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Published on: April 2, 2013
Quantification of viable Legionella pneumophila cells using propidium monoazide combined with quantitative PCR
M Adela Yáñez1, Andreas Nocker, Elena Soria-Soria
1LABAQUA, SA, Alicante, Spain.
Abstract:
One of the greatest challenges of implementing fast molecular detection methods as part of Legionella surveillance systems is to limit detection to live cells. In this work, a protocol for sample treatment with propidium monoazide (PMA) in combination with quantitative PCR (qPCR) has been optimized and validated for L. pneumophila as an alternative of the currently used time-consuming culture method. Results from PMA-qPCR were compared with culture isolation and traditional qPCR. Under the conditions used, sample treatment with 50 μM PMA followed by 5 min of light exposure were assumed optimal resulting in an average reduction of 4.45 log units of the qPCR signal from heat-killed cells. When applied to environmental samples (including water from cooling water towers, hospitals, spas, hot water systems in hotels, and tap water), different degrees of correlations between the three methods were obtained which might be explained by different matrix properties, but also varying degrees of non-culturable cells. It was furthermore shown that PMA displayed substantially lower cytotoxicity with Legionella than the alternative dye ethidium monoazide (EMA) when exposing live cells to the dye followed by plate counting. This result confirmed the findings with other species that PMA is less membrane-permeant and more selective for the intact cells. In conclusion, PMA-qPCR is a promising technique for limiting detection to intact cells and makes Legionella surveillance data substantially more relevant in comparison with qPCR alone. For future research it would be desirable to increase the method's capacity to exclude signals from dead cells in difficult matrices or samples containing high numbers of dead cells.
Insights
Propidium monoazide quantitative PCR (PMA-qPCR) effectively distinguishes live Legionella bacteria from dead ones. This method offers a faster, more relevant alternative to traditional culture methods for Legionella surveillance.
Area of Science:
- Microbiology
- Molecular Biology
- Environmental Science
Background:
- Accurate Legionella surveillance requires distinguishing live bacteria from dead ones, a challenge for rapid molecular methods.
- Current culture-based methods for Legionella detection are time-consuming.
- Quantitative PCR (qPCR) detects DNA, but cannot differentiate between live and dead cells.
Purpose of the Study:
- To optimize and validate a propidium monoazide (PMA) and quantitative PCR (qPCR) protocol for detecting live Legionella pneumophila.
- To compare the efficacy of PMA-qPCR with traditional culture isolation and standard qPCR.
- To assess the suitability of PMA-qPCR for environmental Legionella surveillance.
Main Methods:
- Optimization of sample treatment with propidium monoazide (PMA) and light exposure.
- Validation of the PMA-qPCR protocol using heat-killed and live Legionella pneumophila.
- Comparison of PMA-qPCR results with culture isolation and traditional qPCR on various environmental water samples.
- Assessment of PMA cytotoxicity compared to ethidium monoazide (EMA) using plate counting.
Main Results:
- Optimized conditions (50 μM PMA, 5 min light exposure) reduced qPCR signal from dead cells by an average of 4.45 log units.
- Correlations between PMA-qPCR, culture, and qPCR varied across environmental samples, likely due to matrix effects and non-culturable cells.
- PMA demonstrated significantly lower cytotoxicity to live Legionella compared to EMA.
- PMA-qPCR showed higher selectivity for intact cells, confirming its reduced membrane permeability.
Conclusions:
- PMA-qPCR is a promising method for selective detection of live Legionella, enhancing the relevance of surveillance data compared to qPCR alone.
- The optimized PMA-qPCR protocol offers a viable, faster alternative to culture-based methods for Legionella surveillance.
- Further research is needed to improve dead cell signal exclusion in challenging matrices or samples with high dead cell loads.
