Time to act–assessing variations in qPCR analyses in biological nitrogen removal with examples from partial nitritation/anammox systems

Journal Article (2020)
Author(s)

Shelesh Agrawal (Technische Universität Darmstadt)

David G. Weissbrodt (TU Delft - BT/Environmental Biotechnology)

Medini Annavajhala (Columbia University)

Marlene Mark Jensen (Technical University of Denmark (DTU))

Jose Maria Carvajal Arroyo (Universiteit Gent)

George Wells (Northwestern University)

Kartik Chandran (Columbia University)

Siegfried E. Vlaeminck (Universiteit Antwerpen)

Akihiko Terada (Tokyo University of Agriculture and Technology)

undefined More Authors (External organisation)

DOI related publication
https://doi.org/10.1016/j.watres.2020.116604 Final published version
More Info
expand_more
Publication Year
2020
Language
English
Volume number
190
Article number
116604
Downloads counter
224

Abstract

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.