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Harrington A, Vo V, Moshi MA, Chang CL, Baker H, Ghani N, Itorralba JY, Papp K, Gerrity D, Moser D, Oh EC. Environmental Surveillance of Flood Control Infrastructure Impacted by Unsheltered Individuals Leads to the Detection of SARS-CoV-2 and Novel Mutations in the Spike Gene. ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2024; 11:410-417. [PMID: 38752195 PMCID: PMC11095249 DOI: 10.1021/acs.estlett.3c00938] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 05/18/2024]
Abstract
In the United States, the growing number of people experiencing homelessness has become a socioeconomic crisis with public health ramifications, recently exacerbated by the COVID-19 pandemic. We hypothesized that the environmental surveillance of flood control infrastructure may be an effective approach to understand the prevalence of infectious disease. From December 2021 through July 2022, we tested for SARS-CoV-2 RNA from two flood control channels known to be impacted by unsheltered individuals residing in upstream tunnels. Using qPCR, we detected SARS-CoV-2 RNA in these environmental water samples when significant COVID-19 outbreaks were occurring in the surrounding community. We also performed whole genome sequencing to identify SARS-CoV-2 lineages. Variant compositions were consistent with those of geographically and temporally matched municipal wastewater samples and clinical specimens. However, we also detected 10 of 22 mutations specific to the Alpha variant in the environmental water samples collected during January 2022-one year after the Alpha infection peak. We also identified mutations in the spike gene that have never been identified in published reports. Our findings demonstrate that environmental surveillance of flood control infrastructure may be an effective tool to understand public health conditions among unsheltered individuals-a vulnerable population that is underrepresented in clinical surveillance data.
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Affiliation(s)
- Anthony Harrington
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
| | - Van Vo
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
| | - Michael A. Moshi
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
| | - Ching-Lan Chang
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
| | - Hayley Baker
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
| | - Nabih Ghani
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
| | - Jose Yani Itorralba
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
| | - Katerina Papp
- Southern
Nevada Water Authority, P.O. Box 99954, Las Vegas Nevada 89193, United States
| | - Daniel Gerrity
- Southern
Nevada Water Authority, P.O. Box 99954, Las Vegas Nevada 89193, United States
| | - Duane Moser
- Division
of Hydrologic Sciences, Desert Research
Institute, Las Vegas, Nevada 89119, United States
| | - Edwin C. Oh
- Laboratory
of Neurogenetics and Precision Medicine, College of Sciences, Neuroscience Interdisciplinary
Ph.D. program, Department of Brain Health, Department of Internal Medicine, Kirk Kerkorian
School of Medicine at UNLV, University of
Nevada Las Vegas, Las Vegas, Nevada 89154, United States
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Zhuang X, Vo V, Moshi MA, Dhede K, Ghani N, Akbar S, Chang CL, Young AK, Buttery E, Bendik W, Zhang H, Afzal S, Moser D, Cordes D, Lockett C, Gerrity D, Kan HY, Oh EC. Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.18.24306052. [PMID: 38699326 PMCID: PMC11065002 DOI: 10.1101/2024.04.18.24306052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.
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Shafer MM, Bobholz MJ, Vuyk WC, Gregory DA, Roguet A, Haddock Soto LA, Rushford C, Janssen KH, Emmen IE, Ries HJ, Pilch HE, Mullen PA, Fahney RB, Wei W, Lambert M, Wenzel J, Halfmann P, Kawaoka Y, Wilson NA, Friedrich TC, Pray IW, Westergaard R, O'Connor DH, Johnson MC. Tracing the origin of SARS-CoV-2 omicron-like spike sequences detected in an urban sewershed: a targeted, longitudinal surveillance study of a cryptic wastewater lineage. THE LANCET. MICROBE 2024; 5:e335-e344. [PMID: 38484748 PMCID: PMC11049544 DOI: 10.1016/s2666-5247(23)00372-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/03/2023] [Accepted: 11/15/2023] [Indexed: 04/08/2024]
Abstract
BACKGROUND The origin of novel SARS-CoV-2 spike sequences found in wastewater, without corresponding detection in clinical specimens, remains unclear. We sought to determine the origin of one such cryptic wastewater lineage by tracking and characterising its persistence and genomic evolution over time. METHODS We first detected a cryptic lineage, WI-CL-001, in municipal wastewater in Wisconsin, USA, in January, 2022. To determine the source of WI-CL-001, we systematically sampled wastewater from targeted sub-sewershed lines and maintenance holes using compositing autosamplers. Viral concentrations in wastewater samples over time were measured by RT digital PCR. In addition to using metagenomic 12s rRNA sequencing to determine the virus's host species, we also sequenced SARS-CoV-2 spike receptor binding domains, and, where possible, whole viral genomes to identify and characterise the evolution of this lineage. FINDINGS We traced WI-CL-001 to its source at a single commercial building. There we detected the cryptic lineage at concentrations as high as 2·7 × 109 genome copies per L. The majority of 12s rRNA sequences detected in wastewater leaving the identified source building were human. Additionally, we generated over 100 viral receptor binding domain and whole-genome sequences from wastewater samples containing the cryptic lineage collected over the 13 consecutive months this virus was detectable (January, 2022, to January, 2023). These sequences contained a combination of fixed nucleotide substitutions characteristic of Pango lineage B.1.234, which circulated in humans in Wisconsin at low levels from October, 2020, to February, 2021. Despite this, mutations in the spike gene and elsewhere resembled those subsequently found in omicron variants. INTERPRETATION We propose that prolonged detection of WI-CL-001 in wastewater indicates persistent shedding of SARS-CoV-2 from a single human initially infected by an ancestral B.1.234 virus. The accumulation of convergent omicron-like mutations in WI-CL-001's ancestral B.1.234 genome probably reflects persistent infection and extensive within-host evolution. People who shed cryptic lineages could be an important source of highly divergent viruses that sporadically emerge and spread. FUNDING The Rockefeller Foundation, Wisconsin Department of Health Services, Centers for Disease Control and Prevention, National Institute on Drug Abuse, and the Center for Research on Influenza Pathogenesis and Transmission.
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Affiliation(s)
- Martin M Shafer
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, USA
| | - Max J Bobholz
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - William C Vuyk
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Devon A Gregory
- School of Medicine, University of Missouri, Columbia, MO, USA
| | - Adelaide Roguet
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, USA
| | - Luis A Haddock Soto
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Kayley H Janssen
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, USA
| | - Isla E Emmen
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Hunter J Ries
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Hannah E Pilch
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, USA
| | - Paige A Mullen
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, USA
| | - Rebecca B Fahney
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, USA
| | - Wanting Wei
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Matthew Lambert
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Department of Health Services, Madison, WI, USA
| | - Jeff Wenzel
- Missouri Department of Health and Senior Services, Jefferson City, MO, USA
| | - Peter Halfmann
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Nancy A Wilson
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Thomas C Friedrich
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Ian W Pray
- Wisconsin Department of Health Services, Madison, WI, USA
| | - Ryan Westergaard
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Department of Health Services, Madison, WI, USA
| | - David H O'Connor
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Marc C Johnson
- School of Medicine, University of Missouri, Columbia, MO, USA.
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Gitter A, Bauer C, Wu F, Ramphul R, Chavarria C, Zhang K, Petrosino J, Mezzari M, Gallegos G, Terwilliger AL, Clark JR, Feliz K, Avadhanula V, Piedra T, Weesner K, Maresso A, Mena KD. Assessment of a SARS-CoV-2 wastewater monitoring program in El Paso, Texas, from November 2020 to June 2022. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2024; 34:564-574. [PMID: 36595614 DOI: 10.1080/09603123.2022.2159017] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The border city of El Paso, Texas, and its water utility, El Paso Water, initiated a SARS-CoV-2 wastewater monitoring program to assess virus trends and the appropriateness of a wastewater monitoring program for the community. Nearly weekly sample collection at four wastewater treatment facilities (WWTFs), serving distinct regions of the city, was analyzed for SARS-CoV-2 genes using the CDC 2019-Novel coronavirus Real-Time RT-PCR diagnostic panel. Virus concentrations ranged from 86.7 to 268,000 gc/L, varying across time and at each WWTF. The lag time between virus concentrations in wastewater and reported COVID-19 case rates (per 100,00 population) ranged from 4-24 days for the four WWTFs, with the strongest trend occurring from November 2021 - June 2022. This study is an assessment of the utility of a geographically refined SARS-CoV-2 wastewater monitoring program to supplement public health efforts that will manage the virus as it becomes endemic in El Paso.
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Affiliation(s)
- Anna Gitter
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Cici Bauer
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Fuqing Wu
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Ryan Ramphul
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Carlos Chavarria
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Kehe Zhang
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | | | | | - Gabriela Gallegos
- Department of Management, Policy & Community Health, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | | | | | - Karen Feliz
- Baylor College of Medicine, Houston, TX, USA
| | | | - Tony Piedra
- Baylor College of Medicine, Houston, TX, USA
| | | | | | - Kristina D Mena
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
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Férez JA, Cuevas-Ferrando E, Ayala-San Nicolás M, Simón Andreu PJ, López R, Truchado P, Sánchez G, Allende A. Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach. Viruses 2023; 15:1499. [PMID: 37515186 PMCID: PMC10386001 DOI: 10.3390/v15071499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
The COVID-19 pandemic has posed a significant global threat, leading to several initiatives for its control and management. One such initiative involves wastewater-based epidemiology, which has gained attention for its potential to provide early warning of virus outbreaks and real-time information on its spread. In this study, wastewater samples from two wastewater treatment plants (WWTPs) located in the southeast of Spain (region of Murcia), namely Murcia, and Cartagena, were analyzed using RT-qPCR and high-throughput sequencing techniques to describe the evolution of SARS-CoV-2 in the South-East of Spain. Additionally, phylogenetic analysis and machine learning approaches were applied to develop a pre-screening tool for the identification of differences among the variant composition of different wastewater samples. The results confirmed that the levels of SARS-CoV-2 in these wastewater samples changed concerning the number of SARS-CoV-2 cases detected in the population, and variant occurrences were in line with clinical reported data. The sequence analyses helped to describe how the different SARS-CoV-2 variants have been replaced over time. Additionally, the phylogenetic analysis showed that samples obtained at close sampling times exhibited a higher similarity than those obtained more distantly in time. A second analysis using a machine learning approach based on the mutations found in the SARS-CoV-2 spike protein was also conducted. Hierarchical clustering (HC) was used as an efficient unsupervised approach for data analysis. Results indicated that samples obtained in October 2022 in Murcia and Cartagena were significantly different, which corresponded well with the different virus variants circulating in the two locations. The proposed methods in this study are adequate for comparing consensus sequence types of the SARS-CoV-2 sequences as a preliminary evaluation of potential changes in the variants that are circulating in a given population at a specific time point.
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Affiliation(s)
- Jose A Férez
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
| | - Enric Cuevas-Ferrando
- Environmental Virology and Food Safety Lab (VISAFELab), Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, Av. Agustín Escardino 7, 46980 Valencia, Spain
| | - María Ayala-San Nicolás
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
| | - Pedro J Simón Andreu
- Entidad Regional de Saneamiento y Depuración de Murcia (ESAMUR), Avda. Juan Carlos I, s/n. Ed. Torre Jemeca, 30009 Murcia, Spain
| | - Román López
- Entidad Regional de Saneamiento y Depuración de Murcia (ESAMUR), Avda. Juan Carlos I, s/n. Ed. Torre Jemeca, 30009 Murcia, Spain
| | - Pilar Truchado
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
| | - Gloria Sánchez
- Environmental Virology and Food Safety Lab (VISAFELab), Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, Av. Agustín Escardino 7, 46980 Valencia, Spain
| | - Ana Allende
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
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Vo V, Harrington A, Chang CL, Baker H, Moshi MA, Ghani N, Itorralba JY, Tillett RL, Dahlmann E, Basazinew N, Gu R, Familara TD, Boss S, Vanderford F, Ghani M, Tang AJ, Matthews A, Papp K, Khan E, Koutras C, Kan HY, Lockett C, Gerrity D, Oh EC. Identification and genome sequencing of an influenza H3N2 variant in wastewater from elementary schools during a surge of influenza A cases in Las Vegas, Nevada. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 872:162058. [PMID: 36758698 PMCID: PMC9909754 DOI: 10.1016/j.scitotenv.2023.162058] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 05/25/2023]
Abstract
Real-time surveillance of infectious diseases at schools or in communities is often hampered by delays in reporting due to resource limitations and infrastructure issues. By incorporating quantitative PCR and genome sequencing, wastewater surveillance has been an effective complement to public health surveillance at the community and building-scale for pathogens such as poliovirus, SARS-CoV-2, and even the monkeypox virus. In this study, we asked whether wastewater surveillance programs at elementary schools could be leveraged to detect RNA from influenza viruses shed in wastewater. We monitored for influenza A and B viral RNA in wastewater from six elementary schools from January to May 2022. Quantitative PCR led to the identification of influenza A viral RNA at three schools, which coincided with the lifting of COVID-19 restrictions and a surge in influenza A infections in Las Vegas, Nevada, USA. We performed genome sequencing of wastewater RNA, leading to the identification of a 2021-2022 vaccine-resistant influenza A (H3N2) 3C.2a1b.2a.2 subclade. We next tested wastewater samples from a treatment plant that serviced the elementary schools, but we were unable to detect the presence of influenza A/B RNA. Together, our results demonstrate the utility of near-source wastewater surveillance for the detection of local influenza transmission in schools, which has the potential to be investigated further with paired school-level influenza incidence data.
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Affiliation(s)
- Van Vo
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; Nevada Institute of Personalized Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Anthony Harrington
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Ching-Lan Chang
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Hayley Baker
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Michael A Moshi
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Nabih Ghani
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Jose Yani Itorralba
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Richard L Tillett
- Nevada Institute of Personalized Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Elizabeth Dahlmann
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Natnael Basazinew
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Richard Gu
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Tiffany D Familara
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Sage Boss
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Fritz Vanderford
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Moonis Ghani
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Austin J Tang
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Alice Matthews
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Katerina Papp
- Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, USA
| | - Eakalak Khan
- Department of Civil and Environmental Engineering and Construction, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Carolina Koutras
- R-Zero Systems, Inc., 345 W Bearcat Dr Suite #100, South Salt Lake, UT 84115, USA
| | - Horng-Yuan Kan
- Southern Nevada Health District, Las Vegas, NV 89106, USA
| | | | - Daniel Gerrity
- Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, USA
| | - Edwin C Oh
- Laboratory of Neurogenetics and Precision Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; Nevada Institute of Personalized Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; Department of Internal Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.
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7
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Immunological Studies to Understand Hybrid/Recombinant Variants of SARS-CoV-2. Vaccines (Basel) 2022; 11:vaccines11010045. [PMID: 36679891 PMCID: PMC9867374 DOI: 10.3390/vaccines11010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
The zoonotic SARS-CoV-2 virus was present before the onset of the pandemic. It undergoes evolution, adaptation, and selection to develop variants that gain high transmission rates and virulence, resulting in the pandemic. Structurally, the spike protein of the virus is required for binding to ACE2 receptors of the host cells. The gene coding for the spike is known to have a high propensity of mutations, as a result generating numerous variants. The variants can be generated by random point mutations or recombination during replication. However, SARS-CoV-2 can also produce hybrid variants on co-infection of the host by two distinct lineages of the virus. The genomic sequences of the two variants undergo recombination to produce the hybrid variants. Additionally, these sub-variants also contain numerous mutations from both the parent variants, as well as some novel mutations unique to the hybrids. The hybrid variants (XD, XE, and XF) can be identified through numerous techniques, such as peak PCR, NAAT, and hybrid capture SARS-CoV-2 NGS (next generation sequencing) assay, etc., but the most accurate approach is genome sequencing. There are numerous immunological diagnostic assays, such as ELISA, chemiluminescence immunoassay, flow-cytometry-based approaches, electrochemiluminescence immunoassays, neutralization assays, etc., that are also designed and developed to provide an understanding of the hybrid variants, their pathogenesis, and other reactions. The objective of our study is to comprehensively analyze the variants of SARS-CoV-2, especially the hybrid variants. We have also discussed the techniques available for the identification of hybrids, as well as the immunological assays and studies for analyzing the hybrid variants.
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