A Combination of Metabolomics and Machine Learning Results in the Identification of a New Cyst Nematode Hatching Factor.

Potato Cyst Nematodes (PCNs) are an economically important pest for potato growers. A crucial event in the life cycle of the nematode is hatching, after which the juvenile will move toward the host root and infect it. The hatching of PCNs is induced by known and unknown compounds in the root exudates of host plant species, called hatching factors (HFs, induce hatching independently), such as solanoeclepin A (solA), or hatching stimulants (HSs, enhance hatching activity of HFs). Unraveling the identity of unknown HSs and HFs and their natural variation is important for the selection of cultivars that produce low amounts of HFs and HSs, thus contributing to more sustainable agriculture. In this study, we used a new approach aimed at the identification of new HFs and HSs for PCNs in potato. Hereto, root exudates of a series of different potato cultivars were analyzed for their PCN hatch-inducing activity and their solA content. The exudates were also analyzed using untargeted metabolomics, and subsequently the data were integrated using machine learning, specifically random forest feature selection, and Pearson's correlation testing. As expected, solA highly correlates with hatching. Furthermore, this resulted in the discovery of a number of metabolite features present in the root exudate that correlate with hatching and solA content, and one of these is a compound of