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Moradi Marjaneh M, Challenger JD, Salas A, Gómez-Carballa A, Sivananthan A, Rivero-Calle I, Barbeito-Castiñeiras G, Foo CY, Wu Y, Liew F, Jackson HR, Habgood-Coote D, D'Souza G, Nichols SJ, Wright VJ, Levin M, Kaforou M, Thwaites RS, Okell LC, Martinón-Torres F, Cunnington AJ. Analysis of blood and nasal epithelial transcriptomes to identify mechanisms associated with control of SARS-CoV-2 viral load in the upper respiratory tract. J Infect 2023; 87:538-550. [PMID: 37863321 DOI: 10.1016/j.jinf.2023.10.009] [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: 07/14/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023]
Abstract
OBJECTIVES The amount of SARS-CoV-2 detected in the upper respiratory tract (URT viral load) is a key driver of transmission of infection. Current evidence suggests that mechanisms constraining URT viral load are different from those controlling lower respiratory tract viral load and disease severity. Understanding such mechanisms may help to develop treatments and vaccine strategies to reduce transmission. Combining mathematical modelling of URT viral load dynamics with transcriptome analyses we aimed to identify mechanisms controlling URT viral load. METHODS COVID-19 patients were recruited in Spain during the first wave of the pandemic. RNA sequencing of peripheral blood and targeted NanoString nCounter transcriptome analysis of nasal epithelium were performed and gene expression analysed in relation to paired URT viral load samples collected within 15 days of symptom onset. Proportions of major immune cells in blood were estimated from transcriptional data using computational differential estimation. Weighted correlation network analysis (adjusted for cell proportions) and fixed transcriptional repertoire analysis were used to identify associations with URT viral load, quantified as standard deviations (z-scores) from an expected trajectory over time. RESULTS Eighty-two subjects (50% female, median age 54 years (range 3-73)) with COVID-19 were recruited. Paired URT viral load samples were available for 16 blood transcriptome samples, and 17 respiratory epithelial transcriptome samples. Natural Killer (NK) cells were the only blood cell type significantly correlated with URT viral load z-scores (r = -0.62, P = 0.010). Twenty-four blood gene expression modules were significantly correlated with URT viral load z-score, the most significant being a module of genes connected around IFNA14 (Interferon Alpha-14) expression (r = -0.60, P = 1e-10). In fixed repertoire analysis, prostanoid-related gene expression was significantly associated with higher viral load. In nasal epithelium, only GNLY (granulysin) gene expression showed significant negative correlation with viral load. CONCLUSIONS Correlations between the transcriptional host response and inter-individual variations in SARS-CoV-2 URT viral load, revealed many molecular mechanisms plausibly favouring or constraining viral replication. Existing evidence corroborates many of these mechanisms, including likely roles for NK cells, granulysin, prostanoids and interferon alpha-14. Inhibition of prostanoid production and administration of interferon alpha-14 may be attractive transmission-blocking interventions.
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Affiliation(s)
- Mahdi Moradi Marjaneh
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK; Section of Virology, Department of Infectious Diseases, Imperial College London, London, UK.
| | - Joseph D Challenger
- Medical Research Council Centre for Global Infections Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Antonio Salas
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain; Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, Spain
| | - Alberto Gómez-Carballa
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain; Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, Spain
| | - Abilash Sivananthan
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
| | - Irene Rivero-Calle
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, Spain; Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Galicia, Spain
| | - Gema Barbeito-Castiñeiras
- Servicio de Microbiología y Parasitología, Complejo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Galicia, Spain
| | - Cher Y Foo
- School of Medicine, Imperial College London, London, UK
| | - Yue Wu
- Department of Surgery and Cancer, Imperial College London, St. Mary's Hospital, London, UK
| | - Felicity Liew
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Heather R Jackson
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Dominic Habgood-Coote
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Giselle D'Souza
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Samuel J Nichols
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Victoria J Wright
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Michael Levin
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Myrsini Kaforou
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Ryan S Thwaites
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Lucy C Okell
- Medical Research Council Centre for Global Infections Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Federico Martinón-Torres
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, Spain; Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Galicia, Spain
| | - Aubrey J Cunnington
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK.
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Naveca FG, Nascimento VA, Nascimento F, Ogrzewalska M, Pauvolid-Corrêa A, Araújo MF, Arantes I, Batista ÉR, Magalhães AÁ, Vinhal F, Mattos TP, Riediger I, Debur MDC, Grinsztejn B, Veloso VG, Brasil P, Rodrigues RR, Rovaris DB, Fernandes SB, Fernandes C, Santos JHA, Abdalla LF, Costa-Filho R, Silva M, Souza V, Costa ÁA, Mejía M, Brandão MJ, Gonçalves LF, Silva GA, de Jesus MS, Pessoa K, Corado ADLG, Duarte DCG, Machado AB, Zukeram KDA, Valente N, Lopes RS, Pereira EC, Appolinario LR, Rocha AS, Tort LFL, Sekizuka T, Itokawa K, Hashino M, Kuroda M, Dezordi FZ, Wallau GL, Delatorre E, Gräf T, Siqueira MM, Bello G, Resende PC. SARS-CoV-2 intra-host diversity, antibody response, and disease severity after reinfection by the variant of concern Gamma in Brazil. Sci Rep 2023; 13:7306. [PMID: 37147348 PMCID: PMC10160723 DOI: 10.1038/s41598-023-33443-1] [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: 07/01/2022] [Accepted: 04/12/2023] [Indexed: 05/07/2023] Open
Abstract
The rapid spread of the SARS-CoV-2 Variant of Concern (VOC) Gamma in Amazonas during early 2021 fueled a second large COVID-19 epidemic wave and raised concern about the potential role of reinfections. Very few cases of reinfection associated with the VOC Gamma have been reported to date, and their potential impact on clinical, immunological, and virological parameters remains largely unexplored. Here we describe 25 cases of SARS-CoV-2 reinfection in Brazil. SARS-CoV-2 genomic analysis confirmed that individuals were primo-infected with distinct viral lineages between March and December 2020 (B.1.1, B.1.1.28, B.1.1.33, B.1.195, and P.2) and reinfected with the VOC Gamma between 3 to 12 months after primo-infection. We found a similar mean cycle threshold (Ct) value and limited intra-host viral diversity in both primo-infection and reinfection samples. Sera of 14 patients tested 10-75 days after reinfection displayed detectable neutralizing antibodies (NAb) titers against SARS-CoV-2 variants that circulated before (B.1.*), during (Gamma), and after (Delta and Omicron) the second epidemic wave in Brazil. All individuals had milder or no symptoms after reinfection, and none required hospitalization. These findings demonstrate that individuals reinfected with the VOC Gamma may display relatively high RNA viral loads at the upper respiratory tract after reinfection, thus contributing to onward viral transmissions. Despite this, our study points to a low overall risk of severe Gamma reinfections, supporting that the abrupt increase in hospital admissions and deaths observed in Amazonas and other Brazilian states during the Gamma wave was mostly driven by primary infections. Our findings also indicate that most individuals analyzed developed a high anti-SARS-CoV-2 NAb response after reinfection that may provide some protection against reinfection or disease by different SARS-CoV-2 variants.
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Affiliation(s)
- Felipe Gomes Naveca
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil.
- Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Rio de Janeiro, Brazil.
| | - Valdinete Alves Nascimento
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Fernanda Nascimento
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Maria Ogrzewalska
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Alex Pauvolid-Corrêa
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Mia Ferreira Araújo
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Ighor Arantes
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
- Laboratório de AIDS e Imunologia Molecular, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | | | | | | | - Tirza Peixoto Mattos
- Laboratório Central de Saúde Pública do Amazonas (LACEN-AM, Manaus, Amazonas, Brazil
| | - Irina Riediger
- Laboratório Central de Saúde Pública do Paraná (LACEN-PR) Curitiba, Paraná, Brazil
| | - Maria do Carmo Debur
- Laboratório Central de Saúde Pública do Paraná (LACEN-PR) Curitiba, Paraná, Brazil
| | - Beatriz Grinsztejn
- Instituto Nacional de Infectologia Evandro Chagas (INI), Fiocruz, Rio de Janeiro, Brazil
| | - Valdiléa G Veloso
- Instituto Nacional de Infectologia Evandro Chagas (INI), Fiocruz, Rio de Janeiro, Brazil
| | - Patrícia Brasil
- Instituto Nacional de Infectologia Evandro Chagas (INI), Fiocruz, Rio de Janeiro, Brazil
| | | | - Darcita Buerger Rovaris
- Laboratório Central de Saúde Pública do Estado de Santa Catarina (LACEN-SC), Florianópolis, Santa Catarina, Brazil
| | - Sandra Bianchini Fernandes
- Laboratório Central de Saúde Pública do Estado de Santa Catarina (LACEN-SC), Florianópolis, Santa Catarina, Brazil
| | - Cristiano Fernandes
- Fundação de Vigilância em Saúde do Amazonas-Dra Rosemary Costa Pinto, Manaus, Amazonas, Brazil
| | | | | | | | - Marineide Silva
- Laboratório Central de Saúde Pública do Amazonas (LACEN-AM, Manaus, Amazonas, Brazil
| | - Victor Souza
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Ágatha Araújo Costa
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Matilde Mejía
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Maria Júlia Brandão
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Luciana Fé Gonçalves
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
- Fundação de Vigilância em Saúde do Amazonas-Dra Rosemary Costa Pinto, Manaus, Amazonas, Brazil
| | - George Allan Silva
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Michele Silva de Jesus
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Karina Pessoa
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - André de Lima Guerra Corado
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Debora Camila Gomes Duarte
- Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
| | - Ana Beatriz Machado
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Ketiuce de Azevedo Zukeram
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Natalia Valente
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Renata Serrano Lopes
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Elisa Cavalcante Pereira
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Luciana Reis Appolinario
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Alice Sampaio Rocha
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Luis Fernando Lopez Tort
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
- CENUR Litoral Norte, Universidad de la República, Salto, Uruguay
| | - Tsuyoshi Sekizuka
- Pathogen Genomics Center, National Institute of Infectious Diseases, 1-23-1 Toyama, Shinjuku-Ku, Tokyo, 162-8640, Japan
| | - Kentaro Itokawa
- Pathogen Genomics Center, National Institute of Infectious Diseases, 1-23-1 Toyama, Shinjuku-Ku, Tokyo, 162-8640, Japan
| | - Masanori Hashino
- Pathogen Genomics Center, National Institute of Infectious Diseases, 1-23-1 Toyama, Shinjuku-Ku, Tokyo, 162-8640, Japan
| | - Makoto Kuroda
- Pathogen Genomics Center, National Institute of Infectious Diseases, 1-23-1 Toyama, Shinjuku-Ku, Tokyo, 162-8640, Japan
| | | | - Gabriel Luz Wallau
- Instituto Aggeu Magalhães, Fundação Oswaldo Cruz, Recife, Pernambuco, Brazil
| | - Edson Delatorre
- Departamento de Biologia, Centro de Ciências Exatas, Naturais e da Saúde, Universidade Federal do Espírito Santo, Alegre, Brazil
| | - Tiago Gräf
- Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Marilda Mendonça Siqueira
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Gonzalo Bello
- Laboratório de AIDS e Imunologia Molecular, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | - Paola Cristina Resende
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
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A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia. HEALTHCARE ANALYTICS 2022; 2:100080. [PMID: 37520622 PMCID: PMC9293790 DOI: 10.1016/j.health.2022.100080] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 05/27/2023]
Abstract
This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states.
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SARS-CoV-2 viral load is associated with risk of transmission to household and community contacts. BMC Infect Dis 2022; 22:672. [PMID: 35931971 PMCID: PMC9354300 DOI: 10.1186/s12879-022-07663-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 07/29/2022] [Indexed: 12/23/2022] Open
Abstract
Background Factors that lead to successful SARS-CoV-2 transmission are still not well described. We investigated the association between a case’s viral load and the risk of transmission to contacts in the context of other exposure-related factors. Methods Data were generated through routine testing and contact tracing at a large university. Case viral loads were obtained from cycle threshold values associated with a positive polymerase chain reaction test result from October 1, 2020 to April 15, 2021. Cases were included if they had at least one contact who tested 3–14 days after the exposure. Case-contact pairs were formed by linking index cases with contacts. Chi-square tests were used to evaluate differences in proportions of contacts testing positive. Generalized estimating equation models with a log link were used to evaluate whether viral load and other exposure-related factors were associated with a contact testing positive. Results Median viral load among the 212 cases included in the study was 5.6 (1.8–10.4) log10 RNA copies per mL of saliva. Among 365 contacts, 70 (19%) tested positive following their exposure; 36 (51%) were exposed to a case that was asymptomatic or pre-symptomatic on the day of exposure. The proportion of contacts that tested positive increased monotonically with index case viral load (12%, 23% and 25% corresponding to < 5, 5–8 and > 8 log10 copies per mL, respectively; X2 = 7.18, df = 2, p = 0.03). Adjusting for cough, time between test and exposure, and physical contact, the risk of transmission to a close contact was significantly associated with viral load (RR = 1.27, 95% CI 1.22–1.32). Conclusions Further research is needed to understand whether these relationships persist for newer variants. For those variants whose transmission advantage is mediated through a high viral load, public health measures could be scaled accordingly. Index cases with higher viral loads could be prioritized for contact tracing and recommendations to quarantine contacts could be made according to the likelihood of transmission based on risk factors such as viral load.
Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07663-1.
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Jia H, Miller EA, Chan CC, Ng SY, Prabakaran M, Tao M, Cheong ISY, Lim SM, Chen MW, Gao X, R A, McBee ME, Preiser PR, Sikes HD, Kongsuphol P. Development and translation of a paper-based top readout vertical flow assay for SARS-CoV-2 surveillance. LAB ON A CHIP 2022; 22:1321-1332. [PMID: 35226037 DOI: 10.1039/d2lc00073c] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Surveillance of SARS-CoV-2 infection is critical for controlling the current pandemic. Antigen rapid tests (ARTs) provide a means for surveillance. Available lateral flow assay format ARTs rely heavily on nitrocellulose paper, raising challenges in supply shortage. Vertical flow assay (VFA) with cellulose paper as test material attracts much attention as a complementary test approach. However, current reported VFAs are facing challenges in reading the test signal from the bottom face of the test cassette, complicating the test workflow and hindering translation into rapid test application. Here, we address this gap with an enhanced VFA against SARS-CoV-2 N protein that adapts a cellulose pull-down test format allowing (1) one-step sample application at the top of the test cassette and (2) readout of the test signal from the top. We also demonstrate the feasibility of translating the enhanced VFA into a point-of-care application that can help in SARS-CoV-2 surveillance.
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Affiliation(s)
- Huan Jia
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
| | - Eric A Miller
- Department of Chemical Engineering, Massachusetts Institute of Technology (MIT), 25 Ames Street, Building 66, Cambridge, MA 02139, USA
| | - Chia Ching Chan
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
| | - Say Yong Ng
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
| | - Mookkan Prabakaran
- Molecular Pathogenesis Group, Temasek Life Science Laboratory, National University of Singapore, 1 Research Link, 117604 Singapore
| | - Meng Tao
- Molecular Pathogenesis Group, Temasek Life Science Laboratory, National University of Singapore, 1 Research Link, 117604 Singapore
| | - Ian Shen-Yi Cheong
- Molecular Pathogenesis Group, Temasek Life Science Laboratory, National University of Singapore, 1 Research Link, 117604 Singapore
| | - Sing Mei Lim
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
| | - Ming Wei Chen
- School of Biological Science (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, 637551 Singapore
| | - Xiaohong Gao
- School of Biological Science (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, 637551 Singapore
| | - Abirami R
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
| | - Megan E McBee
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
| | - Peter R Preiser
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
- School of Biological Science (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, 637551 Singapore
| | - Hadley D Sikes
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
- Department of Chemical Engineering, Massachusetts Institute of Technology (MIT), 25 Ames Street, Building 66, Cambridge, MA 02139, USA
| | - Patthara Kongsuphol
- Antimicrobial Resistance Interdisciplinary Research Group (AMR-IRG), Singapore-MIT Alliance in Research and Technology (SMART), 1 CREATE Way, 138602 Singapore.
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Yin N, Dellicour S, Daubie V, Franco N, Wautier M, Faes C, Van Cauteren D, Nymark L, Hens N, Gilbert M, Hallin M, Vandenberg O. Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends. Front Med (Lausanne) 2021; 8:743988. [PMID: 34790677 PMCID: PMC8591051 DOI: 10.3389/fmed.2021.743988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/05/2021] [Indexed: 11/20/2022] Open
Abstract
Introduction: We assessed the usefulness of SARS-CoV-2 RT-PCR cycle thresholds (Ct) values trends produced by the LHUB-ULB (a consolidated microbiology laboratory located in Brussels, Belgium) for monitoring the epidemic's dynamics at local and national levels and for improving forecasting models. Methods: SARS-CoV-2 RT-PCR Ct values produced from April 1, 2020, to May 15, 2021, were compared with national COVID-19 confirmed cases notifications according to their geographical and time distribution. These Ct values were evaluated against both a phase diagram predicting the number of COVID-19 patients requiring intensive care and an age-structured model estimating COVID-19 prevalence in Belgium. Results: Over 155,811 RT-PCR performed, 12,799 were positive and 7,910 Ct values were available for analysis. The 14-day median Ct values were negatively correlated with the 14-day mean daily positive tests with a lag of 17 days. In addition, the 14-day mean daily positive tests in LHUB-ULB were strongly correlated with the 14-day mean confirmed cases in the Brussels-Capital and in Belgium with coinciding start, peak, and end of the different waves of the epidemic. Ct values decreased concurrently with the forecasted phase-shifts of the diagram. Similarly, the evolution of 14-day median Ct values was negatively correlated with daily estimated prevalence for all age-classes. Conclusion: We provide preliminary evidence that trends of Ct values can help to both follow and predict the epidemic's trajectory at local and national levels, underlining that consolidated microbiology laboratories can act as epidemic sensors as they gather data that are representative of the geographical area they serve.
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Affiliation(s)
- Nicolas Yin
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium.,Department of Microbiology, Immunology and Transplantation, Division of Clinical and Epidemiological Virology, Rega Institute, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Valery Daubie
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Franco
- Department of Mathematics, Namur Centre for Complex Systems (Naxys), University of Namur, Namur, Belgium.,Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium
| | - Magali Wautier
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium
| | - Dieter Van Cauteren
- Scientific Directorate of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Liv Nymark
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.,Department of Health Management and Health Economics, University of Oslo, Oslo, Norway
| | - Niel Hens
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium.,Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
| | - Marie Hallin
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Centre for Environmental Health and Occupational Health, School of Public Health, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Olivier Vandenberg
- Centre for Environmental Health and Occupational Health, School of Public Health, Université Libre de Bruxelles (ULB), Brussels, Belgium.,Clinical Research and Innovation Unit, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, United Kingdom
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