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Meganck RM, Edwards CE, Mallory ML, Lee RE, Dang H, Bailey AB, Wykoff JA, Gallant SC, Zhu DR, Yount BL, Kato T, Shaffer KM, Nakano S, Cawley AM, Sontake V, Wang JR, Hagan RS, Miller MB, Tata PR, Randell SH, Tse LV, Ehre C, Okuda K, Boucher RC, Baric RS. SARS-CoV-2 variant of concern fitness and adaptation in primary human airway epithelia. Cell Rep 2024; 43:114076. [PMID: 38607917 PMCID: PMC11165423 DOI: 10.1016/j.celrep.2024.114076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/09/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 pandemic is characterized by the emergence of novel variants of concern (VOCs) that replace ancestral strains. Here, we dissect the complex selective pressures by evaluating variant fitness and adaptation in human respiratory tissues. We evaluate viral properties and host responses to reconstruct forces behind D614G through Omicron (BA.1) emergence. We observe differential replication in airway epithelia, differences in cellular tropism, and virus-induced cytotoxicity. D614G accumulates the most mutations after infection, supporting zoonosis and adaptation to the human airway. We perform head-to-head competitions and observe the highest fitness for Gamma and Delta. Under these conditions, RNA recombination favors variants encoding the B.1.617.1 lineage 3' end. Based on viral growth kinetics, Alpha, Gamma, and Delta exhibit increased fitness compared to D614G. In contrast, the global success of Omicron likely derives from increased transmission and antigenic variation. Our data provide molecular evidence to support epidemiological observations of VOC emergence.
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Affiliation(s)
- Rita M Meganck
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Caitlin E Edwards
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Michael L Mallory
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Rhianna E Lee
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Hong Dang
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Alexis B Bailey
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Jason A Wykoff
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Samuel C Gallant
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Deanna R Zhu
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Boyd L Yount
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Takafumi Kato
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kendall M Shaffer
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Satoko Nakano
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Anne Marie Cawley
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | | | - Jeremy R Wang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Robert S Hagan
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Division of Pulmonary Diseases and Critical Care Medicine, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Melissa B Miller
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | | | - Scott H Randell
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Longping V Tse
- Department of Molecular Microbiology & Immunology, Saint Louis University, St. Louis, MO 63104, USA
| | - Camille Ehre
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kenichi Okuda
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Richard C Boucher
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Ralph S Baric
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
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Korosec CS, Wahl LM, Heffernan JM. Within-host evolution of SARS-CoV-2: how often are de novo mutations transmitted from symptomatic infections? Virus Evol 2024; 10:veae006. [PMID: 38425472 PMCID: PMC10904108 DOI: 10.1093/ve/veae006] [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: 11/15/2023] [Revised: 12/20/2023] [Accepted: 01/12/2024] [Indexed: 03/02/2024] Open
Abstract
Despite a relatively low mutation rate, the large number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has allowed for substantial genetic change, leading to a multitude of emerging variants. Using a recently determined mutation rate (per site replication), as well as within-host parameter estimates for symptomatic SARS-CoV-2 infection, we apply a stochastic transmission-bottleneck model to describe the survival probability of de novo SARS-CoV-2 mutations as a function of bottleneck size and selection coefficient. For narrow bottlenecks, we find that mutations affecting per-target-cell attachment rate (with phenotypes associated with fusogenicity and ACE2 binding) have similar transmission probabilities to mutations affecting viral load clearance (with phenotypes associated with humoral evasion). We further find that mutations affecting the eclipse rate (with phenotypes associated with reorganization of cellular metabolic processes and synthesis of viral budding precursor material) are highly favoured relative to all other traits examined. We find that mutations leading to reduced removal rates of infected cells (with phenotypes associated with innate immune evasion) have limited transmission advantage relative to mutations leading to humoral evasion. Predicted transmission probabilities, however, for mutations affecting innate immune evasion are more consistent with the range of clinically estimated household transmission probabilities for de novo mutations. This result suggests that although mutations affecting humoral evasion are more easily transmitted when they occur, mutations affecting innate immune evasion may occur more readily. We examine our predictions in the context of a number of previously characterized mutations in circulating strains of SARS-CoV-2. Our work offers both a null model for SARS-CoV-2 mutation rates and predicts which aspects of viral life history are most likely to successfully evolve, despite low mutation rates and repeated transmission bottlenecks.
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Affiliation(s)
- Chapin S Korosec
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada
| | - Lindi M Wahl
- Applied Mathematics, Western University, 1151 Richmond St, London, ON N6A 5B7, Canada
| | - Jane M Heffernan
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada
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Zhang L, Cao H, Medlin K, Pearson J, Aristotelous AC, Chen A, Wessler T, Forest MG. Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data. Viruses 2023; 16:69. [PMID: 38257769 PMCID: PMC10820884 DOI: 10.3390/v16010069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/24/2024] Open
Abstract
Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each "virtual individual", which we define as each fixed set of model parameters (1) and (2) above. The "virtual population" and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated "virtual population database" of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus-cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.
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Affiliation(s)
- Leyi Zhang
- Department of Mathematics and Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Han Cao
- Department of Mathematics and Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Karen Medlin
- Department of Mathematics and Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason Pearson
- Department of Mathematics and Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Simulations Plus, Inc., 6 Davis Dr., Durham, NC 27709, USA
| | | | - Alexander Chen
- Department of Mathematics, California State University, Dominguez Hills, CA 90747, USA
| | - Timothy Wessler
- Department of Applied Mathematics, University of Colorado at Boulder, Boulder, CO 80309, USA
| | - M. Gregory Forest
- Department of Mathematics and Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Departments of Applied Physical Sciences and Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Vasquez PA, Walker B, Bloom K, Kolbin D, Caughman N, Freeman R, Lysy M, Hult C, Newhall KA, Papanikolas M, Edelmaier C, Forest MG. The power of weak, transient interactions across biology: A paradigm of emergent behavior. PHYSICA D. NONLINEAR PHENOMENA 2023; 454:133866. [PMID: 38274029 PMCID: PMC10806540 DOI: 10.1016/j.physd.2023.133866] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
A growing list of diverse biological systems and their equally diverse functionalities provides realizations of a paradigm of emergent behavior. In each of these biological systems, pervasive ensembles of weak, short-lived, spatially local interactions act autonomously to convey functionalities at larger spatial and temporal scales. In this article, a range of diverse systems and functionalities are presented in a cursory manner with literature citations for further details. Then two systems and their properties are discussed in more detail: yeast chromosome biology and human respiratory mucus.
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Affiliation(s)
- Paula A. Vasquez
- Department of Mathematics, University of South Carolina, United States of America
| | - Ben Walker
- Department of Mathematics, University of California at Irvine, United States of America
| | - Kerry Bloom
- Department of Biology, University of North Carolina at Chapel Hill, United States of America
| | - Daniel Kolbin
- Department of Biology, University of North Carolina at Chapel Hill, United States of America
| | - Neall Caughman
- Department of Mathematics, University of North Carolina at Chapel Hill, United States of America
| | - Ronit Freeman
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, United States of America
| | - Martin Lysy
- Department of Statistics and Actuarial Science, University of Waterloo, Canada
| | - Caitlin Hult
- Department of Mathematics, Gettysburg College, United States of America
| | - Katherine A. Newhall
- Department of Mathematics, University of North Carolina at Chapel Hill, United States of America
| | - Micah Papanikolas
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, United States of America
| | - Christopher Edelmaier
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, United States of America
- Center for Computational Biology, Flatiron Institute, United States of America
| | - M. Gregory Forest
- Department of Mathematics, University of North Carolina at Chapel Hill, United States of America
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, United States of America
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