Search Results (2)

Recycling of unnecessary or dysfunctional cellular structures through autophagy plays a critical role in cellular homeostasis and environmental resilience. Therefore, the autophagy trait may have been unintentionally selected in wheat breeding programs for higher yields in arid climates. This hypothesis was tested by measuring the response of three common autophagy markers, ATG7, ATG8, and NBR1, to a heat wave under reduced soil moisture content in 16 genetically diverse spring wheat landraces originating from different geographical locations. We observed in the greenhouse trials that ATG8 and NBR1 exhibited genotype-specific responses to a 1 h, 40 °C heat wave, while ATG7 did not show a consistent response. Three genotypes from Uruguay, Mozambique, and Afghanistan showed a pattern consistent with higher autophagic activity: decreased or stable abundance of both ATG8 and NBR1 proteins, coupled with increased transcription of ATG8 and NBR1. In contrast, three genotypes from Pakistan, Ethiopia, and Egypt exhibited elevated ATG8 protein levels alongside reduced or unaltered ATG8 transcript levels, indicating a potential suppression or no change in autophagic activity. Principal component analysis demonstrated a correlation between lower abundance of ATG8 and NBR1 proteins and higher yield in the field trials. We found that (i) the combination of heat and drought activated autophagy only in several genotypes, suggesting that despite being a resilience mechanism, autophagy is a heat-sensitive process; (ii) higher autophagic activity correlates positively with greater yield; (iii) the lack of autophagic activity in some high-yielding genotypes suggests contribution of alternative stress-resilient mechanisms; and (iv) enhanced autophagic activity in response to heat and drought was independently selected by wheat breeding programs in different geographic locations. Full article

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making. Full article