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A systematic sequencing-based approach for microbial contaminant detection and functional inference

BACKGROUND: Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are oft...

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Autores principales: Park, Sung-Joon, Onizuka, Satoru, Seki, Masahide, Suzuki, Yutaka, Iwata, Takanori, Nakai, Kenta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743104/
https://www.ncbi.nlm.nih.gov/pubmed/31519179
http://dx.doi.org/10.1186/s12915-019-0690-0
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author Park, Sung-Joon
Onizuka, Satoru
Seki, Masahide
Suzuki, Yutaka
Iwata, Takanori
Nakai, Kenta
author_facet Park, Sung-Joon
Onizuka, Satoru
Seki, Masahide
Suzuki, Yutaka
Iwata, Takanori
Nakai, Kenta
author_sort Park, Sung-Joon
collection PubMed
description BACKGROUND: Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed. RESULTS: We present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we estimate that 1000–100,000 contaminating microbial reads are detected per million host reads sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection of lymphoma cells. CONCLUSIONS: We provide a computational method for profiling microbial contamination on NGS data and suggest that sources of contamination in laboratory reagents and the experimental environment alter the molecular landscape of host cells leading to phenotypic changes. These findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12915-019-0690-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-67431042019-09-16 A systematic sequencing-based approach for microbial contaminant detection and functional inference Park, Sung-Joon Onizuka, Satoru Seki, Masahide Suzuki, Yutaka Iwata, Takanori Nakai, Kenta BMC Biol Methodology Article BACKGROUND: Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed. RESULTS: We present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we estimate that 1000–100,000 contaminating microbial reads are detected per million host reads sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection of lymphoma cells. CONCLUSIONS: We provide a computational method for profiling microbial contamination on NGS data and suggest that sources of contamination in laboratory reagents and the experimental environment alter the molecular landscape of host cells leading to phenotypic changes. These findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12915-019-0690-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-09-13 /pmc/articles/PMC6743104/ /pubmed/31519179 http://dx.doi.org/10.1186/s12915-019-0690-0 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Park, Sung-Joon
Onizuka, Satoru
Seki, Masahide
Suzuki, Yutaka
Iwata, Takanori
Nakai, Kenta
A systematic sequencing-based approach for microbial contaminant detection and functional inference
title A systematic sequencing-based approach for microbial contaminant detection and functional inference
title_full A systematic sequencing-based approach for microbial contaminant detection and functional inference
title_fullStr A systematic sequencing-based approach for microbial contaminant detection and functional inference
title_full_unstemmed A systematic sequencing-based approach for microbial contaminant detection and functional inference
title_short A systematic sequencing-based approach for microbial contaminant detection and functional inference
title_sort systematic sequencing-based approach for microbial contaminant detection and functional inference
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743104/
https://www.ncbi.nlm.nih.gov/pubmed/31519179
http://dx.doi.org/10.1186/s12915-019-0690-0
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