Application of untargeted metabolomics for the detection of pathogenic Naegleria fowleri in an operational drinking water distribution system

Abstract

Found in drinking water distribution systems (DWDSs), swimming pools, and recreational waters, N. fowleri, is the causative agent of primary amoebic meningoencephalitis (PAM). Although cases of N. fowleri infections are rare, the fatality is comparatively high (>95%) and surveillance is essential to minimize N. fowleri infections. However, conventional N. fowleri detection methods are less satisfying owing to their time-consuming and lab intensive characteristics as well as the lack of the ability to determine viability. As a result, an alternative detection approach capable of determining viability as well as species identification is required to better ensure public health. Based on our previous research focusing on distinguishing laboratory cultured N. fowleri from N. lovaniensis and N. italica, this study applies untargeted metabolomics methods to field samples from operational DWDSs. A list of diagnostic features was found to preliminarily discriminate the N. fowleri positive from N. fowleri negative and N. lovaniensis positive field samples with satisfying predictive accuracy. The results outlined in this manuscript further validate and improve the metabolite-based N. fowleri detection approach, potentially aiding water utilities in the detection and management of N. fowleri in drinking water.