Fruit Herbivory Alters Plant Electrome: Evidence for Fruit-Shoot Long-Distance Electrical Signaling in Tomato Plants

Abstract

The electrical activity of tomato plants subjected to fruit herbivory was investigated. The study aimed to test the hypothesis that tomato fruits transmit long-distance electrical signals to the shoot when subjected to herbivory. For such, time series classification by machine learning techniques and analyses related to the oxidative response were employed. Tomato plants (cv. “Micro-Tom”) were placed into a Faraday's cage and an electrode pair was inserted in the fruit's peduncle. Helicoverpa armigera caterpillars were placed on the fruit (either green and ripe) for 24 h. The time series were recorded before and after the fruit's exposure of the caterpillars. The plant material for chemical analyses was collected 24 and 48 h after the end of the acquisition of electrophysiological data. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. The following features from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis): average, maximum and minimum value, variance, skewness, and kurtosis. Additionally, these features were used in Machine Learning (ML) analyses for looking for classifiable patterns between tomato plants before and after fruit herbivory. Also, we compared the electrome before and after herbivory in the green and ripe fruits. To evaluate an oxidative response in different organs, hydrogen peroxide, superoxide anion, catalase, ascorbate peroxidase, guaiacol peroxidase, and superoxide dismutase activity were evaluated in fruit and leaves. The results show with 90% of accuracy that the electrome registered in the fruit's peduncle before herbivory is different from the electrome during predation on the fruits. Interestingly, there was also a sharp difference in the electrome of the green and ripe fruits' peduncles before, but not during, the herbivory, which demonstrates that the signals generated by the herbivory stand over the others. Biochemical analysis showed that herbivory in the fruit triggered an oxidative response in other parts of the plant. Here, we demonstrate that the fruit perceives biotic stimuli and transmits electrical signals to the shoot of tomato plants. This study raises new possibilities for studies involving electrical signals in signaling and systemic response, as well as for the applicability of ML to classify electrophysiological data and its use in early diagnosis.

Systemic Signals Induced by Single and Combined Abiotic Stimuli in Common Bean Plants

“…In addition, each stimulus can affect the plant in a specific way and trigger different responses in each module [ 3 ]. Therefore, the focus of recent research has been understanding how plants respond to different stimuli, which signals are activated, and if there is a pattern that can be identified [ 15 , 16 , 17 , 18 , 27 ].…”

“…The TDAF method, developed by our research group, aims to demonstrate the dynamics of the electrome run characteristics (features). The latest studies published in the literature suggest that 3 min long measurements already contain enough information to identify behaviors in the plant electrome [ 16 , 17 , 18 ]. Thus, the TDAF assumes the use of the smallest possible unit of the analyzed series, considering the measurement equipment, and uses the dispersion theory [ 51 ] to analyze the dataset as a whole.…”

“…Recent studies analyzing plant electrome [ 1 , 13 , 14 , 15 , 16 , 17 ] revealed its potential as a tool for early stress diagnosis in plants since the electrome exhibits specific patterns of responses that function as a classifiable electrical signature by standard time series analysis and machine learning techniques [ 15 , 16 , 17 , 18 , 19 ].…”

Machine Learning for Automatic Classification of Tomato Ripening Stages Using Electrophysiological Recordings

“…Several studies in the field of plant electrophysiology are demonstrating the potential practical uses of plant electrical signaling (Reissig et al, 2021). For instance, Tran et al (2019) proposed the electrophysiological evaluation of tomato plants status using supervised machine learning.…”

“…Due to the high complexity of a typical plant electrome profile, ML techniques emerge as a powerful tool for data classification of plant electrophysiological recordings (Chatterjee et al, 2015;Pereira et al, 2018;Tran et al, 2019;Simmi et al, 2020;Parise et al, 2021;Reissig et al, 2021). ML is already being used in some potential applications in fruit ripening, but mostly in studies using images for training and ML classification (Taghadomi-Saberi et al, 2018;Vaviya et al, 2019;Alam Siddiquee et al, 2020;Cho et al, 2020).…”