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Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification

OBJECTIVE: To mine specific proteins and their protein-coding genes as suitable molecular biomarkers for the Burkholderia cepacia Complex (BCC) bacteria detection based on mega analysis of microbial proteomic and genomic data comparisons and to develop a real-time recombinase polymerase amplificatio...

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Autores principales: Fan, Yiling, Wang, Shujuan, Song, Minghui, Zhou, Liangliang, Liu, Chengzhi, Yang, Yan, Yu, Shuijing, Yang, Meicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539473/
https://www.ncbi.nlm.nih.gov/pubmed/37779692
http://dx.doi.org/10.3389/fmicb.2023.1270760
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author Fan, Yiling
Wang, Shujuan
Song, Minghui
Zhou, Liangliang
Liu, Chengzhi
Yang, Yan
Yu, Shuijing
Yang, Meicheng
author_facet Fan, Yiling
Wang, Shujuan
Song, Minghui
Zhou, Liangliang
Liu, Chengzhi
Yang, Yan
Yu, Shuijing
Yang, Meicheng
author_sort Fan, Yiling
collection PubMed
description OBJECTIVE: To mine specific proteins and their protein-coding genes as suitable molecular biomarkers for the Burkholderia cepacia Complex (BCC) bacteria detection based on mega analysis of microbial proteomic and genomic data comparisons and to develop a real-time recombinase polymerase amplification (rt-RPA) assay for rapid isothermal screening for pharmaceutical and personal care products. METHODS: We constructed an automatic screening framework based on Python to compare the microbial proteomes of 78 BCC strains and 263 non-BCC strains to identify BCC-specific protein sequences. In addition, the specific protein-coding gene and its core DNA sequence were validated in silico with a self-built genome database containing 158 thousand bacteria. The appropriate methodology for BCC detection using rt-RPA was evaluated by 58 strains in pure culture and 33 batches of artificially contaminated pharmaceutical and personal care products. RESULTS: We identified the protein SecY and its protein-coding gene secY through the automatic comparison framework. The virtual evaluation of the conserved region of the secY gene showed more than 99.8% specificity from the genome database, and it can distinguish all known BCC species from other bacteria by phylogenetic analysis. Furthermore, the detection limit of the rt-RPA assay targeting the secY gene was 5.6 × 10(2) CFU of BCC bacteria in pure culture or 1.2 pg of BCC bacteria genomic DNA within 30 min. It was validated to detect <1 CFU/portion of BCC bacteria from artificially contaminated samples after a pre-enrichment process. The relative trueness and sensitivity of the rt-RPA assay were 100% in practice compared to the reference methods. CONCLUSION: The automatic comparison framework for molecular biomarker mining is straightforward, universal, applicable, and efficient. Based on recognizing the BCC-specific protein SecY and its gene, we successfully established the rt-RPA assay for rapid detection in pharmaceutical and personal care products.
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spelling pubmed-105394732023-09-30 Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification Fan, Yiling Wang, Shujuan Song, Minghui Zhou, Liangliang Liu, Chengzhi Yang, Yan Yu, Shuijing Yang, Meicheng Front Microbiol Microbiology OBJECTIVE: To mine specific proteins and their protein-coding genes as suitable molecular biomarkers for the Burkholderia cepacia Complex (BCC) bacteria detection based on mega analysis of microbial proteomic and genomic data comparisons and to develop a real-time recombinase polymerase amplification (rt-RPA) assay for rapid isothermal screening for pharmaceutical and personal care products. METHODS: We constructed an automatic screening framework based on Python to compare the microbial proteomes of 78 BCC strains and 263 non-BCC strains to identify BCC-specific protein sequences. In addition, the specific protein-coding gene and its core DNA sequence were validated in silico with a self-built genome database containing 158 thousand bacteria. The appropriate methodology for BCC detection using rt-RPA was evaluated by 58 strains in pure culture and 33 batches of artificially contaminated pharmaceutical and personal care products. RESULTS: We identified the protein SecY and its protein-coding gene secY through the automatic comparison framework. The virtual evaluation of the conserved region of the secY gene showed more than 99.8% specificity from the genome database, and it can distinguish all known BCC species from other bacteria by phylogenetic analysis. Furthermore, the detection limit of the rt-RPA assay targeting the secY gene was 5.6 × 10(2) CFU of BCC bacteria in pure culture or 1.2 pg of BCC bacteria genomic DNA within 30 min. It was validated to detect <1 CFU/portion of BCC bacteria from artificially contaminated samples after a pre-enrichment process. The relative trueness and sensitivity of the rt-RPA assay were 100% in practice compared to the reference methods. CONCLUSION: The automatic comparison framework for molecular biomarker mining is straightforward, universal, applicable, and efficient. Based on recognizing the BCC-specific protein SecY and its gene, we successfully established the rt-RPA assay for rapid detection in pharmaceutical and personal care products. Frontiers Media S.A. 2023-09-14 /pmc/articles/PMC10539473/ /pubmed/37779692 http://dx.doi.org/10.3389/fmicb.2023.1270760 Text en Copyright © 2023 Fan, Wang, Song, Zhou, Liu, Yang, Yu and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Fan, Yiling
Wang, Shujuan
Song, Minghui
Zhou, Liangliang
Liu, Chengzhi
Yang, Yan
Yu, Shuijing
Yang, Meicheng
Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification
title Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification
title_full Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification
title_fullStr Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification
title_full_unstemmed Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification
title_short Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification
title_sort specific biomarker mining and rapid detection of burkholderia cepacia complex by recombinase polymerase amplification
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539473/
https://www.ncbi.nlm.nih.gov/pubmed/37779692
http://dx.doi.org/10.3389/fmicb.2023.1270760
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