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.
Highlights
Plants are sessile organisms with enormous evolutionary success (Bar-On et al, 2018)
Several models have achieved high accuracy values (Figure 3), demonstrating that the green fruits before herbivory (GB) and green fruits after herbivory (GA) groups are classified. These data indicate that the herbivore in the mature green fruit considerably modifies the electrome registered in the peduncle, demonstrating that the electrical signal generated in the fruit was transmitted to the rest of the aerial part of the plant
Plant herbivory caused by insects is responsible for large production losses worldwide (Arnemann et al, 2019)
Summary
Plants are sessile organisms with enormous evolutionary success (Bar-On et al, 2018). Since each plant module is able to sense local environmental changes, it is relevant that the many signals coming from the modules be integrated and interpreted effectively, enabling an efficient response in order to maintain the plant surviving as a whole This is possible through a complex network of short and long-distance signal transduction by hydraulic, chemical, and electrical signaling (Choi et al, 2016). The analysis of specific signals can underestimate the complexity of many overlapping electrical signals operating simultaneously, which creates a web of systemic information where multiple electrical signals are layered in time and space (De Loof, 2016; Souza et al, 2017) In this sense, it was proposed the term “plant electrome,” which is based on the general definition of “electrome” by De Loof (2016) as the totality of ionic currents of any living entity, from the cell up to the whole organism level. Plant electrome analysis has been shown as an efficient tool allowing diagnose of different plant stresses (both abiotic and biotic) since the electrome dynamic is sensitive to a plethora of stimuli, exhibiting specific pattern responses recognizable by standard time series analyzes techniques and, specially, by machine learning methods for data classification (Pereira et al, 2018; Simmi et al, 2020; Parise et al, 2021)
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