Use of matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry for bacterial monitoring in routine analysis at a drinking water treatment plant.

The study of bacterial communities throughout a drinking water treatment plant could provide a basic understanding of the effects of water processing that could then be used to improve the management of such plants. However, it is necessary to develop new analytical techniques that are sufficiently efficient, robust and fast for their effective and useful application in routine analysis. The aim of this study is therefore to assess the performance of matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), as compared to the PhenePlate™ system, for routine analysis in a drinking water treatment plant. To this end we studied a total of 277 colonies isolated in different seasons and from different points throughout the water treatment process, including: raw water, sand filtration, ultrafiltration, reverse osmosis and chlorination. The colonies were analysed using MALDI-TOF MS by direct deposition of the cells on the plate. The colonies were also biochemically fingerprinted using the PhenePlate™ system, clustered according to their similarity and a representative strain was selected for 16S rRNA gene sequencing and API gallery-based identification. The use of MALDI-TOF MS was reliable compared to the PhenePlate™ system and has the advantage of being faster and relatively cheap. Bacteria typing by MALDI-TOF MS is therefore a promising method to replace conventional routine phenotypic methods for the identification of bacteria in drinking water laboratories, thanks to its robustness. The major limiting factor for MALDI-TOF MS is the lack of a suitable mass spectra database; although each laboratory can develop its own library. This methodology will provide a tracking tool for companies to use in risk management and the detection of possible failures in both the water treatment processes and the distribution network, as well as offering characterization of the intrinsic microbial populations.