Time to act–assessing variations in qPCR analyses in biological nitrogen removal with examples from partial nitritation/anammox systems
•Assessment of variation in qPCR analysis of biological nitrogen removal microbiome.•Comparison of qPCR results between labs highlights the limitations with comparability.•The impact of DNA extraction was highest was followed by choice of primers.•The extent of variability between labs depends upon...
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Veröffentlicht in: | Water research (Oxford) 2021-02, Vol.190, p.116604-116604, Article 116604 |
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Sprache: | eng |
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Zusammenfassung: | •Assessment of variation in qPCR analysis of biological nitrogen removal microbiome.•Comparison of qPCR results between labs highlights the limitations with comparability.•The impact of DNA extraction was highest was followed by choice of primers.•The extent of variability between labs depends upon the sample type.•The extent of variability between labs differed for each target microorganism.
Quantitative PCR (qPCR) is broadly used as the gold standard to quantify microbial community fractions in environmental microbiology and biotechnology. Benchmarking efforts to ensure the comparability of qPCR data for environmental bioprocesses are still scarce. Also, for partial nitritation/anammox (PN/A) systems systematic investigations are still missing, rendering meta-analysis of reported trends and generic insights potentially precarious. We report a baseline investigation of the variability of qPCR-based analyses for microbial communities applied to PN/A systems. Round-robin testing was performed for three PN/A biomass samples in six laboratories, using the respective in-house DNA extraction and qPCR protocols. The concentration of extracted DNA was significantly different between labs, ranged between 2.7 and 328 ng mg−1 wet biomass. The variability among the qPCR abundance data of different labs was very high (1−7 log fold) but differed for different target microbial guilds. DNA extraction caused maximum variation (3–7 log fold), followed by the primers (1–3 log fold). These insights will guide environmental scientists and engineers as well as treatment plant operators in the interpretation of qPCR data.
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ISSN: | 0043-1354 1879-2448 |
DOI: | 10.1016/j.watres.2020.116604 |