High MT-sHSP23.6 expression increases antioxidant system in ‘Micro-Tom’ tomato fruits during post-harvest hypoxia

Reissig, Gabriela Niemeyer; Posso, Douglas Antônio; Borella, Júnior; Silveira, R. Vieira Dutra da; Rombaldi, César Valmor; Bacarin, Marcos Antônio

doi:10.1016/j.scienta.2018.07.035

Cited by 9 publications

(6 citation statements)

References 48 publications

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“…Black * represents statistical difference between control and herbivory 24 h or control and herbivory 48 h. Red * represents statistical difference between control 24 and 48 h or herbivory 24 and 48 h. C, control treatment; H, herbivory treatment. different stimuli and able to adjust their metabolism (Pedreschi and Lurie, 2015;Reissig et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

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

Reissig

Oliveira

et al. 2021

Front. Sustain. Food Syst.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Reissig

Oliveira

et al. 2021

Front. Sustain. Food Syst.

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“…Micro-tom plants overexpressing sHSP23.6 maintained photosystem activity, the electron transport chain, and CO 2 assimilation when subjected to high-temperature cycles [ 204 ] and flooding [ 205 ]. In addition, these plants had a better modulation of the antioxidant system and higher lycopene content in fruits when subjected to hypoxia and moderate water deficit [ 206 , 207 ]. More recently, the same research group compared the effects of drought imposed during flowering on the photosynthetic activities of MT wildtype plants and those exhibiting silenced or overexpressed sHSP23.6 [ 208 ].…”

Section: Manipulating Mitochondrial Metabolism To Improve Plant Resis...mentioning

confidence: 99%

Metabolism and Signaling of Plant Mitochondria in Adaptation to Environmental Stresses

Barreto

Koltun

Nonato

et al. 2022

IJMS

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The interaction of mitochondria with cellular components evolved differently in plants and mammals; in plants, the organelle contains proteins such as ALTERNATIVE OXIDASES (AOXs), which, in conjunction with internal and external ALTERNATIVE NAD(P)H DEHYDROGENASES, allow canonical oxidative phosphorylation (OXPHOS) to be bypassed. Plant mitochondria also contain UNCOUPLING PROTEINS (UCPs) that bypass OXPHOS. Recent work revealed that OXPHOS bypass performed by AOXs and UCPs is linked with new mechanisms of mitochondrial retrograde signaling. AOX is functionally associated with the NO APICAL MERISTEM transcription factors, which mediate mitochondrial retrograde signaling, while UCP1 can regulate the plant oxygen-sensing mechanism via the PRT6 N-Degron. Here, we discuss the crosstalk or the independent action of AOXs and UCPs on mitochondrial retrograde signaling associated with abiotic stress responses. We also discuss how mitochondrial function and retrograde signaling mechanisms affect chloroplast function. Additionally, we discuss how mitochondrial inner membrane transporters can mediate mitochondrial communication with other organelles. Lastly, we review how mitochondrial metabolism can be used to improve crop resilience to environmental stresses. In this respect, we particularly focus on the contribution of Brazilian research groups to advances in the topic of mitochondrial metabolism and signaling.

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“…The tomato plants were grown in a greenhouse at the campus of Capão do Leão of the Federal University of Pelotas (31 • 52 ′ 32 ′′ S and 52 • 21 ′ 24 ′′ W, altitude 13 m). The average temperature in the greenhouse during the experimental period was 28.5 ± 12.9 • C and the irradiance, from natural light, was on average 800 µmol photons m −2 s −1 (Reissig et al, 2018).…”

Section: Plant Materials and Experimental Conditionsmentioning

confidence: 99%

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

Reissig

Oliveira

Costa

et al. 2021

Front. Sustain. Food Syst.

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The physiological processes underlying fruit ripening can lead to different electrical signatures at each ripening stage, making it possible to classify tomato fruit through the analysis of electrical signals. Here, the electrical activity of tomato fruit (Solanum lycopersicum var. cerasiforme) during ripening was investigated as tissue voltage variations, and Machine Learning (ML) techniques were used for the classification of different ripening stages. Tomato fruit was harvested at the mature green stage and placed in a Faraday's cage under laboratory-controlled conditions. Two electrodes per fruit were inserted 1 cm apart from each other. The measures were carried out continuously until the entire fruits reached the light red stage. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. Descriptive analysis from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis). Finally, ApEn, PCA1, PCA2, and PCA3 were obtained. These features were used in ML analyses for looking for classifiable patterns of the three different ripening stages: mature green, breaker, and light red. The results showed that it is possible to classify the ripening stages using the fruit's electrical activity. It was also observed, using precision, sensitivity, and F1-score techniques, that the breaker stage was the most classifiable among all stages. It was found a more accurate distinction between mature green × breaker than between breaker × light red. The ML techniques used seem to be a novel tool for classifying ripening stages. The features obtained from electrophysiological time series have the potential to be used for supervised training, being able to help in more accurate classification of fruit ripening stages.

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High MT-sHSP23.6 expression increases antioxidant system in ‘Micro-Tom’ tomato fruits during post-harvest hypoxia

Cited by 9 publications

References 48 publications

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

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

Metabolism and Signaling of Plant Mitochondria in Adaptation to Environmental Stresses

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

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