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Gauthier NPG, Chan W, Locher K, Smailus D, Coope R, Charles M, Jassem A, Kopetzky J, Chorlton SD, Manges AR. Validation of an automated, end-to-end metagenomic sequencing assay for agnostic detection of respiratory viruses. J Infect Dis 2024:jiae226. [PMID: 38696336 DOI: 10.1093/infdis/jiae226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 04/26/2024] [Indexed: 05/04/2024] Open
Abstract
BACKGROUND Current molecular diagnostics are limited in the number and type of detectable pathogens. Metagenomic next generation sequencing (mNGS) is an emerging, and increasingly feasible, pathogen-agnostic diagnostic approach. Translational barriers prohibit the widespread adoption of this technology in clinical laboratories. We validate an end-to-end mNGS assay for detection of respiratory viruses. Our assay is optimized to reduce turnaround time, lower cost-per-sample, increase throughput, and deploy secure and actionable bioinformatic results. METHODS We validated our assay using residual nasopharyngeal swab specimens from Vancouver General Hospital (n = 359), RT-PCR-positive, or negative for Influenza, SARS-CoV-2, and RSV. We quantified sample stability, assay precision, the effect of background nucleic acid levels, and analytical limits of detection. Diagnostic performance metrics were estimated. RESULTS We report that our mNGS assay is highly precise, semi-quantitative, with analytical limits of detection ranging from 103-104 copies/mL. Our assay is highly specific (100%) and sensitive (61.9% Overall: 86.8%; RT-PCR Ct < 30). Multiplexing capabilities enable processing of up to 55-specimens simultaneously on an Oxford Nanopore GridION device, with results reported within 12-hours. CONCLUSIONS This study outlines the diagnostic performance and feasibility of mNGS for respiratory viral diagnostics, infection control, and public health surveillance. We addressed translational barriers to widespread mNGS adoption.
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Affiliation(s)
- Nick P G Gauthier
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Wilson Chan
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia
| | - Kerstin Locher
- Division of Medical Microbiology, Vancouver General Hospital, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Duane Smailus
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, British Columbia, Canada
| | - Robin Coope
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, British Columbia, Canada
| | - Marthe Charles
- Division of Medical Microbiology, Vancouver General Hospital, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Agatha Jassem
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Jennifer Kopetzky
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | | | - Amee R Manges
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
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Schmidt PJ, Acosta N, Chik AHS, D’Aoust PM, Delatolla R, Dhiyebi HA, Glier MB, Hubert CRJ, Kopetzky J, Mangat CS, Pang XL, Peterson SW, Prystajecky N, Qiu Y, Servos MR, Emelko MB. Realizing the value in "non-standard" parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology. Front Microbiol 2023; 14:1048661. [PMID: 36937263 PMCID: PMC10020645 DOI: 10.3389/fmicb.2023.1048661] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.
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Affiliation(s)
- Philip J. Schmidt
- Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Nicole Acosta
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada
| | | | - Patrick M. D’Aoust
- Department of Civil Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Robert Delatolla
- Department of Civil Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Hadi A. Dhiyebi
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Melissa B. Glier
- Public Health Laboratory, BC Centre for Disease Control, Vancouver, BC, Canada
| | - Casey R. J. Hubert
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Jennifer Kopetzky
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Chand S. Mangat
- Wastewater Surveillance Unit, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Xiao-Li Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada
- Alberta Precision Laboratories, Public Health Laboratory, Alberta Health Services, Edmonton, AB, Canada
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada
| | - Shelley W. Peterson
- Wastewater Surveillance Unit, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Natalie Prystajecky
- Public Health Laboratory, BC Centre for Disease Control, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Yuanyuan Qiu
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada
| | - Mark R. Servos
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Monica B. Emelko
- Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
- *Correspondence: Monica B. Emelko,
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Wood KJ, Nur M, Gil J, Fletcher K, Lakeman K, Gann D, Gothberg A, Khuu T, Kopetzky J, Naqvi S, Pandya A, Zhang C, Maisonneuve B, Pel M, Michelmore R. Effector prediction and characterization in the oomycete pathogen Bremia lactucae reveal host-recognized WY domain proteins that lack the canonical RXLR motif. PLoS Pathog 2020; 16:e1009012. [PMID: 33104763 PMCID: PMC7644090 DOI: 10.1371/journal.ppat.1009012] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 11/05/2020] [Accepted: 09/29/2020] [Indexed: 12/11/2022] Open
Abstract
Pathogens that infect plants and animals use a diverse arsenal of effector proteins to suppress the host immune system and promote infection. Identification of effectors in pathogen genomes is foundational to understanding mechanisms of pathogenesis, for monitoring field pathogen populations, and for breeding disease resistance. We identified candidate effectors from the lettuce downy mildew pathogen Bremia lactucae by searching the predicted proteome for the WY domain, a structural fold found in effectors that has been implicated in immune suppression as well as effector recognition by host resistance proteins. We predicted 55 WY domain containing proteins in the genome of B. lactucae and found substantial variation in both sequence and domain architecture. These candidate effectors exhibit several characteristics of pathogen effectors, including an N-terminal signal peptide, lineage specificity, and expression during infection. Unexpectedly, only a minority of B. lactucae WY effectors contain the canonical N-terminal RXLR motif, which is a conserved feature in the majority of cytoplasmic effectors reported in Phytophthora spp. Functional analysis of 21 effectors containing WY domains revealed 11 that elicited cell death on wild accessions and domesticated lettuce lines containing resistance genes, indicative of recognition of these effectors by the host immune system. Only two of the 11 recognized effectors contained the canonical RXLR motif, suggesting that there has been an evolutionary divergence in sequence motifs between genera; this has major consequences for robust effector prediction in oomycete pathogens.
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Affiliation(s)
- Kelsey J. Wood
- The Genome Center, University of California, Davis, Davis, California, United States of America
- Integrative Genetics & Genomics Graduate Group, University of California, Davis, Davis, California, United States of America
| | - Munir Nur
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | - Juliana Gil
- The Genome Center, University of California, Davis, Davis, California, United States of America
- Plant Pathology Graduate Group, University of California, Davis, Davis, California, United States of America
| | - Kyle Fletcher
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | | | - Dasan Gann
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | - Ayumi Gothberg
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | - Tina Khuu
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | - Jennifer Kopetzky
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | - Sanye Naqvi
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | - Archana Pandya
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | - Chi Zhang
- The Genome Center, University of California, Davis, Davis, California, United States of America
| | | | | | - Richard Michelmore
- The Genome Center, University of California, Davis, Davis, California, United States of America
- Departments of Plant Sciences, Molecular & Cellular Biology, Medical Microbiology & Immunology, University of California, Davis, Davis, California, United States of America
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Hughes G, Kopetzky J, McRoberts N. Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis. Entropy (Basel) 2020; 22:E938. [PMID: 33286707 PMCID: PMC7597205 DOI: 10.3390/e22090938] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 11/24/2022]
Abstract
The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple graphical format. The analysis is based on the observation that mutual information may be viewed both as a function of ROC curve summary statistics (sensitivity and specificity) and prevalence, and as a function of predictive values and prevalence. Mutual information calculated from a 2 × 2 prediction-realization table for a specified risk score threshold on an ROC curve is the same as the mutual information calculated at the same risk score threshold on a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold that maximizes mutual information is the same on both the ROC curve and the corresponding PROC curve. Phytopathologists and clinicians who have previously relied solely on ROC curve summary statistics when formulating risk thresholds for application in practical agricultural or clinical decision-making contexts are thus presented with a methodology that brings predictive values within the scope of that formulation.
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Affiliation(s)
- Gareth Hughes
- SRUC (Scotland’s Rural College), The King’s Buildings, Edinburgh EH9 3JG, UK
| | - Jennifer Kopetzky
- Department of Plant Pathology, University of California, Davis, CA 95616, USA; (J.K.); (N.M.)
| | - Neil McRoberts
- Department of Plant Pathology, University of California, Davis, CA 95616, USA; (J.K.); (N.M.)
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Samuel, Kopetzky J, Schwartz AA. TOTAL DEAFNESS DUE TO TRAUMA, WITH NORMAL STATIC LABYRINTHINE FINDINGS. REPORT OF TWO CASES. Laryngoscope 1923. [DOI: 10.1288/00005537-192305000-00002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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