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Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing

BACKGROUND: Vector-borne diseases (VBDs) impact both human and veterinary medicine and pose special public health challenges. The main bacterial vector-borne pathogens (VBPs) of importance in veterinary medicine include Anaplasma spp., Bartonella spp., Ehrlichia spp., and Spotted Fever Group Rickett...

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Autores principales: Vasconcelos, Elton J. R., Roy, Chayan, Geiger, Joseph A., Oney, Kristina M., Koo, Melody, Ren, Songyang, Oakley, Brian B., Diniz, Pedro Paulo V. P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325813/
https://www.ncbi.nlm.nih.gov/pubmed/34332568
http://dx.doi.org/10.1186/s12917-021-02969-9
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author Vasconcelos, Elton J. R.
Roy, Chayan
Geiger, Joseph A.
Oney, Kristina M.
Koo, Melody
Ren, Songyang
Oakley, Brian B.
Diniz, Pedro Paulo V. P.
author_facet Vasconcelos, Elton J. R.
Roy, Chayan
Geiger, Joseph A.
Oney, Kristina M.
Koo, Melody
Ren, Songyang
Oakley, Brian B.
Diniz, Pedro Paulo V. P.
author_sort Vasconcelos, Elton J. R.
collection PubMed
description BACKGROUND: Vector-borne diseases (VBDs) impact both human and veterinary medicine and pose special public health challenges. The main bacterial vector-borne pathogens (VBPs) of importance in veterinary medicine include Anaplasma spp., Bartonella spp., Ehrlichia spp., and Spotted Fever Group Rickettsia. Taxon-targeted PCR assays are the current gold standard for VBP diagnostics but limitations on the detection of genetically diverse organisms support a novel approach for broader detection of VBPs. We present a methodology for genetic characterization of VBPs using Next-Generation Sequencing (NGS) and computational approaches. A major advantage of NGS is the ability to detect multiple organisms present in the same clinical sample in an unsupervised (i.e. non-targeted) and semi-quantitative way. The Standard Operating Procedure (SOP) presented here combines industry-standard microbiome analysis tools with our ad-hoc bioinformatic scripts to form a complete analysis pipeline accessible to veterinary scientists and freely available for download and use at https://github.com/eltonjrv/microbiome.westernu/tree/SOP. RESULTS: We tested and validated our SOP by mimicking single, double, and triple infections in genomic canine DNA using serial dilutions of plasmids containing the entire 16 S rRNA gene sequence of (A) phagocytophilum, (B) v. berkhoffii, and E. canis. NGS with broad-range 16 S rRNA primers followed by our bioinformatics SOP was capable of detecting these pathogens in biological replicates of different dilutions. These results illustrate the ability of NGS to detect and genetically characterize multi-infections with different amounts of pathogens in a single sample. CONCLUSIONS: Bloodborne microbiomics & metagenomics approaches may help expand the molecular diagnostic toolbox in veterinary and human medicine. In this paper, we present both in vitro and in silico detailed protocols that can be combined into a single workflow that may provide a significant improvement in VBP diagnostics and also facilitate future applications of microbiome research in veterinary medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12917-021-02969-9.
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spelling pubmed-83258132021-08-02 Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing Vasconcelos, Elton J. R. Roy, Chayan Geiger, Joseph A. Oney, Kristina M. Koo, Melody Ren, Songyang Oakley, Brian B. Diniz, Pedro Paulo V. P. BMC Vet Res Methodology Article BACKGROUND: Vector-borne diseases (VBDs) impact both human and veterinary medicine and pose special public health challenges. The main bacterial vector-borne pathogens (VBPs) of importance in veterinary medicine include Anaplasma spp., Bartonella spp., Ehrlichia spp., and Spotted Fever Group Rickettsia. Taxon-targeted PCR assays are the current gold standard for VBP diagnostics but limitations on the detection of genetically diverse organisms support a novel approach for broader detection of VBPs. We present a methodology for genetic characterization of VBPs using Next-Generation Sequencing (NGS) and computational approaches. A major advantage of NGS is the ability to detect multiple organisms present in the same clinical sample in an unsupervised (i.e. non-targeted) and semi-quantitative way. The Standard Operating Procedure (SOP) presented here combines industry-standard microbiome analysis tools with our ad-hoc bioinformatic scripts to form a complete analysis pipeline accessible to veterinary scientists and freely available for download and use at https://github.com/eltonjrv/microbiome.westernu/tree/SOP. RESULTS: We tested and validated our SOP by mimicking single, double, and triple infections in genomic canine DNA using serial dilutions of plasmids containing the entire 16 S rRNA gene sequence of (A) phagocytophilum, (B) v. berkhoffii, and E. canis. NGS with broad-range 16 S rRNA primers followed by our bioinformatics SOP was capable of detecting these pathogens in biological replicates of different dilutions. These results illustrate the ability of NGS to detect and genetically characterize multi-infections with different amounts of pathogens in a single sample. CONCLUSIONS: Bloodborne microbiomics & metagenomics approaches may help expand the molecular diagnostic toolbox in veterinary and human medicine. In this paper, we present both in vitro and in silico detailed protocols that can be combined into a single workflow that may provide a significant improvement in VBP diagnostics and also facilitate future applications of microbiome research in veterinary medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12917-021-02969-9. BioMed Central 2021-07-31 /pmc/articles/PMC8325813/ /pubmed/34332568 http://dx.doi.org/10.1186/s12917-021-02969-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology Article
Vasconcelos, Elton J. R.
Roy, Chayan
Geiger, Joseph A.
Oney, Kristina M.
Koo, Melody
Ren, Songyang
Oakley, Brian B.
Diniz, Pedro Paulo V. P.
Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing
title Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing
title_full Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing
title_fullStr Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing
title_full_unstemmed Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing
title_short Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing
title_sort data analysis workflow for the detection of canine vector-borne pathogens using 16 s rrna next-generation sequencing
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325813/
https://www.ncbi.nlm.nih.gov/pubmed/34332568
http://dx.doi.org/10.1186/s12917-021-02969-9
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