1
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Feldstein LR, Ruffin J, Wiegand RE, Borkowf CB, James-Gist J, Babu TM, Briggs-Hagen M, Chappell J, Chu HY, Englund JA, Kuntz JL, Lauring AS, Lo N, Carone M, Lockwood C, Martin ET, Midgley CM, Monto AS, Naleway AL, Ogilvie T, Saydah S, Schmidt MA, Schmitz JE, Smith N, Sohn I, Starita L, Talbot HK, Weil AA, Grijalva CG. Effectiveness of mRNA COVID-19 Vaccines and Hybrid Immunity in Preventing SARS-CoV-2 Infection and Symptomatic COVID-19 Among Adults in the United States. J Infect Dis 2025; 231:e743-e753. [PMID: 39774936 PMCID: PMC11998566 DOI: 10.1093/infdis/jiaf007] [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: 11/07/2024] [Revised: 12/26/2024] [Accepted: 01/06/2025] [Indexed: 01/11/2025] Open
Abstract
BACKGROUND Understanding protection against SARS-CoV-2 infection by vaccine and hybrid immunity is important for informing public health strategies as new variants emerge. METHODS We analyzed data from 3 cohort studies spanning 1 September 2022 to 31 July 2023 to estimate COVID-19 vaccine effectiveness (VE) against SARS-CoV-2 infection and symptomatic COVID-19 among adults with and without prior infection in the United States. Participants collected weekly nasal swabs irrespective of symptoms, participated in annual blood draws, and completed periodic surveys, which included vaccination status and infection history. Swabs were tested molecularly for SARS-CoV-2. VE was estimated by Cox proportional hazards models for the hazard ratios of infections, adjusting for covariates. VE was calculated considering prior infection and recency of vaccination. RESULTS Among 3344 adults, the adjusted VE of a bivalent vaccine against infection was 37.2% (95% CI, 12.3%-55.7%) within 7 to 59 days of vaccination and 21.1% (95% CI, -0.5% to 37.1%) within 60 to 179 days of vaccination when compared with participants who were unvaccinated or had received an original monovalent vaccine dose ≥180 days prior. Overall, the adjusted VE of a bivalent vaccine against infection, in conjunction with prior infection, was 62.2% (95% CI, 46.0%-74.5%) within 7 to 179 days of vaccination and 39.4% (95% CI, 12.5%-61.6%) at ≥180 days when compared with naive participants who were unvaccinated or had received a monovalent vaccine dose ≥180 days prior. CONCLUSIONS Adults with prior infection and recent vaccination had high protection against infection and symptomatic illness. Recent vaccination alone provided moderate protection.
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Affiliation(s)
- Leora R Feldstein
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jasmine Ruffin
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Ryan E Wiegand
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Craig B Borkowf
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jade James-Gist
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Tara M Babu
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Melissa Briggs-Hagen
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - James Chappell
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Helen Y Chu
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Janet A Englund
- Department of Pediatrics, University of Washington, Seattle Children's Research Institute, Seattle, Washington
| | | | - Adam S Lauring
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Natalie Lo
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Marco Carone
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Christina Lockwood
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Emily T Martin
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Claire M Midgley
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Arnold S Monto
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | | | - Tara Ogilvie
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Sharon Saydah
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Mark A Schmidt
- Kaiser Permanente Center for Health Research, Portland, Oregon
| | | | - Ning Smith
- Kaiser Permanente Center for Health Research, Portland, Oregon
| | - Ine Sohn
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lea Starita
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - H Keipp Talbot
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ana A Weil
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
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2
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Bendall EE, Dimcheff D, Papalambros L, Fitzsimmons WJ, Zhu Y, Schmitz J, Halasa N, Chappell J, Martin ET, Biddle JE, Smith-Jeffcoat SE, Rolfes MA, Mellis A, Talbot HK, Grijalva C, Lauring AS. In depth sequencing of a serially sampled household cohort reveals the within-host dynamics of Omicron SARS-CoV-2 and rare selection of novel spike variants. PLoS Pathog 2025; 21:e1013134. [PMID: 40294030 PMCID: PMC12074595 DOI: 10.1371/journal.ppat.1013134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2025] [Revised: 05/13/2025] [Accepted: 04/16/2025] [Indexed: 04/30/2025] Open
Abstract
SARS-CoV-2 has undergone repeated and rapid evolution to circumvent host immunity. However, outside of prolonged infections in immunocompromised hosts, within-host positive selection has rarely been detected. Here we combine daily longitudinal sampling of individuals with replicate sequencing to increase the accuracy of and lower the threshold for variant calling. We sequenced 577 specimens from 105 individuals in a household cohort during the BA.1/BA.2 variant period. Individuals exhibited extremely low viral diversity, and we estimated a low within-host evolutionary rate. Within-host dynamics were dominated by genetic drift and purifying selection. Positive selection was rare but highly concentrated in spike. A Wright Fisher Approximate Bayesian Computational model identified positive selection at 14 loci with 7 in spike, including S:448 and S:339. This detectable immune-mediated selection is unusual in acute respiratory infections and may be caused by the relatively narrow antibody repertoire in individuals during the early Omicron phase of the SARS-CoV-2 pandemic.
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Affiliation(s)
- Emily E. Bendall
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Derek Dimcheff
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Leigh Papalambros
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - William J. Fitzsimmons
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yuwei Zhu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jonathan Schmitz
- Department of Pathology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Natasha Halasa
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - James Chappell
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Emily T. Martin
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jessica E. Biddle
- Centers for Disease Control and Prevention, Atlanta, GeorgiaUnited States of America
| | | | - Melissa A. Rolfes
- Centers for Disease Control and Prevention, Atlanta, GeorgiaUnited States of America
| | - Alexandra Mellis
- Centers for Disease Control and Prevention, Atlanta, GeorgiaUnited States of America
| | - H. Keipp Talbot
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee Tennessee, United States of America
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Carlos Grijalva
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Adam S. Lauring
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
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3
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Jones LR. Intra-host variability of SARS-CoV-2: Patterns, causes and impact on COVID-19. Virology 2025; 603:110366. [PMID: 39724740 DOI: 10.1016/j.virol.2024.110366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2024] [Revised: 12/06/2024] [Accepted: 12/17/2024] [Indexed: 12/28/2024]
Abstract
Intra-host viral variability is related to pathogenicity, persistence, drug resistance, and the emergence of new clades. This work reviews the large amount of data on SARS-CoV-2 intra-host variability accumulated to date, addressing known and potential implications in COVID-19 and the emergence of VOCs and lineage-defining mutations. Topics covered include the distribution of intra-host polymorphisms across the genome, the corresponding mutational signatures, their patterns of emergence and extinction throughout infection, and the processes governing their abundance, frequency, and type (synonymous, nonsynonymous, indels, nonsense). Besides, evidence is reviewed that the virus can replicate and mutate in isolation at different anatomical compartments, which may imply that what we have learned from respiratory samples could be part of a broader picture.
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Affiliation(s)
- Leandro R Jones
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Avenida Rivadavia 1917, C1083ACA Ciudad Autónoma de Buenos Aires, Argentina; Laboratorio de Virología y Genética Molecular (LVGM), Facultad de Ciencias Naturales y Ciencias de la Salud, Universidad Nacional de la Patagonia San Juan Bosco, Belgrano 160, Trelew, CP, 9100, Argentina.
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4
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Naderi S, Sagan SM, Shapiro BJ. Within-host genetic diversity of SARS-CoV-2 across animal species. Virus Evol 2024; 11:veae117. [PMID: 39830312 PMCID: PMC11739616 DOI: 10.1093/ve/veae117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 11/19/2024] [Accepted: 12/30/2024] [Indexed: 01/22/2025] Open
Abstract
Infectious disease transmission to different host species makes eradication very challenging and expands the diversity of evolutionary trajectories taken by the pathogen. Since the beginning of the ongoing COVID-19 pandemic, SARS-CoV-2 has been transmitted from humans to many different animal species, in which viral variants of concern could potentially evolve. Previously, using available whole genome consensus sequences of SARS-CoV-2 from four commonly sampled animals (mink, deer, cat, and dog), we inferred similar numbers of transmission events from humans to each animal species. Using a genome-wide association study, we identified 26 single nucleotide variants (SNVs) that tend to occur in deer-more than any other animal-suggesting a high rate of viral adaptation to deer. The reasons for this rapid adaptive evolution remain unclear, but within-host evolution-the ultimate source of the viral diversity that transmits globally-could provide clues. Here, we quantify intra-host SARS-CoV-2 genetic diversity across animal species and show that deer harbor more intra-host SNVs (iSNVs) than other animals, providing a larger pool of genetic diversity for natural selection to act upon. Mixed infections involving more than one viral lineage are unlikely to explain the higher diversity within deer. Rather, a combination of higher mutation rates, longer infections, and species-specific selective pressures are likely explanations. Combined with extensive deer-to-deer transmission, the high levels of within-deer viral diversity help explain the apparent rapid adaptation of SARS-CoV-2 to deer.
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Affiliation(s)
- Sana Naderi
- Department of Microbiology and Immunology, McGill University, 3775 University Street Montreal, QC H3A 2B4, Canada
| | - Selena M Sagan
- Department of Microbiology and Immunology, McGill University, 3775 University Street Montreal, QC H3A 2B4, Canada
- Department of Microbiology and Immunology, The University of British Columbia, 2350 Health Sciences Mall #1365 Vancouver, BC V6T 1Z3, Canada
| | - B. Jesse Shapiro
- Department of Microbiology and Immunology, McGill University, 3775 University Street Montreal, QC H3A 2B4, Canada
- McGill Genome Centre, 740 avenue Dr Penfield Montreal, QC H3A 0G1, Canada
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5
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Bendall EE, Dimcheff D, Papalambros L, Fitzsimmons WJ, Zhu Y, Schmitz J, Halasa N, Chappell J, Martin ET, Biddle JE, Smith-Jeffcoat SE, Rolfes MA, Mellis A, Talbot HK, Grijalva C, Lauring AS. In depth sequencing of a serially sampled household cohort reveals the within-host dynamics of Omicron SARS-CoV-2 and rare selection of novel spike variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.21.624722. [PMID: 39605326 PMCID: PMC11601520 DOI: 10.1101/2024.11.21.624722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
SARS-CoV-2 has undergone repeated and rapid evolution to circumvent host immunity. However, outside of prolonged infections in immunocompromised hosts, within-host positive selection has rarely been detected. The low diversity within-hosts and strong genetic linkage among genomic sites make accurately detecting positive selection difficult. Longitudinal sampling is a powerful method for detecting selection that has seldom been used for SARS-CoV-2. Here we combine longitudinal sampling with replicate sequencing to increase the accuracy of and lower the threshold for variant calling. We sequenced 577 specimens from 105 individuals from a household cohort primarily during the BA.1/BA.2 variant period. There was extremely low diversity and a low rate of divergence. Specimens had 0-12 intrahost single nucleotide variants (iSNV) at >0.5% frequency, and the majority of the iSNV were at frequencies <2%. Within-host dynamics were dominated by genetic drift and purifying selection. Positive selection was rare but highly concentrated in spike. Two individuals with BA.1 infections had S:371F, a lineage defining substitution for BA.2. A Wright Fisher Approximate Bayesian Computational model identified positive selection at 14 loci with 7 in spike, including S:448 and S:339. We also detected significant genetic hitchhiking between synonymous changes and nonsynonymous iSNV under selection. The detectable immune-mediated selection may be caused by the relatively narrow antibody repertoire in individuals during the early Omicron phase of the SARS-CoV-2 pandemic. As both the virus and population immunity evolve, understanding the corresponding shifts in SARS-CoV-2 within-host dynamics will be important.
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Affiliation(s)
- Emily E. Bendall
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Derek Dimcheff
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Leigh Papalambros
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Yuwei Zhu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan Schmitz
- Department of Pathology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Natasha Halasa
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James Chappell
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Emily T. Martin
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | | | | | | | | | - H. Keipp Talbot
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carlos Grijalva
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam S. Lauring
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
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6
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Banga J, Brock-Fisher T, Petros BA, Dai EY, Leonelli AT, Dobbins ST, Messer KS, Nathanson AB, Capone A, Littlehale N, Appiah-Danquah V, Dim S, Moreno GK, Crowther M, DeRuff KC, MacInnis BL, Springer M, Sabeti PC, Stephenson KE. Severe Acute Respiratory Syndrome Coronavirus 2 Household Transmission During the Omicron Era in Massachusetts: A Prospective, Case-Ascertained Study Using Genomic Epidemiology. Open Forum Infect Dis 2024; 11:ofae591. [PMID: 39529936 PMCID: PMC11551451 DOI: 10.1093/ofid/ofae591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Accepted: 10/03/2024] [Indexed: 11/16/2024] Open
Abstract
Background Households are a major setting for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, but there remains a lack of knowledge regarding the dynamics of viral transmission, particularly in the setting of preexisting SARS-CoV-2 immunity and evolving variants. Methods We conducted a prospective, case-ascertained household transmission study in the greater Boston area in March-July 2022. Anterior nasal swabs, along with clinical and demographic data, were collected for 14 days. Nasal swabs were tested for SARS-CoV-2 by polymerase chain reaction (PCR). Whole genome sequencing was performed on high-titer samples. Results We enrolled 33 households in a primary analysis set, with a median participant age of 25 years (range, 2-66 years), 98% of whom had received at least 2 doses of a coronavirus disease 2019 (COVID-19) vaccine. Fifty-eight percent of households had a secondary case during follow-up and the secondary attack rate (SAR) for contacts was 39%. We further examined a strict analysis set of 21 households that had only 1 PCR-positive case at baseline, finding an SAR of 22.5%. Genomic epidemiology further determined that there were multiple sources of infection for household contacts, including the index case and outside introductions. When limiting estimates to only highly probable transmissions given epidemiologic and genomic data, the SAR was 18.4%. Conclusions Household contacts of a person newly diagnosed with COVID-19 are at high risk for SARS-CoV-2 infection in the following 2 weeks. This is, however, not only due to infection from the household index case, but also because the presence of an infected household member implies increased SARS-CoV-2 community transmission.
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Affiliation(s)
- Jaspreet Banga
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Taylor Brock-Fisher
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
- Department of Organismic and Evolutionary Biology, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Brittany A Petros
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
- Harvard/Massachusetts Institute of Technology MD-PhD Program, Boston, Massachusetts, USA
| | - Eric Y Dai
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Ariana T Leonelli
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Sabrina T Dobbins
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Katelyn S Messer
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Audrey B Nathanson
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Amelia Capone
- Beth Israel Lahey Health Primary Care, Chelsea, Massachusetts, USA
| | - Nancy Littlehale
- Beth Israel Lahey Health Primary Care, Chelsea, Massachusetts, USA
| | - Viola Appiah-Danquah
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Siang Dim
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Gage K Moreno
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Maura Crowther
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Katherine C DeRuff
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Bronwyn L MacInnis
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Michael Springer
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA
| | - Pardis C Sabeti
- The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
- Department of Organismic and Evolutionary Biology, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - Kathryn E Stephenson
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
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7
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Machkovech HM, Hahn AM, Garonzik Wang J, Grubaugh ND, Halfmann PJ, Johnson MC, Lemieux JE, O'Connor DH, Piantadosi A, Wei W, Friedrich TC. Persistent SARS-CoV-2 infection: significance and implications. THE LANCET. INFECTIOUS DISEASES 2024; 24:e453-e462. [PMID: 38340735 DOI: 10.1016/s1473-3099(23)00815-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/14/2023] [Accepted: 12/15/2023] [Indexed: 02/12/2024]
Abstract
SARS-CoV-2 causes persistent infections in a subset of individuals, which is a major clinical and public health problem that should be prioritised for further investigation for several reasons. First, persistent SARS-CoV-2 infection often goes unrecognised, and therefore might affect a substantial number of people, particularly immunocompromised individuals. Second, the formation of tissue reservoirs (including in non-respiratory tissues) might underlie the pathophysiology of the persistent SARS-CoV-2 infection and require new strategies for diagnosis and treatment. Finally, persistent SARS-CoV-2 replication, particularly in the setting of suboptimal immune responses, is a possible source of new, divergent virus variants that escape pre-existing immunity on the individual and population levels. Defining optimal diagnostic and treatment strategies for patients with persistent virus replication and monitoring viral evolution are therefore urgent medical and public health priorities.
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Affiliation(s)
- Heather M Machkovech
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Anne M Hahn
- Department of Epidemiology of Microbial Diseases, School of Public Health, Yale University, New Haven, CT, USA
| | | | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, School of Public Health, Yale University, New Haven, CT, USA
| | - Peter J Halfmann
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Marc C Johnson
- Department of Molecular Microbiology and Immunology, University of Missouri-School of Medicine, Columbia, MO, USA
| | - Jacob E Lemieux
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - David H O'Connor
- Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Anne Piantadosi
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Wanting Wei
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Thomas C Friedrich
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA.
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8
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Kakee S, Kanai K, Tsuneki-Tokunaga A, Okuno K, Namba N, Tomita K, Chikumi H, Kageyama S. Difference in TMPRSS2 usage by Delta and Omicron variants of SARS-CoV-2: Implication for a sudden increase among children. PLoS One 2024; 19:e0299445. [PMID: 38870131 PMCID: PMC11175390 DOI: 10.1371/journal.pone.0299445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 05/07/2024] [Indexed: 06/15/2024] Open
Abstract
It has been postulated from a combination of evidence that a sudden increase in COVID-19 cases among pediatric patients after onset of the Omicron wave was attributed to a reduced requirement for TMPRSS2-mediated entry in pediatric airways with lower expression levels of TMPRSS2. Epidemic strains were isolated from the indigenous population in an area, and the levels of TMPRSS2 required for Delta and Omicron variants were assessed. As a result, Delta variants proliferated fully in cultures of TMPRSS2-positive Vero cells but not in TMPRSS2-negative Vero cell culture (350-fold, Delta vs 9.6-fold, Omicron). There was no obvious age-dependent selection of Omicron strains affected by the TMPRSS2 (9.6-fold, Adults vs. 12-fold, Children). A phylogenetic tree was generated and Blast searches (up to 100 references) for the spread of strains in the study area showed that each strain had almost identical homology (>99.5%) with foreign isolates, although indigenous strains had obvious differences from each other. This suggested that the differences had been present abroad for a long period. Therefore, the lower requirement for TMPRSS2 by Omicron strains might be applicable to epidemic strains globally. In conclusion, the property of TMPRSS2-independent cleavage makes Omicron proliferate with ease and allows epidemics among children with fewer TMPRSS2 on epithelial surfaces of the respiratory organs.
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Affiliation(s)
- Sosuke Kakee
- Division of Virology, Department of Microbiology and Immunology, Faculty of Medicine, Tottori University, Yonago, Japan
- Division of Pediatrics and Perinatology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Kyosuke Kanai
- Division of Virology, Department of Microbiology and Immunology, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Akeno Tsuneki-Tokunaga
- Division of Virology, Department of Microbiology and Immunology, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Keisuke Okuno
- Division of Pediatrics and Perinatology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Noriyuki Namba
- Division of Pediatrics and Perinatology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Katsuyuki Tomita
- Department of Respiratory Medicine, National Hospital Organization Yonago Medical Center, Yonago, Japan
| | | | - Seiji Kageyama
- Division of Virology, Department of Microbiology and Immunology, Faculty of Medicine, Tottori University, Yonago, Japan
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9
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Liu Y, Sapoval N, Gallego-García P, Tomás L, Posada D, Treangen TJ, Stadler LB. Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater. Nat Commun 2024; 15:4545. [PMID: 38806450 PMCID: PMC11133379 DOI: 10.1038/s41467-024-48334-w] [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: 11/15/2023] [Accepted: 04/29/2024] [Indexed: 05/30/2024] Open
Abstract
Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.
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Affiliation(s)
- Yunxi Liu
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Nicolae Sapoval
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Pilar Gallego-García
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Laura Tomás
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310, Vigo, Spain
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX, 77005, USA.
| | - Lauren B Stadler
- Department of Civil and Environmental Engineering, Rice University, Houston, TX, 77005, USA.
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10
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Marques AD, Graham-Wooten J, Fitzgerald AS, Sobel Leonard A, Cook EJ, Everett JK, Rodino KG, Moncla LH, Kelly BJ, Collman RG, Bushman FD. SARS-CoV-2 evolution during prolonged infection in immunocompromised patients. mBio 2024; 15:e0011024. [PMID: 38364100 PMCID: PMC10936176 DOI: 10.1128/mbio.00110-24] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 02/18/2024] Open
Abstract
Prolonged infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in immunocompromised patients provides an opportunity for viral evolution, potentially leading to the generation of new pathogenic variants. To investigate the pathways of viral evolution, we carried out a study on five patients experiencing prolonged SARS-CoV-2 infection (quantitative polymerase chain reaction-positive for 79-203 days) who were immunocompromised due to treatment for lymphoma or solid organ transplantation. For each timepoint analyzed, we generated at least two independent viral genome sequences to assess the heterogeneity and control for sequencing error. Four of the five patients likely had prolonged infection; the fifth apparently experienced a reinfection. The rates of accumulation of substitutions in the viral genome per day were higher in hospitalized patients with prolonged infection than those estimated for the community background. The spike coding region accumulated a significantly greater number of unique mutations than other viral coding regions, and the mutation density was higher. Two patients were treated with monoclonal antibodies (bebtelovimab and sotrovimab); by the next sampled timepoint, each virus population showed substitutions associated with monoclonal antibody resistance as the dominant forms (spike K444N and spike E340D). All patients received remdesivir, but remdesivir-resistant substitutions were not detected. These data thus help elucidate the trends of emergence, evolution, and selection of mutational variants within long-term infected immunocompromised individuals. IMPORTANCE SARS-CoV-2 is responsible for a global pandemic, driven in part by the emergence of new viral variants. Where do these new variants come from? One model is that long-term viral persistence in infected individuals allows for viral evolution in response to host pressures, resulting in viruses more likely to replicate efficiently in humans. In this study, we characterize replication in several hospitalized and long-term infected individuals, documenting efficient pathways of viral evolution.
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Affiliation(s)
- Andrew D. Marques
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jevon Graham-Wooten
- Division of Pulmonary, Allergy, and Critical Care, Philadelphia, Pennsylvania, USA
| | | | - Ashley Sobel Leonard
- Division of Infectious Diseases, Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Emma J. Cook
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John K. Everett
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle G. Rodino
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Louise H. Moncla
- Department of Pathobiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Brendan J. Kelly
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ronald G. Collman
- Division of Pulmonary, Allergy, and Critical Care, Philadelphia, Pennsylvania, USA
| | - Frederic D. Bushman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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11
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Feldstein LR, Britton A, Grant L, Wiegand R, Ruffin J, Babu TM, Briggs Hagen M, Burgess JL, Caban-Martinez AJ, Chu HY, Ellingson KD, Englund JA, Hegmann KT, Jeddy Z, Lauring AS, Lutrick K, Martin ET, Mathenge C, Meece J, Midgley CM, Monto AS, Newes-Adeyi G, Odame-Bamfo L, Olsho LEW, Phillips AL, Rai RP, Saydah S, Smith N, Steinhardt L, Tyner H, Vandermeer M, Vaughan M, Yoon SK, Gaglani M, Naleway AL. Effectiveness of Bivalent mRNA COVID-19 Vaccines in Preventing SARS-CoV-2 Infection in Children and Adolescents Aged 5 to 17 Years. JAMA 2024; 331:408-416. [PMID: 38319331 PMCID: PMC10848053 DOI: 10.1001/jama.2023.27022] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/11/2023] [Indexed: 02/07/2024]
Abstract
Importance Bivalent mRNA COVID-19 vaccines were recommended in the US for children and adolescents aged 12 years or older on September 1, 2022, and for children aged 5 to 11 years on October 12, 2022; however, data demonstrating the effectiveness of bivalent COVID-19 vaccines are limited. Objective To assess the effectiveness of bivalent COVID-19 vaccines against SARS-CoV-2 infection and symptomatic COVID-19 among children and adolescents. Design, Setting, and Participants Data for the period September 4, 2022, to January 31, 2023, were combined from 3 prospective US cohort studies (6 sites total) and used to estimate COVID-19 vaccine effectiveness among children and adolescents aged 5 to 17 years. A total of 2959 participants completed periodic surveys (demographics, household characteristics, chronic medical conditions, and COVID-19 symptoms) and submitted weekly self-collected nasal swabs (irrespective of symptoms); participants submitted additional nasal swabs at the onset of any symptoms. Exposure Vaccination status was captured from the periodic surveys and supplemented with data from state immunization information systems and electronic medical records. Main Outcome and Measures Respiratory swabs were tested for the presence of the SARS-CoV-2 virus using reverse transcriptase-polymerase chain reaction. SARS-CoV-2 infection was defined as a positive test regardless of symptoms. Symptomatic COVID-19 was defined as a positive test and 2 or more COVID-19 symptoms within 7 days of specimen collection. Cox proportional hazards models were used to estimate hazard ratios for SARS-CoV-2 infection and symptomatic COVID-19 among participants who received a bivalent COVID-19 vaccine dose vs participants who received no vaccine or monovalent vaccine doses only. Models were adjusted for age, sex, race, ethnicity, underlying health conditions, prior SARS-CoV-2 infection status, geographic site, proportion of circulating variants by site, and local virus prevalence. Results Of the 2959 participants (47.8% were female; median age, 10.6 years [IQR, 8.0-13.2 years]; 64.6% were non-Hispanic White) included in this analysis, 25.4% received a bivalent COVID-19 vaccine dose. During the study period, 426 participants (14.4%) had laboratory-confirmed SARS-CoV-2 infection. Among these 426 participants, 184 (43.2%) had symptomatic COVID-19, 383 (89.9%) were not vaccinated or had received only monovalent COVID-19 vaccine doses (1.38 SARS-CoV-2 infections per 1000 person-days), and 43 (10.1%) had received a bivalent COVID-19 vaccine dose (0.84 SARS-CoV-2 infections per 1000 person-days). Bivalent vaccine effectiveness against SARS-CoV-2 infection was 54.0% (95% CI, 36.6%-69.1%) and vaccine effectiveness against symptomatic COVID-19 was 49.4% (95% CI, 22.2%-70.7%). The median observation time after vaccination was 276 days (IQR, 142-350 days) for participants who received only monovalent COVID-19 vaccine doses vs 50 days (IQR, 27-74 days) for those who received a bivalent COVID-19 vaccine dose. Conclusion and Relevance The bivalent COVID-19 vaccines protected children and adolescents against SARS-CoV-2 infection and symptomatic COVID-19. These data demonstrate the benefit of COVID-19 vaccine in children and adolescents. All eligible children and adolescents should remain up to date with recommended COVID-19 vaccinations.
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Affiliation(s)
- Leora R. Feldstein
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Amadea Britton
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Lauren Grant
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Ryan Wiegand
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jasmine Ruffin
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Tara M. Babu
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle
| | - Melissa Briggs Hagen
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | | | - Helen Y. Chu
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle
| | | | | | | | | | - Adam S. Lauring
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor
| | | | - Emily T. Martin
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor
| | | | - Jennifer Meece
- Marshfield Clinic Research Institute, Marshfield, Wisconsin
| | - Claire M. Midgley
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Arnold S. Monto
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor
| | | | | | | | | | | | - Sharon Saydah
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Ning Smith
- Kaiser Permanente Center for Health Research, Portland, Oregon
| | - Laura Steinhardt
- Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Harmony Tyner
- St Luke’s Regional Health Care System, Duluth, Minnesota
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12
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Murray JM, Murray DD, Schvoerer E, Akand EH. SARS-CoV-2 Delta and Omicron community transmission networks as added value to contact tracing. J Infect 2024; 88:173-179. [PMID: 38242366 DOI: 10.1016/j.jinf.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/14/2024] [Indexed: 01/21/2024]
Abstract
OBJECTIVES Calculations of SARS-CoV-2 transmission networks at a population level have been limited. Networks that estimate infections between individuals and whether this results in a mutation, can be a way to evaluate fitness of a mutational clone by how much it expands in number as well as determining the likelihood a transmission results in a new variant. METHODS Australian Delta and Omicron SARS-CoV-2 sequences were downloaded from GISAID. Transmission networks of infection between individuals were estimated using a novel mathematical method. RESULTS Many of the sequences were identical, with clone sizes following power law distributions driven by negative binomial probability distributions for both the number of infections per individual and the number of mutations per transmission (median 0.74 nucleotide changes for Delta and 0.71 for Omicron). Using these distributions, an agent-based model was able to replicate the observed clonal network structure, providing a basis for more detailed COVID-19 modelling. Possible recombination events, tracked by insertion/deletion (indel) patterns, were identified for each variant in these outbreaks. CONCLUSIONS This modelling approach reveals key transmission characteristics of SARS-CoV-2 and may complement traditional contact tracing. This methodology can also be applied to other diseases as genetic sequencing of viruses becomes more commonplace.
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Affiliation(s)
- John M Murray
- School of Mathematics and Statistics, UNSW Sydney, NSW 2052, Australia.
| | - Daniel D Murray
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Evelyne Schvoerer
- Laboratory of Virology, University Hospital of Nancy Brabois, F-54500 Vandoeuvre-les-Nancy, France; Lorraine University, Laboratory of Physical Chemistry and Microbiology for Materials and the Environment, LCPME UMR 7564, CNRS, 405 Rue de Vandoeuvre, F-54600 Villers-lès-Nancy, France
| | - Elma H Akand
- School of Mathematics and Statistics, UNSW Sydney, NSW 2052, Australia
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13
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Li Y, Choudhary MC, Regan J, Boucau J, Nathan A, Speidel T, Liew MY, Edelstein GE, Kawano Y, Uddin R, Deo R, Marino C, Getz MA, Reynolds Z, Barry M, Gilbert RF, Tien D, Sagar S, Vyas TD, Flynn JP, Hammond SP, Novack LA, Choi B, Cernadas M, Wallace ZS, Sparks JA, Vyas JM, Seaman MS, Gaiha GD, Siedner MJ, Barczak AK, Lemieux JE, Li JZ. SARS-CoV-2 viral clearance and evolution varies by type and severity of immunodeficiency. Sci Transl Med 2024; 16:eadk1599. [PMID: 38266109 PMCID: PMC10982957 DOI: 10.1126/scitranslmed.adk1599] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024]
Abstract
Despite vaccination and antiviral therapies, immunocompromised individuals are at risk for prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but the immune defects that predispose an individual to persistent coronavirus disease 2019 (COVID-19) remain incompletely understood. In this study, we performed detailed viro-immunologic analyses of a prospective cohort of participants with COVID-19. The median times to nasal viral RNA and culture clearance in individuals with severe immunosuppression due to hematologic malignancy or transplant (S-HT) were 72 and 40 days, respectively, both of which were significantly longer than clearance rates in individuals with severe immunosuppression due to autoimmunity or B cell deficiency (S-A), individuals with nonsevere immunodeficiency, and nonimmunocompromised groups (P < 0.01). Participants who were severely immunocompromised had greater SARS-CoV-2 evolution and a higher risk of developing resistance against therapeutic monoclonal antibodies. Both S-HT and S-A participants had diminished SARS-CoV-2-specific humoral responses, whereas only the S-HT group had reduced T cell-mediated responses. This highlights the varied risk of persistent COVID-19 across distinct immunosuppressive conditions and suggests that suppression of both B and T cell responses results in the highest contributing risk of persistent infection.
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Affiliation(s)
- Yijia Li
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Manish C. Choudhary
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - James Regan
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Julie Boucau
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Anusha Nathan
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
- Program in Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA 02115, USA
| | - Tessa Speidel
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - May Yee Liew
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gregory E. Edelstein
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yumeko Kawano
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Rockib Uddin
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rinki Deo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin Marino
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Matthew A. Getz
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Zahra Reynolds
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Mamadou Barry
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Rebecca F. Gilbert
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Dessie Tien
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Shruti Sagar
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Tammy D. Vyas
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - James P. Flynn
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sarah P. Hammond
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Lewis A. Novack
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Bina Choi
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Manuela Cernadas
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Zachary S. Wallace
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jeffrey A. Sparks
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jatin M. Vyas
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Michael S. Seaman
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Gaurav D. Gaiha
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Mark J. Siedner
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Amy K. Barczak
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Jacob E. Lemieux
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jonathan Z. Li
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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14
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Farjo M, Koelle K, Martin MA, Gibson LL, Walden KKO, Rendon G, Fields CJ, Alnaji FG, Gallagher N, Luo CH, Mostafa HH, Manabe YC, Pekosz A, Smith RL, McManus DD, Brooke CB. Within-host evolutionary dynamics and tissue compartmentalization during acute SARS-CoV-2 infection. J Virol 2024; 98:e0161823. [PMID: 38174928 PMCID: PMC10805032 DOI: 10.1128/jvi.01618-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
The global evolution of SARS-CoV-2 depends in part upon the evolutionary dynamics within individual hosts with varying immune histories. To characterize the within-host evolution of acute SARS-CoV-2 infection, we sequenced saliva and nasal samples collected daily from vaccinated and unvaccinated individuals early during infection. We show that longitudinal sampling facilitates high-confidence genetic variant detection and reveals evolutionary dynamics missed by less-frequent sampling strategies. Within-host dynamics in both unvaccinated and vaccinated individuals appeared largely stochastic; however, in rare cases, minor genetic variants emerged to frequencies sufficient for forward transmission. Finally, we detected significant genetic compartmentalization of viral variants between saliva and nasal swab sample sites in many individuals. Altogether, these data provide a high-resolution profile of within-host SARS-CoV-2 evolutionary dynamics.IMPORTANCEWe detail the within-host evolutionary dynamics of SARS-CoV-2 during acute infection in 31 individuals using daily longitudinal sampling. We characterized patterns of mutational accumulation for unvaccinated and vaccinated individuals, and observed that temporal variant dynamics in both groups were largely stochastic. Comparison of paired nasal and saliva samples also revealed significant genetic compartmentalization between tissue environments in multiple individuals. Our results demonstrate how selection, genetic drift, and spatial compartmentalization all play important roles in shaping the within-host evolution of SARS-CoV-2 populations during acute infection.
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Affiliation(s)
- Mireille Farjo
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Katia Koelle
- Department of Biology, Emory University, Atlanta, Georgia, USA
| | - Michael A. Martin
- Department of Biology, Emory University, Atlanta, Georgia, USA
- Population Biology, Ecology, and Evolution Graduate Program, Emory University, Atlanta, Georgia, USA
| | - Laura L. Gibson
- Division of Infectious Diseases and Immunology, Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Department of Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Kimberly K. O. Walden
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Gloria Rendon
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Christopher J. Fields
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Fadi G. Alnaji
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Nicholas Gallagher
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chun Huai Luo
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Heba H. Mostafa
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yukari C. Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Andrew Pekosz
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Rebecca L. Smith
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - David D. McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Christopher B. Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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15
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Holmes KE, VanInsberghe D, Ferreri LM, Elie B, Ganti K, Lee CY, Lowen AC. Viral expansion after transfer is a primary driver of influenza A virus transmission bottlenecks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.19.567585. [PMID: 38014182 PMCID: PMC10680852 DOI: 10.1101/2023.11.19.567585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
For many viruses, narrow bottlenecks acting during transmission sharply reduce genetic diversity in a recipient host relative to the donor. Since genetic diversity represents adaptive potential, such losses of diversity are though to limit the opportunity for viral populations to undergo antigenic change and other adaptive processes. Thus, a detailed picture of evolutionary dynamics during transmission is critical to understanding the forces driving viral evolution at an epidemiologic scale. To advance this understanding, we used a novel barcoded virus library and a guinea pig model of transmission to decipher where in the transmission process diversity is lost for influenza A viruses. In inoculated guinea pigs, we show that a high level of viral genetic diversity is maintained across time. Continuity in the barcodes detected furthermore indicates that stochastic effects are not pronounced within inoculated hosts. Importantly, in both aerosol-exposed and direct contact-exposed animals, we observed many barcodes at the earliest time point(s) positive for infectious virus, indicating robust transfer of diversity through the environment. This high viral diversity is short-lived, however, with a sharp decline seen 1-2 days after initiation of infection. Although major losses of diversity at transmission are well described for influenza A virus, our data indicate that events that occur following viral transfer and during the earliest stages of natural infection have a predominant role in this process. This finding suggests that immune selection may have greater opportunity to operate during influenza A transmission than previously recognized.
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Affiliation(s)
- Katie E. Holmes
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - David VanInsberghe
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Lucas M. Ferreri
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Baptiste Elie
- MIVEGEC, CNRS, IRD, Université de Montpellier, Montpellier, France
| | - Ketaki Ganti
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Chung-Young Lee
- Department of Microbiology, School of Medicine, Kyungpook National University, Jung-gu, Daegu, Republic of Korea
| | - Anice C. Lowen
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
- Emory Center of Excellence for Influenza Research and Response (CEIRR), Atlanta, GA, USA
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16
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Bragazzi Cunha J, Leix K, Sherman EJ, Mirabelli C, Frum T, Zhang CJ, Kennedy AA, Lauring AS, Tai AW, Sexton JZ, Spence JR, Wobus CE, Emmer BT. Type I interferon signaling induces a delayed antiproliferative response in respiratory epithelial cells during SARS-CoV-2 infection. J Virol 2023; 97:e0127623. [PMID: 37975674 PMCID: PMC10734423 DOI: 10.1128/jvi.01276-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/22/2023] [Indexed: 11/19/2023] Open
Abstract
ABSTRACT Disease progression during SARS-CoV-2 infection is tightly linked to the fate of lung epithelial cells, with severe cases of COVID-19 characterized by direct injury of the alveolar epithelium and an impairment in its regeneration from progenitor cells. The molecular pathways that govern respiratory epithelial cell death and proliferation during SARS-CoV-2 infection, however, remain unclear. We now report a high-throughput CRISPR screen for host genetic modifiers of the survival and proliferation of SARS-CoV-2-infected Calu-3 respiratory epithelial cells. The top four genes identified in our screen encode components of the same type I interferon (IFN-I) signaling complex—IFNAR1, IFNAR2, JAK1, and TYK2. The fifth gene, ACE2, was an expected control encoding the SARS-CoV-2 viral receptor. Surprisingly, despite the antiviral properties of IFN-I signaling, its disruption in our screen was associated with an increase in Calu-3 cell fitness. We validated this effect and found that IFN-I signaling did not sensitize SARS-CoV-2-infected cultures to cell death but rather inhibited the proliferation of surviving cells after the early peak of viral replication and cytopathic effect. We also found that IFN-I signaling alone, in the absence of viral infection, was sufficient to induce this delayed antiproliferative response in both Calu-3 cells and iPSC-derived type 2 alveolar epithelial cells. Together, these findings highlight a cell autonomous antiproliferative response by respiratory epithelial cells to persistent IFN-I signaling during SARS-CoV-2 infection. This response may contribute to the deficient alveolar regeneration that has been associated with COVID-19 lung injury and represents a promising area for host-targeted therapeutic development.
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Affiliation(s)
- Juliana Bragazzi Cunha
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Kyle Leix
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Emily J. Sherman
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Carmen Mirabelli
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Tristan Frum
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Charles J. Zhang
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
| | - Andrew A. Kennedy
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Adam S. Lauring
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Andrew W. Tai
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
- VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Jonathan Z. Sexton
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
| | - Jason R. Spence
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan, USA
| | - Christiane E. Wobus
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Brian T. Emmer
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
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17
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Terbot JW, Cooper BS, Good JM, Jensen JD. A Simulation Framework for Modeling the Within-Patient Evolutionary Dynamics of SARS-CoV-2. Genome Biol Evol 2023; 15:evad204. [PMID: 37950882 PMCID: PMC10664409 DOI: 10.1093/gbe/evad204] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 10/31/2023] [Accepted: 11/07/2023] [Indexed: 11/13/2023] Open
Abstract
The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to considerable interest in detecting novel beneficial mutations and other genomic changes that may signal the development of variants of concern (VOCs). The ability to accurately detect these changes within individual patient samples is important in enabling early detection of VOCs. Such genomic scans for rarely acting positive selection are best performed via comparison of empirical data with simulated data wherein commonly acting evolutionary factors, including mutation and recombination, reproductive and infection dynamics, and purifying and background selection, can be carefully accounted for and parameterized. Although there has been work to quantify these factors in SARS-CoV-2, they have yet to be integrated into a baseline model describing intrahost evolutionary dynamics. To construct such a baseline model, we develop a simulation framework that enables one to establish expectations for underlying levels and patterns of patient-level variation. By varying eight key parameters, we evaluated 12,096 different model-parameter combinations and compared them with existing empirical data. Of these, 592 models (∼5%) were plausible based on the resulting mean expected number of segregating variants. These plausible models shared several commonalities shedding light on intrahost SARS-CoV-2 evolutionary dynamics: severe infection bottlenecks, low levels of reproductive skew, and a distribution of fitness effects skewed toward strongly deleterious mutations. We also describe important areas of model uncertainty and highlight additional sequence data that may help to further refine a baseline model. This study lays the groundwork for the improved analysis of existing and future SARS-CoV-2 within-patient data.
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Affiliation(s)
- John W Terbot
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, Arizona, USA
- Division of Biological Sciences, University of Montana, Missoula, Montana, USA
| | - Brandon S Cooper
- Division of Biological Sciences, University of Montana, Missoula, Montana, USA
| | - Jeffrey M Good
- Division of Biological Sciences, University of Montana, Missoula, Montana, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, Arizona, USA
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18
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Hare D, Dembicka KM, Brennan C, Campbell C, Sutton-Fitzpatrick U, Stapleton PJ, De Gascun CF, Dunne CP. Whole-genome sequencing to investigate transmission of SARS-CoV-2 in the acute healthcare setting: a systematic review. J Hosp Infect 2023; 140:139-155. [PMID: 37562592 DOI: 10.1016/j.jhin.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/03/2023] [Accepted: 08/04/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Whole-genome sequencing (WGS) has been used widely to elucidate transmission of SARS-CoV-2 in acute healthcare settings, and to guide infection, prevention, and control (IPC) responses. AIM To systematically appraise available literature, published between January 1st, 2020 and June 30th, 2022, describing the implementation of WGS in acute healthcare settings to characterize nosocomial SARS-CoV-2 transmission. METHODS Searches of the PubMed, Embase, Ovid MEDLINE, EBSCO MEDLINE, and Cochrane Library databases identified studies in English reporting the use of WGS to investigate SARS-CoV-2 transmission in acute healthcare environments. Publications involved data collected up to December 31st, 2021, and findings were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. FINDINGS In all, 3088 non-duplicate records were retrieved; 97 met inclusion criteria, involving 62 outbreak analyses and 35 genomic surveillance studies. No publications from low-income countries were identified. In 87/97 (90%), WGS supported hypotheses for nosocomial transmission, while in 46 out of 97 (47%) suspected transmission events were excluded. An IPC intervention was attributed to the use of WGS in 18 out of 97 (18%); however, only three (3%) studies reported turnaround times ≤7 days facilitating near real-time IPC action, and none reported an impact on the incidence of nosocomial COVID-19 attributable to WGS. CONCLUSION WGS can elucidate transmission of SARS-CoV-2 in acute healthcare settings to enhance epidemiological investigations. However, evidence was not identified to support sequencing as an intervention to reduce the incidence of SARS-CoV-2 in hospital or to alter the trajectory of active outbreaks.
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Affiliation(s)
- D Hare
- UCD National Virus Reference Laboratory, University College Dublin, Ireland; School of Medicine, University of Limerick, Limerick, Ireland.
| | - K M Dembicka
- School of Medicine, University of Limerick, Limerick, Ireland
| | - C Brennan
- UCD National Virus Reference Laboratory, University College Dublin, Ireland
| | - C Campbell
- UCD National Virus Reference Laboratory, University College Dublin, Ireland
| | | | | | - C F De Gascun
- UCD National Virus Reference Laboratory, University College Dublin, Ireland
| | - C P Dunne
- School of Medicine, University of Limerick, Limerick, Ireland; Centre for Interventions in Infection, Inflammation & Immunity (4i), University of Limerick, Limerick, Ireland
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19
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Roder AE, Johnson KEE, Knoll M, Khalfan M, Wang B, Schultz-Cherry S, Banakis S, Kreitman A, Mederos C, Youn JH, Mercado R, Wang W, Chung M, Ruchnewitz D, Samanovic MI, Mulligan MJ, Lässig M, Luksza M, Das S, Gresham D, Ghedin E. Optimized quantification of intra-host viral diversity in SARS-CoV-2 and influenza virus sequence data. mBio 2023; 14:e0104623. [PMID: 37389439 PMCID: PMC10470513 DOI: 10.1128/mbio.01046-23] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/02/2023] [Indexed: 07/01/2023] Open
Abstract
High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant-calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller and use of replicate sequencing have the most significant impact on single-nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false-negative rates. When replicates are not available, using a combination of multiple callers with more stringent cutoffs is recommended. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intra-host viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intra-host variation, viral diversity, and viral evolution. IMPORTANCE When viruses replicate inside a host cell, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus nor strongly beneficial can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in the inclusion of false-positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant-calling tools. We used simulated and synthetic data to test their performance against a true set of variants and then used these studies to inform variant identification in data from SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.
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Affiliation(s)
- A. E. Roder
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
| | - K. E. E. Johnson
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, USA
| | - M. Knoll
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, USA
| | - M. Khalfan
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, USA
| | - B. Wang
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, USA
| | - S. Schultz-Cherry
- Department of Infectious Diseases, St Jude Children Research Hospital, Memphis, Tennessee, USA
| | - S. Banakis
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
| | - A. Kreitman
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
| | - C. Mederos
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
| | - J.-H. Youn
- Department of Laboratory Medicine, NIH, Bethesda, Maryland, USA
| | - R. Mercado
- Department of Laboratory Medicine, NIH, Bethesda, Maryland, USA
| | - W. Wang
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
| | - M. Chung
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
| | - D. Ruchnewitz
- Institute for Biological Physics, University of Cologne, Cologne, Germany
| | - M. I. Samanovic
- Department of Medicine, New York University Langone Vaccine Center, New York, New York, USA
| | - M. J. Mulligan
- Department of Medicine, New York University Langone Vaccine Center, New York, New York, USA
| | - M. Lässig
- Institute for Biological Physics, University of Cologne, Cologne, Germany
| | - M. Luksza
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - S. Das
- Department of Laboratory Medicine, NIH, Bethesda, Maryland, USA
| | - D. Gresham
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, USA
| | - E. Ghedin
- Systems Genomics Section, Laboratory of Parasitic Diseases, DIR, NIAID, NIH, Bethesda, Maryland, USA
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, USA
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20
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Xi B, Zeng X, Chen Z, Zeng J, Huang L, Du H. SARS-CoV-2 within-host diversity of human hosts and its implications for viral immune evasion. mBio 2023; 14:e0067923. [PMID: 37273216 PMCID: PMC10470530 DOI: 10.1128/mbio.00679-23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 04/17/2023] [Indexed: 06/06/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving, bringing great challenges to the control of the virus. In the present study, we investigated the characteristics of SARS-CoV-2 within-host diversity of human hosts and its implications for immune evasion using about 2,00,000 high-depth next-generation genome sequencing data of SARS-CoV-2. A total of 44% of the samples showed within-host variations (iSNVs), and the average number of iSNVs in the samples with iSNV was 1.90. C-to-U is the dominant substitution pattern for iSNVs. C-to-U/G-to-A and A-to-G/U-to-C preferentially occur in 5'-CG-3' and 5'-AU-3' motifs, respectively. In addition, we found that SARS-CoV-2 within-host variations are under negative selection. About 15.6% iSNVs had an impact on the content of the CpG dinucleotide (CpG) in SARS-CoV-2 genomes. We detected signatures of faster loss of CpG-gaining iSNVs, possibly resulting from zinc-finger antiviral protein-mediated antiviral activities targeting CpG, which could be the major reason for CpG depletion in SARS-CoV-2 consensus genomes. The non-synonymous iSNVs in the S gene can largely alter the S protein's antigenic features, and many of these iSNVs are distributed in the amino-terminal domain (NTD) and receptor-binding domain (RBD). These results suggest that SARS-CoV-2 interacts actively with human hosts and attempts to take different evolutionary strategies to escape human innate and adaptive immunity. These new findings further deepen and widen our understanding of the within-host evolutionary features of SARS-CoV-2. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of the coronavirus disease 2019, has evolved rapidly since it was discovered. Recent studies have pointed out that some mutations in the SARS-CoV-2 S protein could confer SARS-CoV-2 the ability to evade the human adaptive immune system. In addition, it is observed that the content of the CpG dinucleotide in SARS-CoV-2 genome sequences has decreased over time, reflecting the adaptation to the human host. The significance of our research is revealing the characteristics of SARS-CoV-2 within-host diversity of human hosts, identifying the causes of CpG depletion in SARS-CoV-2 consensus genomes, and exploring the potential impacts of non-synonymous within-host variations in the S gene on immune escape, which could further deepen and widen our understanding of the evolutionary features of SARS-CoV-2.
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Affiliation(s)
- Binbin Xi
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Xi Zeng
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Zixi Chen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Jiong Zeng
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Lizhen Huang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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21
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Dimcheff DE, Blair CN, Zhu Y, Chappell JD, Gaglani M, McNeal T, Ghamande S, Steingrub JS, Shapiro NI, Duggal A, Busse LW, Frosch AEP, Peltan ID, Hager DN, Gong MN, Exline MC, Khan A, Wilson JG, Qadir N, Ginde AA, Douin DJ, Mohr NM, Mallow C, Martin ET, Johnson NJ, Casey JD, Stubblefield WB, Gibbs KW, Kwon JH, Talbot HK, Halasa N, Grijalva CG, Baughman A, Womack KN, Hart KW, Swan SA, Surie D, Thornburg NJ, McMorrow ML, Self WH, Lauring AS, for the Investigating Respiratory Viruses in the Acutely Ill (IVY) Network. Total and Subgenomic RNA Viral Load in Patients Infected With SARS-CoV-2 Alpha, Delta, and Omicron Variants. J Infect Dis 2023; 228:235-244. [PMID: 36883903 PMCID: PMC10420395 DOI: 10.1093/infdis/jiad061] [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: 02/20/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic and subgenomic RNA levels are frequently used as a correlate of infectiousness. The impact of host factors and SARS-CoV-2 lineage on RNA viral load is unclear. METHODS Total nucleocapsid (N) and subgenomic N (sgN) RNA levels were measured by quantitative reverse transcription polymerase chain reaction (RT-qPCR) in specimens from 3204 individuals hospitalized with coronavirus disease 2019 (COVID-19) at 21 hospitals. RT-qPCR cycle threshold (Ct) values were used to estimate RNA viral load. The impact of time of sampling, SARS-CoV-2 variant, age, comorbidities, vaccination, and immune status on N and sgN Ct values were evaluated using multiple linear regression. RESULTS Mean Ct values at presentation for N were 24.14 (SD 4.53) for non-variants of concern, 25.15 (SD 4.33) for Alpha, 25.31 (SD 4.50) for Delta, and 26.26 (SD 4.42) for Omicron. N and sgN RNA levels varied with time since symptom onset and infecting variant but not with age, comorbidity, immune status, or vaccination. When normalized to total N RNA, sgN levels were similar across all variants. CONCLUSIONS RNA viral loads were similar among hospitalized adults, irrespective of infecting variant and known risk factors for severe COVID-19. Total N and subgenomic RNA N viral loads were highly correlated, suggesting that subgenomic RNA measurements add little information for the purposes of estimating infectivity.
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Affiliation(s)
- Derek E Dimcheff
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Christopher N Blair
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Yuwei Zhu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - James D Chappell
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Manjusha Gaglani
- Baylor Scott and White Health, Texas A&M University College of Medicine, Temple, Texas, USA
| | - Tresa McNeal
- Baylor Scott and White Health, Texas A&M University College of Medicine, Temple, Texas, USA
| | - Shekhar Ghamande
- Baylor Scott and White Health, Texas A&M University College of Medicine, Temple, Texas, USA
| | - Jay S Steingrub
- Department of Medicine, Baystate Medical Center, Springfield, Massachusetts, USA
| | - Nathan I Shapiro
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Abhijit Duggal
- Department of Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Anne E P Frosch
- Department of Medicine, Hennepin County Medical Center, Minneapolis, Minnesota, USA
| | - Ithan D Peltan
- Department of Medicine, Intermountain Medical Center, Murray, Utah, USA
- Department of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - David N Hager
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michelle N Gong
- Department of Medicine, Montefiore Health System, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Matthew C Exline
- Department of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Akram Khan
- Department of Medicine, Oregon Health and Sciences University, Portland, Oregon, USA
| | - Jennifer G Wilson
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Nida Qadir
- Department of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Adit A Ginde
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - David J Douin
- Department of Anesthesiology, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Nicholas M Mohr
- Department of Emergency Medicine, University of Iowa, Iowa City, Iowa, USA
| | | | - Emily T Martin
- School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Nicholas J Johnson
- Department of Emergency Medicine and Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, Washington, USA
| | - Jonathan D Casey
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - William B Stubblefield
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kevin W Gibbs
- Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Jennie H Kwon
- Department of Medicine, Washington University, St Louis, Missouri, USA
| | - H Keipp Talbot
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Natasha Halasa
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Carlos G Grijalva
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adrienne Baughman
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kelsey N Womack
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kimberly W Hart
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sydney A Swan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Diya Surie
- COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Natalie J Thornburg
- COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Meredith L McMorrow
- COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Wesley H Self
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam S Lauring
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
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22
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Li Y, Choudhary MC, Regan J, Boucau J, Nathan A, Speidel T, Liew MY, Edelstein GE, Kawano Y, Uddin R, Deo R, Marino C, Getz MA, Reynold Z, Barry M, Gilbert RF, Tien D, Sagar S, Vyas TD, Flynn JP, Hammond SP, Novack LA, Choi B, Cernadas M, Wallace ZS, Sparks JA, Vyas JM, Seaman MS, Gaiha GD, Siedner MJ, Barczak AK, Lemieux JE, Li JZ. SARS-CoV-2 Viral Clearance and Evolution Varies by Extent of Immunodeficiency. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.31.23293441. [PMID: 37577493 PMCID: PMC10418302 DOI: 10.1101/2023.07.31.23293441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Despite vaccination and antiviral therapies, immunocompromised individuals are at risk for prolonged SARS-CoV-2 infection, but the immune defects that predispose to persistent COVID-19 remain incompletely understood. In this study, we performed detailed viro-immunologic analyses of a prospective cohort of participants with COVID-19. The median time to nasal viral RNA and culture clearance in the severe hematologic malignancy/transplant group (S-HT) were 72 and 40 days, respectively, which were significantly longer than clearance rates in the severe autoimmune/B-cell deficient (S-A), non-severe, and non-immunocompromised groups (P<0.001). Participants who were severely immunocompromised had greater SARS-CoV-2 evolution and a higher risk of developing antiviral treatment resistance. Both S-HT and S-A participants had diminished SARS-CoV-2-specific humoral, while only the S-HT group had reduced T cell-mediated responses. This highlights the varied risk of persistent COVID-19 across immunosuppressive conditions and suggests that suppression of both B and T cell responses results in the highest contributing risk of persistent infection.
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Affiliation(s)
- Yijia Li
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Manish C Choudhary
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - James Regan
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Julie Boucau
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Anusha Nathan
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Program in Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA 02115, USA
| | - Tessa Speidel
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - May Yee Liew
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gregory E Edelstein
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yumeko Kawano
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rockib Uddin
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rinki Deo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Caitlin Marino
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Matthew A Getz
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Zahra Reynold
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mamadou Barry
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rebecca F Gilbert
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dessie Tien
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shruti Sagar
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tammy D Vyas
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - James P Flynn
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sarah P Hammond
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lewis A Novack
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bina Choi
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Manuela Cernadas
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zachary S Wallace
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey A Sparks
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jatin M Vyas
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Michael S Seaman
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Gaurav D Gaiha
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Mark J Siedner
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Amy K Barczak
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Jacob E Lemieux
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan Z Li
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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23
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Biancolella M, Colona VL, Luzzatto L, Watt JL, Mattiuz G, Conticello SG, Kaminski N, Mehrian-Shai R, Ko AI, Gonsalves GS, Vasiliou V, Novelli G, Reichardt JKV. COVID-19 annual update: a narrative review. Hum Genomics 2023; 17:68. [PMID: 37488607 PMCID: PMC10367267 DOI: 10.1186/s40246-023-00515-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/16/2023] [Indexed: 07/26/2023] Open
Abstract
Three and a half years after the pandemic outbreak, now that WHO has formally declared that the emergency is over, COVID-19 is still a significant global issue. Here, we focus on recent developments in genetic and genomic research on COVID-19, and we give an outlook on state-of-the-art therapeutical approaches, as the pandemic is gradually transitioning to an endemic situation. The sequencing and characterization of rare alleles in different populations has made it possible to identify numerous genes that affect either susceptibility to COVID-19 or the severity of the disease. These findings provide a beginning to new avenues and pan-ethnic therapeutic approaches, as well as to potential genetic screening protocols. The causative virus, SARS-CoV-2, is still in the spotlight, but novel threatening virus could appear anywhere at any time. Therefore, continued vigilance and further research is warranted. We also note emphatically that to prevent future pandemics and other world-wide health crises, it is imperative to capitalize on what we have learnt from COVID-19: specifically, regarding its origins, the world's response, and insufficient preparedness. This requires unprecedented international collaboration and timely data sharing for the coordination of effective response and the rapid implementation of containment measures.
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Affiliation(s)
| | - Vito Luigi Colona
- Department of Biomedicine and Prevention, School of Medicine and Surgery, Tor Vergata University of Rome, Via Montpellier 1, 00133, Rome, Italy
| | - Lucio Luzzatto
- Department of Haematology and Blood Transfusion, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
- University of Florence, 50121, Florence, Italy
| | - Jessica Lee Watt
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Smithfield, QLD, 4878, Australia
| | | | - Silvestro G Conticello
- Core Research Laboratory, Istituto per lo Studio, la Prevenzione e la Rete Oncologica (ISPRO), Florence, Italy
- Institute of Clinical Physiology - National Council of Research (IFC-CNR), 56124, Pisa, Italy
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ruty Mehrian-Shai
- Pediatric Hemato-Oncology, Edmond and Lilly Safra Children's Hospital, Sheba Medical Center, Tel Hashomer 2 Sheba Road, 52621, Ramat Gan, Israel
| | - Albert I Ko
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA
- Instituto Gonçalo MonizFundação Oswaldo Cruz, Salvador, Bahia, Brazil
| | - Gregg S Gonsalves
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, USA
| | - Giuseppe Novelli
- Department of Biomedicine and Prevention, School of Medicine and Surgery, Tor Vergata University of Rome, Via Montpellier 1, 00133, Rome, Italy.
- IRCCS Neuromed, 86077, Pozzilli, IS, Italy.
- Department of Pharmacology, School of Medicine, University of Nevada, 89557, Reno, NV, USA.
| | - Juergen K V Reichardt
- Australian Institute of Tropical Health and Medicine, James Cook University, Smithfield, QLD, 4878, Australia
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24
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Gupta A, Basu R, Bashyam MD. Assessing the evolution of SARS-CoV-2 lineages and the dynamic associations between nucleotide variations. Access Microbiol 2023; 5:acmi000513.v3. [PMID: 37601437 PMCID: PMC10436015 DOI: 10.1099/acmi.0.000513.v3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 02/20/2023] [Indexed: 08/22/2023] Open
Abstract
Despite seminal advances towards understanding the infection mechanism of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), it continues to cause significant morbidity and mortality worldwide. Though mass immunization programmes have been implemented in several countries, the viral transmission cycle has shown a continuous progression in the form of multiple waves. A constant change in the frequencies of dominant viral lineages, arising from the accumulation of nucleotide variations (NVs) through favourable selection, is understandably expected to be a major determinant of disease severity and possible vaccine escape. Indeed, worldwide efforts have been initiated to identify specific virus lineage(s) and/or NVs that may cause a severe clinical presentation or facilitate vaccination breakthrough. Since host genetics is expected to play a major role in shaping virus evolution, it is imperative to study the role of genome-wide SARS-CoV-2 NVs across various populations. In the current study, we analysed the whole genome sequence of 3543 SARS-CoV-2-infected samples obtained from the state of Telangana, India (including 210 from our previous study), collected over an extended period from April 2020 to October 2021. We present a unique perspective on the evolution of prevalent virus lineages and NVs during this period. We also highlight the presence of specific NVs likely to be associated favourably with samples classified as vaccination breakthroughs. Finally, we report genome-wide intra-host variations at novel genomic positions. The results presented here provide critical insights into virus evolution over an extended period and pave the way to rigorously investigate the role of specific NVs in vaccination breakthroughs.
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Affiliation(s)
- Asmita Gupta
- Laboratory of Molecular Oncology, Centre of DNA Fingerprinting and Diagnostics, Hyderabad, India
| | - Reelina Basu
- Laboratory of Molecular Oncology, Centre of DNA Fingerprinting and Diagnostics, Hyderabad, India
| | - Murali Dharan Bashyam
- Laboratory of Molecular Oncology, Centre of DNA Fingerprinting and Diagnostics, Hyderabad, India
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25
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Terbot JW, Cooper BS, Good JM, Jensen JD. A simulation framework for modeling the within-patient evolutionary dynamics of SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548462. [PMID: 37503016 PMCID: PMC10370031 DOI: 10.1101/2023.07.13.548462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The global impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to considerable interest in detecting novel beneficial mutations and other genomic changes that may signal the development of variants of concern (VOCs). The ability to accurately detect these changes within individual patient samples is important in enabling early detection of VOCs. Such genomic scans for positive selection are best performed via comparison of empirical data to simulated data wherein evolutionary factors, including mutation and recombination rates, reproductive and infection dynamics, and purifying and background selection, can be carefully accounted for and parameterized. While there has been work to quantify these factors in SARS-CoV-2, they have yet to be integrated into a baseline model describing intra-host evolutionary dynamics. To construct such a baseline model, we develop a simulation framework that enables one to establish expectations for underlying levels and patterns of patient-level variation. By varying eight key parameters, we evaluated 12,096 different model-parameter combinations and compared them to existing empirical data. Of these, 592 models (~5%) were plausible based on the resulting mean expected number of segregating variants. These plausible models shared several commonalities shedding light on intra-host SARS-CoV-2 evolutionary dynamics: severe infection bottlenecks, low levels of reproductive skew, and a distribution of fitness effects skewed towards strongly deleterious mutations. We also describe important areas of model uncertainty and highlight additional sequence data that may help to further refine a baseline model. This study lays the groundwork for the improved analysis of existing and future SARS-CoV-2 within-patient data.
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Affiliation(s)
- John W Terbot
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Brandon S. Cooper
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey M. Good
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey D. Jensen
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
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26
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Gonzalez-Reiche AS, Alshammary H, Schaefer S, Patel G, Polanco J, Carreño JM, Amoako AA, Rooker A, Cognigni C, Floda D, van de Guchte A, Khalil Z, Farrugia K, Assad N, Zhang J, Alburquerque B, Sominsky LA, Gleason C, Srivastava K, Sebra R, Ramirez JD, Banu R, Shrestha P, Krammer F, Paniz-Mondolfi A, Sordillo EM, Simon V, van Bakel H. Sequential intrahost evolution and onward transmission of SARS-CoV-2 variants. Nat Commun 2023; 14:3235. [PMID: 37270625 PMCID: PMC10239218 DOI: 10.1038/s41467-023-38867-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 05/16/2023] [Indexed: 06/05/2023] Open
Abstract
Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have been reported in immune-compromised individuals and people undergoing immune-modulatory treatments. Although intrahost evolution has been documented, direct evidence of subsequent transmission and continued stepwise adaptation is lacking. Here we describe sequential persistent SARS-CoV-2 infections in three individuals that led to the emergence, forward transmission, and continued evolution of a new Omicron sublineage, BA.1.23, over an eight-month period. The initially transmitted BA.1.23 variant encoded seven additional amino acid substitutions within the spike protein (E96D, R346T, L455W, K458M, A484V, H681R, A688V), and displayed substantial resistance to neutralization by sera from boosted and/or Omicron BA.1-infected study participants. Subsequent continued BA.1.23 replication resulted in additional substitutions in the spike protein (S254F, N448S, F456L, M458K, F981L, S982L) as well as in five other virus proteins. Our findings demonstrate not only that the Omicron BA.1 lineage can diverge further from its already exceptionally mutated genome but also that patients with persistent infections can transmit these viral variants. Thus, there is, an urgent need to implement strategies to prevent prolonged SARS-CoV-2 replication and to limit the spread of newly emerging, neutralization-resistant variants in vulnerable patients.
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Affiliation(s)
- Ana S Gonzalez-Reiche
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hala Alshammary
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sarah Schaefer
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Gopi Patel
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jose Polanco
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Juan Manuel Carreño
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Angela A Amoako
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Aria Rooker
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Christian Cognigni
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel Floda
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Adriana van de Guchte
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zain Khalil
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Keith Farrugia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nima Assad
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jian Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bremy Alburquerque
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Levy A Sominsky
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Charles Gleason
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Komal Srivastava
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- The Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Juan David Ramirez
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Radhika Banu
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Paras Shrestha
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alberto Paniz-Mondolfi
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Emilia Mia Sordillo
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Viviana Simon
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- The Global Health Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Harm van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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27
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Pearson J, Wessler T, Chen A, Boucher RC, Freeman R, Lai SK, Pickles R, Forest MG. Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection. J Theor Biol 2023; 565:111470. [PMID: 36965846 PMCID: PMC10033495 DOI: 10.1016/j.jtbi.2023.111470] [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: 02/23/2023] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 03/25/2023]
Abstract
The SARS-CoV-2 coronavirus continues to evolve with scores of mutations of the spike, membrane, envelope, and nucleocapsid structural proteins that impact pathogenesis. Infection data from nasal swabs, nasal PCR assays, upper respiratory samples, ex vivo cell cultures and nasal epithelial organoids reveal extreme variabilities in SARS-CoV-2 RNA titers within and between the variants. Some variabilities are naturally prone to clinical testing protocols and experimental controls. Here we focus on nasal viral load sensitivity arising from the timing of sample collection relative to onset of infection and from heterogeneity in the kinetics of cellular infection, uptake, replication, and shedding of viral RNA copies. The sources of between-variant variability are likely due to SARS-CoV-2 structural protein mutations, whereas within-variant population variability is likely due to heterogeneity in cellular response to that particular variant. With the physiologically faithful, agent-based mechanistic model of inhaled exposure and infection from (Chen et al., 2022), we perform statistical sensitivity analyses of the progression of nasal viral titers in the first 0-48 h post infection, focusing on three kinetic mechanisms. Model simulations reveal shorter latency times of infected cells (including cellular uptake, viral RNA replication, until the onset of viral RNA shedding) exponentially accelerate nasal viral load. Further, the rate of infectious RNA copies shed per day has a proportional influence on nasal viral load. Finally, there is a very weak, negative correlation of viral load with the probability of infection per virus-cell encounter, the model proxy for spike-receptor binding affinity.
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Affiliation(s)
- Jason Pearson
- Department of Mathematics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Timothy Wessler
- Department of Mathematics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alex Chen
- Department of Mathematics, California State University-Dominguez Hills, Carson, CA 90747, USA
| | - Richard C Boucher
- Marsico Lung Institute, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ronit Freeman
- Department of Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Samuel K Lai
- Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA and North Carolina State University, Raleigh, NC 27606, USA; Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Raymond Pickles
- Marsico Lung Institute, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - M Gregory Forest
- Department of Mathematics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; Department of Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA and North Carolina State University, Raleigh, NC 27606, USA.
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28
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Terbot JW, Johri P, Liphardt SW, Soni V, Pfeifer SP, Cooper BS, Good JM, Jensen JD. Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples. PLoS Pathog 2023; 19:e1011265. [PMID: 37018331 PMCID: PMC10075409 DOI: 10.1371/journal.ppat.1011265] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023] Open
Abstract
Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.
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Affiliation(s)
- John W Terbot
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Parul Johri
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Schuyler W Liphardt
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Vivak Soni
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Susanne P Pfeifer
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Brandon S Cooper
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey M Good
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey D Jensen
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
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29
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Gu H, Quadeer AA, Krishnan P, Ng DYM, Chang LDJ, Liu GYZ, Cheng SMS, Lam TTY, Peiris M, McKay MR, Poon LLM. Within-host genetic diversity of SARS-CoV-2 lineages in unvaccinated and vaccinated individuals. Nat Commun 2023; 14:1793. [PMID: 37002233 PMCID: PMC10063955 DOI: 10.1038/s41467-023-37468-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
Viral and host factors can shape SARS-CoV-2 evolution. However, little is known about lineage-specific and vaccination-specific mutations that occur within individuals. Here, we analysed deep sequencing data from 2,820 SARS-CoV-2 respiratory samples with different viral lineages to describe the patterns of within-host diversity under different conditions, including vaccine-breakthrough infections. In unvaccinated individuals, variant of Concern (VOC) Alpha, Delta, and Omicron respiratory samples were found to have higher within-host diversity and were under neutral to purifying selection at the full genome level compared to non-VOC SARS-CoV-2. Breakthrough infections in 2-dose or 3-dose Comirnaty and CoronaVac vaccinated individuals did not increase levels of non-synonymous mutations and did not change the direction of selection pressure. Vaccine-induced antibody or T cell responses did not appear to have significant impact on within-host SARS-CoV-2 sequence diversification. Our findings suggest that vaccination does not increase exploration of SARS-CoV-2 protein sequence space and may not facilitate emergence of viral variants.
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Affiliation(s)
- Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Pavithra Krishnan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Daisy Y M Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lydia D J Chang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gigi Y Z Liu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Samuel M S Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tommy T Y Lam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Malik Peiris
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Matthew R McKay
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, 3010, Australia
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, 3000, Australia
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Leo L M Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China.
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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30
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Gupta A, Konnova A, Smet M, Berkell M, Savoldi A, Morra M, Van Averbeke V, De Winter FH, Peserico D, Danese E, Hotterbeekx A, Righi E, De Nardo P, Tacconelli E, Malhotra-Kumar S, Kumar-Singh S. Host immunological responses facilitate development of SARS-CoV-2 mutations in patients receiving monoclonal antibody treatments. J Clin Invest 2023; 133:166032. [PMID: 36727404 PMCID: PMC10014108 DOI: 10.1172/jci166032] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 01/05/2023] [Indexed: 02/03/2023] Open
Abstract
BackgroundThe role of host immunity in emergence of evasive SARS-CoV-2 Spike mutations under therapeutic monoclonal antibody (mAb) pressure remains to be explored.MethodsIn a prospective, observational, monocentric ORCHESTRA cohort study, conducted between March 2021 and November 2022, mild-to-moderately ill COVID-19 patients (n = 204) receiving bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, or sotrovimab were longitudinally studied over 28 days for viral loads, de novo Spike mutations, mAb kinetics, seroneutralization against infecting variants of concern, and T cell immunity. Additionally, a machine learning-based circulating immune-related biomarker (CIB) profile predictive of evasive Spike mutations was constructed and confirmed in an independent data set (n = 19) that included patients receiving sotrovimab or tixagevimab/cilgavimab.ResultsPatients treated with various mAbs developed evasive Spike mutations with remarkable speed and high specificity to the targeted mAb-binding sites. Immunocompromised patients receiving mAb therapy not only continued to display significantly higher viral loads, but also showed higher likelihood of developing de novo Spike mutations. Development of escape mutants also strongly correlated with neutralizing capacity of the therapeutic mAbs and T cell immunity, suggesting immune pressure as an important driver of escape mutations. Lastly, we showed that an antiinflammatory and healing-promoting host milieu facilitates Spike mutations, where 4 CIBs identified patients at high risk of developing escape mutations against therapeutic mAbs with high accuracy.ConclusionsOur data demonstrate that host-driven immune and nonimmune responses are essential for development of mutant SARS-CoV-2. These data also support point-of-care decision making in reducing the risk of mAb treatment failure and improving mitigation strategies for possible dissemination of escape SARS-CoV-2 mutants.FundingThe ORCHESTRA project/European Union's Horizon 2020 research and innovation program.
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Affiliation(s)
- Akshita Gupta
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and.,Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Angelina Konnova
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and.,Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Mathias Smet
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and.,Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Matilda Berkell
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and.,Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Alessia Savoldi
- Division of Infectious Diseases, Department of Diagnostics and Public Health and
| | - Matteo Morra
- Division of Infectious Diseases, Department of Diagnostics and Public Health and
| | - Vincent Van Averbeke
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and
| | - Fien Hr De Winter
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and
| | - Denise Peserico
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
| | - Elisa Danese
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
| | - An Hotterbeekx
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and
| | - Elda Righi
- Division of Infectious Diseases, Department of Diagnostics and Public Health and
| | | | - Pasquale De Nardo
- Division of Infectious Diseases, Department of Diagnostics and Public Health and
| | - Evelina Tacconelli
- Division of Infectious Diseases, Department of Diagnostics and Public Health and
| | - Surbhi Malhotra-Kumar
- Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Samir Kumar-Singh
- Molecular Pathology Group, Cell Biology & Histology, Faculty of Medicine and Health Sciences and.,Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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31
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Cunha JB, Leix K, Sherman EJ, Mirabelli C, Kennedy AA, Lauring AS, Tai AW, Wobus CE, Emmer BT. Type I interferon signaling induces a delayed antiproliferative response in Calu-3 cells during SARS-CoV-2 infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530557. [PMID: 36909579 PMCID: PMC10002732 DOI: 10.1101/2023.02.28.530557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Disease progression during SARS-CoV-2 infection is tightly linked to the fate of lung epithelial cells, with severe cases of COVID-19 characterized by direct injury of the alveolar epithelium and an impairment in its regeneration from progenitor cells. The molecular pathways that govern respiratory epithelial cell death and proliferation during SARS-CoV-2 infection, however, remain poorly understood. We now report a high-throughput CRISPR screen for host genetic modifiers of the survival and proliferation of SARS-CoV-2-infected Calu-3 respiratory epithelial cells. The top 4 genes identified in our screen encode components of the same type I interferon signaling complex - IFNAR1, IFNAR2, JAK1, and TYK2. The 5th gene, ACE2, was an expected control encoding the SARS-CoV-2 viral receptor. Surprisingly, despite the antiviral properties of IFN-I signaling, its disruption in our screen was associated with an increase in Calu-3 cell fitness. We validated this effect and found that IFN-I signaling did not sensitize SARS-CoV-2-infected cultures to cell death but rather inhibited the proliferation of surviving cells after the early peak of viral replication and cytopathic effect. We also found that IFN-I signaling alone, in the absence of viral infection, was sufficient to induce this delayed antiproliferative response. Together, these findings highlight a cell autonomous antiproliferative response by respiratory epithelial cells to persistent IFN-I signaling during SARS-CoV-2 infection. This response may contribute to the deficient alveolar regeneration that has been associated with COVID-19 lung injury and represents a promising area for host-targeted therapeutic development.
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Affiliation(s)
| | - Kyle Leix
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor MI
| | - Emily J. Sherman
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor MI
| | - Carmen Mirabelli
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor MI
| | - Andrew A. Kennedy
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor MI
| | - Adam S. Lauring
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor MI
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor MI
| | - Andrew W. Tai
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor MI
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor MI
- VA Ann Arbor Healthcare System, Ann Arbor MI
| | - Christiane E. Wobus
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor MI
| | - Brian T. Emmer
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor MI
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32
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Farjo M, Brooke CB. Low Viral Diversity Limits the Effectiveness of Sequence-Based Transmission Inference for SARS-CoV-2. mSphere 2023; 8:e0054422. [PMID: 36695609 PMCID: PMC9942562 DOI: 10.1128/msphere.00544-22] [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] [Indexed: 01/26/2023] Open
Abstract
Tracking the spread of infection amongst individuals within and between communities has been a major challenge during viral outbreaks. With the unprecedented scale of viral sequence data collection during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the possibility of using phylogenetics to reconstruct past transmission events has been explored as a more rigorous alternative to traditional contact tracing; however, the reliability of sequence-based inference of transmission networks has yet to be directly evaluated. E. E. Bendall, G. Paz-Bailey, G. A. Santiago, C. A. Porucznik, et al. (mSphere 7:e00400-22, 2022, https://doi.org/10.1128/mSphere.00400-22) evaluate the potential of this technique by applying best practices sequence comparison methods to three geographically distinct cohorts that include known transmission pairs and demonstrate that linked pairs are often indistinguishable from unrelated samples. This study clearly establishes how low viral diversity limits the utility of genomic methods of epidemiological inference for SARS-CoV-2.
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Affiliation(s)
- Mireille Farjo
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Christopher B. Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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33
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Arcos S, Han AX, Te Velthuis AJW, Russell CA, Lauring AS. Mutual information networks reveal evolutionary relationships within the influenza A virus polymerase. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.16.528850. [PMID: 36824962 PMCID: PMC9949103 DOI: 10.1101/2023.02.16.528850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
The influenza A (IAV) RNA polymerase is an essential driver of IAV evolution. Mutations that the polymerase introduces into viral genome segments during replication are the ultimate source of genetic variation, including within the three subunits of the IAV polymerase (PB2, PB1, and PA). Evolutionary analysis of the IAV polymerase is complicated, because changes in mutation rate, replication speed, and drug resistance involve epistatic interactions among its subunits. In order to study the evolution of the human seasonal H3N2 polymerase since the 1968 pandemic, we identified pairwise evolutionary relationships among ∼7000 H3N2 polymerase sequences using mutual information (MI), which measures the information gained about the identity of one residue when a second residue is known. To account for uneven sampling of viral sequences over time, we developed a weighted MI metric (wMI) and demonstrate that wMI outperforms raw MI through simulations using a well-sampled SARS-CoV-2 dataset. We then constructed wMI networks of the H3N2 polymerase to extend the inherently pairwise wMI statistic to encompass relationships among larger groups of residues. We included HA in the wMI network to distinguish between functional wMI relationships within the polymerase and those potentially due to hitchhiking on antigenic changes in HA. The wMI networks reveal coevolutionary relationships among residues with roles in replication and encapsidation. Inclusion of HA highlighted polymerase-only subgraphs containing residues with roles in the enzymatic functions of the polymerase and host adaptability. This work provides insight into the factors that drive and constrain the rapid evolution of influenza viruses.
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34
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Walter KS, Kim E, Verma R, Altamirano J, Leary S, Carrington YJ, Jagannathan P, Singh U, Holubar M, Subramanian A, Khosla C, Maldonado Y, Andrews JR. Challenges in Harnessing Shared Within-Host Severe Acute Respiratory Syndrome Coronavirus 2 Variation for Transmission Inference. Open Forum Infect Dis 2023; 10:ofad001. [PMID: 36751652 PMCID: PMC9898879 DOI: 10.1093/ofid/ofad001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/06/2023] [Indexed: 01/09/2023] Open
Abstract
Background The limited variation observed among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) consensus sequences makes it difficult to reconstruct transmission linkages in outbreak settings. Previous studies have recovered variation within individual SARS-CoV-2 infections but have not yet measured the informativeness of within-host variation for transmission inference. Methods We performed tiled amplicon sequencing on 307 SARS-CoV-2 samples, including 130 samples from 32 individuals in 14 households and 47 longitudinally sampled individuals, from 4 prospective studies with household membership data, a proxy for transmission linkage. Results Consensus sequences from households had limited diversity (mean pairwise distance, 3.06 single-nucleotide polymorphisms [SNPs]; range, 0-40). Most (83.1%, 255 of 307) samples harbored at least 1 intrahost single-nucleotide variant ([iSNV] median, 117; interquartile range [IQR], 17-208), above a minor allele frequency threshold of 0.2%. Pairs in the same household shared significantly more iSNVs (mean, 1.20 iSNVs; 95% confidence interval [CI], 1.02-1.39) than did pairs in different households infected with the same viral clade (mean, 0.31 iSNVs; 95% CI, .28-.34), a signal that decreases with increasingly stringent minor allele frequency thresholds. The number of shared iSNVs was significantly associated with an increased odds of household membership (adjusted odds ratio, 1.35; 95% CI, 1.23-1.49). However, the poor concordance of iSNVs detected across sequencing replicates (24.8% and 35.0% above a 0.2% and 1% threshold) confirms technical concerns that current sequencing and bioinformatic workflows do not consistently recover low-frequency within-host variants. Conclusions Shared within-host variation may augment the information in consensus sequences for predicting transmission linkages. Improving sensitivity and specificity of within-host variant identification will improve the informativeness of within-host variation.
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Affiliation(s)
| | - Eugene Kim
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Renu Verma
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Jonathan Altamirano
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
| | - Sean Leary
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Yuan J Carrington
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Prasanna Jagannathan
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA
| | - Upinder Singh
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA
| | - Marisa Holubar
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Aruna Subramanian
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Chaitan Khosla
- Stanford ChEM-H, Stanford University, Stanford, California, USA
- Department of Chemistry and Chemical Engineering, Stanford University, Stanford, California, USA
| | - Yvonne Maldonado
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Jason R Andrews
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
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35
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Bendall EE, Callear AP, Getz A, Goforth K, Edwards D, Monto AS, Martin ET, Lauring AS. Rapid transmission and tight bottlenecks constrain the evolution of highly transmissible SARS-CoV-2 variants. Nat Commun 2023; 14:272. [PMID: 36650162 PMCID: PMC9844183 DOI: 10.1038/s41467-023-36001-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
Transmission bottlenecks limit the spread of novel mutations and reduce the efficiency of selection along a transmission chain. While increased force of infection, receptor binding, or immune evasion may influence bottleneck size, the relationship between transmissibility and the transmission bottleneck is unclear. Here we compare the transmission bottleneck of non-VOC SARS-CoV-2 lineages to those of Alpha, Delta, and Omicron. We sequenced viruses from 168 individuals in 65 households. Most virus populations had 0-1 single nucleotide variants (iSNV). From 64 transmission pairs with detectable iSNV, we identify a per clade bottleneck of 1 (95% CI 1-1) for Alpha, Delta, and Omicron and 2 (95% CI 2-2) for non-VOC. These tight bottlenecks reflect the low diversity at the time of transmission, which may be more pronounced in rapidly transmissible variants. Tight bottlenecks will limit the development of highly mutated VOC in transmission chains, adding to the evidence that selection over prolonged infections may drive their evolution.
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Affiliation(s)
- Emily E Bendall
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Amy P Callear
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Amy Getz
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Kendra Goforth
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Drew Edwards
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Arnold S Monto
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Emily T Martin
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Adam S Lauring
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA.
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
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36
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Bendall EE, Paz-Bailey G, Santiago GA, Porucznik CA, Stanford JB, Stockwell MS, Duque J, Jeddy Z, Veguilla V, Major C, Rivera-Amill V, Rolfes MA, Dawood FS, Lauring AS. SARS-CoV-2 Genomic Diversity in Households Highlights the Challenges of Sequence-Based Transmission Inference. mSphere 2022; 7:e0040022. [PMID: 36377913 PMCID: PMC9769559 DOI: 10.1128/msphere.00400-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/24/2022] [Indexed: 11/16/2022] Open
Abstract
The reliability of sequence-based inference of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is not clear. Sequence data from infections among household members can define the expected genomic diversity of a virus along a defined transmission chain. SARS-CoV-2 cases were identified prospectively among 2,369 participants in 706 households. Specimens with a reverse transcription-PCR cycle threshold of ≤30 underwent whole-genome sequencing. Intrahost single-nucleotide variants (iSNV) were identified at a ≥5% frequency. Phylogenetic trees were used to evaluate the relationship of household and community sequences. There were 178 SARS-CoV-2 cases in 706 households. Among 147 specimens sequenced, 106 yielded a whole-genome consensus with coverage suitable for identifying iSNV. Twenty-six households had sequences from multiple cases within 14 days. Consensus sequences were indistinguishable among cases in 15 households, while 11 had ≥1 consensus sequence that differed by 1 to 2 mutations. Sequences from households and the community were often interspersed on phylogenetic trees. Identification of iSNV improved inference in 2 of 15 households with indistinguishable consensus sequences and in 6 of 11 with distinct ones. In multiple-infection households, whole-genome consensus sequences differed by 0 to 1 mutations. Identification of shared iSNV occasionally resolved linkage, but the low genomic diversity of SARS-CoV-2 limits the utility of "sequence-only" transmission inference. IMPORTANCE We performed whole-genome sequencing of SARS-CoV-2 from prospectively identified cases in three longitudinal household cohorts. In a majority of multi-infection households, SARS-CoV-2 consensus sequences were indistinguishable, and they differed by 1 to 2 mutations in the rest. Importantly, even with modest genomic surveillance of the community (3 to 5% of cases sequenced), it was not uncommon to find community sequences interspersed with household sequences on phylogenetic trees. Identification of shared minority variants only occasionally resolved these ambiguities in transmission linkage. Overall, the low genomic diversity of SARS-CoV-2 limits the utility of "sequence-only" transmission inference. Our work highlights the need to carefully consider both epidemiologic linkage and sequence data to define transmission chains in households, hospitals, and other transmission settings.
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Affiliation(s)
- Emily E. Bendall
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | | | | | - Christina A. Porucznik
- Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Joseph B. Stanford
- Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Melissa S. Stockwell
- Division of Child and Adolescent Health, Department of Pediatrics, Columbia University Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
- Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, New York, USA
| | | | - Zuha Jeddy
- Abt Associates, Rockville, Maryland, USA
| | - Vic Veguilla
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chelsea Major
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Vanessa Rivera-Amill
- Ponce Research Institute, Ponce Health Sciences University, Ponce, Puerto Rico, USA
| | | | | | - Adam S. Lauring
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
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37
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Stein SR, Ramelli SC, Grazioli A, Chung JY, Singh M, Yinda CK, Winkler CW, Sun J, Dickey JM, Ylaya K, Ko SH, Platt AP, Burbelo PD, Quezado M, Pittaluga S, Purcell M, Munster VJ, Belinky F, Ramos-Benitez MJ, Boritz EA, Lach IA, Herr DL, Rabin J, Saharia KK, Madathil RJ, Tabatabai A, Soherwardi S, McCurdy MT, NIH COVID-19 Autopsy Consortium BabyakAshley L.12Perez ValenciaLuis J.12CurranShelly J.3RichertMary E.3YoungWillie J.5YoungSarah P.5GasmiBillel5Sampaio De MeloMichelly5DesarSabina5TadrosSaber5NasirNadia5JinXueting5RajanSharika5DikogluEsra5OzkayaNeval5SmithGrace5EmanuelElizabeth R.21KelsallBrian L.21OliveraJustin A.22BlawasMegan22StarRobert A.22HaysNicole10SingireddyShreya10WuJocelyn10RajaKatherine10CurtoRyan10ChungJean E.23BorthAmy J.23BowersKimberly A.23WeicholdAnne M.23MinorPaula A.23MoshrefMir Ahmad N.23KellyEmily E.23SajadiMohammad M.1415ScaleaThomas M.24TranDouglas16DahiSiamak16DeatrickKristopher B.16KrauseEric M.25HerroldJoseph A.17HochbergEric S.17CornachioneChristopher R.17LevineAndrea R.17RichardsJustin E.26ElderJohn27BurkeAllen P.27MazzeffiMichael A.28ChristensonRobert H.29ChancerZackary A.30AbdulmahdiMustafa31SophaSabrina31GoldbergTyler31SangwanYashvir32SudanoKristen19BlumeDiane19RadinBethany19ArnoukMadhat19EaganJames W.Jr33PalermoRobert34HarrisAnthony D.35PohidaThomas36Garmendia-CedillosMarcial36DoldGeorge37SaglioEric37PhamPhuoc37, Peterson KE, Cohen JI, de Wit E, Vannella KM, Hewitt SM, Kleiner DE, Chertow DS. SARS-CoV-2 infection and persistence in the human body and brain at autopsy. Nature 2022; 612:758-763. [PMID: 36517603 PMCID: PMC9749650 DOI: 10.1038/s41586-022-05542-y] [Citation(s) in RCA: 493] [Impact Index Per Article: 164.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 11/08/2022] [Indexed: 12/15/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is known to cause multi-organ dysfunction1-3 during acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with some patients experiencing prolonged symptoms, termed post-acute sequelae of SARS-CoV-2 (refs. 4,5). However, the burden of infection outside the respiratory tract and time to viral clearance are not well characterized, particularly in the brain3,6-14. Here we carried out complete autopsies on 44 patients who died with COVID-19, with extensive sampling of the central nervous system in 11 of these patients, to map and quantify the distribution, replication and cell-type specificity of SARS-CoV-2 across the human body, including the brain, from acute infection to more than seven months following symptom onset. We show that SARS-CoV-2 is widely distributed, predominantly among patients who died with severe COVID-19, and that virus replication is present in multiple respiratory and non-respiratory tissues, including the brain, early in infection. Further, we detected persistent SARS-CoV-2 RNA in multiple anatomic sites, including throughout the brain, as late as 230 days following symptom onset in one case. Despite extensive distribution of SARS-CoV-2 RNA throughout the body, we observed little evidence of inflammation or direct viral cytopathology outside the respiratory tract. Our data indicate that in some patients SARS-CoV-2 can cause systemic infection and persist in the body for months.
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Affiliation(s)
- Sydney R. Stein
- grid.410305.30000 0001 2194 5650Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA ,grid.419681.30000 0001 2164 9667Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Sabrina C. Ramelli
- grid.410305.30000 0001 2194 5650Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA
| | - Alison Grazioli
- grid.419635.c0000 0001 2203 7304Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD USA
| | - Joon-Yong Chung
- grid.417768.b0000 0004 0483 9129Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Manmeet Singh
- grid.94365.3d0000 0001 2297 5165Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institute of Health, Hamilton, MT USA
| | - Claude Kwe Yinda
- grid.94365.3d0000 0001 2297 5165Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institute of Health, Hamilton, MT USA
| | - Clayton W. Winkler
- grid.94365.3d0000 0001 2297 5165Laboratory of Persistent Viral Diseases, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institute of Health, Hamilton, MT USA
| | - Junfeng Sun
- grid.410305.30000 0001 2194 5650Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA
| | - James M. Dickey
- grid.410305.30000 0001 2194 5650Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA ,grid.419681.30000 0001 2164 9667Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Kris Ylaya
- grid.417768.b0000 0004 0483 9129Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Sung Hee Ko
- grid.419681.30000 0001 2164 9667Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Andrew P. Platt
- grid.410305.30000 0001 2194 5650Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA ,grid.419681.30000 0001 2164 9667Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Peter D. Burbelo
- grid.419633.a0000 0001 2205 0568National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD USA
| | - Martha Quezado
- grid.417768.b0000 0004 0483 9129Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Stefania Pittaluga
- grid.417768.b0000 0004 0483 9129Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Madeleine Purcell
- grid.411024.20000 0001 2175 4264University of Maryland School of Medicine, Baltimore, MD USA
| | - Vincent J. Munster
- grid.94365.3d0000 0001 2297 5165Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institute of Health, Hamilton, MT USA
| | - Frida Belinky
- grid.419681.30000 0001 2164 9667Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Marcos J. Ramos-Benitez
- grid.410305.30000 0001 2194 5650Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA ,grid.419681.30000 0001 2164 9667Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA ,grid.280785.00000 0004 0533 7286Postdoctoral Research Associate Training Program, National Institute of General Medical Sciences, National Institutes of Health, Bethesda, MD USA
| | - Eli A. Boritz
- grid.419681.30000 0001 2164 9667Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Izabella A. Lach
- grid.410305.30000 0001 2194 5650Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA ,grid.419681.30000 0001 2164 9667Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Daniel L. Herr
- grid.411024.20000 0001 2175 4264R Adams Cowley Shock Trauma Center, Department of Medicine and Program in Trauma, University of Maryland School of Medicine, Baltimore, MD USA
| | - Joseph Rabin
- grid.411024.20000 0001 2175 4264R Adams Cowley Shock Trauma Center, Department of Surgery and Program in Trauma, University of Maryland School of Medicine, Baltimore, MD USA
| | - Kapil K. Saharia
- grid.411024.20000 0001 2175 4264Department of Medicine, Division of Infectious Disease, University of Maryland School of Medicine, Baltimore, MD USA ,grid.411024.20000 0001 2175 4264Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD USA
| | - Ronson J. Madathil
- grid.411024.20000 0001 2175 4264Department of Surgery, Division of Cardiac Surgery, University of Maryland School of Medicine, Baltimore, MD USA
| | - Ali Tabatabai
- grid.411024.20000 0001 2175 4264Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of Maryland School of Medicine, Baltimore, MD USA
| | - Shahabuddin Soherwardi
- grid.417209.90000 0004 0429 3816Hospitalist Department, TidalHealth Peninsula Regional, Salisbury, MD USA
| | - Michael T. McCurdy
- grid.411024.20000 0001 2175 4264Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of Maryland School of Medicine, Baltimore, MD USA ,grid.416700.40000 0004 0440 9540Division of Critical Care Medicine, Department of Medicine, University of Maryland St. Joseph Medical Center, Towson, MD USA
| | | | - Karin E. Peterson
- grid.94365.3d0000 0001 2297 5165Laboratory of Persistent Viral Diseases, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institute of Health, Hamilton, MT USA
| | - Jeffrey I. Cohen
- grid.419681.30000 0001 2164 9667Medical Virology Section, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Emmie de Wit
- grid.94365.3d0000 0001 2297 5165Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institute of Health, Hamilton, MT USA
| | - Kevin M. Vannella
- grid.410305.30000 0001 2194 5650Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA ,grid.419681.30000 0001 2164 9667Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Stephen M. Hewitt
- grid.417768.b0000 0004 0483 9129Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - David E. Kleiner
- grid.417768.b0000 0004 0483 9129Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Daniel S. Chertow
- grid.410305.30000 0001 2194 5650Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD USA ,grid.419681.30000 0001 2164 9667Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
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38
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Zhang Y, Jiang N, Qi W, Li T, Zhang Y, Wu J, Zhang H, Zhou M, Cui P, Yu T, Fu Z, Zhou Y, Lin K, Wang H, Wei T, Zhu Z, Ai J, Qiu C, Zhang W. SARS-CoV-2 intra-host single-nucleotide variants associated with disease severity. Virus Evol 2022; 8:veac106. [PMID: 36505092 PMCID: PMC9728387 DOI: 10.1093/ve/veac106] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/24/2022] [Accepted: 11/26/2022] [Indexed: 11/30/2022] Open
Abstract
Variants of severe acute respiratory syndrome coronavirus 2 frequently arise within infected individuals. Here, we explored the level and pattern of intra-host viral diversity in association with disease severity. Then, we analyzed information underlying these nucleotide changes to infer the impetus including mutational signatures and immune selection from neutralizing antibody or T-cell recognition. From 23 January to 31 March 2020, a set of cross-sectional samples were collected from individuals with homogeneous founder virus regardless of disease severity. Intra-host single-nucleotide variants (iSNVs) were enumerated using deep sequencing. Human leukocyte antigen (HLA) alleles were genotyped by Sanger sequencing. Medical records were collected and reviewed by attending physicians. A total of 836 iSNVs (3-106 per sample) were identified and distributed in a highly individualized pattern. The number of iSNVs paced with infection duration peaked within days and declined thereafter. These iSNVs did not stochastically arise due to a strong bias toward C > U/G > A and U > C/A > G substitutions in reciprocal proportion with escalating disease severity. Eight nonsynonymous iSNVs in the receptor-binding domain could escape from neutralization, and eighteen iSNVs were significantly associated with specific HLA alleles. The level and pattern of iSNVs reflect the in vivo viral-host interaction and the disease pathogenesis.
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Affiliation(s)
| | | | | | | | - Yumeng Zhang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jing Wu
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Haocheng Zhang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Mingzhe Zhou
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Peng Cui
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Tong Yu
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zhangfan Fu
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yang Zhou
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Ke Lin
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Hongyu Wang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Tongqing Wei
- State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | | | | | - Chao Qiu
- *Corresponding authors: E-mail: ; ; ;
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39
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Turcinovic J, Schaeffer B, Taylor BP, Bouton TC, Odom-Mabey AR, Weber SE, Lodi S, Ragan EJ, Connor JH, Jacobson KR, Hanage WP. Understanding Early Pandemic Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in a Medical Center by Incorporating Public Sequencing Databases to Mitigate Bias. J Infect Dis 2022; 226:1704-1711. [PMID: 35993116 PMCID: PMC9452097 DOI: 10.1093/infdis/jiac348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/19/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Throughout the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, healthcare workers (HCWs) have faced risk of infection from within the workplace via patients and staff as well as from the outside community, complicating our ability to resolve transmission chains in order to inform hospital infection control policy. Here we show how the incorporation of sequences from public genomic databases aided genomic surveillance early in the pandemic when circulating viral diversity was limited. METHODS We sequenced a subset of discarded, diagnostic SARS-CoV-2 isolates between March and May 2020 from Boston Medical Center HCWs and combined this data set with publicly available sequences from the surrounding community deposited in GISAID with the goal of inferring specific transmission routes. RESULTS Contextualizing our data with publicly available sequences reveals that 73% (95% confidence interval, 63%-84%) of coronavirus disease 2019 cases in HCWs are likely novel introductions rather than nosocomial spread. CONCLUSIONS We argue that introductions of SARS-CoV-2 into the hospital environment are frequent and that expanding public genomic surveillance can better aid infection control when determining routes of transmission.
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Affiliation(s)
- Jacquelyn Turcinovic
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Beau Schaeffer
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Bradford P Taylor
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Tara C Bouton
- Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA
| | - Aubrey R Odom-Mabey
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Sarah E Weber
- Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA
| | - Sara Lodi
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Elizabeth J Ragan
- Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA
| | - John H Connor
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Department of Microbiology, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Karen R Jacobson
- Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA
| | - William P Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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40
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Bendall EE, Callear A, Getz A, Goforth K, Edwards D, Monto AS, Martin ET, Lauring AS. Rapid transmission and tight bottlenecks constrain the evolution of highly transmissible SARS-CoV-2 variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.10.12.511991. [PMID: 36263068 PMCID: PMC9580385 DOI: 10.1101/2022.10.12.511991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Transmission bottlenecks limit the spread of novel mutations and reduce the efficiency of natural selection along a transmission chain. Many viruses exhibit tight bottlenecks, and studies of early SARS-CoV-2 lineages identified a bottleneck of 1-3 infectious virions. While increased force of infection, host receptor binding, or immune evasion may influence bottleneck size, the relationship between transmissibility and the transmission bottleneck is unclear. Here, we compare the transmission bottleneck of non-variant-of-concern (non-VOC) SARS-CoV-2 lineages to those of the Alpha, Delta, and Omicron variants. We sequenced viruses from 168 individuals in 65 multiply infected households in duplicate to high depth of coverage. In 110 specimens collected close to the time of transmission, within-host diversity was extremely low. At a 2% frequency threshold, 51% had no intrahost single nucleotide variants (iSNV), and 42% had 1-2 iSNV. In 64 possible transmission pairs with detectable iSNV, we identified a bottleneck of 1 infectious virion (95% CI 1-1) for Alpha, Delta, and Omicron lineages and 2 (95% CI 2-2) in non-VOC lineages. The latter was driven by a single iSNV shared in one non-VOC household. The tight transmission bottleneck in SARS-CoV-2 is due to low genetic diversity at the time of transmission, a relationship that may be more pronounced in rapidly transmissible variants. The tight bottlenecks identified here will limit the development of highly mutated VOC in typical transmission chains, adding to the evidence that selection over prolonged infections in immunocompromised patients may drive their evolution.
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Affiliation(s)
- Emily E. Bendall
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Amy Callear
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Amy Getz
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Kendra Goforth
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Drew Edwards
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Arnold S. Monto
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Emily T. Martin
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Adam S. Lauring
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
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41
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Thommana A, Shakya M, Gandhi J, Fung CK, Chain PSG, Maljkovic Berry I, Conte MA. Intrahost SARS-CoV-2 k-mer Identification Method (iSKIM) for Rapid Detection of Mutations of Concern Reveals Emergence of Global Mutation Patterns. Viruses 2022; 14:2128. [PMID: 36298683 PMCID: PMC9609618 DOI: 10.3390/v14102128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/23/2022] [Accepted: 09/24/2022] [Indexed: 11/27/2022] Open
Abstract
Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM ("intrahost SARS-CoV-2 k-mer identification method") to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.
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Affiliation(s)
- Ashley Thommana
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
- Montgomery Blair High School, Silver Spring, MD 20901, USA
| | - Migun Shakya
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Jaykumar Gandhi
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Christian K. Fung
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Patrick S. G. Chain
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Irina Maljkovic Berry
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD 21702, USA
| | - Matthew A. Conte
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
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42
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Sun Y, Wang M, Lin W, Dong W, Xu J. Massive-scale genomic analysis reveals SARS-CoV-2 mutation characteristics and evolutionary trends. MLIFE 2022; 1:311-322. [PMID: 37732331 PMCID: PMC9538474 DOI: 10.1002/mlf2.12040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 11/05/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic resulted in significant societal costs. Hence, an in-depth understanding of SARS-CoV-2 virus mutation and its evolution will help determine the direction of the COVID-19 pandemic. In this study, we identified 296,728 de novo mutations in more than 2,800,000 high-quality SARS-CoV-2 genomes. All possible factors affecting the mutation frequency of SARS-CoV-2 in human hosts were analyzed, including zinc finger antiviral proteins, sequence context, amino acid change, and translation efficiency. As a result, we proposed that when adenine (A) and tyrosine (T) bases are in the context of AM (M stands for adenine or cytosine) or TA motif, A or T base has lower mutation frequency. Furthermore, we hypothesized that translation efficiency can affect the mutation frequency of the third position of the codon by the selection, which explains why SARS-CoV-2 prefers AT3 codons usage. In addition, we found a host-specific asymmetric dinucleotide mutation frequency in the SARS-CoV-2 genome, which provides a new basis for determining the origin of the SARS-CoV-2. Finally, we summarize all possible factors affecting mutation frequency and provide insights into the mutation characteristics and evolutionary trends of SARS-CoV-2.
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Affiliation(s)
- Yamin Sun
- Research Institute of Public HealthNankai UniversityTianjinChina
| | - Min Wang
- TEDA Institute of Biological Sciences and BiotechnologyNankai UniversityTianjinChina
- Engineering and Research Center for Microbial Functional Genomics and Detection, Ministry of EducationNankai UniversityTianjinChina
| | - Wenchao Lin
- Engineering and Research Center for Microbial Functional Genomics and Detection, Ministry of EducationNankai UniversityTianjinChina
| | - Wei Dong
- Engineering and Research Center for Microbial Functional Genomics and Detection, Ministry of EducationNankai UniversityTianjinChina
| | - Jianguo Xu
- Research Institute of Public HealthNankai UniversityTianjinChina
- State Key Laboratory for Infectious Disease Prevention and Control, Chinese Center for Disease Control and PreventionNational Institute for Communicable Disease Control and PreventionBeijingChina
- Research Units of Discovery of Unknown Bacteria and FunctionChinese Academy of Medical SciencesBeijingChina
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43
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Data analysis of viral complementary DNA/RNA sequences for low-frequency intrahost-single nucleotide variants in COVID-19. Proc Natl Acad Sci U S A 2022; 119:e2205269119. [PMID: 35914186 PMCID: PMC9407639 DOI: 10.1073/pnas.2205269119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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44
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Mushegian AA, Long SW, Olsen RJ, Christensen PA, Subedi S, Chung M, Davis J, Musser J, Ghedin E. Within-host genetic diversity of SARS-CoV-2 in the context of large-scale hospital-associated genomic surveillance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.08.17.22278898. [PMID: 36032964 PMCID: PMC9413716 DOI: 10.1101/2022.08.17.22278898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The COVID-19 pandemic has resulted in extensive surveillance of the genomic diversity of SARS-CoV-2. Sequencing data generated as part of these efforts can also capture the diversity of the SARS-CoV-2 virus populations replicating within infected individuals. To assess this within-host diversity of SARS-CoV-2 we quantified low frequency (minor) variants from deep sequence data of thousands of clinical samples collected by a large urban hospital system over the course of a year. Using a robust analytical pipeline to control for technical artefacts, we observe that at comparable viral loads, specimens from patients hospitalized due to COVID-19 had a greater number of minor variants than samples from outpatients. Since individuals with highly diverse viral populations could be disproportionate drivers of new viral lineages in the patient population, these results suggest that transmission control should pay special attention to patients with severe or protracted disease to prevent the spread of novel variants.
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Affiliation(s)
- Alexandra A. Mushegian
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Scott W. Long
- Laboratory of Molecular and Translational Human Infectious Diseases Research, Center for Infectious Diseases, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Houston Methodist Hospital Houston, Texas, 77030
| | - Randall J. Olsen
- Laboratory of Molecular and Translational Human Infectious Diseases Research, Center for Infectious Diseases, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Houston Methodist Hospital Houston, Texas, 77030
| | - Paul A. Christensen
- Laboratory of Molecular and Translational Human Infectious Diseases Research, Center for Infectious Diseases, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Houston Methodist Hospital Houston, Texas, 77030
| | - Sishir Subedi
- Laboratory of Molecular and Translational Human Infectious Diseases Research, Center for Infectious Diseases, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Houston Methodist Hospital Houston, Texas, 77030
| | - Matthew Chung
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - James Davis
- Division of Data Science and Learning, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, Illinois, 60439
- University of Chicago Consortium for Advanced Science and Engineering, 5801 South Ellis Avenue, Chicago, Illinois, 60637
| | - James Musser
- Laboratory of Molecular and Translational Human Infectious Diseases Research, Center for Infectious Diseases, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Houston Methodist Hospital Houston, Texas, 77030
| | - Elodie Ghedin
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
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45
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Thommana A, Shakya M, Gandhi J, Fung CK, Chain PSG, Berry IM, Conte MA. Intrahost SARS-CoV-2 k-mer identification method (iSKIM) for rapid detection of mutations of concern reveals emergence of global mutation patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.08.16.504117. [PMID: 36032969 PMCID: PMC9413717 DOI: 10.1101/2022.08.16.504117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM ("intrahost SARS-CoV-2 k-mer identification method") to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.
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Affiliation(s)
- Ashley Thommana
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Montgomery Blair High School, Silver Spring, MD, USA
| | - Migun Shakya
- Los Alamos National Laboratory, Biosecurity and Public Health Group, Bioscience Division, Los Alamos, NM, USA
| | - Jaykumar Gandhi
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Christian K Fung
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Patrick S G Chain
- Los Alamos National Laboratory, Biosecurity and Public Health Group, Bioscience Division, Los Alamos, NM, USA
| | - Irina Maljkovic Berry
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Matthew A Conte
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
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46
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Gu H, Quadeer AA, Krishnan P, Ng DY, Chang LD, Liu GY, Cheng SS, Lam TT, Peiris M, McKay MR, Poon LL. Within-host diversity of SARS-CoV-2 lineages and effect of vaccination. RESEARCH SQUARE 2022:rs.3.rs-1927944. [PMID: 35982671 PMCID: PMC9387541 DOI: 10.21203/rs.3.rs-1927944/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Viral and host factors can shape SARS-CoV-2 within-host viral diversity and virus evolution. However, little is known about lineage-specific and vaccination-specific mutations that occur within individuals. Here we analysed deep sequencing data from 2,146 SARS-CoV-2 samples with different viral lineages to describe the patterns of within-host diversity in different conditions, including vaccine-breakthrough infections. Variant of Concern (VOC) Alpha, Delta, and Omicron samples were found to have higher within-host nucleotide diversity while being under weaker purifying selection at full genome level compared to non-VOC SARS-CoV-2 viruses. Breakthrough Delta and Omicron infections in Comirnaty and CoronaVac vaccinated individuals appeared to have higher within-host purifying selection at the full-genome and/or Spike gene levels. Vaccine-induced antibody or T cell responses did not appear to have significant impact on within-host SARS-CoV-2 evolution. Our findings suggest that vaccination does not increase SARS-CoV-2 protein sequence space and may not facilitate emergence of more viral variants.
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Affiliation(s)
- Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Pavithra Krishnan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Daisy Y.M. Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Lydia D.J Chang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Gigi Y.Z. Liu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Samuel S.M. Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Tommy T.Y. Lam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Malik Peiris
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Matthew R. McKay
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Leo L.M. Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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47
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Singh M, Novitsky V, Carpenter-Azevedo K, Howison M, Huard RC, King E, Kantor R. SARS-CoV-2 Variants in Rhode Island; May 2022 Update. RHODE ISLAND MEDICAL JOURNAL (2013) 2022; 105:6-11. [PMID: 35834172 PMCID: PMC11285058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Genomic surveillance allows identification of circulating SARS-CoV-2 variants. We provide an update on the evolution of SARS-CoV-2 in Rhode Island (RI). METHODS All publicly available SARS-CoV-2 RI sequences were retrieved from https://www.gisaid.org. Genomic analyses were conducted to identify variants of concern (VOC), variants being monitored (VBM), or non-VOC/non-VBM, and investigate their evolution. RESULTS Overall, 17,340 SARS-CoV-2 RI sequences were available between 2/2020-5/2022 across five (globally recognized) major waves, including 1,462 (8%) sequences from 36 non-VOC/non-VBM until 5/2021; 10,565 (61%) sequences from 8 VBM between 5/2021-12/2021, most commonly Delta; and 5,313 (31%) sequences from the VOC Omicron from 12/2021 onwards. Genomic analyses demonstrated 71 Delta and 44 Omicron sub-lineages, with occurrence of variant-defining mutations in other variants. CONCLUSION Statewide SARS-CoV-2 genomic surveillance allows for continued characterization of circulating variants and monitoring of viral evolution, which inform the local health force and guide public health on mitigation efforts against COVID-19.
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Affiliation(s)
- Manjot Singh
- Warren Alpert Medical School, Brown University, Providence, RI
| | | | | | - Mark Howison
- Research Improving People's Life, Providence, RI
| | - Richard C Huard
- Rhode Island Department of Health State Health Laboratories, Providence, RI
| | - Ewa King
- Rhode Island Department of Health State Health Laboratories, Providence, RI
| | - Rami Kantor
- Warren Alpert Medical School, Brown University, Providence, RI
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48
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Focosi D, Franchini M. Potential use of convalescent plasma for SARS-CoV-2 prophylaxis and treatment in immunocompromised and vulnerable populations. Expert Rev Vaccines 2022; 21:877-884. [PMID: 34015243 PMCID: PMC8171015 DOI: 10.1080/14760584.2021.1932475] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/17/2021] [Indexed: 12/21/2022]
Abstract
INTRODUCTION : The ongoing SARS-CoV-2 pandemic is a serious threat for the health of immunocompromised patients. Among neutralizing antibody-based therapeutics, convalescent plasma containing polyclonal anti-SARS-CoV-2 immunoglobulins has promising results in both congenital and iatrogenic immunodeficiencies in oncohematological and transplant patients. AREAS COVERED : This article discusses case reports, case series and controlled studies detailing the efficacy of convalescent plasma in immunocompromised patients. EXPERT OPINION : Convalescent plasma, when administered at high neutralizing antibody titers, is a safe and effective treatment for frail immunocompromised patients. Genetic monitoring of refractory patients is recommended to intercept intra-host emergence of SARS-CoV-2 variants.
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Affiliation(s)
- Daniele Focosi
- North-Western Tuscany Blood Bank, Pisa University Hospital, Pisa, Italy
| | - Massimo Franchini
- Department of Hematology and Transfusion Medicine, Carlo Poma Hospital, Mantua, Italy
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49
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Feng S, Ali MS, Evdokimova M, Reid GE, Clark NM, Uprichard SL, Baker SC. Sequencing during Times of Change: Evaluating SARS-CoV-2 Clinical Samples during the Transition from the Delta to Omicron Wave. Viruses 2022; 14:1408. [PMID: 35891388 PMCID: PMC9320617 DOI: 10.3390/v14071408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 11/18/2022] Open
Abstract
The pandemic of SARS-CoV-2 is characterized by the emergence of new variants of concern (VOCs) that supplant previous waves of infection. Here, we describe our investigation of the lineages and host-specific mutations identified in a particularly vulnerable population of predominantly older and immunosuppressed SARS-CoV-2-infected patients seen at our medical center in Chicago during the transition from the Delta to Omicron wave. We compare two primer schemes, ArticV4.1 and VarSkip2, used for short read amplicon sequencing, and describe our strategy for bioinformatics analysis that facilitates identifying lineage-associated mutations and host-specific mutations that arise during infection. This study illustrates the ongoing evolution of SARS-CoV-2 VOCs in our community and documents novel constellations of mutations that arise in individual patients. The ongoing evaluation of the evolution of SARS-CoV-2 during this pandemic is important for informing our public health strategies.
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Affiliation(s)
- Shuchen Feng
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (S.F.); (M.S.A.); (M.E.); (S.L.U.)
| | - Mudassir S. Ali
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (S.F.); (M.S.A.); (M.E.); (S.L.U.)
| | - Monika Evdokimova
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (S.F.); (M.S.A.); (M.E.); (S.L.U.)
| | - Gail E. Reid
- Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (G.E.R.); (N.M.C.)
- Infectious Disease and Immunology Research Institute, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA
| | - Nina M. Clark
- Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (G.E.R.); (N.M.C.)
- Infectious Disease and Immunology Research Institute, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA
| | - Susan L. Uprichard
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (S.F.); (M.S.A.); (M.E.); (S.L.U.)
- Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (G.E.R.); (N.M.C.)
- Infectious Disease and Immunology Research Institute, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA
| | - Susan C. Baker
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA; (S.F.); (M.S.A.); (M.E.); (S.L.U.)
- Infectious Disease and Immunology Research Institute, Stritch School of Medicine, Loyola University Chicago, Chicago, IL 60153, USA
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50
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Harari S, Tahor M, Rutsinsky N, Meijer S, Miller D, Henig O, Halutz O, Levytskyi K, Ben-Ami R, Adler A, Paran Y, Stern A. Drivers of adaptive evolution during chronic SARS-CoV-2 infections. Nat Med 2022; 28:1501-1508. [PMID: 35725921 PMCID: PMC9307477 DOI: 10.1038/s41591-022-01882-4] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 05/23/2022] [Indexed: 11/17/2022]
Abstract
In some immunocompromised patients with chronic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, considerable adaptive evolution occurs. Some substitutions found in chronic infections are lineage-defining mutations in variants of concern (VOCs), which has led to the hypothesis that VOCs emerged from chronic infections. In this study, we searched for drivers of VOC-like emergence by consolidating sequencing results from a set of 27 chronic infections. Most substitutions in this set reflected lineage-defining VOC mutations; however, a subset of mutations associated with successful global transmission was absent from chronic infections. We further tested the ability to associate antibody evasion mutations with patient-specific and virus-specific features and found that viral rebound is strongly correlated with the emergence of antibody evasion. We found evidence for dynamic polymorphic viral populations in most patients, suggesting that a compromised immune system selects for antibody evasion in particular niches in a patient’s body. We suggest that a tradeoff exists between antibody evasion and transmissibility and that extensive monitoring of chronic infections is necessary to further understanding of VOC emergence. Analysis of mutations that arise in chronic SARS-CoV-2 infections shows both overlap and differences with mutations present in pandemic viral variants of concern, highlighting their distinct drivers of evolution.
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Affiliation(s)
- Sheri Harari
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel.,Edmond J. Safra Center for Bioinformatics at Tel Aviv University, Tel Aviv, Israel
| | - Maayan Tahor
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
| | - Natalie Rutsinsky
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
| | - Suzy Meijer
- Department of Infectious Diseases and Epidemiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Danielle Miller
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel.,Edmond J. Safra Center for Bioinformatics at Tel Aviv University, Tel Aviv, Israel
| | - Oryan Henig
- Department of Infectious Diseases and Epidemiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ora Halutz
- Clinical Microbiology Laboratory, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Katia Levytskyi
- Department of Infectious Diseases and Epidemiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ronen Ben-Ami
- Department of Infectious Diseases and Epidemiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amos Adler
- Department of Infectious Diseases and Epidemiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yael Paran
- Department of Infectious Diseases and Epidemiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Adi Stern
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel. .,Edmond J. Safra Center for Bioinformatics at Tel Aviv University, Tel Aviv, Israel.
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