Literature DB >> 31056322

Short communication: Bioinformatics-based mining of novel gene targets for identification of Cronobacter turicensis using PCR.

Qiming Chen1, Lu Jun1, Yongjun Qiu1, Liming Zhao2.   

Abstract

Cronobacter turicensis is a food-borne pathogen found in dairy products. It has been reported to cause bacteremia and enteritis in immunocompromised people, especially infants. Cronobacter turicensis has been isolated from various food sources, and contaminated powdered infant formula was found to be the most common source of infection among infants. Although some gene targets are used for the identification of C. turicensis, they are not specific at the species level. In this study, we analyzed the genome sequence of C. turicensis by bioinformatics and identified 13 specific gene targets. Primer sets targeting these sequences were designed and selected based on their specificity. Finally, primer set CT11, targeting gene CTU_19580, which codes for a hypothetical protein, was selected for development of the PCR assay because it alone produced positive PCR results for C. turicensis. To our knowledge, this is the first time that this gene target has been used to develop PCR detection assays for C. turicensis. The specific PCR assay had detection limits as low as 760 fg/µL for genomic DNA (approximately 158 copies/μL of DNA) and could detect C. turicensis in powdered infant formula with initial cell concentrations as low as 8.5 cfu per 10 g of powdered infant formula after 10 h of enrichment. Thus, this PCR assay is highly sensitive and can be used for rapid detection of C. turicensis.
Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cronobacter turicensis; PCR detection; bioinformatics; gene target

Mesh:

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Year:  2019        PMID: 31056322     DOI: 10.3168/jds.2018-15929

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  1 in total

1.  Prediction of PCR amplification from primer and template sequences using recurrent neural network.

Authors:  Kotetsu Kayama; Miyuki Kanno; Naoto Chisaki; Misaki Tanaka; Reika Yao; Kiwamu Hanazono; Gerry Amor Camer; Daiji Endoh
Journal:  Sci Rep       Date:  2021-04-05       Impact factor: 4.379

  1 in total

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