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Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics
Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8888594/ https://www.ncbi.nlm.nih.gov/pubmed/35233021 http://dx.doi.org/10.1038/s41598-022-07260-x |
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author | Olausson, Josefin Brunet, Sofia Vracar, Diana Tian, Yarong Abrahamsson, Sanna Meghadri, Sri Harsha Sikora, Per Lind Karlberg, Maria Jakobsson, Hedvig E. Tang, Ka-Wei |
author_facet | Olausson, Josefin Brunet, Sofia Vracar, Diana Tian, Yarong Abrahamsson, Sanna Meghadri, Sri Harsha Sikora, Per Lind Karlberg, Maria Jakobsson, Hedvig E. Tang, Ka-Wei |
author_sort | Olausson, Josefin |
collection | PubMed |
description | Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare microbes and novel pathogens. However, several major hurdles remain, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. Twenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. The detection threshold for each sample was calculated by using the total leukocyte content in the sample and environmental contaminants found in the bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to the calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to the calculations. False positive pathogens of computational or environmental origin were readily identified, by using a commonly available cell control. For bacteria, additional filters including a comparison between classifiers removed the remaining false positives and alleviated pathogen identification. Here we show a generalizable method for identification of pathogen species using DNA metagenomic sequencing. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens requires multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads. |
format | Online Article Text |
id | pubmed-8888594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88885942022-03-03 Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics Olausson, Josefin Brunet, Sofia Vracar, Diana Tian, Yarong Abrahamsson, Sanna Meghadri, Sri Harsha Sikora, Per Lind Karlberg, Maria Jakobsson, Hedvig E. Tang, Ka-Wei Sci Rep Article Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare microbes and novel pathogens. However, several major hurdles remain, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. Twenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. The detection threshold for each sample was calculated by using the total leukocyte content in the sample and environmental contaminants found in the bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to the calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to the calculations. False positive pathogens of computational or environmental origin were readily identified, by using a commonly available cell control. For bacteria, additional filters including a comparison between classifiers removed the remaining false positives and alleviated pathogen identification. Here we show a generalizable method for identification of pathogen species using DNA metagenomic sequencing. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens requires multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads. Nature Publishing Group UK 2022-03-01 /pmc/articles/PMC8888594/ /pubmed/35233021 http://dx.doi.org/10.1038/s41598-022-07260-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Olausson, Josefin Brunet, Sofia Vracar, Diana Tian, Yarong Abrahamsson, Sanna Meghadri, Sri Harsha Sikora, Per Lind Karlberg, Maria Jakobsson, Hedvig E. Tang, Ka-Wei Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics |
title | Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics |
title_full | Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics |
title_fullStr | Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics |
title_full_unstemmed | Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics |
title_short | Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics |
title_sort | optimization of cerebrospinal fluid microbial dna metagenomic sequencing diagnostics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8888594/ https://www.ncbi.nlm.nih.gov/pubmed/35233021 http://dx.doi.org/10.1038/s41598-022-07260-x |
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