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Siedentop B, Rüegg D, Bonhoeffer S, Chabas H. My host's enemy is my enemy: plasmids carrying CRISPR-Cas as a defence against phages. Proc Biol Sci 2024; 291:20232449. [PMID: 38262608 PMCID: PMC10805597 DOI: 10.1098/rspb.2023.2449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
Bacteria are infected by mobile genetic elements like plasmids and virulent phages, and those infections significantly impact bacterial ecology and evolution. Recent discoveries reveal that some plasmids carry anti-phage immune systems like CRISPR-Cas, suggesting that plasmids may participate in the coevolutionary arms race between virulent phages and bacteria. Intuitively, this seems reasonable as virulent phages kill the plasmid's obligate host. However, the efficiency of CRISPR-Cas systems carried by plasmids can be expected to be lower than those carried by the chromosome due to continuous segregation loss, creating susceptible cells for phage amplification. To evaluate the anti-phage protection efficiency of CRISPR-Cas on plasmids, we develop a stochastic model describing the dynamics of a virulent phage infection against which a conjugative plasmid defends using CRISPR-Cas. We show that CRISPR-Cas on plasmids provides robust protection, except in limited parameter sets. In these cases, high segregation loss favours phage outbreaks by generating a population of defenceless cells on which the phage can evolve and escape CRISPR-Cas immunity. We show that the phage's ability to exploit segregation loss depends strongly on the evolvability of both CRISPR-Cas and the phage itself.
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Affiliation(s)
- Berit Siedentop
- Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland
| | - Dario Rüegg
- Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland
| | | | - Hélène Chabas
- Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland
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2
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Siedentop B, Kachalov VN, Witzany C, Egger M, Kouyos RD, Bonhoeffer S. The effect of combining antibiotics on resistance: A systematic review and meta-analysis. medRxiv 2023:2023.07.10.23292374. [PMID: 37503165 PMCID: PMC10370225 DOI: 10.1101/2023.07.10.23292374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
When and under which conditions antibiotic combination therapy decelerates rather than accelerates resistance evolution is not well understood. We examined the effect of combining antibiotics on within-patient resistance development across various bacterial pathogens and antibiotics. We searched CENTRAL, EMBASE and PubMed for (quasi)-randomised controlled trials (RCTs) published from database inception to November 24th, 2022. Trials comparing antibiotic treatments with different numbers of antibiotics were included. A patient was considered to have acquired resistance if, at the follow-up culture, a resistant bacterium was detected that had not been present in the baseline culture. We combined results using a random effects model and performed meta-regression and stratified analyses. The trials' risk of bias was assessed with the Cochrane tool. 42 trials were eligible and 29, including 5054 patients, were qualified for statistical analysis. In most trials, resistance development was not the primary outcome and studies lacked power. The combined odds ratio (OR) for the acquisition of resistance comparing the group with the higher number of antibiotics with the comparison group was 1.23 (95% CI 0.68-2.25), with substantial between-study heterogeneity (I2 =77%). We identified tentative evidence for potential beneficial or detrimental effects of antibiotic combination therapy for specific pathogens or medical conditions. The evidence for combining a higher number of antibiotics compared to fewer from RCTs is scarce and overall, is compatible with both benefit or harm. Trials powered to detect differences in resistance development or well-designed observational studies are required to clarify the impact of combination therapy on resistance.
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Affiliation(s)
- Berit Siedentop
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Viacheslav N. Kachalov
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Christopher Witzany
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | - Matthias Egger
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
- Population Health Sciences, University of Bristol, Bristol, UK
- Centre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Roger D. Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
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3
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Scire J, Huisman JS, Grosu A, Angst DC, Lison A, Li J, Maathuis MH, Bonhoeffer S, Stadler T. estimateR: an R package to estimate and monitor the effective reproductive number. BMC Bioinformatics 2023; 24:310. [PMID: 37568078 PMCID: PMC10416499 DOI: 10.1186/s12859-023-05428-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Accurate estimation of the effective reproductive number ([Formula: see text]) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the [Formula: see text] estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. RESULTS The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. CONCLUSIONS The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of [Formula: see text].
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Affiliation(s)
- Jérémie Scire
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Jana S Huisman
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
- Department of Physics, Massachusetts Institute of Technology, Cambridge, USA
| | - Ana Grosu
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Daniel C Angst
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Jinzhou Li
- Department of Mathematics, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Marloes H Maathuis
- Department of Mathematics, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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Pasin C, Consiglio CR, Huisman J, de Lange AMG, Peckham H, Vallejo-Yagüe E, Abela IA, Islander U, Neuner-Jehle N, Pujantell M, Roth O, Schirmer M, Tepekule B, Zeeb M, Hachfeld A, Aebi-Popp K, Kouyos RD, Bonhoeffer S. Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications. R Soc Open Sci 2023; 10:221628. [PMID: 37416827 PMCID: PMC10320357 DOI: 10.1098/rsos.221628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/14/2023] [Indexed: 07/08/2023]
Abstract
Although sex and gender are recognized as major determinants of health and immunity, their role is rarely considered in clinical practice and public health. We identified six bottlenecks preventing the inclusion of sex and gender considerations from basic science to clinical practice, precision medicine and public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex and gender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-related bottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and gender identity. (iii) A translational bottleneck, limited by animal models and the underrepresentation of gender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statistical analyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation of pregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemic bias and discriminations affect not only academic research but also decision makers. We specify guidelines for researchers, scientific journals, funding agencies and academic institutions to address these bottlenecks. Following such guidelines will support the development of more efficient and equitable care strategies for all.
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Affiliation(s)
- Chloé Pasin
- Collegium Helveticum, 8092 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Camila R. Consiglio
- Department of Women's and Children's Health, Karolinska Institutet, 17165 Stockholm, Sweden
| | - Jana S. Huisman
- Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland
- Physics of Living Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ann-Marie G. de Lange
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, 1011 Lausanne, Switzerland
- Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - Hannah Peckham
- Centre for Adolescent Rheumatology Versus Arthritis at UCL, UCLH and GOSH, London WC1E 6JF, UK
| | | | - Irene A. Abela
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Ulrika Islander
- Department of Rheumatology and Inflammation Research, University of Gothenburg, 40530 Gothenburg, Sweden
- SciLifeLab, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Nadia Neuner-Jehle
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Maria Pujantell
- Institute of Immunology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Leibniz Institute of Virology, 20251 Hamburg, Germany
| | - Olivia Roth
- Marine Evolutionary Biology, Zoological Institute, Christian-Albrechts-University Kiel, 24118 Kiel, Germany
| | - Melanie Schirmer
- Emmy Noether Group for Computational Microbiome Research, ZIEL – Institute for Food and Health, Technical University of Munich, 85354 Freising, Germany
| | - Burcu Tepekule
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Marius Zeeb
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Anna Hachfeld
- Department of Infectious Diseases, University Hospital and University of Bern, 3012 Bern, Switzerland
| | - Karoline Aebi-Popp
- Department of Infectious Diseases, University Hospital and University of Bern, 3012 Bern, Switzerland
- Department of Obstetrics and Gynecology, Lindenhofspital, 3012 Bern, Switzerland
| | - Roger D. Kouyos
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Collegium Helveticum, 8092 Zurich, Switzerland
- Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland
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5
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Huisman JS, Vaughan TG, Egli A, Tschudin-Sutter S, Stadler T, Bonhoeffer S. The effect of sequencing and assembly on the inference of horizontal gene transfer on chromosomal and plasmid phylogenies. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210245. [PMID: 35989605 PMCID: PMC9393563 DOI: 10.1098/rstb.2021.0245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The spread of antibiotic resistance genes on plasmids is a threat to human and animal health. Phylogenies of bacteria and their plasmids contain clues regarding the frequency of plasmid transfer events, as well as the co-evolution of plasmids and their hosts. However, whole genome sequencing data from diverse ecological or clinical bacterial samples are rarely used to study plasmid phylogenies and resistance gene transfer. This is partially due to the difficulty of extracting plasmids from short-read sequencing data. Here, we use both short- and long-read sequencing data of 24 clinical extended-spectrum β-lactamase (ESBL)-producing Escherichia coli to estimate chromosomal and plasmid phylogenies. We compare the impact of different sequencing and assembly methodologies on these phylogenies and on the inference of horizontal gene transfer. We find that chromosomal phylogenies can be estimated robustly with all methods, whereas plasmid phylogenies have more variable topology and branch lengths across the methods used. Specifically, hybrid methods that use long reads to resolve short-read assemblies (HybridSPAdes and Unicycler) perform better than those that started from long reads during assembly graph generation (Canu). By contrast, the inference of plasmid and antibiotic resistance gene transfer using a parsimony-based criterion is mostly robust to the choice of sequencing and assembly method. This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’.
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Affiliation(s)
- Jana S Huisman
- Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland.,Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.,Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Timothy G Vaughan
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.,Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Adrian Egli
- Division of Clinical Microbiology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.,Department of Biomedicine, University of Basel, Hebelstrasse 20, 4031 Basel, Switzerland
| | - Sarah Tschudin-Sutter
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.,Department of Clinical Research, University of Basel, Schanzenstrasse 55, 4031 Basel, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.,Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Sebastian Bonhoeffer
- Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
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6
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Vasse M, Bonhoeffer S, Frenoy A. Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments. ISME Commun 2022; 2:80. [PMID: 37938266 PMCID: PMC9723650 DOI: 10.1038/s43705-022-00157-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/20/2022] [Accepted: 07/29/2022] [Indexed: 11/09/2023]
Abstract
Stress is thought to increase mutation rate and thus to accelerate evolution. In the context of antibiotic resistance, sub-inhibitory treatments could then lead to enhanced evolvability, thereby fuelling the adaptation of pathogens. Combining wet-lab experiments, stochastic simulations and a meta-analysis of the literature, we found that the increase in mutation rates triggered by antibiotic treatments is often cancelled out by reduced population size, resulting in no overall increase in genetic diversity. A careful analysis of the effect of ecological factors on genetic diversity showed that the potential for regrowth during recovery phase after treatment plays a crucial role in evolvability, being the main factor associated with increased genetic diversity in experimental data.
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Affiliation(s)
- Marie Vasse
- Institute for Integrative Biology, ETH Zürich, Zurich, Switzerland
| | | | - Antoine Frenoy
- Institute for Integrative Biology, ETH Zürich, Zurich, Switzerland.
- Université Grenoble Alpes, CNRS UMR 5525, Grenoble, France.
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7
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Huisman JS, Scire J, Angst DC, Li J, Neher RA, Maathuis MH, Bonhoeffer S, Stadler T. Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2. eLife 2022; 11:71345. [PMID: 35938911 DOI: 10.1101/2020.11.26.20239368] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/01/2022] [Indexed: 05/28/2023] Open
Abstract
The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.
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Affiliation(s)
- Jana S Huisman
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Jérémie Scire
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Daniel C Angst
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Jinzhou Li
- Department of Mathematics, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Richard A Neher
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Biozentrum, University of Basel, Basel, Switzerland
| | - Marloes H Maathuis
- Department of Mathematics, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Tanja Stadler
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
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8
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Huisman JS, Scire J, Angst DC, Li J, Neher RA, Maathuis MH, Bonhoeffer S, Stadler T. Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2. eLife 2022; 11:71345. [PMID: 35938911 PMCID: PMC9467515 DOI: 10.7554/elife.71345] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/01/2022] [Indexed: 11/20/2022] Open
Abstract
The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data. Over the past two and a half years, countries around the globe have struggled to control the transmission of the SARS-CoV-2 virus within their borders. To manage the situation, it is important to have an accurate picture of how fast the virus is spreading. This can be achieved by calculating the effective reproductive number (Re), which describes how many people, on average, someone with COVID-19 is likely to infect. If the Re is greater than one, the virus is infecting increasingly more people, but if it is smaller than one, the number of cases is declining. Scientists use various strategies to estimate the Re, which each have their own strengths and weaknesses. One of the main difficulties is that infections are typically recorded only when people test positive for COVID-19, are hospitalized with the virus, or die. This means that the data provides a delayed representation of when infections are happening. Furthermore, changes in these records occur later than measures that change the infection dynamics. As a result, researchers need to take these delays into account when estimating Re. Here, Huisman, Scire et al. have developed a new method for estimating the Re based on available data records, statistically taking into account the above-mentioned delays. An online dashboard with daily updates was then created so that policy makers and the population could monitor the values over time. For over two years, Huisman, Scire et al. have been applying their tool and dashboard to COVID-19 data from 170 countries. They found that public health interventions, such as mask requirements and lockdowns, did help reduce the Re in Europe. But the effects were not uniform across the globe, likely because of variations in how restrictions were implemented and followed during the pandemic. In early 2020, the Re only dropped below one after countries put lockdowns or other severe measures in place. The Re values added to the dashboard over the last two years have been used pro-actively to inform public health policies in Switzerland and to monitor the spread of SARS-CoV-2 in South Africa. The team has also recently released programming software based on this method that can be used to track future disease outbreaks, and extended the method to estimate the Re using SARS-CoV-2 levels in wastewater.
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Affiliation(s)
- Jana Sanne Huisman
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Jérémie Scire
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Daniel C Angst
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Jinzhou Li
- Department of Mathematics, ETH Zurich, Zurich, Switzerland
| | | | | | | | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
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9
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Chabas H, Müller V, Bonhoeffer S, Regoes RR. Epidemiological and evolutionary consequences of different types of CRISPR-Cas systems. PLoS Comput Biol 2022; 18:e1010329. [PMID: 35881633 PMCID: PMC9355216 DOI: 10.1371/journal.pcbi.1010329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/05/2022] [Accepted: 06/27/2022] [Indexed: 11/19/2022] Open
Abstract
Bacteria have adaptive immunity against viruses (phages) in the form of CRISPR-Cas immune systems. Currently, 6 types of CRISPR-Cas systems are known and the molecular study of three of these has revealed important molecular differences. It is unknown if and how these molecular differences change the outcome of phage infection and the evolutionary pressure the CRISPR-Cas systems faces. To determine the importance of these molecular differences, we model a phage outbreak entering a population defending exclusively with a type I/II or a type III CRISPR-Cas system. We show that for type III CRISPR-Cas systems, rapid phage extinction is driven by the probability to acquire at least one resistance spacer. However, for type I/II CRISPR-Cas systems, rapid phage extinction is characterized by an a threshold-like behaviour: any acquisition probability below this threshold leads to phage survival whereas any acquisition probability above it, results in phage extinction. We also show that in the absence of autoimmunity, high acquisition rates evolve. However, when CRISPR-Cas systems are prone to autoimmunity, intermediate levels of acquisition are optimal during a phage outbreak. As we predict an optimal probability of spacer acquisition 2 factors of magnitude above the one that has been measured, we discuss the origin of such a discrepancy. Finally, we show that in a biologically relevant parameter range, a type III CRISPR-Cas system can outcompete a type I/II CRISPR-Cas system with a slightly higher probability of acquisition. CRISPR-Cas systems are adaptive immune systems that use a complex 3-step molecular mechanism to defend prokaryotes against phages. Viral infections of populations defending themselves with CRISPR-Cas can result in rapid phage extinction or in medium-term phage maintenance. To investigate what controls the fate of the phage population, we use mathematical modeling of type I/II and type III CRISPR-Cas systems, and show that two parameters control the epidemiological short-term outcome: the type of CRISPR-Cas systems and CRISPR-Cas probability of resistance acquisition. Furthermore, the latter impacts host fitness. From this, we derive that 1) for both types, CRISPR-Cas acquisition probability is a key predictor of the efficiency and of the cost of a CRISPR-Cas system, 2) during an outbreak, there is an optimal probability of resistance acquisition balancing the cost of autoimmunity and immune efficiency and 3) type I/II CRISPR-Cas systems are likely to evolve higher acquisition probability than type III.
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Affiliation(s)
- Hélène Chabas
- Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland
- * E-mail:
| | - Viktor Müller
- Institute of Biology, Eötvös Loránd University, Budapest, Hungary
| | | | - Roland R. Regoes
- Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland
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Pascual-García A, Schwartzman J, Enke TN, Iffland-Stettner A, Cordero OX, Bonhoeffer S. Turnover in Life-Strategies Recapitulates Marine Microbial Succession Colonizing Model Particles. Front Microbiol 2022; 13:812116. [PMID: 35814698 PMCID: PMC9260654 DOI: 10.3389/fmicb.2022.812116] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/29/2022] [Indexed: 12/02/2022] Open
Abstract
Particulate organic matter (POM) in the ocean sustains diverse communities of bacteria that mediate the remineralization of organic complex matter. However, the variability of these particles and of the environmental conditions surrounding them present a challenge to the study of the ecological processes shaping particle-associated communities and their function. In this work, we utilize data from experiments in which coastal water communities are grown on synthetic particles to ask which are the most important ecological drivers of their assembly and associated traits. Combining 16S rRNA amplicon sequencing with shotgun metagenomics, together with an analysis of the full genomes of a subset of isolated strains, we were able to identify two-to-three distinct community classes, corresponding to early vs. late colonizers. We show that these classes are shaped by environmental selection (early colonizers) and facilitation (late colonizers) and find distinctive traits associated with each class. While early colonizers have a larger proportion of genes related to the uptake of nutrients, motility, and environmental sensing with few pathways enriched for metabolism, late colonizers devote a higher proportion of genes for metabolism, comprising a wide array of different pathways including the metabolism of carbohydrates, amino acids, and xenobiotics. Analysis of selected pathways suggests the existence of a trophic-chain topology connecting both classes for nitrogen metabolism, potential exchange of branched chain amino acids for late colonizers, and differences in bacterial doubling times throughout the succession. The interpretation of these traits suggests a distinction between early and late colonizers analogous to other classifications found in the literature, and we discuss connections with the classical distinction between r- and K-strategists.
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Affiliation(s)
- Alberto Pascual-García
- Institute of Integrative Biology, Eidgenössische Technische Hochschule (ETH)-Zürich, Zurich, Switzerland
- *Correspondence: Alberto Pascual-García
| | - Julia Schwartzman
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Tim N. Enke
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
- Institute of Biogeochemistry and Pollutant Dynamics, Eidgenössische Technische Hochschule (ETH)-Zürich, Zurich, Switzerland
| | - Arion Iffland-Stettner
- Institute of Integrative Biology, Eidgenössische Technische Hochschule (ETH)-Zürich, Zurich, Switzerland
| | - Otto X. Cordero
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Eidgenössische Technische Hochschule (ETH)-Zürich, Zurich, Switzerland
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11
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Bouman JA, Kadelka S, Stringhini S, Pennacchio F, Meyer B, Yerly S, Kaiser L, Guessous I, Azman AS, Bonhoeffer S, Regoes RR. Applying mixture model methods to SARS-CoV-2 serosurvey data from Geneva. Epidemics 2022; 39:100572. [PMID: 35580458 PMCID: PMC9076579 DOI: 10.1016/j.epidem.2022.100572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 02/01/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
Serosurveys are an important tool to estimate the true extent of the current SARS-CoV-2 pandemic. So far, most serosurvey data have been analyzed with cutoff-based methods, which dichotomize individual measurements into sero-positives or negatives based on a predefined cutoff. However, mixture model methods can gain additional information from the same serosurvey data. Such methods refrain from dichotomizing individual values and instead use the full distribution of the serological measurements from pre-pandemic and COVID-19 controls to estimate the cumulative incidence. This study presents an application of mixture model methods to SARS-CoV-2 serosurvey data from the SEROCoV-POP study from April and May 2020 in Geneva (2766 individuals). Besides estimating the total cumulative incidence in these data (8.1% (95% CI: 6.8%–9.9%)), we applied extended mixture model methods to estimate an indirect indicator of disease severity, which is the fraction of cases with a distribution of antibody levels similar to hospitalized COVID-19 patients. This fraction is 51.2% (95% CI: 15.2%–79.5%) across the full serosurvey, but differs between three age classes: 21.4% (95% CI: 0%–59.6%) for individuals between 5 and 40 years old, 60.2% (95% CI: 21.5%–100%) for individuals between 41 and 65 years old and 100% (95% CI: 20.1%–100%) for individuals between 66 and 90 years old. Additionally, we find a mismatch between the inferred negative distribution of the serosurvey and the validation data of pre-pandemic controls. Overall, this study illustrates that mixture model methods can provide additional insights from serosurvey data.
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12
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Huisman JS, Benz F, Duxbury SJN, de Visser JAGM, Hall AR, Fischer EAJ, Bonhoeffer S. Estimating plasmid conjugation rates: A new computational tool and a critical comparison of methods. Plasmid 2022; 121:102627. [PMID: 35271855 DOI: 10.1016/j.plasmid.2022.102627] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 01/22/2022] [Accepted: 03/01/2022] [Indexed: 11/27/2022]
Abstract
Plasmids are important vectors for the spread of genes among diverse populations of bacteria. However, there is no standard method to determine the rate at which they spread horizontally via conjugation. Here, we compare commonly used methods on simulated and experimental data, and show that the resulting conjugation rate estimates often depend strongly on the time of measurement, the initial population densities, or the initial ratio of donor to recipient populations. Differences in growth rate, e.g. induced by sub-lethal antibiotic concentrations or temperature, can also significantly bias conjugation rate estimates. We derive a new 'end-point' measure to estimate conjugation rates, which extends the well-known Simonsen method to include the effects of differences in population growth and conjugation rates from donors and transconjugants. We further derive analytical expressions for the parameter range in which these approximations remain valid. We present an easy to use R package and web interface which implement both new and previously existing methods to estimate conjugation rates. The result is a set of tools and guidelines for accurate and comparable measurement of plasmid conjugation rates.
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Affiliation(s)
- Jana S Huisman
- Institute of Integrative Biology, ETH Zürich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Fabienne Benz
- Institute of Integrative Biology, ETH Zürich, Zurich, Switzerland
| | - Sarah J N Duxbury
- Laboratory of Genetics, Wageningen University, Wageningen, the Netherlands
| | | | - Alex R Hall
- Institute of Integrative Biology, ETH Zürich, Zurich, Switzerland
| | - Egil A J Fischer
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
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13
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Ashcroft P, Bonhoeffer S. Constrained optimization of divisional load in hierarchically organized tissues during homeostasis. J R Soc Interface 2022; 19:20210784. [PMID: 35193391 PMCID: PMC8864360 DOI: 10.1098/rsif.2021.0784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
It has been hypothesized that the structure of tissues and the hierarchy of differentiation from stem cell to terminally differentiated cell play a significant role in reducing the incidence of cancer in that tissue. One specific mechanism by which this risk can be reduced is by minimizing the number of divisions—and hence the mutational risk—that cells accumulate as they divide to maintain tissue homeostasis. Here, we investigate a mathematical model of cell division in a hierarchical tissue, calculating and minimizing the divisional load while constraining parameters such that homeostasis is maintained. We show that the minimal divisional load is achieved by binary division trees with progenitor cells incapable of self-renewal. Contrary to the protection hypothesis, we find that an increased stem cell turnover can lead to lower divisional load. Furthermore, we find that the optimal tissue structure depends on the time horizon of the duration of homeostasis, with faster stem cell division favoured in short-lived organisms and more progenitor compartments favoured in longer-lived organisms.
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Affiliation(s)
- Peter Ashcroft
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
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14
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Ashcroft P, Lehtinen S, Bonhoeffer S. Test-trace-isolate-quarantine (TTIQ) intervention strategies after symptomatic COVID-19 case identification. PLoS One 2022; 17:e0263597. [PMID: 35148359 PMCID: PMC8836351 DOI: 10.1371/journal.pone.0263597] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 01/22/2022] [Indexed: 12/23/2022] Open
Abstract
The test-trace-isolate-quarantine (TTIQ) strategy, where confirmed-positive pathogen carriers are isolated from the community and their recent close contacts are identified and pre-emptively quarantined, is used to break chains of transmission during a disease outbreak. The protocol is frequently followed after an individual presents with disease symptoms, at which point they will be tested for the pathogen. This TTIQ strategy, along with hygiene and social distancing measures, make up the non-pharmaceutical interventions that are utilised to suppress the ongoing COVID-19 pandemic. Here we develop a tractable mathematical model of disease transmission and the TTIQ intervention to quantify how the probability of detecting and isolating a case following symptom onset, the fraction of contacts that are identified and quarantined, and the delays inherent to these processes impact epidemic growth. In the model, the timing of disease transmission and symptom onset, as well as the frequency of asymptomatic cases, is based on empirical distributions of SARS-CoV-2 infection dynamics, while the isolation of confirmed cases and quarantine of their contacts is implemented by truncating their respective infectious periods. We find that a successful TTIQ strategy requires intensive testing: the majority of transmission is prevented by isolating symptomatic individuals and doing so in a short amount of time. Despite the lesser impact, additional contact tracing and quarantine increases the parameter space in which an epidemic is controllable and is necessary to control epidemics with a high reproductive number. TTIQ could remain an important intervention for the foreseeable future of the COVID-19 pandemic due to slow vaccine rollout and highly-transmissible variants with the potential for vaccine escape. Our results can be used to assess how TTIQ can be improved and optimised, and the methodology represents an improvement over previous quantification methods that is applicable to future epidemic scenarios.
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Affiliation(s)
- Peter Ashcroft
- Institute of Integrative Biology, ETH Zurich, Zürich, Switzerland
- * E-mail: (PA); (SB)
| | - Sonja Lehtinen
- Institute of Integrative Biology, ETH Zurich, Zürich, Switzerland
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, ETH Zurich, Zürich, Switzerland
- * E-mail: (PA); (SB)
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15
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Igler C, Huisman JS, Siedentop B, Bonhoeffer S, Lehtinen S. Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200478. [PMID: 34839701 PMCID: PMC8628072 DOI: 10.1098/rstb.2020.0478] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 07/09/2021] [Indexed: 12/27/2022] Open
Abstract
As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid-host interactions but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection-from systems that exclude infection by other plasmids to interactions in the regulation of plasmid copy number-are well characterized at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics and the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts ecological and evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection on plasmid variants. This article is part of the theme issue 'The secret lives of microbial mobile genetic elements'.
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Affiliation(s)
- Claudia Igler
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | - Jana S. Huisman
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Berit Siedentop
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | - Sonja Lehtinen
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
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16
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Pires J, Huisman JS, Bonhoeffer S, Van Boeckel TP. Increase in antimicrobial resistance in Escherichia coli in food animals between 1980 and 2018 assessed using genomes from public databases. J Antimicrob Chemother 2021; 77:646-655. [PMID: 34894245 DOI: 10.1093/jac/dkab451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 11/09/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Next-generation sequencing has considerably increased the number of genomes available in the public domain. However, efforts to use these genomes for surveillance of antimicrobial resistance have thus far been limited and geographically heterogeneous. We inferred global resistance trends in Escherichia coli in food animals using genomes from public databases. METHODS We retrieved 7632 E. coli genomes from public databases (NCBI, PATRIC and EnteroBase) and screened for antimicrobial resistance genes (ARGs) using ResFinder. Selection bias towards resistance, virulence or specific strains was accounted for by screening BioProject descriptions. Temporal trends for MDR, resistance to antimicrobial classes and ARG prevalence were inferred using generalized linear models for all genomes, including those not subjected to selection bias. RESULTS MDR increased by 1.6 times between 1980 and 2018, as genomes carried, on average, ARGs conferring resistance to 2.65 antimicrobials in swine, 2.22 in poultry and 1.58 in bovines. Highest resistance levels were observed for tetracyclines (42.2%-69.1%), penicillins (19.4%-47.5%) and streptomycin (28.6%-56.6%). Resistance trends were consistent after accounting for selection bias, although lower mean absolute resistance estimates were associated with genomes not subjected to selection bias (difference of 3.16%±3.58% across years, hosts and antimicrobial classes). We observed an increase in extended-spectrum cephalosporin ARG blaCMY-2 and a progressive substitution of tetB by tetA. Estimates of resistance prevalence inferred from genomes in the public domain were in good agreement with reports from systematic phenotypic surveillance. CONCLUSIONS Our analysis illustrates the potential of using the growing volume of genomes in public databases to track AMR trends globally.
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Affiliation(s)
- João Pires
- Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland
| | - Jana S Huisman
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Thomas P Van Boeckel
- Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland.,Center for Disease Dynamics, Economics & Policy, New Delhi, India
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17
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Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C, Kahn R, Niehus R, Hay JA, De Salazar PM, Hellewell J, Meakin S, Munday JD, Bosse NI, Sherrat K, Thompson RN, White LF, Huisman JS, Scire J, Bonhoeffer S, Stadler T, Wallinga J, Funk S, Lipsitch M, Cobey S. Correction: Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol 2021; 17:e1009679. [PMID: 34879070 PMCID: PMC8654153 DOI: 10.1371/journal.pcbi.1009679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pcbi.1008409.].
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18
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Bakkeren E, Herter JA, Huisman JS, Steiger Y, Gül E, Newson JPM, Brachmann AO, Piel J, Regoes R, Bonhoeffer S, Diard M, Hardt WD. Pathogen invasion-dependent tissue reservoirs and plasmid-encoded antibiotic degradation boost plasmid spread in the gut. eLife 2021; 10:e69744. [PMID: 34872631 PMCID: PMC8651294 DOI: 10.7554/elife.69744] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 11/10/2021] [Indexed: 11/30/2022] Open
Abstract
Many plasmids encode antibiotic resistance genes. Through conjugation, plasmids can be rapidly disseminated. Previous work identified gut luminal donor/recipient blooms and tissue-lodged plasmid-bearing persister cells of the enteric pathogen Salmonella enterica serovar Typhimurium (S.Tm) that survive antibiotic therapy in host tissues, as factors promoting plasmid dissemination among Enterobacteriaceae. However, the buildup of tissue reservoirs and their contribution to plasmid spread await experimental demonstration. Here, we asked if re-seeding-plasmid acquisition-invasion cycles by S.Tm could serve to diversify tissue-lodged plasmid reservoirs, and thereby promote plasmid spread. Starting with intraperitoneal mouse infections, we demonstrate that S.Tm cells re-seeding the gut lumen initiate clonal expansion. Extended spectrum beta-lactamase (ESBL) plasmid-encoded gut luminal antibiotic degradation by donors can foster recipient survival under beta-lactam antibiotic treatment, enhancing transconjugant formation upon re-seeding. S.Tm transconjugants can subsequently re-enter host tissues introducing the new plasmid into the tissue-lodged reservoir. Population dynamics analyses pinpoint recipient migration into the gut lumen as rate-limiting for plasmid transfer dynamics in our model. Priority effects may be a limiting factor for reservoir formation in host tissues. Overall, our proof-of-principle data indicates that luminal antibiotic degradation and shuttling between the gut lumen and tissue-resident reservoirs can promote the accumulation and spread of plasmids within a host over time.
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Affiliation(s)
- Erik Bakkeren
- Institute of Microbiology, Department of Biology, ETH ZurichZurichSwitzerland
| | | | - Jana Sanne Huisman
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH ZurichZurichSwitzerland
| | - Yves Steiger
- Institute of Microbiology, Department of Biology, ETH ZurichZurichSwitzerland
| | - Ersin Gül
- Institute of Microbiology, Department of Biology, ETH ZurichZurichSwitzerland
| | | | | | - Jörn Piel
- Institute of Microbiology, Department of Biology, ETH ZurichZurichSwitzerland
| | - Roland Regoes
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH ZurichZurichSwitzerland
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH ZurichZurichSwitzerland
| | - Médéric Diard
- Botnar Research Centre for Child HealthBaselSwitzerland
- Biozentrum, University of BaselBaselSwitzerland
| | - Wolf-Dietrich Hardt
- Institute of Microbiology, Department of Biology, ETH ZurichZurichSwitzerland
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19
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Abstract
Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic-resistant strains that delay or impede effective treatment. To ensure successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (combination therapy) but also the random assignment of drugs to different patients (mixing) and a regular switch in the default drug used in the hospital from drug A to drug B and back (cycling). For more than 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modelling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation and its analysis. We thereafter discuss how progress can be made and suggest future modelling directions.
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Affiliation(s)
- Hildegard Uecker
- Institute of Integrative Biology, ETH Zurich, Universitätstrasse 16, Zurich 8092, Switzerland.,Max Planck Institute for Evolutionary Biology, August-Thienemann-Strasse 2, Plön 24306, Germany
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, ETH Zurich, Universitätstrasse 16, Zurich 8092, Switzerland
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20
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Lehtinen S, Huisman JS, Bonhoeffer S. Evolutionary mechanisms that determine which bacterial genes are carried on plasmids. Evol Lett 2021; 5:290-301. [PMID: 34136276 PMCID: PMC8190454 DOI: 10.1002/evl3.226] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 04/29/2021] [Indexed: 01/05/2023] Open
Abstract
The evolutionary pressures that determine the location (chromosomal or plasmid‐borne) of bacterial genes are not fully understood. We investigate these pressures through mathematical modeling in the context of antibiotic resistance, which is often found on plasmids. Our central finding is that gene location is under positive frequency‐dependent selection: the higher the frequency of one form of resistance compared to the other, the higher its relative fitness. This can keep moderately beneficial genes on plasmids, despite occasional plasmid loss. For these genes, positive frequency dependence leads to a priority effect: whichever form is acquired first—through either mutation or horizontal gene transfer—has time to increase in frequency and thus becomes difficult to displace. Higher rates of horizontal transfer of plasmid‐borne than chromosomal genes therefore predict moderately beneficial genes will be found on plasmids. Gene flow between plasmid and chromosome allows chromosomal forms to arise, but positive frequency‐dependent selection prevents these from establishing. Further modeling shows that this effect is particularly pronounced when genes are shared across a large number of species, suggesting that antibiotic resistance genes are often found on plasmids because they are moderately beneficial across many species. We also revisit previous theoretical work—relating to the role of local adaptation in explaining gene location and to plasmid persistence—in light of our findings.
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Affiliation(s)
- Sonja Lehtinen
- Department of Environmental System Science Institute for Integrative Biology, ETH Zürich Universitätstrasse 16 Zürich 8006 Switzerland
| | - Jana S Huisman
- Department of Environmental System Science Institute for Integrative Biology, ETH Zürich Universitätstrasse 16 Zürich 8006 Switzerland.,Swiss Institute of Bioinformatics Quartier Sorge Lausanne 1015 Switzerland
| | - Sebastian Bonhoeffer
- Department of Environmental System Science Institute for Integrative Biology, ETH Zürich Universitätstrasse 16 Zürich 8006 Switzerland
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21
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Angst DC, Tepekule B, Sun L, Bogos B, Bonhoeffer S. Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting. Proc Natl Acad Sci U S A 2021; 118:e2023467118. [PMID: 33766914 PMCID: PMC8020770 DOI: 10.1073/pnas.2023467118] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
The rapid rise of antibiotic resistance, combined with the increasing cost and difficulties to develop new antibiotics, calls for treatment strategies that enable more sustainable antibiotic use. The development of such strategies, however, is impeded by the lack of suitable experimental approaches that allow testing their effects under realistic epidemiological conditions. Here, we present an approach to compare the effect of alternative multidrug treatment strategies in vitro using a robotic liquid-handling platform. We use this framework to study resistance evolution and spread implementing epidemiological population dynamics for treatment, transmission, and patient admission and discharge, as may be observed in hospitals. We perform massively parallel experimental evolution over up to 40 d and complement this with a computational model to infer the underlying population-dynamical parameters. We find that in our study, combination therapy outperforms monotherapies, as well as cycling and mixing, in minimizing resistance evolution and maximizing uninfecteds, as long as there is no influx of double resistance into the focal treated community.
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Affiliation(s)
- Daniel C Angst
- Institute of Integrative Biology, Department for Environmental System Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Burcu Tepekule
- Institute of Integrative Biology, Department for Environmental System Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Lei Sun
- Institute of Integrative Biology, Department for Environmental System Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Balázs Bogos
- Institute of Integrative Biology, Department for Environmental System Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Department for Environmental System Science, ETH Zurich, 8092 Zurich, Switzerland
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22
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Courcol JD, Invernizzi CF, Landry ZC, Minisini M, Baumgartner DA, Bonhoeffer S, Chabriw B, Clerc EE, Daniels M, Getta P, Girod M, Kazala K, Markram H, Pasqualini A, Martínez-Pérez C, Peaudecerf FJ, Peaudecerf MS, Pfreundt U, Roller BRK, Słomka J, Vasse M, Wheeler JD, Metzger CMJA, Stocker R, Schürmann F. ARC: An Open Web-Platform for Request/Supply Matching for a Prioritized and Controlled COVID-19 Response. Front Public Health 2021; 9:607677. [PMID: 33665184 PMCID: PMC7921781 DOI: 10.3389/fpubh.2021.607677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/18/2021] [Indexed: 11/30/2022] Open
Abstract
In 2020 the world was hit by the COVID-19 pandemic putting entire governments and civil societies in crisis mode. Around the globe unprecedented shortages of equipment and qualified personnel were reported in hospitals and diagnostic laboratories. When a crisis is global, supply chains are strained worldwide and external help may not be readily available. In Switzerland, as part of the efforts of the Swiss National COVID-19 Science Task Force, we developed a tailor-made web-based tool where needs and offers for critical laboratory equipment and expertise can be brought together, coordinated, prioritized, and validated. This Academic Resources for COVID-19 (ARC) Platform presents the specialized needs of diagnostic laboratories to academic research groups at universities, allowing the sourcing of said needs from unconventional supply channels, while keeping the entities tasked with coordination of the crisis response in control of each part of the process. An instance of the ARC Platform is operated in Switzerland (arc.epfl.ch) catering to the diagnostic efforts in Switzerland and sourcing from the Swiss academic sector. The underlying technology has been released as open source so that others can adopt the customizable web-platform for need/supply match-making in their own relief efforts, during the COVID-19 pandemic or any future disaster.
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Affiliation(s)
- Jean-Denis Courcol
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Cédric F Invernizzi
- Spiez Laboratory, Federal Office for Civil Protection FOCP, Spiez, Switzerland
| | - Zachary C Landry
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | | | - Dieter A Baumgartner
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Institute for Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | | | - Estelle E Clerc
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Michael Daniels
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland.,Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland
| | - Pavlo Getta
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - Kinga Kazala
- Section Software Services, Department of IT Services, ETH Zürich, Zurich, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - Clara Martínez-Pérez
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - François J Peaudecerf
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Margit S Peaudecerf
- Institute for Biochemistry, Department of Biology, ETH Zürich, Zurich, Switzerland
| | - Ulrike Pfreundt
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Benjamin R K Roller
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland.,Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland
| | - Jonasz Słomka
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Marie Vasse
- Institute for Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | - Jeanette D Wheeler
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - César M J A Metzger
- Spiez Laboratory, Federal Office for Civil Protection FOCP, Spiez, Switzerland
| | - Roman Stocker
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Felix Schürmann
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
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23
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Ashcroft P, Lehtinen S, Angst DC, Low N, Bonhoeffer S. Quantifying the impact of quarantine duration on COVID-19 transmission. eLife 2021; 10:e63704. [PMID: 33543709 PMCID: PMC7963476 DOI: 10.7554/elife.63704] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/04/2021] [Indexed: 12/13/2022] Open
Abstract
The large number of individuals placed into quarantine because of possible severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) exposure has high societal and economic costs. There is ongoing debate about the appropriate duration of quarantine, particularly since the fraction of individuals who eventually test positive is perceived as being low. We use empirically determined distributions of incubation period, infectivity, and generation time to quantify how the duration of quarantine affects onward transmission from traced contacts of confirmed SARS-CoV-2 cases and from returning travellers. We also consider the roles of testing followed by release if negative (test-and-release), reinforced hygiene, adherence, and symptoms in calculating quarantine efficacy. We show that there are quarantine strategies based on a test-and-release protocol that, from an epidemiological viewpoint, perform almost as well as a 10-day quarantine, but with fewer person-days spent in quarantine. The findings apply to both travellers and contacts, but the specifics depend on the context.
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Affiliation(s)
- Peter Ashcroft
- Institute of Integrative Biology, ETH ZürichZürichSwitzerland
| | - Sonja Lehtinen
- Institute of Integrative Biology, ETH ZürichZürichSwitzerland
| | - Daniel C Angst
- Institute of Integrative Biology, ETH ZürichZürichSwitzerland
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of BernBernSwitzerland
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24
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Bouman JA, Riou J, Bonhoeffer S, Regoes RR. Estimating the cumulative incidence of SARS-CoV-2 with imperfect serological tests: Exploiting cutoff-free approaches. PLoS Comput Biol 2021; 17:e1008728. [PMID: 33635863 PMCID: PMC7946301 DOI: 10.1371/journal.pcbi.1008728] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 03/10/2021] [Accepted: 01/20/2021] [Indexed: 01/10/2023] Open
Abstract
Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method-referred to as mixture-model approach-for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys.
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Affiliation(s)
- Judith A. Bouman
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Julien Riou
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | | | - Roland R. Regoes
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
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25
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Abstract
The timing of transmission plays a key role in the dynamics and controllability of an epidemic. However, observing generation times-the time interval between the infection of an infector and an infectee in a transmission pair-requires data on infection times, which are generally unknown. The timing of symptom onset is more easily observed; generation times are therefore often estimated based on serial intervals-the time interval between symptom onset of an infector and an infectee. This estimation follows one of two approaches: (i) approximating the generation time distribution by the serial interval distribution or (ii) deriving the generation time distribution from the serial interval and incubation period-the time interval between infection and symptom onset in a single individual-distributions. These two approaches make different-and not always explicitly stated-assumptions about the relationship between infectiousness and symptoms, resulting in different generation time distributions with the same mean but unequal variances. Here, we clarify the assumptions that each approach makes and show that neither set of assumptions is plausible for most pathogens. However, the variances of the generation time distribution derived under each assumption can reasonably be considered as upper (approximation with serial interval) and lower (derivation from serial interval) bounds. Thus, we suggest a pragmatic solution is to use both approaches and treat these as edge cases in downstream analysis. We discuss the impact of the variance of the generation time distribution on the controllability of an epidemic through strategies based on contact tracing, and we show that underestimating this variance is likely to overestimate controllability.
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Affiliation(s)
- Sonja Lehtinen
- Institute for Integrative Biology, Department of Environmental System Science, ETH Zürich, Zürich, Switzerland
| | - Peter Ashcroft
- Institute for Integrative Biology, Department of Environmental System Science, ETH Zürich, Zürich, Switzerland
| | - Sebastian Bonhoeffer
- Institute for Integrative Biology, Department of Environmental System Science, ETH Zürich, Zürich, Switzerland
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26
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Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C, Kahn R, Niehus R, Hay JA, De Salazar PM, Hellewell J, Meakin S, Munday JD, Bosse NI, Sherrat K, Thompson RN, White LF, Huisman JS, Scire J, Bonhoeffer S, Stadler T, Wallinga J, Funk S, Lipsitch M, Cobey S. Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol 2020; 16:e1008409. [PMID: 33301457 PMCID: PMC7728287 DOI: 10.1371/journal.pcbi.1008409] [Citation(s) in RCA: 229] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.
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Affiliation(s)
- Katelyn M. Gostic
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, United States of America
| | - Lauren McGough
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, United States of America
| | - Edward B. Baskerville
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, United States of America
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Keya Joshi
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America
| | - Christine Tedijanto
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America
| | - Rebecca Kahn
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America
| | - Rene Niehus
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America
| | - James A. Hay
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America
| | - Pablo M. De Salazar
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America
| | - Joel Hellewell
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Sophie Meakin
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - James D. Munday
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Nikos I. Bosse
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Katharine Sherrat
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Robin N. Thompson
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Laura F. White
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America
| | - Jana S. Huisman
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
| | - Jérémie Scire
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | | | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, United States of America
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27
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van Boeckel T, Pires J, Silvester R, Zhao C, Song J, Criscuolo N, Gilbert M, Bonhoeffer S, Laxminarayan R. Global trends in antimicrobial resistance in animals in low- and middle-income countries. Int J Infect Dis 2020. [DOI: 10.1016/j.ijid.2020.09.086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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28
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Affiliation(s)
- Christopher Witzany
- Freie Universität Berlin, Institut für Biologie, Evolutionary Biology, Berlin, Germany
| | | | - Jens Rolff
- Freie Universität Berlin, Institut für Biologie, Evolutionary Biology, Berlin, Germany
- Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
- * E-mail:
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29
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Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C, Kahn R, Niehus R, Hay J, De Salazar PM, Hellewell J, Meakin S, Munday J, Bosse NI, Sherrat K, Thompson RN, White LF, Huisman JS, Scire J, Bonhoeffer S, Stadler T, Wallinga J, Funk S, Lipsitch M, Cobey S. Practical considerations for measuring the effective reproductive number, R t. medRxiv 2020:2020.06.18.20134858. [PMID: 32607522 PMCID: PMC7325187 DOI: 10.1101/2020.06.18.20134858] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Estimation of the effective reproductive number, R t , is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using R t to assess the effectiveness of interventions and to inform policy. However, estimation of R t from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of R t , we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to spread. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting R t estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in R t estimation.
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Affiliation(s)
- Katelyn M. Gostic
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Lauren McGough
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | | | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Keya Joshi
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - Christine Tedijanto
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - Rebecca Kahn
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - Rene Niehus
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - James Hay
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - Pablo M. De Salazar
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - Joel Hellewell
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Sophie Meakin
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - James Munday
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Nikos I. Bosse
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Katharine Sherrat
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Robin N. Thompson
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Laura F. White
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jana S. Huisman
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
| | - Jérémie Scire
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | | | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
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30
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Ashcroft P, Huisman JS, Lehtinen S, Bouman JA, Althaus CL, Regoes RR, Bonhoeffer S. COVID-19 infectivity profile correction. Swiss Med Wkly 2020; 150:w20336. [PMID: 32757177 DOI: 10.4414/smw.2020.20336] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Peter Ashcroft
- Institute of Integrative Biology, ETH Zurich, Switzerland
| | - Jana S Huisman
- Institute of Integrative Biology, ETH Zurich, Switzerland
| | - Sonja Lehtinen
- Institute of Integrative Biology, ETH Zurich, Switzerland
| | | | - Christian L Althaus
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
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31
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von Wyl V, Bonhoeffer S, Bugnion E, Puhan MA, Salathé M, Stadler T, Troncoso C, Vayena E, Low N. A research agenda for digital proximity tracing apps. Swiss Med Wkly 2020; 150:w20324. [PMID: 32672340 DOI: 10.4414/smw.2020.20324] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Viktor von Wyl
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Switzerland / Institute for Implementation Science in Health Care, University of Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Eidgenössische Technische Hochschule Zürich (ETHZ), Switzerland
| | - Edouard Bugnion
- École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Switzerland
| | - Marcel Salathé
- École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Theresa Stadler
- École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | | | - Effy Vayena
- Institute of Translational Medicine, Eidgenössische Technische Hochschule Zürich (ETHZ), Switzerland
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
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32
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Pascual-García A, Bonhoeffer S, Bell T. Metabolically cohesive microbial consortia and ecosystem functioning. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190245. [PMID: 32200744 PMCID: PMC7133520 DOI: 10.1098/rstb.2019.0245] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2020] [Indexed: 01/10/2023] Open
Abstract
Recent theory and experiments have reported a reproducible tendency for the coexistence of microbial species under controlled environmental conditions. This observation has been explained in the context of competition for resources and metabolic complementarity given that, in microbial communities (MCs), many excreted by-products of metabolism may also be resources. MCs therefore play a key role in promoting their own stability and in shaping the niches of the constituent taxa. We suggest that an intermediate level of organization between the species and the community level may be pervasive, where tightly knit metabolic interactions create discrete consortia that are stably maintained. We call these units Metabolically Cohesive Consortia (MeCoCos) and we discuss the environmental context in which we expect their formation, and the ecological and evolutionary consequences of their existence. We argue that the ability to identify MeCoCos would open new avenues to link the species-, community- and ecosystem-level properties, with consequences for our understanding of microbial ecology and evolution, and an improved ability to predict ecosystem functioning in the wild. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.
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Affiliation(s)
| | | | - Thomas Bell
- Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, UK
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33
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Garcia V, Bonhoeffer S, Fu F. Cancer-induced immunosuppression can enable effectiveness of immunotherapy through bistability generation: A mathematical and computational examination. J Theor Biol 2020; 492:110185. [PMID: 32035826 PMCID: PMC7079339 DOI: 10.1016/j.jtbi.2020.110185] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 01/09/2020] [Accepted: 02/03/2020] [Indexed: 12/22/2022]
Abstract
The presence of an immunological barrier in cancer- immune system interaction (CISI) is consistent with the bistability patterns in that system. In CISI models, bistability patterns are consistent with immunosuppressive effects dominating immunoproliferative effects. Bistability could be harnessed to devise effective combination immunotherapy approaches.
Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these interactions are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can plausibly reproduce biological behavior that is consistent with the notion of an immunological barrier. This behavior is also in accord with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into a cancer-free state. Additionally, in combination with the administration of immune effector cells, modifications in cancer cell killing may be harnessed for immunotherapy without the need for resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers.
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Affiliation(s)
- Victor Garcia
- Institute of Applied Simulation, Zurich University of Applied Sciences, Einsiedlerstrasse 31a, 8820 Wädenswil, Switzerland; ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland; Institute for Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, 3012 Bern, Switzerland; Department of Biology, Stanford University, 371 Serra Mall, Stanford CA 94305, USA.
| | | | - Feng Fu
- Department of Mathematics, Dartmouth College, 27 N. Main Street, 6188 Kemeny Hall, Hanover, NH 03755-3551, USA; ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
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34
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Abstract
Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzyme's catalytic rate alone may contribute to resistance if drug binding is not rate limiting. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.
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Affiliation(s)
- Lei Sun
- Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
| | - Peter Ashcroft
- Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
| | - Martin Ackermann
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzerland.,Department of Environmental Microbiology, EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
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35
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Tepekule B, Abel Zur Wiesch P, Kouyos RD, Bonhoeffer S. Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota. Proc Natl Acad Sci U S A 2019; 116:23106-23116. [PMID: 31666328 PMCID: PMC6859334 DOI: 10.1073/pnas.1912188116] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
To understand how antibiotic use affects the risk of a resistant infection, we present a computational model of the population dynamics of gut microbiota including antibiotic resistance-conferring plasmids. We then describe how this model is parameterized based on published microbiota data. Finally, we investigate how treatment history affects the prevalence of resistance among opportunistic enterobacterial pathogens. We simulate treatment histories and identify which properties of prior antibiotic exposure are most influential in determining the prevalence of resistance. We find that resistance prevalence can be predicted by 3 properties, namely the total days of drug exposure, the duration of the drug-free period after last treatment, and the center of mass of the treatment pattern. Overall this work provides a framework for capturing the role of the microbiome in the selection of antibiotic resistance and highlights the role of treatment history for the prevalence of resistance.
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Affiliation(s)
- Burcu Tepekule
- Department of Environmental Systems Science, Eidgenössische Technische Hochschule Zurich, 8092 Zurich, Switzerland;
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway
- Centre for Molecular Medicine Norway, 0318 Oslo, Norway
| | - Roger D Kouyos
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Department of Environmental Systems Science, Eidgenössische Technische Hochschule Zurich, 8092 Zurich, Switzerland
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36
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Shilaih M, Angst DC, Marzel A, Bonhoeffer S, Günthard HF, Kouyos RD. Antibacterial effects of antiretrovirals, potential implications for microbiome studies in HIV. Antivir Ther 2019; 23:91-94. [PMID: 28497768 DOI: 10.3851/imp3173] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/17/2017] [Indexed: 10/19/2022]
Abstract
BACKGROUND Despite being used by more than 18 million people our understanding of the extent of the effects of antiretrovirals on the human body and other organisms remains incomplete. In addition, the direct effect of antiretrovirals on the gut microbiota of HIV-infected individuals has been largely overlooked in microbiome studies concerned with HIV-infected individuals. METHODS Here we tested 25 antiretrovirals on Bacillus subtilis and Escherichia coli using a broth microdilution assay to assess whether these drugs have an antibacterial effect. RESULTS We found that several widely used antiretroviral drugs have in vitro antibacterial activity against both gram-positive and gram-negative commensal bacteria. Efavirenz inhibited the growth of B. subtilis with a minimum inhibitory concentration (MIC) of 16 µg/ml (in all three replicates), while 2',3'-dideoxyinosine and zidovudine inhibited the growth of E. coli with an MIC of 16-32 µg/ml and 0.016-0.125 µg/ml (respectively). CONCLUSIONS Given the large and increasing number of individuals on antiretrovirals, and the lifelong nature of HIV treatment, this proof-of-concept report could have several potential implications, including an impact of antiretrovirals on bacterial coinfections, as well as potentials for drug discovery and repositioning.
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Affiliation(s)
- Mohaned Shilaih
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Daniel C Angst
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Alex Marzel
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | | | - Huldrych F Günthard
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Roger D Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
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37
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Van Boeckel TP, Pires J, Silvester R, Zhao C, Song J, Criscuolo NG, Gilbert M, Bonhoeffer S, Laxminarayan R. Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science 2019; 365:365/6459/eaaw1944. [DOI: 10.1126/science.aaw1944] [Citation(s) in RCA: 369] [Impact Index Per Article: 73.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/19/2019] [Accepted: 07/31/2019] [Indexed: 12/17/2022]
Abstract
The global scale-up in demand for animal protein is the most notable dietary trend of our time. Antimicrobial consumption in animals is threefold that of humans and has enabled large-scale animal protein production. The consequences for the development of antimicrobial resistance in animals have received comparatively less attention than in humans. We analyzed 901 point prevalence surveys of pathogens in developing countries to map resistance in animals. China and India represented the largest hotspots of resistance, with new hotspots emerging in Brazil and Kenya. From 2000 to 2018, the proportion of antimicrobials showing resistance above 50% increased from 0.15 to 0.41 in chickens and from 0.13 to 0.34 in pigs. Escalating resistance in animals is anticipated to have important consequences for animal health and, eventually, for human health.
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38
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Bakkeren E, Huisman JS, Fattinger SA, Hausmann A, Furter M, Egli A, Slack E, Sellin ME, Bonhoeffer S, Regoes RR, Diard M, Hardt WD. Salmonella persisters promote the spread of antibiotic resistance plasmids in the gut. Nature 2019; 573:276-280. [PMID: 31485077 PMCID: PMC6744281 DOI: 10.1038/s41586-019-1521-8] [Citation(s) in RCA: 138] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 08/05/2019] [Indexed: 12/17/2022]
Abstract
The emergence of antibiotic resistant bacteria by mutations or by acquisition of genetic material like resistance plasmids represents a major public health issue 1,2 (Extended Data Fig. 1a). Persisters are bacterial subpopulations surviving antibiotics by reversibly adapting their physiology 3–10. They promote the emergence of antibiotic resistant mutants 11. We asked if persisters can also promote the spread of resistance plasmids. In contrast to mutations, resistance plasmid transfer requires the co-occurrence of two different bacterial strains: a donor and a recipient (Extended Data Fig. 1a). For our experiments, we chose the facultative intracellular entero-pathogen Salmonella enterica serovar Typhimurium (S.Tm) and E. coli, a common microbiota member 12. S.Tm forms persisters surviving antibiotic therapy in several host tissues. We show that tissue-associated, S.Tm persisters account for long-lived reservoirs of plasmid donors or recipients. Persistent S.Tm reservoir formation requires Salmonella Pathogenicity Island (SPI) -1/2 in the gut-associated tissues or SPI-2 at systemic sites. Re-seeding of these bacteria into the gut lumen allows co-occurrence of donors with gut-resident recipients, thereby favouring plasmid transfer between various Enterobacteriaceae. We observe up to 99% transconjugants within 2-3 days after re-seeding. Mathematical modeling shows that rare re-seeding events may suffice for a high frequency of conjugation. Vaccination reduces tolerant reservoir formation after oral Salmonella infection and subsequent plasmid transfer. We conclude that even without selection for plasmid-encoded resistance genes, small persistent pathogen reservoirs can foster the spread of promiscuous resistance plasmids in the gut.
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Affiliation(s)
- Erik Bakkeren
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Jana S Huisman
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stefan A Fattinger
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland.,Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Annika Hausmann
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Markus Furter
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Adrian Egli
- Division of Clinical Microbiology, University Hospital Basel, Basel, Switzerland.,Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Emma Slack
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland.,Institute of Food, Nutrition and Health, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Mikael E Sellin
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Roland R Regoes
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Médéric Diard
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland. .,Biozentrum, University of Basel, Basel, Switzerland.
| | - Wolf-Dietrich Hardt
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland.
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39
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Bertels F, Marzel A, Leventhal G, Mitov V, Fellay J, Günthard HF, Böni J, Yerly S, Klimkait T, Aubert V, Battegay M, Rauch A, Cavassini M, Calmy A, Bernasconi E, Schmid P, Scherrer AU, Müller V, Bonhoeffer S, Kouyos R, Regoes RR. Dissecting HIV Virulence: Heritability of Setpoint Viral Load, CD4+ T-Cell Decline, and Per-Parasite Pathogenicity. Mol Biol Evol 2019; 35:27-37. [PMID: 29029206 PMCID: PMC5850767 DOI: 10.1093/molbev/msx246] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Pathogen strains may differ in virulence because they attain different loads in their hosts, or because they induce different disease-causing mechanisms independent of their load. In evolutionary ecology, the latter is referred to as “per-parasite pathogenicity”. Using viral load and CD4+ T-cell measures from 2014 HIV-1 subtype B-infected individuals enrolled in the Swiss HIV Cohort Study, we investigated if virulence—measured as the rate of decline of CD4+ T cells—and per-parasite pathogenicity are heritable from donor to recipient. We estimated heritability by donor–recipient regressions applied to 196 previously identified transmission pairs, and by phylogenetic mixed models applied to a phylogenetic tree inferred from HIV pol sequences. Regressing the CD4+ T-cell declines and per-parasite pathogenicities of the transmission pairs did not yield heritability estimates significantly different from zero. With the phylogenetic mixed model, however, our best estimate for the heritability of the CD4+ T-cell decline is 17% (5–30%), and that of the per-parasite pathogenicity is 17% (4–29%). Further, we confirm that the set-point viral load is heritable, and estimate a heritability of 29% (12–46%). Interestingly, the pattern of evolution of all these traits differs significantly from neutrality, and is most consistent with stabilizing selection for the set-point viral load, and with directional selection for the CD4+ T-cell decline and the per-parasite pathogenicity. Our analysis shows that the viral genotype affects virulence mainly by modulating the per-parasite pathogenicity, while the indirect effect via the set-point viral load is minor.
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Affiliation(s)
- Frederic Bertels
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Alex Marzel
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | | | - Venelin Mitov
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Jacques Fellay
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Huldrych F Günthard
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Jürg Böni
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Sabine Yerly
- Division of Infectious Diseases, Laboratory of Virology, Geneva University Hospital, Geneva, Switzerland
| | - Thomas Klimkait
- Molecular Virology, Department of Biomedicine - Petersplatz, University of Basel, Basel, Switzerland
| | - Vincent Aubert
- Division of Immunology and Allergy, University Hospital Lausanne, Lausanne, Switzerland
| | - Manuel Battegay
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland
| | - Andri Rauch
- Department of Infectious Diseases, Berne University Hospital and University of Berne, Berne, Switzerland
| | - Matthias Cavassini
- Division of Infectious Diseases, University Hospital Lausanne, Lausanne, Switzerland
| | - Alexandra Calmy
- HIV/AIDS Unit, Infectious Disease Service, Geneva University Hospital, Geneva, Switzerland
| | - Enos Bernasconi
- Division of Infectious Diseases, Regional Hospital Lugano, Lugano, Switzerland
| | - Patrick Schmid
- Division of Infectious Diseases, Cantonal Hospital St Gallen, St Gallen, Switzerland
| | - Alexandra U Scherrer
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Viktor Müller
- Institute of Biology, Eötvös Loránd University, Budapest, Hungary.,Evolutionary Systems Research Group, MTA Centre for Ecological Research, Tihany, Hungary
| | | | - Roger Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Roland R Regoes
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
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40
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Van Boeckel T, Do Couto Pires J, Zhao C, Silvester R, Gilbert M, Bonhoeffer S, Laxminarayan R, Song J. A global map of antimicrobial resistance in animals raised for food. Front Vet Sci 2019. [DOI: 10.3389/conf.fvets.2019.05.00092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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41
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Hozé N, Bonhoeffer S, Regoes R. Assessing the public health impact of tolerance-based therapies with mathematical models. PLoS Comput Biol 2018; 14:e1006119. [PMID: 29727455 PMCID: PMC5955582 DOI: 10.1371/journal.pcbi.1006119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 05/16/2018] [Accepted: 04/03/2018] [Indexed: 12/18/2022] Open
Abstract
Disease tolerance is a defense strategy against infections that aims at maintaining host health even at high pathogen replication or load. Tolerance mechanisms are currently intensively studied with the long-term goal of exploiting them therapeutically. Because tolerance-based treatment imposes less selective pressure on the pathogen it has been hypothesised to be “evolution-proof”. However, the primary public health goal is to reduce the incidence and mortality associated with a disease. From this perspective, tolerance-based treatment bears the risk of increasing the prevalence of the disease, which may lead to increased mortality. We assessed the promise of tolerance-based treatment strategies using mathematical models. Conventional treatment was implemented as an increased recovery rate, while tolerance-based treatment was assumed to reduce the disease-related mortality of infected hosts without affecting recovery. We investigated the endemic phase of two types of infections: acute and chronic. Additionally, we considered the effect of pathogen resistance against conventional treatment. We show that, for low coverage of tolerance-based treatment, chronic infections can cause even more deaths than without treatment. Overall, we found that conventional treatment always outperforms tolerance-based treatment, even when we allow the emergence of pathogen resistance. Our results cast doubt on the potential benefit of tolerance-based over conventional treatment. Any clinical application of tolerance-based treatment of infectious diseases has to consider the associated detrimental epidemiological feedback. Conventional therapies improve patient health by eliminating the pathogen, or, at least, reducing its burden. Recently, alternative therapies that exploit host tolerance mechanisms have received attention from the medical community as a promising strategy. These treatments aim at reducing the level of illness due to the infection, rather than eliminating the pathogen directly. Using a mathematical model, we show that although these treatments are beneficial at the individual level, they can have undesired public health consequences. In particular we show that tolerance-based treatment gives more time for the disease to spread in the population, which in turn increase its prevalence. Moreover, in the case of a low coverage of the treatment of a chronic infection, the overall mortality can increase.
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Affiliation(s)
- Nathanaël Hozé
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
- * E-mail: (NH); (RR)
| | | | - Roland Regoes
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
- * E-mail: (NH); (RR)
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42
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Sun L, Alexander HK, Bogos B, Kiviet DJ, Ackermann M, Bonhoeffer S. Effective polyploidy causes phenotypic delay and influences bacterial evolvability. PLoS Biol 2018; 16:e2004644. [PMID: 29470493 PMCID: PMC5839593 DOI: 10.1371/journal.pbio.2004644] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 03/06/2018] [Accepted: 02/01/2018] [Indexed: 11/18/2022] Open
Abstract
Whether mutations in bacteria exhibit a noticeable delay before expressing their corresponding mutant phenotype was discussed intensively in the 1940s to 1950s, but the discussion eventually waned for lack of supportive evidence and perceived incompatibility with observed mutant distributions in fluctuation tests. Phenotypic delay in bacteria is widely assumed to be negligible, despite the lack of direct evidence. Here, we revisited the question using recombineering to introduce antibiotic resistance mutations into E. coli at defined time points and then tracking expression of the corresponding mutant phenotype over time. Contrary to previous assumptions, we found a substantial median phenotypic delay of three to four generations. We provided evidence that the primary source of this delay is multifork replication causing cells to be effectively polyploid, whereby wild-type gene copies transiently mask the phenotype of recessive mutant gene copies in the same cell. Using modeling and simulation methods, we explored the consequences of effective polyploidy for mutation rate estimation by fluctuation tests and sequencing-based methods. For recessive mutations, despite the substantial phenotypic delay, the per-copy or per-genome mutation rate is accurately estimated. However, the per-cell rate cannot be estimated by existing methods. Finally, with a mathematical model, we showed that effective polyploidy increases the frequency of costly recessive mutations in the standing genetic variation (SGV), and thus their potential contribution to evolutionary adaptation, while drastically reducing the chance that de novo recessive mutations can rescue populations facing a harsh environmental change such as antibiotic treatment. Overall, we have identified phenotypic delay and effective polyploidy as previously overlooked but essential components in bacterial evolvability, including antibiotic resistance evolution. What is the time delay between the occurrence of a genetic mutation in a bacterial cell and manifestation of its phenotypic effect? We show that antibiotic resistance mutations in Escherichia coli show a remarkably long phenotypic delay of three to four bacterial generations. The primary underlying mechanism of this delay is effective polyploidy. If a mutation arises on one of the multiple chromosomes in a polyploid cell, the presence of nonmutated, wild-type gene copies on other chromosomes may mask the phenotype of the mutation. We show here that mutation rate estimation needs to consider polyploidy, which influences the potential for bacterial adaptation. The fact that a new mutation may become useful only in the “great-great-grandchildren” suggests that preexisting mutations are more important for surviving sudden environmental catastrophes.
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Affiliation(s)
- Lei Sun
- Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
| | | | - Balazs Bogos
- Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
| | - Daniel J. Kiviet
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzerland
- Department of Environmental Microbiology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Martin Ackermann
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzerland
- Department of Environmental Microbiology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
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43
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Ashcroft P, Manz MG, Bonhoeffer S. Clonal dominance and transplantation dynamics in hematopoietic stem cell compartments. PLoS Comput Biol 2017; 13:e1005803. [PMID: 28991922 PMCID: PMC5654265 DOI: 10.1371/journal.pcbi.1005803] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 10/19/2017] [Accepted: 09/29/2017] [Indexed: 01/16/2023] Open
Abstract
Hematopoietic stem cells in mammals are known to reside mostly in the bone marrow, but also transitively passage in small numbers in the blood. Experimental findings have suggested that they exist in a dynamic equilibrium, continuously migrating between these two compartments. Here we construct an individual-based mathematical model of this process, which is parametrised using existing empirical findings from mice. This approach allows us to quantify the amount of migration between the bone marrow niches and the peripheral blood. We use this model to investigate clonal hematopoiesis, which is a significant risk factor for hematologic cancers. We also analyse the engraftment of donor stem cells into non-conditioned and conditioned hosts, quantifying the impact of different treatment scenarios. The simplicity of the model permits a thorough mathematical analysis, providing deeper insights into the dynamics of both the model and of the real-world system. We predict the time taken for mutant clones to expand within a host, as well as chimerism levels that can be expected following transplantation therapy, and the probability that a preconditioned host is reconstituted by donor cells. Clonal hematopoiesis—where mature myeloid cells in the blood deriving from a single stem cell are over-represented—is a major risk factor for overt hematologic malignancies. To quantify how likely this phenomena is, we combine existing observations with a novel stochastic model and extensive mathematical analysis. This approach allows us to observe the hidden dynamics of the hematopoietic system. We conclude that for a clone to be detectable within the lifetime of a mouse, it requires a selective advantage. I.e. the clonal expansion cannot be explained by neutral drift alone. Furthermore, we use our model to describe the dynamics of hematopoiesis after stem cell transplantation. In agreement with earlier findings, we observe that niche-space saturation decreases engraftment efficiency. We further discuss the implications of our findings for human hematopoiesis where the quantity and role of stem cells is frequently debated.
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Affiliation(s)
- Peter Ashcroft
- Institut für Integrative Biologie, ETH Zürich, Zürich, Switzerland
- * E-mail:
| | - Markus G. Manz
- Division of Hematology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
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44
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Uecker H, Bonhoeffer S. Modeling antimicrobial cycling and mixing: Differences arising from an individual-based versus a population-based perspective. Math Biosci 2017; 294:85-91. [PMID: 28962827 DOI: 10.1016/j.mbs.2017.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Revised: 06/30/2017] [Accepted: 09/11/2017] [Indexed: 12/21/2022]
Abstract
In order to manage bacterial infections in hospitals in the face of antibiotic resistance, the two treatment protocols "mixing" and "cycling" have received considerable attention both from modelers and clinicians. However, the terms are not used in exactly the same way by both groups. This comes because the standard modeling approach disregards the perspective of individual patients. In this article, we investigate a model that comes closer to clinical practice and compare the predictions to the standard model. We set up two deterministic models, implemented as a set of differential equations, for the spread of bacterial infections in a hospital. Following the traditional approach, the first model takes a population-based perspective. The second model, in contrast, takes the drug use of individual patients into account. The alternative model can indeed lead to different predictions than the standard model. We provide examples for which in the new model, the opposite strategy maximizes the number of uninfected patients or minimizes the rate of spread of double resistance. While the traditional models provide valuable insight, care is hence needed in the interpretation of results.
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Affiliation(s)
- Hildegard Uecker
- Institute of Integrative Biology, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland.
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland.
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45
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Van Boeckel TP, Glennon EE, Chen D, Gilbert M, Robinson TP, Grenfell BT, Levin SA, Bonhoeffer S, Laxminarayan R. Reducing antimicrobial use in food animals. Science 2017; 357:1350-1352. [PMID: 28963240 PMCID: PMC6510296 DOI: 10.1126/science.aao1495] [Citation(s) in RCA: 323] [Impact Index Per Article: 46.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
| | - Emma E Glennon
- Center for Disease Dynamics, Economics and Policy, Washington, DC 20005, USA.,Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK
| | - Dora Chen
- Center for Disease Dynamics, Economics and Policy, Washington, DC 20005, USA.,Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Marius Gilbert
- Université Libre de Bruxelles, Brussels 1050, Belgium.,Fonds National de la Recherche Scientifique, Brussels 1050, Belgium
| | - Timothy P Robinson
- International Livestock Research Institute, Nairobi 00100, Kenya.,Food and Agriculture Organization of the United Nations, Rome 00153, Italy
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.,Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Ramanan Laxminarayan
- Center for Disease Dynamics, Economics and Policy, Washington, DC 20005, USA. .,Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
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46
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Allen RC, Engelstädter J, Bonhoeffer S, McDonald BA, Hall AR. Reversing resistance: different routes and common themes across pathogens. Proc Biol Sci 2017; 284:20171619. [PMID: 28954914 PMCID: PMC5627214 DOI: 10.1098/rspb.2017.1619] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 08/23/2017] [Indexed: 11/12/2022] Open
Abstract
Resistance spreads rapidly in pathogen or pest populations exposed to biocides, such as fungicides and antibiotics, and in many cases new biocides are in short supply. How can resistance be reversed in order to prolong the effectiveness of available treatments? Some key parameters affecting reversion of resistance are well known, such as the fitness cost of resistance. However, the population biological processes that actually cause resistance to persist or decline remain poorly characterized, and consequently our ability to manage reversion of resistance is limited. Where do susceptible genotypes that replace resistant lineages come from? What is the epidemiological scale of reversion? What information do we need to predict the mechanisms or likelihood of reversion? Here, we define some of the population biological processes that can drive reversion, using examples from a wide range of taxa and biocides. These processes differ primarily in the origin of revertant genotypes, but also in their sensitivity to factors such as coselection and compensatory evolution that can alter the rate of reversion, and the likelihood that resistance will re-emerge upon re-exposure to biocides. We therefore argue that discriminating among different types of reversion allows for better prediction of where resistance is most likely to persist.
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Affiliation(s)
- Richard C Allen
- Institute of Integrative Biology, ETH Zürich, CH-8092 Zurich, Switzerland
| | - Jan Engelstädter
- School of Biological Sciences, The University of Queensland, Brisbane, Queensland 4072, Australia
| | | | - Bruce A McDonald
- Institute of Integrative Biology, ETH Zürich, CH-8092 Zurich, Switzerland
| | - Alex R Hall
- Institute of Integrative Biology, ETH Zürich, CH-8092 Zurich, Switzerland
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Liechti JI, Leventhal GE, Bonhoeffer S. Host population structure impedes reversion to drug sensitivity after discontinuation of treatment. PLoS Comput Biol 2017; 13:e1005704. [PMID: 28827796 PMCID: PMC5602665 DOI: 10.1371/journal.pcbi.1005704] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 09/18/2017] [Accepted: 07/26/2017] [Indexed: 01/21/2023] Open
Abstract
Intense use of antibiotics for the treatment of diseases such as tuberculosis, malaria, Staphylococcus aureus or gonorrhea has led to rapidly increasing population levels of drug resistance. This has generally necessitated a switch to new drugs and the discontinuation of older ones, after which resistance often only declines slowly or even persists indefinitely. These long-term effects are usually ascribed to low fitness costs of resistance in absence of the drug. Here we show that structure in the host population, in particular heterogeneity in number of contacts, also plays an important role in the reversion dynamics. Host contact structure acts both during the phase of intense treatment, leading to non-random distributions of the resistant strain among the infected population, and after the discontinuation of the drug, by affecting the competition dynamics resulting in a mitigation of fitness advantages. As a consequence, we observe both a lower rate of reversion and a lower probability that reversion to sensitivity on the population level occurs after treatment is stopped. Our simulations show that the impact of heterogeneity in the host structure is maximal in the biologically most plausible parameter range, namely when fitness costs of resistance are small.
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Affiliation(s)
- Jonas I. Liechti
- Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland
- * E-mail:
| | - Gabriel E. Leventhal
- Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
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Fingerhuth SM, Low N, Bonhoeffer S, Althaus CL. Detection of antibiotic resistance is essential for gonorrhoea point-of-care testing: a mathematical modelling study. BMC Med 2017; 15:142. [PMID: 28747205 PMCID: PMC5530576 DOI: 10.1186/s12916-017-0881-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 05/19/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Antibiotic resistance is threatening to make gonorrhoea untreatable. Point-of-care (POC) tests that detect resistance promise individually tailored treatment, but might lead to more treatment and higher levels of resistance. We investigate the impact of POC tests on antibiotic-resistant gonorrhoea. METHODS We used data about the prevalence and incidence of gonorrhoea in men who have sex with men (MSM) and heterosexual men and women (HMW) to calibrate a mathematical gonorrhoea transmission model. With this model, we simulated four clinical pathways for the diagnosis and treatment of gonorrhoea: POC test with (POC+R) and without (POC-R) resistance detection, culture and nucleic acid amplification tests (NAATs). We calculated the proportion of resistant infections and cases averted after 5 years, and compared how fast resistant infections spread in the populations. RESULTS The proportion of resistant infections after 30 years is lowest for POC+R (median MSM: 0.18%, HMW: 0.12%), and increases for culture (MSM: 1.19%, HMW: 0.13%), NAAT (MSM: 100%, HMW: 99.27%), and POC-R (MSM: 100%, HMW: 99.73%). Per 100 000 persons, NAAT leads to 36 366 (median MSM) and 1228 (median HMW) observed cases after 5 years. Compared with NAAT, POC+R averts more cases after 5 years (median MSM: 3353, HMW: 118). POC tests that detect resistance with intermediate sensitivity slow down resistance spread more than NAAT. POC tests with very high sensitivity for the detection of resistance are needed to slow down resistance spread more than by using culture. CONCLUSIONS POC with high sensitivity to detect antibiotic resistance can keep gonorrhoea treatable longer than culture or NAAT. POC tests without reliable resistance detection should not be introduced because they can accelerate the spread of antibiotic-resistant gonorrhoea.
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Affiliation(s)
- Stephanie M Fingerhuth
- Institute of Integrative Biology, ETH Zurich, Zurich, 8092, Switzerland. .,Institute of Social and Preventive Medicine, University of Bern, Bern, 3012, Switzerland.
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, 3012, Switzerland
| | | | - Christian L Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, 3012, Switzerland
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Müller V, de Boer RJ, Bonhoeffer S, Szathmáry E. An evolutionary perspective on the systems of adaptive immunity. Biol Rev Camb Philos Soc 2017; 93:505-528. [PMID: 28745003 DOI: 10.1111/brv.12355] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 06/28/2017] [Accepted: 06/30/2017] [Indexed: 12/22/2022]
Abstract
We propose an evolutionary perspective to classify and characterize the diverse systems of adaptive immunity that have been discovered across all major domains of life. We put forward a new function-based classification according to the way information is acquired by the immune systems: Darwinian immunity (currently known from, but not necessarily limited to, vertebrates) relies on the Darwinian process of clonal selection to 'learn' by cumulative trial-and-error feedback; Lamarckian immunity uses templated targeting (guided adaptation) to internalize heritable information on potential threats; finally, shotgun immunity operates through somatic mechanisms of variable targeting without feedback. We argue that the origin of Darwinian (but not Lamarckian or shotgun) immunity represents a radical innovation in the evolution of individuality and complexity, and propose to add it to the list of major evolutionary transitions. While transitions to higher-level units entail the suppression of selection at lower levels, Darwinian immunity re-opens cell-level selection within the multicellular organism, under the control of mechanisms that direct, rather than suppress, cell-level evolution for the benefit of the individual. From a conceptual point of view, the origin of Darwinian immunity can be regarded as the most radical transition in the history of life, in which evolution by natural selection has literally re-invented itself. Furthermore, the combination of clonal selection and somatic receptor diversity enabled a transition from limited to practically unlimited capacity to store information about the antigenic environment. The origin of Darwinian immunity therefore comprises both a transition in individuality and the emergence of a new information system - the two hallmarks of major evolutionary transitions. Finally, we present an evolutionary scenario for the origin of Darwinian immunity in vertebrates. We propose a revival of the concept of the 'Big Bang' of vertebrate immunity, arguing that its origin involved a 'difficult' (i.e. low-probability) evolutionary transition that might have occurred only once, in a common ancestor of all vertebrates. In contrast to the original concept, we argue that the limiting innovation was not the generation of somatic diversity, but the regulatory circuitry needed for the safe operation of amplifiable immune responses with somatically acquired targeting. Regulatory complexity increased abruptly by genomic duplications at the root of the vertebrate lineage, creating a rare opportunity to establish such circuitry. We discuss the selection forces that might have acted at the origin of the transition, and in the subsequent stepwise evolution leading to the modern immune systems of extant vertebrates.
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Affiliation(s)
- Viktor Müller
- Parmenides Center for the Conceptual Foundations of Science, 82049 Pullach/Munich, Germany.,Department of Plant Systematics, Ecology and Theoretical Biology, Institute of Biology, Eötvös Loránd University, 1117 Budapest, Hungary.,Evolutionary Systems Research Group, MTA Centre for Ecological Research, 8237 Tihany, Hungary
| | - Rob J de Boer
- Theoretical Biology, Department of Biology, Utrecht University, 3584 CH Utrecht, The Netherlands
| | - Sebastian Bonhoeffer
- Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Eörs Szathmáry
- Parmenides Center for the Conceptual Foundations of Science, 82049 Pullach/Munich, Germany.,Department of Plant Systematics, Ecology and Theoretical Biology, Institute of Biology, Eötvös Loránd University, 1117 Budapest, Hungary.,Evolutionary Systems Research Group, MTA Centre for Ecological Research, 8237 Tihany, Hungary
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Richardson TO, Liechti JI, Stroeymeyt N, Bonhoeffer S, Keller L. Short-term activity cycles impede information transmission in ant colonies. PLoS Comput Biol 2017; 13:e1005527. [PMID: 28489896 PMCID: PMC5443549 DOI: 10.1371/journal.pcbi.1005527] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 05/24/2017] [Accepted: 04/20/2017] [Indexed: 12/31/2022] Open
Abstract
Rhythmical activity patterns are ubiquitous in nature. We study an oscillatory biological system: collective activity cycles in ant colonies. Ant colonies have become model systems for research on biological networks because the interactions between the component parts are visible to the naked eye, and because the time-ordered contact network formed by these interactions serves as the substrate for the distribution of information and other resources throughout the colony. To understand how the collective activity cycles influence the contact network transport properties, we used an automated tracking system to record the movement of all the individuals within nine different ant colonies. From these trajectories we extracted over two million ant-to-ant interactions. Time-series analysis of the temporal fluctuations of the overall colony interaction and movement rates revealed that both the period and amplitude of the activity cycles exhibit a diurnal cycle, in which daytime cycles are faster and of greater amplitude than night cycles. Using epidemiology-derived models of transmission over networks, we compared the transmission properties of the observed periodic contact networks with those of synthetic aperiodic networks. These simulations revealed that contrary to some predictions, regularly-oscillating contact networks should impede information transmission. Further, we provide a mechanistic explanation for this effect, and present evidence in support of it.
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Affiliation(s)
| | - Jonas I. Liechti
- Department of Environmental Systems Science, ETH Zürich, Switzerland
| | | | | | - Laurent Keller
- Department of Ecology and Evolution, University of Lausanne, Switzerland
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