Development of a multiplex droplet digital PCR assay for simultaneous detection and quantification of Escherichia coli, E. marmotae, and E. Ruysiae in water samples.

Absolute quantification Digital PCR Fecal indicator organisms Naturalized Escherichia Quantitation methods Water microbial quality

Journal

Journal of microbiological methods
ISSN: 1872-8359
Titre abrégé: J Microbiol Methods
Pays: Netherlands
ID NLM: 8306883

Informations de publication

Date de publication:
01 Mar 2024
Historique:
received: 03 12 2023
revised: 28 02 2024
accepted: 29 02 2024
medline: 4 3 2024
pubmed: 4 3 2024
entrez: 3 3 2024
Statut: aheadofprint

Résumé

Escherichia coli are widely used by water quality managers as Fecal Indicator Bacteria, but current quantification methods do not differentiate them from benign, environmental Escherichia species such as E. marmotae (formerly named cryptic clade V) or E. ruysiae (cryptic clades III and IV). Reliable and specific techniques for their identification are required to avoid confounding microbial water quality assessments. To address this, a multiplex droplet digital PCR (ddPCR) assay targeting lipB (E. coli and E. ruysiae) and bglC (E. marmotae) was designed. The ddPCR performance was assessed using in silico analysis; genomic DNA from 40 local, international, and reference strains of target and non-target coliforms; and spiked water samples in a range relevant to water quality managers (1 to 1000 cells/100 mL). Results were compared to an analogous quantitative PCR (qPCR) and the Colilert method. Both PCR assays showed excellent sensitivity with a limit of detection of 0.05 pg/μL and 0.005 pg/μl for ddPCR and qPCR respectively, and of quantification of 0.5 pg/μL of genomic DNA. The ddPCR allowed differentiation and quantification of three Escherichia species per run by amplitude multiplexing and showed a high concordance with concentrations measured by Colilert once proportional bias was accounted for. In silico specificity testing underlined the possibility to further detect and distinguish Escherichia cryptic clade VI. Finally, the applicability of the ddPCR was successfully tested on environmental water samples where E. marmotae and E. ruysiae potentially confound E. coli counts based on the Most Probable Number method, highlighting the utility of this novel ddPCR as an efficient and rapid discriminatory test to improve water quality assessments.

Identifiants

pubmed: 38432551
pii: S0167-7012(24)00021-6
doi: 10.1016/j.mimet.2024.106909
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

106909

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Auteurs

Marie Moinet (M)

AgResearch Ltd., Food Systems Integrity Team, Hopkirk Research Institute, Tennent Drive, 4442 Palmerston North, New Zealand.

Rose M Collis (RM)

AgResearch Ltd., Food Systems Integrity Team, Hopkirk Research Institute, Tennent Drive, 4442 Palmerston North, New Zealand. Electronic address: Rose.Collis@AgResearch.co.nz.

Lynn Rogers (L)

AgResearch Ltd., Food Systems Integrity Team, Hopkirk Research Institute, Tennent Drive, 4442 Palmerston North, New Zealand; Massey University, (m)EpiLab, School of Veterinary Science, Hopkirk Research Institute, Tennent Drive, 4442 Palmerston North, New Zealand. Electronic address: Lynn.Rogers@agresearch.co.nz.

Megan L Devane (ML)

Institute of Environmental Science and Research Ltd. (ESR), 27 Creyke Rd, Ilam, 8041 Christchurch, New Zealand. Electronic address: Megan.Devane@esr.cri.nz.

Patrick J Biggs (PJ)

Massey University, (m)EpiLab, School of Veterinary Science, Hopkirk Research Institute, Tennent Drive, 4442 Palmerston North, New Zealand; Massey University, School of Natural Sciences, Tennent Drive, 4442 Palmerston North, New Zealand. Electronic address: P.Biggs@massey.ac.nz.

Rebecca Stott (R)

National Institute of Water and Atmospheric Research (NIWA), Gate 10 Silverdale Road, Hillcrest, 3216 Hamilton, New Zealand. Electronic address: Rebecca.Stott@niwa.co.nz.

Jonathan Marshall (J)

Massey University, School of Mathematical and Computational Sciences, Tennent Drive, 4442 Palmerston North, New Zealand. Electronic address: J.C.Marshall@massey.ac.nz.

Richard Muirhead (R)

AgResearch Ltd., Ethical Agriculture, Invermay, 176 Puddle Alley, 9092, Mosgiel, New Zealand. Electronic address: Richard.Muirhead@AgResearch.co.nz.

Adrian L Cookson (AL)

AgResearch Ltd., Food Systems Integrity Team, Hopkirk Research Institute, Tennent Drive, 4442 Palmerston North, New Zealand; Massey University, (m)EpiLab, School of Veterinary Science, Hopkirk Research Institute, Tennent Drive, 4442 Palmerston North, New Zealand. Electronic address: Adrian.Cookson@AgResearch.co.nz.

Classifications MeSH