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da Silva AF, da Silva Neto AM, Aksenen C, Jeronimo P, Dezordi F, Almeida S, Costa H, Salvato R, Campos TD, Wallau G, of the Fiocruz Genomic Network OB. ViralFlow v1.0-a computational workflow for streamlining viral genomic surveillance. NAR Genom Bioinform 2024; 6:lqae056. [PMID: 38800829 PMCID: PMC11127631 DOI: 10.1093/nargab/lqae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/15/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
ViralFlow v1.0 is a computational workflow developed for viral genomic surveillance. Several key changes turned ViralFlow into a general-purpose reference-based genome assembler for all viruses with an available reference genome. New virus-agnostic modules were implemented to further study nucleotide and amino acid mutations. ViralFlow v1.0 runs on a broad range of computational infrastructures, from laptop computers to high-performance computing (HPC) environments, and generates standard and well-formatted outputs suited for both public health reporting and scientific problem-solving. ViralFlow v1.0 is available at: https://viralflow.github.io/index-en.html.
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Affiliation(s)
- Alexandre Freitas da Silva
- Departamento de Entomologia, Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
- Núcleo de Bioinformática (NBI), Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
| | - Antonio Marinho da Silva Neto
- Data Analysis and Engineering, Genomic Surveillance Unit, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
| | | | | | - Filipe Zimmer Dezordi
- Departamento de Entomologia, Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
- Núcleo de Bioinformática (NBI), Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
| | | | - Hudson Marques Paula Costa
- Núcleo de Bioinformática (NBI), Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
| | - Richard Steiner Salvato
- Secretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Laboratório Central de Saúde Pública, Porto Alegre, Rio Grande do Sul 90450-190, Brazil
| | - Tulio de Lima Campos
- Núcleo de Bioinformática (NBI), Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
| | - Gabriel da Luz Wallau
- Departamento de Entomologia, Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
- Núcleo de Bioinformática (NBI), Instituto Aggeu Magalhães (IAM)-Fundação Oswaldo Cruz-FIOCRUZ, Recife, Pernambuco 50670-420, Brazil
- Department of Arbovirology, Bernhard Nocht Institute for Tropical Medicine, WHO Collaborating Center for Arbovirus and Hemorrhagic Fever Reference and Research, National Reference Center for Tropical Infectious Diseases, Bernhard-Nocht-Strasse 74, D-20359 Hamburg, Germany
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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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Oktavianthi S, Lages AC, Kusuma R, Kurniasih TS, Trimarsanto H, Andriani F, Rustandi D, Meriyanti T, Yusuf I, Malik SG, Jo J, Suriapranata I. Whole-Genome Sequencing and Mutation Analyses of SARS-CoV-2 Isolates from Indonesia. Pathogens 2024; 13:279. [PMID: 38668234 PMCID: PMC11053823 DOI: 10.3390/pathogens13040279] [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: 11/06/2023] [Revised: 01/12/2024] [Accepted: 03/07/2024] [Indexed: 04/29/2024] Open
Abstract
The SARS-CoV-2 infection that caused the COVID-19 pandemic has become a significant public health concern. New variants with distinct mutations have emerged, potentially impacting its infectivity, immune evasion capacity, and vaccine response. A whole-genome sequencing study of 292 SARS-CoV-2 isolates collected from selected regions of Indonesia between January and October 2021 was performed to identify the distribution of SARS-CoV-2 variants and common mutations in Indonesia. During January-April 2021, Indonesian lineages B.1.466.2 and B.1.470 dominated, but from May 2021, Delta's AY.23 lineage outcompeted them. An analysis of 7515 published sequences from January 2021 to June 2022 revealed a decline in Delta in November 2021, followed by the emergence of Omicron variants in December 2021. We identified C241T (5'UTR), P314L (NSP12b), F106F (NSP3), and D614G (Spike) mutations in all sequences. The other common substitutions included P681R (76.4%) and T478K (60%) in Spike, D377Y in Nucleocapsid (61%), and I82T in Membrane (60%) proteins. Breakthrough infection and prolonged viral shedding cases were associated with Delta variants carrying the Spike T19R, G142D, L452R, T478K, D614G, P681R, D950N, and V1264L mutations. The dynamic of SARS-CoV-2 variants in Indonesia highlights the importance of continuous genomic surveillance in monitoring and identifying potential strains leading to disease outbreaks.
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Affiliation(s)
- Sukma Oktavianthi
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
- Eijkman Institute for Molecular Biology, Jakarta 10430, Indonesia;
| | - Aksar Chair Lages
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
| | - Rinaldy Kusuma
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
| | - Tri Shinta Kurniasih
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
| | - Hidayat Trimarsanto
- Eijkman Institute for Molecular Biology, Jakarta 10430, Indonesia;
- Menzies School of Health Research, Charles Darwin University, Darwin 0811, Australia
| | - Febi Andriani
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
| | - David Rustandi
- Siloam Hospital Lippo Village, Tangerang 15810, Indonesia; (D.R.); (T.M.)
| | - Tandry Meriyanti
- Siloam Hospital Lippo Village, Tangerang 15810, Indonesia; (D.R.); (T.M.)
| | - Irawan Yusuf
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
| | - Safarina G. Malik
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
- Eijkman Institute for Molecular Biology, Jakarta 10430, Indonesia;
| | - Juandy Jo
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
- Department of Biology, Faculty of Science and Technology, Universitas Pelita Harapan, Tangerang 15811, Indonesia
| | - Ivet Suriapranata
- Mochtar Riady Institute for Nanotechnology, Tangerang 15810, Indonesia; (S.O.); (A.C.L.); (R.K.); (T.S.K.); (F.A.); (I.Y.); (S.G.M.); (J.J.)
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Wang Z, Pei S, Ye R, Chen J, Cheng N, Zhao M, Cao W, Jia Z. Increasing evolution, prevalence, and outbreaks for rift valley fever virus in the process of breaking geographical barriers. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170302. [PMID: 38272089 DOI: 10.1016/j.scitotenv.2024.170302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND Rift valley fever (RVF) is listed as one of prioritized diseases by WHO. This study aims to describe RVF virus' landscape distribution globally, and to insight dynamics change of its evolution, prevalence, and outbreaks in the process of breaking geographical barriers. METHODS A systematic literature review and meta-analyses was conducted to estimate RVF prevalence by hosts using a random-effect model. Molecular clock-based phylogenetic analyses were performed to estimate RVF virus nucleotide substitution rates using nucleotide sequences in NCBI database. RVF virus prevalence, nucleotide substitution rates, and outbreaks were compared before and after breaking geographical barriers twice, respectively. RESULTS RVF virus was reported from 26 kinds of hosts covering 48 countries from 1930 to 2022. Since RVF broke geographical barriers, (1) nucleotide substitution rates significantly increased after firstly spreading out of Africa in 2000, (2) prevalence in humans significantly increased from 1.92 % (95 % CI: 0.86-3.25 %) to 3.03 % (95 % CI: 2.09-4.12 %) after it broke Sahara Desert geographical barriers in 1977, and to 5.24 % (95 % CI: 3.81-6.82 %) after 2000, (3) RVF outbreaks in humans and the number of wildlife hosts presented increasing trends. RVF virus spillover may exist between bats and humans, and accelerate viral substitution rates in humans. During outbreaks, the RVF virus substitution rates accelerated in humans. 60.00 % RVF outbreaks occurred 0-2 months after floods and (or) heavy rainfall. CONCLUSION RVF has the increasing risk to cause pandemics, and global collaboration on "One Health" is needed to prevent potential pandemics.
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Affiliation(s)
- Zekun Wang
- School of Public Health, Peking University, Beijing, China
| | - Shaojun Pei
- School of Public Health, Peking University, Beijing, China
| | - Runze Ye
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Jingyuan Chen
- School of Public Health, Peking University, Beijing, China
| | - Nuo Cheng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Mingchen Zhao
- School of Public Health, Peking University, Beijing, China
| | - Wuchun Cao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Zhongwei Jia
- School of Public Health, Peking University, Beijing, China; Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing, China; Center for Drug Abuse Control and Prevention, National Institute of Health Data Science, Peking University, Beijing, China; Peking University Clinical Research Institute, Beijing, China.
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5
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Connor R, Shakya M, Yarmosh DA, Maier W, Martin R, Bradford R, Brister JR, Chain PSG, Copeland CA, di Iulio J, Hu B, Ebert P, Gunti J, Jin Y, Katz KS, Kochergin A, LaRosa T, Li J, Li PE, Lo CC, Rashid S, Maiorova ES, Xiao C, Zalunin V, Purcell L, Pruitt KD. Recommendations for Uniform Variant Calling of SARS-CoV-2 Genome Sequence across Bioinformatic Workflows. Viruses 2024; 16:430. [PMID: 38543795 PMCID: PMC10975397 DOI: 10.3390/v16030430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 04/01/2024] Open
Abstract
Genomic sequencing of clinical samples to identify emerging variants of SARS-CoV-2 has been a key public health tool for curbing the spread of the virus. As a result, an unprecedented number of SARS-CoV-2 genomes were sequenced during the COVID-19 pandemic, which allowed for rapid identification of genetic variants, enabling the timely design and testing of therapies and deployment of new vaccine formulations to combat the new variants. However, despite the technological advances of deep sequencing, the analysis of the raw sequence data generated globally is neither standardized nor consistent, leading to vastly disparate sequences that may impact identification of variants. Here, we show that for both Illumina and Oxford Nanopore sequencing platforms, downstream bioinformatic protocols used by industry, government, and academic groups resulted in different virus sequences from same sample. These bioinformatic workflows produced consensus genomes with differences in single nucleotide polymorphisms, inclusion and exclusion of insertions, and/or deletions, despite using the same raw sequence as input datasets. Here, we compared and characterized such discrepancies and propose a specific suite of parameters and protocols that should be adopted across the field. Consistent results from bioinformatic workflows are fundamental to SARS-CoV-2 and future pathogen surveillance efforts, including pandemic preparation, to allow for a data-driven and timely public health response.
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Affiliation(s)
- Ryan Connor
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Migun Shakya
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; (M.S.); (P.S.G.C.); (B.H.); (P.-E.L.); (C.-C.L.)
| | - David A. Yarmosh
- American Type Culture Collection, Manassas, VA 20110, USA; (D.A.Y.); (R.B.); (S.R.)
- BEI Resources, Manassas, VA 20110, USA
| | - Wolfgang Maier
- Galaxy Europe Team, University of Freiburg, 79085 Freiburg, Germany;
| | - Ross Martin
- Clinical Virology Department, Gilead Sciences, Foster City, CA 94404, USA; (R.M.); (J.L.); (E.S.M.)
| | - Rebecca Bradford
- American Type Culture Collection, Manassas, VA 20110, USA; (D.A.Y.); (R.B.); (S.R.)
- BEI Resources, Manassas, VA 20110, USA
| | - J. Rodney Brister
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Patrick S. G. Chain
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; (M.S.); (P.S.G.C.); (B.H.); (P.-E.L.); (C.-C.L.)
| | | | - Julia di Iulio
- Vir Biotechnology Inc., San Francisco, CA 94158, USA; (J.d.I.); (L.P.)
| | - Bin Hu
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; (M.S.); (P.S.G.C.); (B.H.); (P.-E.L.); (C.-C.L.)
| | - Philip Ebert
- Eli Lilly and Company, Indianapolis, IN 46225, USA;
| | - Jonathan Gunti
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Yumi Jin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Kenneth S. Katz
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Andrey Kochergin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Tré LaRosa
- Deloitte Consulting LLP, Rosslyn, VA 22209, USA; (C.A.C.); (T.L.)
| | - Jiani Li
- Clinical Virology Department, Gilead Sciences, Foster City, CA 94404, USA; (R.M.); (J.L.); (E.S.M.)
| | - Po-E Li
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; (M.S.); (P.S.G.C.); (B.H.); (P.-E.L.); (C.-C.L.)
| | - Chien-Chi Lo
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; (M.S.); (P.S.G.C.); (B.H.); (P.-E.L.); (C.-C.L.)
| | - Sujatha Rashid
- American Type Culture Collection, Manassas, VA 20110, USA; (D.A.Y.); (R.B.); (S.R.)
| | - Evguenia S. Maiorova
- Clinical Virology Department, Gilead Sciences, Foster City, CA 94404, USA; (R.M.); (J.L.); (E.S.M.)
| | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Vadim Zalunin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
| | - Lisa Purcell
- Vir Biotechnology Inc., San Francisco, CA 94158, USA; (J.d.I.); (L.P.)
| | - Kim D. Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; (R.C.); (J.R.B.); (J.G.); (Y.J.); (K.S.K.); (A.K.); (C.X.); (V.Z.)
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6
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Pinkhover NP, Pontbriand KM, Fletcher KP, Sanchez E, Okello K, Garvey LM, Pum A, Li K, DeOliveira G, Proctor T, Feenstra JDM, Sorel O, Gandhi M, Auclair JR. Comparison of analytical performance and economic value of two biosurveillance methods for tracking SARS-COV-2 variants of concern. Microbiol Spectr 2024; 12:e0348423. [PMID: 38206048 PMCID: PMC10845967 DOI: 10.1128/spectrum.03484-23] [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: 09/25/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
The development of biosurveillance programs with strong analytical performance and economically accessible protocols is essential for monitoring viral pathogens. Throughout the COVID-19 pandemic, whole-genome sequencing (WGS) has been the prevailing technology for SARS-CoV-2 variant of concern (VOC) detection. While WGS offers benefits, it is a lengthy process, financially and technically straining for scalable viral tracking. The aim of this study was to compare the analytical performance and economic feasibility of WGS and PCR mutation panels for distinguishing six known VOCs: Alpha (B.1.1.7 and Q.4), Gamma (P.1), Delta (B.1.617.2 and AY.4.2), and Omicron. (B.1.1.529.1). In all, 78 SARS-CoV-2-positive samples were collected from April to December 2021 at Northeastern University (Cabot Testing Site, Boston, MA, USA) for genotyping PCR and WGS analysis. MagMax Viral/Pathogen II Nucleic Acid Isolation and TaqPath COVID-19 Combo Kits were used for RNA extraction and SARS-CoV-2 confirmation. VOC discrimination was assessed using two TaqMan SARS-CoV-2 single nucleotide polymorphism (SNP) assay layouts, and Ion Torrent WGS. In November 2021, the mutation panel demonstrated marked versatility by detecting the emerging Omicron variant reported by South Africa. SNP panel analysis yielded the following 78 VOC identifications: Alpha B.1.1.7 (N = 20), Alpha Q.4 (N = 3), Gamma P.1 (N = 1), Delta B.1.617.2 (N = 30), Delta AY.4.2 (N = 3), and Omicron B.1.1.529.1 (N = 20) with one undetermined (N = 1) sample. Genotyping mutation panels designated lineages in 77 of 78 samples, 46/78 were confirmed by WGS, while 32 samples failed WGS lineage assignment. RT-PCR genotyping panels offer pronounced throughput and sensitivity and provide an economically advantageous technique for SARS-CoV-2 biosurveillance.IMPORTANCEThe results presented in our manuscript demonstrate how the value of simplistic and reliable molecular assays coupled with the core scientific principle of standardization can be overlooked by the charm of more sophisticated assays and instrumentation. This effect can often be amplified during tumultuous public health events, such as the COVID-19 pandemic. By adapting standardized PCR mutation panels to detect prominently circulating SARS-CoV-2 variants, we were able to better assess the potential health impacts of rising positivity rates and transmission clusters within the Northeastern University population. While several literature publications utilizing genotyping PCR and NGS have a similar scope to ours, many investigations lack sufficiently standardized genotyping PCR and NGS bioinformatics inclusionary/exclusionary criteria for SARS-CoV-2 variant identification. Finally, the economic benefits of standardized PCR mutation panels would allow for global implementation of biosurveillance, rather than reserving biosurveillance to more economically developed nations.
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Affiliation(s)
- Nicholas P. Pinkhover
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Kerriann M. Pontbriand
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Kelli P. Fletcher
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Eduardo Sanchez
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Kenneth Okello
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Liam M. Garvey
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Alex Pum
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Kurvin Li
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Gabriel DeOliveira
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
| | - Teddie Proctor
- Thermo Fisher Scientific, Genetic Testing Solutions, South San Francisco, California, USA
| | - Jelena D. M. Feenstra
- Thermo Fisher Scientific, Genetic Testing Solutions, South San Francisco, California, USA
| | - Océane Sorel
- Thermo Fisher Scientific, Genetic Testing Solutions, South San Francisco, California, USA
| | - Manoj Gandhi
- Thermo Fisher Scientific, Genetic Testing Solutions, South San Francisco, California, USA
| | - Jared R. Auclair
- Department of Chemistry and Chemical Biology, Life Sciences Testing Center, Northeastern University Innovation Campus, Burlington, Massachusetts, USA
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7
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Ling-Hu T, Simons LM, Dean TJ, Rios-Guzman E, Caputo MT, Alisoltani A, Qi C, Malczynski M, Blanke T, Jennings LJ, Ison MG, Achenbach CJ, Larkin PM, Kaul KL, Lorenzo-Redondo R, Ozer EA, Hultquist JF. Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes. Cell Rep Med 2024; 5:101361. [PMID: 38232695 PMCID: PMC10829796 DOI: 10.1016/j.xcrm.2023.101361] [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: 12/02/2022] [Revised: 08/07/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with enhanced transmissibility and immune escape have emerged periodically throughout the coronavirus disease 2019 (COVID-19) pandemic, but the impact of these variants on disease severity has remained unclear. In this single-center, retrospective cohort study, we examined the association between SARS-CoV-2 clade and patient outcome over a two-year period in Chicago, Illinois. Between March 2020 and March 2022, 14,252 residual diagnostic specimens were collected from SARS-CoV-2-positive inpatients and outpatients alongside linked clinical and demographic metadata, of which 2,114 were processed for viral whole-genome sequencing. When controlling for patient demographics and vaccination status, several viral clades were associated with risk for hospitalization, but this association was negated by the inclusion of population-level confounders, including case count, sampling bias, and shifting standards of care. These data highlight the importance of integrating non-virological factors into disease severity and outcome models for the accurate assessment of patient risk.
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Affiliation(s)
- Ted Ling-Hu
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Lacy M Simons
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Taylor J Dean
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Estefany Rios-Guzman
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Matthew T Caputo
- Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Arghavan Alisoltani
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Chao Qi
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Michael Malczynski
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Timothy Blanke
- Diagnostic Molecular Biology Laboratory, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Lawrence J Jennings
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Michael G Ison
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Chad J Achenbach
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Paige M Larkin
- Department of Molecular Microbiology, Northshore University HealthSystem, Evanston, IL 60201, USA
| | - Karen L Kaul
- Department of Pathology, Northshore University HealthSystem, Evanston, IL 60201, USA
| | - Ramon Lorenzo-Redondo
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Egon A Ozer
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Judd F Hultquist
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA.
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8
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Lorenzo-Redondo R, de Sant’Anna Carvalho AM, Hultquist JF, Ozer EA. SARS-CoV-2 genomics and impact on clinical care for COVID-19. J Antimicrob Chemother 2023; 78:ii25-ii36. [PMID: 37995357 PMCID: PMC10667012 DOI: 10.1093/jac/dkad309] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/02/2023] [Indexed: 11/25/2023] Open
Abstract
The emergence and worldwide spread of SARS-CoV-2 during the COVID-19 pandemic necessitated the adaptation and rapid deployment of viral WGS and analysis techniques that had been previously applied on a more limited basis to other viral pathogens, such as HIV and influenza viruses. The need for WGS was driven in part by the low mutation rate of SARS-CoV-2, which necessitated measuring variation along the entire genome sequence to effectively differentiate lineages and characterize viral evolution. Several WGS approaches designed to maximize throughput and accuracy were quickly adopted by surveillance labs around the world. These broad-based SARS-CoV-2 genomic sequencing efforts revealed ongoing evolution of the virus, highlighted by the successive emergence of new viral variants throughout the course of the pandemic. These genomic insights were instrumental in characterizing the effects of viral mutations on transmissibility, immune escape and viral tropism, which in turn helped guide public health policy, the use of monoclonal antibody therapeutics and vaccine development strategies. As the use of direct-acting antivirals for the treatment of COVID-19 became more widespread, the potential for emergence of antiviral resistance has driven ongoing efforts to delineate resistance mutations and to monitor global sequence databases for their emergence. Given the critical role of viral genomics in the international effort to combat the COVID-19 pandemic, coordinated efforts should be made to expand global genomic surveillance capacity and infrastructure towards the anticipation and prevention of future pandemics.
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Affiliation(s)
- Ramon Lorenzo-Redondo
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Alexandre Machado de Sant’Anna Carvalho
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Judd F Hultquist
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Egon A Ozer
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
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9
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Bianconi I, Aschbacher R, Pagani E. Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology. Antibiotics (Basel) 2023; 12:1580. [PMID: 37998782 PMCID: PMC10668849 DOI: 10.3390/antibiotics12111580] [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: 09/26/2023] [Revised: 10/16/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023] Open
Abstract
Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.
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Affiliation(s)
- Irene Bianconi
- Laboratory of Microbiology and Virology, Provincial Hospital of Bolzano (SABES-ASDAA), Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversitätvia Amba Alagi 5, 39100 Bolzano, Italy; (R.A.); (E.P.)
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10
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Scarpa F, Bazzani L, Giovanetti M, Ciccozzi A, Benedetti F, Zella D, Sanna D, Casu M, Borsetti A, Cella E, Pascarella S, Maruotti A, Ciccozzi M. Update on the Phylodynamic and Genetic Variability of Marburg Virus. Viruses 2023; 15:1721. [PMID: 37632063 PMCID: PMC10458864 DOI: 10.3390/v15081721] [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: 07/22/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
The COVID-19 pandemic has not only strained healthcare systems in Africa but has also intensified the impact of emerging and re-emerging diseases. Specifically in Equatorial Guinea, mirroring the situation in other African countries, unique zoonotic outbreaks have occurred during this challenging period. One notable resurgence is Marburg virus disease (MVD), which has further burdened the already fragile healthcare system. The re-emergence of the Marburg virus amid the COVID-19 pandemic is believed to stem from a probable zoonotic spill-over, although the precise transmission routes remain uncertain. Given the gravity of the situation, addressing the existing challenges is paramount. Though the genome sequences from the current outbreak were not available for this study, we analyzed all the available whole genome sequences of this re-emerging pathogen to advocate for a shift towards active surveillance. This is essential to ensure the successful containment of any potential Marburg virus outbreak in Equatorial Guinea and the wider African context. This study, which presents an update on the phylodynamics and the genetic variability of MARV, further confirmed the existence of at least two distinct patterns of viral spread. One pattern demonstrates a slower but continuous and recurring virus circulation, while the other exhibits a faster yet limited and episodic spread. These results highlight the critical need to strengthen genomic surveillance in the region to effectively curb the pathogen's dissemination. Moreover, the study emphasizes the importance of prompt alert management, comprehensive case investigation and analysis, contact tracing, and active case searching. These steps are vital to support the healthcare system's response to this emerging health crisis. By implementing these strategies, we can better arm ourselves against the challenges posed by the resurgence of the Marburg virus and other infectious diseases.
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Affiliation(s)
- Fabio Scarpa
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy;
| | - Liliana Bazzani
- Department of Science and Technology for Humans and the Environment, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.B.); (M.G.)
| | - Marta Giovanetti
- Department of Science and Technology for Humans and the Environment, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.B.); (M.G.)
- Instituto Rene Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-009, MG, Brazil
| | - Alessandra Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (A.C.); (M.C.)
| | - Francesca Benedetti
- Institute of Human Virlogy and Global Virusn Network Center, Deparment of Biochemistry and Molecular Biology, University for Maryland School of Medicine, Baltimore, MD 21201, USA; (F.B.); (D.Z.)
| | - Davide Zella
- Institute of Human Virlogy and Global Virusn Network Center, Deparment of Biochemistry and Molecular Biology, University for Maryland School of Medicine, Baltimore, MD 21201, USA; (F.B.); (D.Z.)
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy;
| | - Marco Casu
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy;
| | - Alessandra Borsetti
- National HIV/AIDS Research Center (CNAIDS), National Institute of Health, 00161 Rome, Italy;
| | - Eleonora Cella
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL 32816, USA;
| | - Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza Università di Roma, 00185 Rome, Italy;
| | | | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (A.C.); (M.C.)
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11
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Tartaninan AC, Mulroney N, Poselenzny K, Akroush M, Unger T, Helseth DL, Sabatini LM, Bouma M, Larkin PM. NGS implementation for monitoring SARS-CoV-2 variants in Chicagoland: An institutional perspective, successes and challenges. Front Public Health 2023; 11:1177695. [PMID: 37151582 PMCID: PMC10157391 DOI: 10.3389/fpubh.2023.1177695] [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: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 05/09/2023] Open
Abstract
Identification of SARS-CoV-2 lineages has shown to provide invaluable information regarding treatment efficacy, viral transmissibility, disease severity, and immune evasion. These benefits provide institutions with an expectation of high informational upside with little insight in regards to practicality with implementation and execution of such high complexity testing in the midst of a pandemic. This article details our institution's experience implementing and using Next Generation Sequencing (NGS) to monitor SARS-CoV-2 lineages in the northern Chicagoland area throughout the pandemic. To date, we have sequenced nearly 7,000 previously known SARS-CoV-2 positive samples from various patient populations (e.g., outpatient, inpatient, and outreach sites) to reduce bias in sampling. As a result, our hospital was guided while making crucial decisions about staffing, masking, and other infection control measures during the pandemic. While beneficial, establishing this NGS procedure was challenging, with countless considerations at every stage of assay development and validation. Reduced staffing prompted transition from a manual to automated high throughput workflow, requiring further validation, lab space, and instrumentation. Data management and IT security were additional considerations that delayed implementation and dictated our bioinformatic capabilities. Taken together, our experience highlights the obstacles and triumphs of SARS-CoV-2 sequencing.
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Affiliation(s)
| | - Nicole Mulroney
- NorthShore University HealthSystem, Evanston, IL, United States
| | | | - Michael Akroush
- NorthShore University HealthSystem, Evanston, IL, United States
| | - Trevor Unger
- NorthShore University HealthSystem, Evanston, IL, United States
| | | | - Linda M. Sabatini
- NorthShore University HealthSystem, Evanston, IL, United States
- Pritzker School of Medicine, The University of Chicago, Chicago, IL, United States
| | - Michael Bouma
- NorthShore University HealthSystem, Evanston, IL, United States
| | - Paige M.K. Larkin
- NorthShore University HealthSystem, Evanston, IL, United States
- Pritzker School of Medicine, The University of Chicago, Chicago, IL, United States
- *Correspondence: Paige M.K. Larkin,
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