Direct detection of Listeria monocytogenes DNA amplification products with quartz crystal microbalances at elevated temperatures
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Publication Details
Author list: Wachiralurpan S., Chansiri K., Lieberzeit P.A.
Publisher: Elsevier
Publication year: 2020
Volume number: 308
ISSN: 0925-4005
Languages: English-Great Britain (EN-GB)
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Abstract
Current methods for identifying Listeria monocytogenes are both time and labor intensive. However, it is highly desirable to detect it rapidly and reliably to prevent and/or identify contamination of foodstuff. Herein we propose a mass-sensitive sensor operating at elevated temperatures, 50−55 °C, for that purpose. Sensitive and selective detection relies on distinguishing genes of genomic extract of L. monocytogenes. A thiol-modified ssDNA probe designed for virulence phosphatidylcholine-phospholipase C (plcB) immobilized on the Quartz Crystal Microbalance (QCM) serves as the recognition element. This hybridizes with synthetic Loop-mediated isothermal amplification (LAMP) products of target DNA on the active surface sensor. Discernible detection limits of approximately 3 × 10−1 to 3 × 100 CFU mL-1 of L. monocytogenes DMST 17303 gDNA were achieved. The QCMDNA sensor showed high sensitivity and selectivity for L. monocytogenes (100 %) with negligible interference by DNA of other foodborne pathogens, such as Salmonella Paratyphi A (24 %), Salmonella Weltevreden (24 %), Salmonella Typhi (16 %), Shigella boydii (22 %), and Shigella flexneri (13 %). The temperature covered is in the range of 50–55 °C for immobilizing DNA probe and DNA target hybridization. Hybridization response times were within 10−30 min, demonstrated by saturation of the respective sensor responses. It turned out that sensitivity of the hybridization response increases up to two times by co-immobilizing the probe and L-cysteine. The latter acts as a spacer to increase probe-probe distance. This work demonstrates the potential of the QCM sensor technique at elevated temperatures as a sensor platform for further development of sensitive, specific and rapid detection of microbial DNA.
Keywords
autoregressive processes, Bayesian methods, Bayesian model averaging, compression algorithms, dymamic model averaging, finance