addresses only two of the three fundamental processes that underlie disease dynamics-transmission and disease, but not recovery. Furthermore, studies tend to focus on only a few scales of biological organization that matter for disease ecology and evolution. Moreover, the field lacks an infrastructure to perform comparative analyses. We aim to begin a discussion of what we want from model systems, which would further progress toward a thorough, holistic understanding of EEID.
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Imidocarb Dipropionate Lacks Efficacy against Theileria haneyi and Fails to Consistently Clear Theileria equi in Horses Co-Infected with T. haneyi. Pathogens 2020; 9:pathogens9121035. [PMID: 33321715 PMCID: PMC7764667 DOI: 10.3390/pathogens9121035] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/05/2020] [Accepted: 12/08/2020] [Indexed: 12/27/2022] Open
Abstract
Control of Theileria equi, the primary cause of equine theileriosis, is largely reliant on acaracide use and chemosterilization with imidocarb dipropionate (ID). However, it is currently unknown if ID is effective against Theileria haneyi, the recently identified second causative agent of equine theileriosis, or if the drug maintains effectiveness against T. equi in the presence of T. haneyi co-infection. The purpose of this study was to address these questions using ID treatment of the following three groups of horses: (1) five T. haneyi infected horses; (2) three T. haneyi-T. equi infected horses; and (3) three T. equi-T. haneyi infected horses. Clearance was first evaluated using nPCR for each Theileria sp. on peripheral blood samples. ID failed to clear T. haneyi in all three groups of horses, and failed to clear T. equi in two of three horses in group two. For definitive confirmation of infection status, horses in groups two and three underwent splenectomy post-treatment. The T. equi-nPCR-positive horses in group two developed severe clinical signs and were euthanized. Remaining horses exhibited moderate signs consistent with T. haneyi. Our results demonstrate that ID therapy lacks efficacy against T. haneyi, and T. haneyi-T. equi co-infection may interfere with ID clearance of T. equi.
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Sharp RT, Shaw MW, van den Bosch F. The effect of competition on the control of invading plant pathogens. J Appl Ecol 2020; 57:1403-1412. [PMID: 32742019 PMCID: PMC7386929 DOI: 10.1111/1365-2664.13618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 02/25/2020] [Indexed: 11/24/2022]
Abstract
New invading pathogen strains must compete with endemic pathogen strains to emerge and spread. As disease control measures are often non-specific, that is, they do not distinguish between strains, applying control not only affects the invading pathogen strain but the endemic as well. We hypothesize that the control of the invasive strain could be compromised due to the non-specific nature of the control.A spatially explicit model, describing the East African cassava mosaic virus-Uganda strain (EACMV-UG) outbreak, is used to evaluate methods of controlling both disease incidence and spread of invading pathogen strains in pathosystems with and without an endemic pathogen strain present.We find that while many newly introduced or intensified control measures (such as resistant cultivars or roguing) decrease the expected incidence, they have the unintended consequence of increasing, or at least not reducing, the speed with which the invasive pathogen spreads geographically. We identify the controls that cause this effect and methods in which these controls may be applied to prevent it.We found that the spatial spread of the invading strain is chiefly governed by the incidence at the wave front. Control can therefore be applied, or intensified, once the wave front has passed without increasing the pathogen's rate of spread.When trade of planting material occurs, it is possible that the planting material is already infected. The only forms of control in this study that reduces the speed of geographic spread, regardless of the presence of an endemic strain, are those that reduce the amount of trade and the distance over which trade takes place. Synthesis and applications. The best control strategy depends on the presence of competing endemic strains. Applying or intensifying the control can slow the rate of spread when absent but increase it if present. Imposing trade restrictions before the epidemic has reached a given area and intensifying other control methods only when the wave front has passed is the most effective way of both slowing down spread and controlling incidence when a competing endemic strain is present and is the safest approach when its presence is unknown.
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Affiliation(s)
- Ryan T. Sharp
- Department of Sustainable Agriculture SciencesRothamsted ResearchHarpendenHertfordshireUK
| | - Michael W. Shaw
- School of Agriculture, Policy and DevelopmentUniversity of ReadingReadingBerkshireUK
| | - Frank van den Bosch
- Department of Environment & AgricultureCentre for Crop and Disease ManagementCurtin UniversityBentley, PerthWAAustralia
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5
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Mulberry N, Rutherford A, Colijn C. Systematic comparison of coexistence in models of drug-sensitive and drug-resistant pathogen strains. Theor Popul Biol 2019; 133:150-158. [PMID: 31887315 DOI: 10.1016/j.tpb.2019.12.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/03/2019] [Accepted: 12/04/2019] [Indexed: 10/25/2022]
Abstract
A number of mathematical models have recently been proposed to explain empirical trends of pathogen diversity. In particular, long-term coexistence of both drug-sensitive and drug-resistant variants of a single pathogen is something of a mystery, given that simple models of pathogens competing for the same ecological niche predict competitive exclusion, and more complex models admitting coexistence require assumptions that may not be justified. Coinfection is among the candidate mechanisms to generate coexistence, as it occurs in many pathogens and provides the opportunity for strains to interact directly. Recently, coinfection and competitive release have been described as creating a form of negative frequency-dependent selection that promotes coexistence, and a range of models containing coinfection have been proposed as having generic stable coexistence of multiple strains. This abundance of new models presents the challenge of comparison and interpretation. To this end, we describe a dimensionless quantity that can be used to compare the amount of coexistence generated by different models. We focus on models that include coinfection, although this framework could be generalized to a larger class of structured models.
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6
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Bushman M, Antia R. A general framework for modelling the impact of co-infections on pathogen evolution. J R Soc Interface 2019; 16:20190165. [PMID: 31238835 PMCID: PMC6597765 DOI: 10.1098/rsif.2019.0165] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Theoretical models suggest that mixed-strain infections, or co-infections, are an important driver of pathogen evolution. However, the within-host dynamics of co-infections vary enormously, which complicates efforts to develop a general understanding of how co-infections affect evolution. Here, we develop a general framework which condenses the within-host dynamics of co-infections into a few key outcomes, the most important of which is the overall R0 of the co-infection. Similar to how fitness is determined by two different alleles in a heterozygote, the R0 of a co-infection is a product of the R0 values of the co-infecting strains, shaped by the interaction of those strains at the within-host level. Extending the analogy, we propose that the overall R0 reflects the dominance of the co-infecting strains, and that the ability of a mutant strain to invade a population is a function of its dominance in co-infections. To illustrate the utility of these concepts, we use a within-host model to show how dominance arises from the within-host dynamics of a co-infection, and then use an epidemiological model to demonstrate that dominance is a robust predictor of the ability of a mutant strain to save a maladapted wild-type strain from extinction (evolutionary emergence).
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Affiliation(s)
- Mary Bushman
- Department of Biology, Emory University , Atlanta, GA , USA
| | - Rustom Antia
- Department of Biology, Emory University , Atlanta, GA , USA
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7
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Niewiadomska AM, Jayabalasingham B, Seidman JC, Willem L, Grenfell B, Spiro D, Viboud C. Population-level mathematical modeling of antimicrobial resistance: a systematic review. BMC Med 2019; 17:81. [PMID: 31014341 PMCID: PMC6480522 DOI: 10.1186/s12916-019-1314-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 03/25/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work. METHODS We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens. RESULTS We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals. CONCLUSIONS The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.
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Affiliation(s)
- Anna Maria Niewiadomska
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA
| | - Bamini Jayabalasingham
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.,Present Address: Elsevier Inc., 230 Park Ave, Suite B00, New York, NY, 10169, USA
| | - Jessica C Seidman
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA
| | | | - Bryan Grenfell
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.,Princeton University, Princeton, NJ, USA
| | - David Spiro
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
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8
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Beams AB, Toth DJA, Khader K, Adler FR. Harnessing Intra-Host Strain Competition to Limit Antibiotic Resistance: Mathematical Model Results. Bull Math Biol 2016; 78:1828-1846. [PMID: 27670431 DOI: 10.1007/s11538-016-0201-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 08/25/2016] [Indexed: 11/24/2022]
Abstract
Antibiotic overuse has promoted the spread of antibiotic resistance. To compound the issue, treating individuals dually infected with antibiotic-resistant and antibiotic-vulnerable strains can make their infections completely resistant through competitive release. We formulate mathematical models of transmission dynamics accounting for dual infections and extensions accounting for lag times between infection and treatment or between cure and ending treatment. Analysis using the Next-Generation Matrix reveals how competition within hosts and the costs of resistance determine whether vulnerable and resistant strains persist, coexist, or drive each other to extinction. Invasion analysis predicts that treatment of dually infected cases will promote resistance. By varying antibiotic strength, the models suggest that physicians have two ways to achieve a particular resistance target: prescribe relatively weak antibiotics to everyone infected with an antibiotic-vulnerable strain or give more potent prescriptions to only those patients singly infected with the vulnerable strain after ruling out the possibility of them being dually infected with resistance. Through selectivity and moderation in antibiotic prescription, resistance might be limited.
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Affiliation(s)
- Alexander B Beams
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA.
| | - Damon J A Toth
- Informatics, Decision Enhancement, and Analytical Sciences (IDEAS) 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Karim Khader
- Informatics, Decision Enhancement, and Analytical Sciences (IDEAS) 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Frederick R Adler
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA.,Department of Biology, University of Utah, Salt Lake City, UT, USA
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9
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Bose J, Kloesener MH, Schulte RD. Multiple-genotype infections and their complex effect on virulence. ZOOLOGY 2016; 119:339-49. [PMID: 27389395 DOI: 10.1016/j.zool.2016.06.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Revised: 06/04/2016] [Accepted: 06/08/2016] [Indexed: 11/17/2022]
Abstract
Multiple infections are common. Although in recent years our understanding of multiple infections has increased significantly, it has also become clear that a diversity of aspects has to be considered to understand the interplay between co-infecting parasite genotypes of the same species and its implications for virulence and epidemiology, resulting in high complexity. Here, we review different interaction mechanisms described for multiple infections ranging from competition to cooperation. We also list factors influencing the interaction between co-infecting parasite genotypes and their influence on virulence. Finally, we emphasise the importance of between-host effects and their evolution for understanding multiple infections and their implications.
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Affiliation(s)
- Joy Bose
- Department of Behavioral Biology, University of Osnabrueck, Barbarastr. 11, D-49076 Osnabrueck, Germany
| | - Michaela H Kloesener
- Department of Behavioral Biology, University of Osnabrueck, Barbarastr. 11, D-49076 Osnabrueck, Germany
| | - Rebecca D Schulte
- Department of Behavioral Biology, University of Osnabrueck, Barbarastr. 11, D-49076 Osnabrueck, Germany.
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10
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Bushman M, Morton L, Duah N, Quashie N, Abuaku B, Koram KA, Dimbu PR, Plucinski M, Gutman J, Lyaruu P, Kachur SP, de Roode JC, Udhayakumar V. Within-host competition and drug resistance in the human malaria parasite Plasmodium falciparum. Proc Biol Sci 2016; 283:20153038. [PMID: 26984625 PMCID: PMC4810865 DOI: 10.1098/rspb.2015.3038] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 02/16/2016] [Indexed: 11/12/2022] Open
Abstract
Infections with the malaria parasite Plasmodium falciparum typically comprise multiple strains, especially in high-transmission areas where infectious mosquito bites occur frequently. However, little is known about the dynamics of mixed-strain infections, particularly whether strains sharing a host compete or grow independently. Competition between drug-sensitive and drug-resistant strains, if it occurs, could be a crucial determinant of the spread of resistance. We analysed 1341 P. falciparum infections in children from Angola, Ghana and Tanzania and found compelling evidence for competition in mixed-strain infections: overall parasite density did not increase with additional strains, and densities of individual chloroquine-sensitive (CQS) and chloroquine-resistant (CQR) strains were reduced in the presence of competitors. We also found that CQR strains exhibited low densities compared with CQS strains (in the absence of chloroquine), which may underlie observed declines of chloroquine resistance in many countries following retirement of chloroquine as a first-line therapy. Our observations support a key role for within-host competition in the evolution of drug-resistant malaria. Malaria control and resistance-management efforts in high-transmission regions may be significantly aided or hindered by the effects of competition in mixed-strain infections. Consideration of within-host dynamics may spur development of novel strategies to minimize resistance while maximizing the benefits of control measures.
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Affiliation(s)
- Mary Bushman
- Department of Biology, Emory University, Atlanta, GA 30322, USA Malaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Lindsay Morton
- Malaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Nancy Duah
- Epidemiology Department, Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana
| | - Neils Quashie
- Epidemiology Department, Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana Centre for Tropical Clinical Pharmacology and Therapeutics, University of Ghana Medical School, Accra, Ghana
| | - Benjamin Abuaku
- Epidemiology Department, Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana
| | - Kwadwo A Koram
- Epidemiology Department, Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana
| | | | - Mateusz Plucinski
- Malaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Julie Gutman
- Malaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Peter Lyaruu
- Ifakara Health Institute, Dar es Salaam, Tanzania
| | - S Patrick Kachur
- Malaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | | | - Venkatachalam Udhayakumar
- Malaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
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11
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Kunkel A, Colijn C, Lipsitch M, Cohen T. How could preventive therapy affect the prevalence of drug resistance? Causes and consequences. Philos Trans R Soc Lond B Biol Sci 2016; 370:20140306. [PMID: 25918446 PMCID: PMC4424438 DOI: 10.1098/rstb.2014.0306] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Various forms of preventive and prophylactic antimicrobial therapies have been proposed to combat HIV (e.g. pre-exposure prophylaxis), tuberculosis (e.g. isoniazid preventive therapy) and malaria (e.g. intermittent preventive treatment). However, the potential population-level effects of preventative therapy (PT) on the prevalence of drug resistance are not well understood. PT can directly affect the rate at which resistance is acquired among those receiving PT. It can also indirectly affect resistance by altering the rate at which resistance is acquired through treatment for active disease and by modifying the level of competition between transmission of drug-resistant and drug-sensitive pathogens. We propose a general mathematical model to explore the ways in which PT can affect the long-term prevalence of drug resistance. Depending on the relative contributions of these three mechanisms, we find that increasing the level of coverage of PT may result in increases, decreases or non-monotonic changes in the overall prevalence of drug resistance. These results demonstrate the complexity of the relationship between PT and drug resistance in the population. Care should be taken when predicting population-level changes in drug resistance from small pilot studies of PT or estimates based solely on its direct effects.
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Affiliation(s)
- Amber Kunkel
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06520, USA
| | - Caroline Colijn
- Department of Mathematics, Imperial College, London SW7 2AZ, UK
| | - Marc Lipsitch
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06520, USA
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12
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Colijn C, Cohen T. How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance. eLife 2015; 4. [PMID: 26393685 PMCID: PMC4641510 DOI: 10.7554/elife.10559] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 09/18/2015] [Indexed: 11/16/2022] Open
Abstract
Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population. DOI:http://dx.doi.org/10.7554/eLife.10559.001 Antibiotics are chemical compounds used to treat bacterial infections. The discovery of antibiotics, starting with penicillin in 1929, revolutionized medicine, making it possible to cure or prevent life-threatening infections such as tetanus and pneumonia. However, many bacteria have become resistant to one or more antibiotics and so can no longer be killed by these drugs. The emergence of antibiotic resistance reflects an evolutionary process that occurs during antibiotic treatment. While the antibiotic will kill most bacteria, some bacteria may naturally have a feature or genetic mutation that allows them to survive in the presence of the antibiotic. These bacteria then reproduce and pass on their resistant traits, eventually leading to the emergence of a new antibiotic-resistant strain of bacteria. Once a resistant strain exists it may be able to spread from one person to another. There is conflicting evidence about how best to prevent antibiotic-resistant bacteria from evolving and spreading. The results of some experiments suggest that treating bacteria with large doses of antibiotics early in an infection is the most effective way to optimize treatment and minimize the risk of an antibiotic-resistant strain developing. However, other studies suggest that exposing bacteria to high levels of antibiotics more efficiently selects for resistance; in this case a more moderate approach should be used when treating bacterial infections. Here, Colijn and Cohen present a mathematical model that suggests that the natural competition between the antibiotic-resistant and antibiotic-sensitive strains of bacteria influence which treatment strategy should be taken. Strains were modeled both within individual hosts and spreading in a community of individuals. In the models, aggressive antibiotic treatment is most effective (in that it minimizes antibiotic resistance) when the antibiotic-resistant strain either does not experience strong competition from the non-resistant strains of bacteria or is not fit enough to be a good competitor. However, a more moderate treatment is appropriate when the two strains are competing and the antibiotic-resistant strain is a fit competitor. Competition may mean that moderate treatment is best to avoid resistance at the community level, even in situations when aggressive treatment is likely best for individuals. Two important future challenges are to better understand the diversity of strains in bacterial infections, and to develop tools to measure to what extent strains are effectively competing with each other. DOI:http://dx.doi.org/10.7554/eLife.10559.002
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Affiliation(s)
- Caroline Colijn
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Ted Cohen
- School of Public Health, Yale University, New Haven, United States
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13
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Birger RB, Kouyos RD, Cohen T, Griffiths EC, Huijben S, Mina MJ, Volkova V, Grenfell B, Metcalf CJE. The potential impact of coinfection on antimicrobial chemotherapy and drug resistance. Trends Microbiol 2015; 23:537-544. [PMID: 26028590 DOI: 10.1016/j.tim.2015.05.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 04/20/2015] [Accepted: 05/05/2015] [Indexed: 01/06/2023]
Abstract
Across a range of pathogens, resistance to chemotherapy is a growing problem in both public health and animal health. Despite the ubiquity of coinfection, and its potential effects on within-host biology, the role played by coinfecting pathogens on the evolution of resistance and efficacy of antimicrobial chemotherapy is rarely considered. In this review, we provide an overview of the mechanisms of interaction of coinfecting pathogens, ranging from immune modulation and resource modulation, to drug interactions. We discuss their potential implications for the evolution of resistance, providing evidence in the rare cases where it is available. Overall, our review indicates that the impact of coinfection has the potential to be considerable, suggesting that this should be taken into account when designing antimicrobial drug treatments.
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Affiliation(s)
- Ruthie B Birger
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Roger D Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.,Institute of Medical Virology, University of Zürich, Zürich, Switzerland
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Emily C Griffiths
- Department of Entomology, Gardner Hall, Derieux Place, North Carolina State University, Raleigh, NC 27695-7613, USA
| | - Silvie Huijben
- ISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic -Universitat de Barcelona, Barcelona, Spain
| | - Michael J Mina
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Medical Scientist Training Program, Emory University School of Medicine, Atlanta, GA, USA
| | - Victoriya Volkova
- Department of Diagnostic Medicine/Pathobiology, Institute of Computational Comparative Medicine, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA
| | - Bryan Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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