Inaba, S. | Search bioRxiv

bioRxiv2025

Glucosylation of Endogenous Haustorium-inducing factors Underpins Kin Avoidance in Parasitic Plants

Parasitic plants rarely attack themselves, suggesting the activity of a kin-avoidance mechanism. In the root parasitic plant Phtheirospermum japonicum, prehaustorium formation is triggered by host-secreted haustorium-inducing factors (HIFs), but it is unresponsive to its own root exudates. Here we report the identification of the spontaneous prehaustorium 1 (spoh1) mutant, which forms prehaustoria without external HIFs. spoh1 harbors a point mutation in the gene encoding UDP-glycosyltransferase GT72B1, an enzyme that glucosylates and thereby inactivates phenolic HIFs. Notably, PjGT72B1 possesses a different substrate specificity from its ortholog of the host Arabidopsis. Introduction of PjGT72B1 in Arabidopsis reduced HIF activity, indicating that HIF glucosylation regulates haustorium induction by hosts. Our findings suggest that parasitic plants have evolved kin-avoidance mechanisms through the glucosylation of endogenous HIFs.

plant biology↗

bioRxiv2023

Automatic quantification of disgust taste reactivity in mice using machine learning

Disgust, a primary negative emotion, plays a vital role in protecting organisms from intoxication and infection. In rodents, this emotion has been quantified by measuring the specific reactions elicited by exposure to unpleasant tastes. These reactions were captured on video and manually analyzed, a process that required considerable time and effort. Here we developed a method to automatically count disgust reactions in mice by using machine learning. The disgust reactions were automatically tracked using DeepLabCut as the coordinates of the nose and both front and rear paws. The automated tracking data were split into test and training data, and the latter were combined with manually labeled data on whether a disgust reaction was present and, if so, which type of disgust reaction was present. Then, a random forest classifier was constructed, and the performance of the classifier was evaluated in the test dataset. The total number of disgust reactions estimated by the classifier highly correlated with those counted manually (Pearsons r = 0.98). The present method will decrease the time and effort required to analyze disgust reactions, thus facilitating the implementation of the taste reactivity test in large-scale screening and long-term experiments that necessitate quantifying a substantial number of disgust reactions.

bioinformatics↗