1
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Diessner EM, Takahashi GR, Cross TJ, Martin RW, Butts CT. Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease. Biochemistry 2023; 62:747-758. [PMID: 36656653 PMCID: PMC9888416 DOI: 10.1021/acs.biochem.2c00479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/29/2022] [Indexed: 01/20/2023]
Abstract
The main protease of SARS-CoV-2 (Mpro) plays a critical role in viral replication; although it is relatively conserved, Mpro has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of Mpro, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics. In addition to basic structural properties such as variation in surface area and torsion angles, we use protein structure networks and active site networks to evaluate functionally relevant characters related to global cohesion and active site constraint. Substitution analysis shows a continuing trend toward more hydrophobic residues that are dependent on the location of the residue in primary, secondary, tertiary, and quaternary structures. Phylogenetic analysis provides additional evidence for the impact of selective pressure on mutation of Mpro. Overall, these analyses suggest evolutionary adaptation of Mpro toward more hydrophobicity and a less-constrained active site in response to the selective pressures of a novel host environment.
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Affiliation(s)
- Elizabeth M Diessner
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Gemma R Takahashi
- Department of Molecular Biology & Biochemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Thomas J Cross
- Department of Chemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Rachel W Martin
- Departments of Chemistry and Molecular Biology & Biochemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Carter T Butts
- Departments of Sociology, Statistics, Computer Science, and EECS, University of California, Irvine, Irvine, California 92697, United States
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2
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Zhao S, Hu I, Lou J, Chong MK, Cao L, He D, Zee BC, Wang MH. The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale. Infect Dis Model 2022; 8:107-121. [PMID: 36632179 PMCID: PMC9811219 DOI: 10.1016/j.idm.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/19/2022] [Accepted: 12/25/2022] [Indexed: 12/28/2022] Open
Abstract
Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.
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Affiliation(s)
- Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Inchi Hu
- Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong, China
| | - Jingzhi Lou
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | - Marc K.C. Chong
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Lirong Cao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Benny C.Y. Zee
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Maggie H. Wang
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China,Corresponding author. JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
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3
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Marion G, Hadley L, Isham V, Mollison D, Panovska-Griffiths J, Pellis L, Tomba GS, Scarabel F, Swallow B, Trapman P, Villela D. Modelling: Understanding pandemics and how to control them. Epidemics 2022; 39:100588. [PMID: 35679714 DOI: 10.1016/j.epidem.2022.100588] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 03/22/2022] [Accepted: 05/26/2022] [Indexed: 12/11/2022] Open
Abstract
New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.
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Affiliation(s)
- Glenn Marion
- Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK.
| | - Liza Hadley
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, UK
| | - Valerie Isham
- Department of Statistical Science, University College London, UK
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, Oxford University, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | | | - Francesca Scarabel
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy
| | - Ben Swallow
- Scottish COVID-19 Response Consortium, UK; School of Mathematics and Statistics, University of Glasgow, UK
| | - Pieter Trapman
- Department of Mathematics, Stockholm University, Stockholm, Sweden
| | - Daniel Villela
- Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
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4
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Brintnell E, Gupta M, Anderson DW. Phylogenetic and Ancestral Sequence Reconstruction of SARS-CoV-2 Reveals Latent Capacity to Bind Human ACE2 Receptor. J Mol Evol 2021; 89:656-664. [PMID: 34739551 PMCID: PMC8570237 DOI: 10.1007/s00239-021-10034-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 10/26/2021] [Indexed: 01/05/2023]
Abstract
SARS-CoV-2 is a unique event, having emerged suddenly as a highly infectious viral pathogen for human populations. Previous phylogenetic analyses show its closest known evolutionary relative to be a virus detected in bats (RaTG13), with a common assumption that SARS-CoV-2 evolved from a zoonotic ancestor via recent genetic changes (likely in the Spike protein receptor-binding domain or RBD) that enabled it to infect humans. We used detailed phylogenetic analysis, ancestral sequence reconstruction, and in situ molecular dynamics simulations to examine the Spike-RBD's functional evolution, finding that the common ancestral virus with RaTG13, dating to no later than 2013, possessed high binding affinity to the human ACE2 receptor. This suggests that SARS-CoV-2 likely possessed a latent capacity to bind to human cellular targets (though this may not have been sufficient for successful infection) and emphasizes the importance of expanding efforts to catalog and monitor viruses circulating in both human and non-human populations.
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Affiliation(s)
- Erin Brintnell
- Bachelor of Health Sciences Program, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Mehul Gupta
- Bachelor of Health Sciences Program, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
| | - Dave W Anderson
- Bachelor of Health Sciences Program, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.
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5
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Glennon EE, Bruijning M, Lessler J, Miller IF, Rice BL, Thompson RN, Wells K, Metcalf CJE. Challenges in modeling the emergence of novel pathogens. Epidemics 2021; 37:100516. [PMID: 34775298 DOI: 10.1016/j.epidem.2021.100516] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/29/2021] [Accepted: 10/22/2021] [Indexed: 01/24/2023] Open
Abstract
The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.
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Affiliation(s)
- Emma E Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK.
| | - Marjolein Bruijning
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Justin Lessler
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ian F Miller
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA; Rocky Mountain Biological Laboratory, Crested Butte, CO 81224, USA
| | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA; Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Robin N Thompson
- Mathematics Institute, University of Warwick, Warwick CV4 7AL, UK; The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Warwick CV4 7AL, UK
| | - Konstans Wells
- Department of Biosciences, Swansea University, Swansea SA28PP, UK
| | - C Jessica E Metcalf
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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6
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Shaw CL, Bilich R, O'Brien B, Cáceres CE, Hall SR, James TY, Duffy MA. Genotypic variation in an ecologically important parasite is associated with host species, lake and spore size. Parasitology 2021; 148:1303-1312. [PMID: 34103104 PMCID: PMC8383271 DOI: 10.1017/s0031182021000949] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/11/2021] [Accepted: 05/31/2021] [Indexed: 11/10/2022]
Abstract
Genetic variation in parasites has important consequences for host–parasite interactions. Prior studies of the ecologically important parasite Metschnikowia bicuspidata have suggested low genetic variation in the species. Here, we collected M. bicuspidata from two host species (Daphnia dentifera and Ceriodaphnia dubia) and two regions (Michigan and Indiana, USA). Within a lake, outbreaks tended to occur in one host species but not the other. Using microsatellite markers, we identified six parasite genotypes grouped within three distinct clades, one of which was rare. Of the two main clades, one was generally associated with D. dentifera, with lakes in both regions containing a single genotype. The other M. bicuspidata clade was mainly associated with C. dubia, with a different genotype dominating in each region. Despite these associations, both D. dentifera- and C. dubia-associated genotypes were found infecting both hosts in lakes. However, in lab experiments, the D. dentifera-associated genotype infected both D. dentifera and C. dubia, but the C. dubia-associated genotype, which had spores that were approximately 30% smaller, did not infect D. dentifera. We hypothesize that variation in spore size might help explain patterns of cross-species transmission. Future studies exploring the causes and consequences of variation in spore size may help explain patterns of infection and the maintenance of genotypic diversity in this ecologically important system.
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Affiliation(s)
- Clara L. Shaw
- Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI48109, USA
| | - Rebecca Bilich
- Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI48109, USA
| | - Bruce O'Brien
- Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI48109, USA
| | - Carla E. Cáceres
- Department of Evolution, Ecology, & Behavior, School of Integrative Biology, University of Illinois Urbana-Champaign, Urbana, IL61801, USA
| | - Spencer R. Hall
- Department of Biology, Indiana University, Bloomington, IN47405, USA
| | - Timothy Y. James
- Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI48109, USA
| | - Meghan A. Duffy
- Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI48109, USA
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7
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Abstract
RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance). IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.
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8
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Fitak RR, Antonides JD, Baitchman EJ, Bonaccorso E, Braun J, Kubiski S, Chiu E, Fagre AC, Gagne RB, Lee JS, Malmberg JL, Stenglein MD, Dusek RJ, Forgacs D, Fountain-Jones NM, Gilbertson MLJ, Worsley-Tonks KEL, Funk WC, Trumbo DR, Ghersi BM, Grimaldi W, Heisel SE, Jardine CM, Kamath PL, Karmacharya D, Kozakiewicz CP, Kraberger S, Loisel DA, McDonald C, Miller S, O'Rourke D, Ott-Conn CN, Páez-Vacas M, Peel AJ, Turner WC, VanAcker MC, VandeWoude S, Pecon-Slattery J. The Expectations and Challenges of Wildlife Disease Research in the Era of Genomics: Forecasting with a Horizon Scan-like Exercise. J Hered 2020; 110:261-274. [PMID: 31067326 DOI: 10.1093/jhered/esz001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 01/08/2019] [Indexed: 12/14/2022] Open
Abstract
The outbreak and transmission of disease-causing pathogens are contributing to the unprecedented rate of biodiversity decline. Recent advances in genomics have coalesced into powerful tools to monitor, detect, and reconstruct the role of pathogens impacting wildlife populations. Wildlife researchers are thus uniquely positioned to merge ecological and evolutionary studies with genomic technologies to exploit unprecedented "Big Data" tools in disease research; however, many researchers lack the training and expertise required to use these computationally intensive methodologies. To address this disparity, the inaugural "Genomics of Disease in Wildlife" workshop assembled early to mid-career professionals with expertise across scientific disciplines (e.g., genomics, wildlife biology, veterinary sciences, and conservation management) for training in the application of genomic tools to wildlife disease research. A horizon scanning-like exercise, an activity to identify forthcoming trends and challenges, performed by the workshop participants identified and discussed 5 themes considered to be the most pressing to the application of genomics in wildlife disease research: 1) "Improving communication," 2) "Methodological and analytical advancements," 3) "Translation into practice," 4) "Integrating landscape ecology and genomics," and 5) "Emerging new questions." Wide-ranging solutions from the horizon scan were international in scope, itemized both deficiencies and strengths in wildlife genomic initiatives, promoted the use of genomic technologies to unite wildlife and human disease research, and advocated best practices for optimal use of genomic tools in wildlife disease projects. The results offer a glimpse of the potential revolution in human and wildlife disease research possible through multi-disciplinary collaborations at local, regional, and global scales.
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Affiliation(s)
| | - Jennifer D Antonides
- Department of Forestry & Natural Resources, Purdue University, West Lafayette, IN
| | - Eric J Baitchman
- The Zoo New England Division of Animal Health and Conservation, Boston, MA
| | - Elisa Bonaccorso
- The Instituto BIOSFERA and Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, vía Interoceánica y Diego de Robles, Quito, Ecuador
| | - Josephine Braun
- The Institute for Conservation Research, San Diego Zoo Global, Escondido, CA
| | - Steven Kubiski
- The Institute for Conservation Research, San Diego Zoo Global, Escondido, CA
| | - Elliott Chiu
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Anna C Fagre
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Roderick B Gagne
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Justin S Lee
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Jennifer L Malmberg
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Mark D Stenglein
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Robert J Dusek
- The U. S. Geological Survey, National Wildlife Health Center, Madison, WI
| | - David Forgacs
- The Interdisciplinary Graduate Program of Genetics, Texas A&M University, College Station, TX
| | | | - Marie L J Gilbertson
- The Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN
| | | | - W Chris Funk
- The Department of Biology, Colorado State University, Fort Collins, CO
| | - Daryl R Trumbo
- The Department of Biology, Colorado State University, Fort Collins, CO
| | | | | | - Sara E Heisel
- The Odum School of Ecology, University of Georgia, Athens, GA
| | - Claire M Jardine
- The Department of Pathobiology, Canadian Wildlife Health Cooperative, University of Guelph, Guelph, Ontario, Canada
| | - Pauline L Kamath
- The School of Food and Agriculture, University of Maine, Orono, ME
| | | | | | - Simona Kraberger
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ
| | - Dagan A Loisel
- The Department of Biology, Saint Michael's College, Colchester, VT
| | - Cait McDonald
- The Department of Ecology & Evolutionary Biology, Cornell University, Ithaca, NY (McDonald)
| | - Steven Miller
- The Department of Biology, Drexel University, Philadelphia, PA
| | | | - Caitlin N Ott-Conn
- The Michigan Department of Natural Resources, Wildlife Disease Laboratory, Lansing, MI
| | - Mónica Páez-Vacas
- The Centro de Investigación de la Biodiversidad y Cambio Climático (BioCamb), Facultad de Ciencias de Medio Ambiente, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, Ecuador
| | - Alison J Peel
- The Environmental Futures Research Institute, Griffith University, Nathan, Queensland, Australia
| | - Wendy C Turner
- The Department of Biological Sciences, University at Albany, State University of New York, Albany, NY
| | - Meredith C VanAcker
- The Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY
| | - Sue VandeWoude
- The College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO
| | - Jill Pecon-Slattery
- The Center for Species Survival, Smithsonian Conservation Biology Institute-National Zoological Park, Front Royal, VA
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9
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Vianna P, Mendes MF, Bragatte MA, Ferreira PS, Salzano FM, Bonamino MH, Vieira GF. pMHC Structural Comparisons as a Pivotal Element to Detect and Validate T-Cell Targets for Vaccine Development and Immunotherapy-A New Methodological Proposal. Cells 2019; 8:cells8121488. [PMID: 31766602 PMCID: PMC6952977 DOI: 10.3390/cells8121488] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/15/2019] [Accepted: 11/16/2019] [Indexed: 12/02/2022] Open
Abstract
The search for epitopes that will effectively trigger an immune response remains the “El Dorado” for immunologists. The development of promising immunotherapeutic approaches requires the appropriate targets to elicit a proper immune response. Considering the high degree of HLA/TCR diversity, as well as the heterogeneity of viral and tumor proteins, this number will invariably be higher than ideal to test. It is known that the recognition of a peptide-MHC (pMHC) by the T-cell receptor is performed entirely in a structural fashion, where the atomic interactions of both structures, pMHC and TCR, dictate the fate of the process. However, epitopes with a similar composition of amino acids can produce dissimilar surfaces. Conversely, sequences with no conspicuous similarities can exhibit similar TCR interaction surfaces. In the last decade, our group developed a database and in silico structural methods to extract molecular fingerprints that trigger T-cell immune responses, mainly referring to physicochemical similarities, which could explain the immunogenic differences presented by different pMHC-I complexes. Here, we propose an immunoinformatic approach that considers a structural level of information, combined with an experimental technology that simulates the presentation of epitopes for a T cell, to improve vaccine production and immunotherapy efficacy.
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Affiliation(s)
- Priscila Vianna
- Laboratory of Human Teratogenesis and Population Medical Genetics, Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil;
| | - Marcus F.A. Mendes
- Laboratory of Bioinformatics (NBLI), Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil (M.A.B.)
| | - Marcelo A. Bragatte
- Laboratory of Bioinformatics (NBLI), Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil (M.A.B.)
| | - Priscila S. Ferreira
- Program of Immunology and Tumor Biology, Division of Experimental and Translational Research, Brazilian National Cancer Institute, Rio de Janeiro 20231-050, Brazil; (P.S.F.); (M.H.B.)
| | - Francisco M. Salzano
- Laboratory of Molecular Evolution, Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil;
| | - Martin H. Bonamino
- Program of Immunology and Tumor Biology, Division of Experimental and Translational Research, Brazilian National Cancer Institute, Rio de Janeiro 20231-050, Brazil; (P.S.F.); (M.H.B.)
- Vice Presidency of Research and Biological Collections, Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil
| | - Gustavo F. Vieira
- Laboratory of Bioinformatics (NBLI), Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil (M.A.B.)
- Laboratory of Health Bioinformatics, Post Graduate Program in Health and Human Development, La Salle University, Canoas 91.501-970, Brazil
- Correspondence: ; Tel.: +55-51-3308-99-38; Fax: +55-51-3308-73-11
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10
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Bushman M, Antia R. A general framework for modelling the impact of co-infections on pathogen evolution. J R Soc Interface 2019; 16:20190165. [PMID: 31238835 PMCID: PMC6597765 DOI: 10.1098/rsif.2019.0165] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/29/2019] [Indexed: 11/12/2022] Open
Abstract
Theoretical models suggest that mixed-strain infections, or co-infections, are an important driver of pathogen evolution. However, the within-host dynamics of co-infections vary enormously, which complicates efforts to develop a general understanding of how co-infections affect evolution. Here, we develop a general framework which condenses the within-host dynamics of co-infections into a few key outcomes, the most important of which is the overall R0 of the co-infection. Similar to how fitness is determined by two different alleles in a heterozygote, the R0 of a co-infection is a product of the R0 values of the co-infecting strains, shaped by the interaction of those strains at the within-host level. Extending the analogy, we propose that the overall R0 reflects the dominance of the co-infecting strains, and that the ability of a mutant strain to invade a population is a function of its dominance in co-infections. To illustrate the utility of these concepts, we use a within-host model to show how dominance arises from the within-host dynamics of a co-infection, and then use an epidemiological model to demonstrate that dominance is a robust predictor of the ability of a mutant strain to save a maladapted wild-type strain from extinction (evolutionary emergence).
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Affiliation(s)
- Mary Bushman
- Department of Biology, Emory University, Atlanta, GA, USA
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11
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Hauser A, Kusejko K, Johnson LF, Wandeler G, Riou J, Goldstein F, Egger M, Kouyos RD. Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa. PLoS Comput Biol 2019; 15:e1007083. [PMID: 31233494 PMCID: PMC6611642 DOI: 10.1371/journal.pcbi.1007083] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 07/05/2019] [Accepted: 05/06/2019] [Indexed: 11/19/2022] Open
Abstract
The scale-up of antiretroviral therapy (ART) in South Africa substantially reduced AIDS-related deaths and new HIV infections. However, its success is threatened by the emergence of resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI). The MARISA (Modelling Antiretroviral drug Resistance In South Africa) model presented here aims at investigating the time trends and factors driving NNRTI resistance in South Africa. MARISA is a compartmental model that includes the key aspects of the local HIV epidemic: continuum of care, disease progression, and gender. The dynamics of NNRTI resistance emergence and transmission are then added to this framework. Model parameters are informed using data from HIV cohorts participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) and literature estimates, or fitted to UNAIDS estimates. Using this novel approach of triangulating clinical and resistance data from various sources, MARISA reproduces the time trends of HIV in South Africa in 2005–2016, with a decrease in new infections, undiagnosed individuals, and AIDS-related deaths. MARISA captures the dynamics of the spread of NNRTI resistance: high levels of acquired drug resistance (ADR, in 83% of first-line treatment failures in 2016), and increasing transmitted drug resistance (TDR, in 8.1% of ART initiators in 2016). Simulation of counter-factual scenarios reflecting alternative public health policies shows that increasing treatment coverage would have resulted in fewer new infections and deaths, at the cost of higher TDR (11.6% in 2016 for doubling the treatment rate). Conversely, improving switching to second-line treatment would have led to lower TDR (6.5% in 2016 for doubling the switching rate) and fewer new infections and deaths. Implementing drug resistance testing would have had little impact. The rapid ART scale-up and inadequate switching to second-line treatment were the key drivers of the spread of NNRTI resistance in South Africa. However, even though some interventions could have substantially reduced the level of NNRTI resistance, no policy including NNRTI-based first line regimens could have prevented this spread. Thus, by combining epidemiological data on HIV in South Africa with biological data on resistance evolution, our modelling approach identified key factors driving NNRTI resistance, highlighting the need of alternative first-line regimens. Resistance to non-nucleoside reverse transcriptase inhibitors (NNRTI) threatens the long-term success of antiretroviral therapy (ART) roll-out in South Africa. We developed a compartmental model integrating the local HIV epidemiology with biological mechanisms of drug resistance. A first dimension of the model accounts for the continuum of care: infection, diagnosis, first-line treatment with suppression or failure, and second-line treatment. Other dimensions include: disease progression (CD4 counts), gender, and acquisition and transmission of NNRTI resistance. Whenever possible, we informed the parameters using the data available from local cohorts. Other parameters were informed using literature or UNAIDS estimates. The model captured the rise of NNRTI resistance during the period. We assessed the impact of counter-factual scenarios reflecting alternative countrywide policies during the period 2005 to 2016, considering either increasing ART coverage, improving management of treatment failure, broadening ART eligibility, or implementing drug resistance testing before ART initiation. We identified key drivers of the NNRTI resistance epidemic: large-scale ART roll-out and insufficient monitoring of first-line treatment failure. The model also suggested that no policy including NNRTI-based first line regimens could have prevented the spread of NNRTI resistance.
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Affiliation(s)
- Anthony Hauser
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
| | - Katharina Kusejko
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Leigh F. Johnson
- Centre for Infectious Disease Epidemiology and Research, University of Cape Town, South Africa
| | - Gilles Wandeler
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
- Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
| | - Fardo Goldstein
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
- Centre for Infectious Disease Epidemiology and Research, University of Cape Town, South Africa
| | - Roger D. Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
- * E-mail:
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12
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Hector TE, Booksmythe I. Digest: Little evidence exists for a virulence-transmission trade-off. Evolution 2019; 73:858-859. [PMID: 30900249 DOI: 10.1111/evo.13724] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 03/14/2019] [Indexed: 11/28/2022]
Abstract
Research predicting the impact and spread of infectious disease has been heavily influenced by the idea of an evolutionary trade-off between a pathogen's virulence and its transmission rate. In a meta-analysis of the key underlying relationships, Acevedo et al. (2019) highlight the surprising lack of empirical evidence for this influential hypothesis.
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Affiliation(s)
- Tobias E Hector
- School of Biological Sciences, Monash University, Clayton, Victoria, 3800, Australia
| | - Isobel Booksmythe
- School of Biological Sciences, Monash University, Clayton, Victoria, 3800, Australia
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13
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Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building. Epidemics 2019; 26:116-127. [PMID: 30446431 PMCID: PMC7105018 DOI: 10.1016/j.epidem.2018.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/06/2018] [Accepted: 10/17/2018] [Indexed: 12/24/2022] Open
Abstract
Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.
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14
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González-Barrio D, Ruiz-Fons F. Coxiella burnetii in wild mammals: A systematic review. Transbound Emerg Dis 2018; 66:662-671. [PMID: 30506629 DOI: 10.1111/tbed.13085] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 11/19/2018] [Accepted: 11/20/2018] [Indexed: 12/26/2022]
Abstract
Coxiella burnetii is a multi-host bacterium that causes Q fever in humans, a zoonosis that is emerging worldwide. The ecology of C. burnetii in wildlife is still poorly understood and the influence of host, environmental and pathogen factors is almost unknown. This study gathers current published information on different aspects of C. burnetii infection in wildlife, even in species with high reservoir potential and a high rate of interaction with livestock and humans, in order to partially fill the existing gap and highlight future needs. Exposure and/or infection by C. burnetii has, to date, been reported in 109 wild mammal species. The limited sample size of most of the existing studies could suggest an undervalued prevalence of C. burnetii infection. Knowledge on the clinical outcome of C. burnetii infection in wildlife is also very limited, but currently includes reproductive failure in waterbuck (Kobus ellipsiprymnus), roan antelope (Hippotragus niger), dama gazelle (Nanger dama) and water buffalo (Bubalus bubalis) and placentitis in the Pacific harbor seal (Phoca vitulina richardsi), Steller sea lion (Eumetopias jubatus) and red deer (Cervus elaphus). The currently available serological tests need to be optimised and validated for each wildlife species. Finally, there is a huge gap in the research on C. burnetii control in wildlife, despite of the increasing evidence that wildlife is a source of C. burnetii for both livestock and humans.
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Affiliation(s)
- David González-Barrio
- Health & Biotechnology (SaBio) Group, Instituto de Investigación en Recursos Cinegéticos IREC (CSIC-UCLM-JCCM), Ciudad Real, Spain
| | - Francisco Ruiz-Fons
- Health & Biotechnology (SaBio) Group, Instituto de Investigación en Recursos Cinegéticos IREC (CSIC-UCLM-JCCM), Ciudad Real, Spain
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15
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Borlase A, Webster JP, Rudge JW. Opportunities and challenges for modelling epidemiological and evolutionary dynamics in a multihost, multiparasite system: Zoonotic hybrid schistosomiasis in West Africa. Evol Appl 2018; 11:501-515. [PMID: 29636802 PMCID: PMC5891036 DOI: 10.1111/eva.12529] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 08/01/2017] [Indexed: 01/01/2023] Open
Abstract
Multihost multiparasite systems are evolutionarily and ecologically dynamic, which presents substantial trans-disciplinary challenges for elucidating their epidemiology and designing appropriate control. Evidence for hybridizations and introgressions between parasite species is gathering, in part in line with improvements in molecular diagnostics and genome sequencing. One major system where this is becoming apparent is within the Genus Schistosoma, where schistosomiasis represents a disease of considerable medical and veterinary importance, the greatest burden of which occurs in sub-Saharan Africa. Interspecific hybridizations and introgressions bring an increased level of complexity over and above that already inherent within multihost, multiparasite systems, also representing an additional source of genetic variation that can drive evolution. This has the potential for profound implications for the control of parasitic diseases, including, but not exclusive to, widening host range, increased transmission potential and altered responses to drug therapy. Here, we present the challenging case example of haematobium group Schistosoma spp. hybrids in West Africa, a system involving multiple interacting parasites and multiple definitive hosts, in a region where zoonotic reservoirs of schistosomiasis were not previously considered to be of importance. We consider how existing mathematical model frameworks for schistosome transmission could be expanded and adapted to zoonotic hybrid systems, exploring how such model frameworks can utilize molecular and epidemiological data, as well as the complexities and challenges this presents. We also highlight the opportunities and value such mathematical models could bring to this and a range of similar multihost, multi and cross-hybridizing parasites systems in our changing world.
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Affiliation(s)
- Anna Borlase
- Department of Pathobiology and Population SciencesCentre for Emerging, Endemic and Exotic DiseasesRoyal Veterinary CollegeUniversity of LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
| | - Joanne P. Webster
- Department of Pathobiology and Population SciencesCentre for Emerging, Endemic and Exotic DiseasesRoyal Veterinary CollegeUniversity of LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
| | - James W. Rudge
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
- Communicable Diseases Policy Research GroupLondon School of Hygiene and Tropical MedicineLondonUK
- Faculty of Public HealthMahidol UniversityBangkokThailand
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16
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Garira W. A complete categorization of multiscale models of infectious disease systems. JOURNAL OF BIOLOGICAL DYNAMICS 2017; 11:378-435. [PMID: 28849734 DOI: 10.1080/17513758.2017.1367849] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Modelling of infectious disease systems has entered a new era in which disease modellers are increasingly turning to multiscale modelling to extend traditional modelling frameworks into new application areas and to achieve higher levels of detail and accuracy in characterizing infectious disease systems. In this paper we present a categorization framework for categorizing multiscale models of infectious disease systems. The categorization framework consists of five integration frameworks and five criteria. We use the categorization framework to give a complete categorization of host-level immuno-epidemiological models (HL-IEMs). This categorization framework is also shown to be applicable in categorizing other types of multiscale models of infectious diseases beyond HL-IEMs through modifying the initial categorization framework presented in this study. Categorization of multiscale models of infectious disease systems in this way is useful in bringing some order to the discussion on the structure of these multiscale models.
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Affiliation(s)
- Winston Garira
- a Modelling Health and Environmental Linkages Research Group (MHELRG), Department of Mathematics and Applied Mathematics , University of Venda , Thohoyandou, South Africa
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17
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Arenas M, Araujo NM, Branco C, Castelhano N, Castro-Nallar E, Pérez-Losada M. Mutation and recombination in pathogen evolution: Relevance, methods and controversies. INFECTION GENETICS AND EVOLUTION 2017; 63:295-306. [PMID: 28951202 DOI: 10.1016/j.meegid.2017.09.029] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 09/20/2017] [Accepted: 09/21/2017] [Indexed: 02/06/2023]
Abstract
Mutation and recombination drive the evolution of most pathogens by generating the genetic variants upon which selection operates. Those variants can, for example, confer resistance to host immune systems and drug therapies or lead to epidemic outbreaks. Given their importance, diverse evolutionary studies have investigated the abundance and consequences of mutation and recombination in pathogen populations. However, some controversies persist regarding the contribution of each evolutionary force to the development of particular phenotypic observations (e.g., drug resistance). In this study, we revise the importance of mutation and recombination in the evolution of pathogens at both intra-host and inter-host levels. We also describe state-of-the-art analytical methodologies to detect and quantify these two evolutionary forces, including biases that are often ignored in evolutionary studies. Finally, we present some of our former studies involving pathogenic taxa where mutation and recombination played crucial roles in the recovery of pathogenic fitness, the generation of interspecific genetic diversity, or the design of centralized vaccines. This review also illustrates several common controversies and pitfalls in the analysis and in the evaluation and interpretation of mutation and recombination outcomes.
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Affiliation(s)
- Miguel Arenas
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain; Instituto de Investigação e Inovação em Saúde (i3S), University of Porto, Porto, Portugal; Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal.
| | - Natalia M Araujo
- Laboratory of Molecular Virology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil.
| | - Catarina Branco
- Instituto de Investigação e Inovação em Saúde (i3S), University of Porto, Porto, Portugal; Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal.
| | - Nadine Castelhano
- Instituto de Investigação e Inovação em Saúde (i3S), University of Porto, Porto, Portugal; Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal.
| | - Eduardo Castro-Nallar
- Universidad Andrés Bello, Center for Bioinformatics and Integrative Biology, Facultad de Ciencias Biológicas, Santiago, Chile.
| | - Marcos Pérez-Losada
- Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Ashburn, VA 20147, Washington, DC, United States; CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão 4485-661, Portugal.
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18
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The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy. Genome Biol 2017; 18:71. [PMID: 28424085 PMCID: PMC5395877 DOI: 10.1186/s13059-017-1196-0] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 03/21/2017] [Indexed: 12/22/2022] Open
Abstract
Background Combination therapy is one of the most effective tools for limiting the emergence of drug resistance in pathogens. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We address this question by studying the population dynamics of Mycobacterium tuberculosis within tuberculosis patients undergoing treatment with different combinations of antibiotics. Results By combining very deep whole genome sequencing (~1000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy has a clear impact on the population dynamics: sufficient drug pressure bears a clear signature of purifying selection leading to apparent genetic stability. In contrast, M. tuberculosis populations subject to less drug pressure show markedly different dynamics, including cases of acquisition of additional drug resistance. Conclusions Our findings show that for a pathogen like M. tuberculosis, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1196-0) contains supplementary material, which is available to authorized users.
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19
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Abstract
Genomic analysis is a powerful tool for understanding viral disease outbreaks. Sequencing of viral samples is now easier and cheaper than ever before and can supplement epidemiological methods by providing nucleotide-level resolution of outbreak-causing pathogens. In this review, we describe methods used to answer crucial questions about outbreaks, such as how they began and how a disease is transmitted. More specifically, we explain current techniques for viral sequencing, phylogenetic analysis, transmission reconstruction, and evolutionary investigation of viral pathogens. By detailing the ways in which genomic data can help us understand viral disease outbreaks, we aim to provide a resource that will facilitate the response to future outbreaks.
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Affiliation(s)
- Shirlee Wohl
- FAS Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138.,Broad Institute, Cambridge, Massachusetts 02142; ,
| | - Stephen F Schaffner
- FAS Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138.,Broad Institute, Cambridge, Massachusetts 02142; , .,Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts 02115
| | - Pardis C Sabeti
- FAS Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138.,Broad Institute, Cambridge, Massachusetts 02142; , .,Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts 02115
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20
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Syller J, Grupa A. Antagonistic within-host interactions between plant viruses: molecular basis and impact on viral and host fitness. MOLECULAR PLANT PATHOLOGY 2016; 17:769-82. [PMID: 26416204 PMCID: PMC6638324 DOI: 10.1111/mpp.12322] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Double infections of related or unrelated viruses frequently occur in single plants, the viral agents being inoculated into the host plant simultaneously (co-infection) or sequentially (super-infection). Plants attacked by viruses activate sophisticated defence pathways which operate at different levels, often at significant fitness costs, resulting in yield reduction in crop plants. The occurrence and severity of the negative effects depend on the type of within-host interaction between the infecting viruses. Unrelated viruses generally interact with each other in a synergistic manner, whereas interactions between related viruses are mostly antagonistic. These can incur substantial fitness costs to one or both of the competitors. A relatively well-known antagonistic interaction is cross-protection, also referred to as super-infection exclusion. This type of interaction occurs when a previous infection with one virus prevents or interferes with subsequent infection by a homologous second virus. The current knowledge on why and how one virus variant excludes or restricts another is scant. Super-infection exclusion between viruses has predominantly been attributed to the induction of RNA silencing, which is a major antiviral defence mechanism in plants. There are, however, presumptions that various mechanisms are involved in this phenomenon. This review outlines the current state of knowledge concerning the molecular mechanisms behind antagonistic interactions between plant viruses. Harmful or beneficial effects of these interactions on viral and host plant fitness are also characterized. Moreover, the review briefly outlines the past and present attempts to utilize antagonistic interactions among viruses to protect crop plants against destructive diseases.
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Affiliation(s)
- Jerzy Syller
- Plant Breeding and Acclimatization Institute-National Research Institute, Laboratory of Phytopathology, Centre Młochów, 05-831, Młochów, Poland
| | - Anna Grupa
- Plant Breeding and Acclimatization Institute-National Research Institute, Laboratory of Phytopathology, Centre Młochów, 05-831, Młochów, Poland
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21
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Metcalf CJE, Graham AL, Martinez-Bakker M, Childs DZ. Opportunities and challenges of Integral Projection Models for modelling host-parasite dynamics. J Anim Ecol 2015; 85:343-55. [PMID: 26620440 PMCID: PMC4991293 DOI: 10.1111/1365-2656.12456] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 09/29/2015] [Indexed: 11/28/2022]
Abstract
Epidemiological dynamics are shaped by and may in turn shape host demography. These feedbacks can result in hard to predict patterns of disease incidence. Mathematical models that integrate infection and demography are consequently a key tool for informing expectations for disease burden and identifying effective measures for control. A major challenge is capturing the details of infection within individuals and quantifying their downstream impacts to understand population‐scale outcomes. For example, parasite loads and antibody titres may vary over the course of an infection and contribute to differences in transmission at the scale of the population. To date, to capture these subtleties, models have mostly relied on complex mechanistic frameworks, discrete categorization and/or agent‐based approaches. Integral Projection Models (IPMs) allow variance in individual trajectories of quantitative traits and their population‐level outcomes to be captured in ways that directly reflect statistical models of trait–fate relationships. Given increasing data availability, and advances in modelling, there is considerable potential for extending this framework to traits of relevance for infectious disease dynamics. Here, we provide an overview of host and parasite natural history contexts where IPMs could strengthen inference of population dynamics, with examples of host species ranging from mice to sheep to humans, and parasites ranging from viruses to worms. We discuss models of both parasite and host traits, provide two case studies and conclude by reviewing potential for both ecological and evolutionary research.
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Affiliation(s)
- C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Office of Population Research, The Woodrow Wilson School, Princeton University, Princeton, NJ, USA
| | - Andrea L Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Dylan Z Childs
- Department of Animal and Plant Sciences, Sheffield University, Sheffield, UK
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22
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Lee W, van Baalen M, Jansen VAA. Siderophore production and the evolution of investment in a public good: An adaptive dynamics approach to kin selection. J Theor Biol 2015; 388:61-71. [PMID: 26471069 DOI: 10.1016/j.jtbi.2015.09.038] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 09/23/2015] [Accepted: 09/29/2015] [Indexed: 01/08/2023]
Abstract
Like many other bacteria, Pseudomonas aeruginosa sequesters iron from the environment through the secretion, and subsequent uptake, of iron-binding molecules. As these molecules can be taken up by other bacteria in the population than those who secreted them, this is a form of cooperation through a public good. Traditionally, this problem has been studied by comparing the relative fitnesses of siderophore-producing and non-producing strains, but this gives no information about the fate of strains that do produce intermediate amounts of siderophores. Here, we investigate theoretically how the amount invested in this form of cooperation evolves. We use a mechanistic description of the laboratory protocols used in experimental evolution studies to describe the competition and cooperation of the bacteria. From this dynamical model we derive the fitness following the adaptive dynamics method. The results show how selection is driven by local siderophore production and local competition. Because siderophore production reduces the growth rate, local competition decreases with the degree of relatedness (which is a dynamical variable in our model). Our model is not restricted to the analysis of small phenotypic differences and allows for theoretical exploration of the effects of large phenotypic differences between cooperators and cheats. We predict that an intermediate ESS level of cooperation (molecule production) should exist. The adaptive dynamics approach allows us to assess evolutionary stability, which is often not possible in other kin-selection models. We found that selection can lead to an intermediate strategy which in our model is always evolutionarily stable, yet can allow invasion of strategies that are much more cooperative. Our model describes the evolution of a public good in the context of the ecology of the microorganism, which allows us to relate the extent of production of the public good to the details of the interactions.
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Affiliation(s)
- William Lee
- School of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UK
| | - Minus van Baalen
- Eco-Evolutionary Mathematics, Institut Biologie de l׳ENS (UMR 8197), Ecole Normale Supérieure, 75005 Paris, France; Eco-Evolutionary Mathematics, Institut Biologie de l׳ENS (UMR 8197), Centre National de la Recherche Scientifique, 75005 Paris, France
| | - Vincent A A Jansen
- School of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UK.
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Abstract
Why is it that some parasites cause high levels of host damage (i.e. virulence) whereas others are relatively benign? There are now numerous reviews of virulence evolution in the literature but it is nevertheless still difficult to find a comprehensive treatment of the theory and data on the subject that is easily accessible to non-specialists. Here we attempt to do so by distilling the vast theoretical literature on the topic into a set of relatively few robust predictions. We then provide a comprehensive assessment of the available empirical literature that tests these predictions. Our results show that there have been some notable successes in integrating theory and data but also that theory and empiricism in this field do not ‘speak’ to each other very well. We offer a few suggestions for how the connection between the two might be improved.
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24
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Childs LM, Abuelezam NN, Dye C, Gupta S, Murray MB, Williams BG, Buckee CO. Modelling challenges in context: lessons from malaria, HIV, and tuberculosis. Epidemics 2015; 10:102-7. [PMID: 25843394 PMCID: PMC4451070 DOI: 10.1016/j.epidem.2015.02.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 02/09/2015] [Accepted: 02/09/2015] [Indexed: 02/08/2023] Open
Abstract
Malaria, HIV, and tuberculosis (TB) collectively account for several million deaths each year, with all three ranking among the top ten killers in low-income countries. Despite being caused by very different organisms, malaria, HIV, and TB present a suite of challenges for mathematical modellers that are particularly pronounced in these infections, but represent general problems in infectious disease modelling, and highlight many of the challenges described throughout this issue. Here, we describe some of the unifying challenges that arise in modelling malaria, HIV, and TB, including variation in dynamics within the host, diversity in the pathogen, and heterogeneity in human contact networks and behaviour. Through the lens of these three pathogens, we provide specific examples of the other challenges in this issue and discuss their implications for informing public health efforts.
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Affiliation(s)
- Lauren M Childs
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Nadia N Abuelezam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Christopher Dye
- Office of the Director General, World Health Organization, Avenue Appia, 1211 Geneva 27, Switzerland
| | - Sunetra Gupta
- Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
| | - Megan B Murray
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Division of Global Health Equity, Brigham & Women's Hospital, Boston, MA 02115, United States
| | - Brian G Williams
- South African Centre for Epidemiological Modelling and Analysis, Stellenbosch, South Africa; Wits Reproductive Health and HIV Institute, University of the Witwatersrand, Johannesburg, South Africa
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States.
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25
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Metcalf CJE, Andreasen V, Bjørnstad ON, Eames K, Edmunds WJ, Funk S, Hollingsworth TD, Lessler J, Viboud C, Grenfell BT. Seven challenges in modeling vaccine preventable diseases. Epidemics 2015; 10:11-5. [PMID: 25843375 PMCID: PMC6777947 DOI: 10.1016/j.epidem.2014.08.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 06/19/2014] [Accepted: 08/18/2014] [Indexed: 11/22/2022] Open
Abstract
Vaccination has been one of the most successful public health measures since the introduction of basic sanitation. Substantial mortality and morbidity reductions have been achieved via vaccination against many infections, and the list of diseases that are potentially controllable by vaccines is growing steadily. We introduce key challenges for modeling in shaping our understanding and guiding policy decisions related to vaccine preventable diseases.
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Affiliation(s)
- C J E Metcalf
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School, Princeton University, Princeton, NJ, USA.
| | - V Andreasen
- Department of Science, Systems and Models, Universitetsvej 1, 27.1, DK-4000 Roskilde, Denmark
| | - O N Bjørnstad
- Centre for Infectious Disease Dynamics, the Pennsylvania State University, State College, PA, USA
| | - K Eames
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - W J Edmunds
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - S Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - T D Hollingsworth
- Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - J Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - C Viboud
- Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - B T Grenfell
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School, Princeton University, Princeton, NJ, USA; Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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26
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Frost SDW, Pybus OG, Gog JR, Viboud C, Bonhoeffer S, Bedford T. Eight challenges in phylodynamic inference. Epidemics 2015; 10:88-92. [PMID: 25843391 PMCID: PMC4383806 DOI: 10.1016/j.epidem.2014.09.001] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Revised: 08/30/2014] [Accepted: 09/02/2014] [Indexed: 02/06/2023] Open
Abstract
The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.
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Affiliation(s)
- Simon D W Frost
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK; Institute of Public Health, University of Cambridge, Cambridge, UK.
| | | | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, USA
| | | | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA
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27
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Hollingsworth TD, Pulliam JRC, Funk S, Truscott JE, Isham V, Lloyd AL. Seven challenges for modelling indirect transmission: vector-borne diseases, macroparasites and neglected tropical diseases. Epidemics 2014; 10:16-20. [PMID: 25843376 PMCID: PMC4383804 DOI: 10.1016/j.epidem.2014.08.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Revised: 08/22/2014] [Accepted: 08/23/2014] [Indexed: 12/04/2022] Open
Abstract
Many of the challenges which face modellers of directly transmitted pathogens also arise when modelling the epidemiology of pathogens with indirect transmission – whether through environmental stages, vectors, intermediate hosts or multiple hosts. In particular, understanding the roles of different hosts, how to measure contact and infection patterns, heterogeneities in contact rates, and the dynamics close to elimination are all relevant challenges, regardless of the mode of transmission. However, there remain a number of challenges that are specific and unique to modelling vector-borne diseases and macroparasites. Moreover, many of the neglected tropical diseases which are currently targeted for control and elimination are vector-borne, macroparasitic, or both, and so this article includes challenges which will assist in accelerating the control of these high-burden diseases. Here, we discuss the challenges of indirect measures of infection in humans, whether through vectors or transmission life stages and in estimating the contribution of different host groups to transmission. We also discuss the issues of “evolution-proof” interventions against vector-borne disease.
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Affiliation(s)
- T Déirdre Hollingsworth
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK.
| | - Juliet R C Pulliam
- Department of Biology, University of Florida, Gainesville, FL 32611, USA; Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - James E Truscott
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, W2 1PG London, UK
| | - Valerie Isham
- Department of Statistical Science, University College London, WC1E 6BT, UK
| | - Alun L Lloyd
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA; Department of Mathematics and Biomathematics Graduate Program, North Carolina State University, NC 27695, USA
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