Abstract
The effects of radiation dosages on plant species are quantitatively presented as the lethal dose or the dose required for growth reduction in mutation breeding. However, lethal dose and growth reduction fail to provide dynamic growth behavior information such as growth rate after irradiation. Irradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms. Analysis of digital phenotyping data revealed unique growth patterns following treatments below LD50 value at 641 Gy. Plants treated with 100-Gy gamma irradiation showed almost identical growth pattern compared with wild type; the hormesis effect was observed >21 days after sowing. In 200 Gy-treated plants, a uniform growth pattern but smaller rosette areas than the wild type were seen (p < 0.05). The shift between vegetative and reproductive stages was not retarded by irradiation at 200 and 300 Gy although growth inhibition was detected under the same irradiation dose. Results were validated using 200 and 300 Gy doses with HTP in a separate study. To our knowledge, this is the first study to apply a HTP platform to measure and analyze the dosage effect of radiation in plants. The method enabled an in-depth analysis of growth patterns, which could not be detected previously due to a lack of time-series data. This information will improve our knowledge about the effects of radiation in model plant species and crops.
Highlights
Food security issues are emerging globally due to climate change, population growth, economic growth in developing countries, and increasing demand for bioenergy [1]
high-throughput phenotyping (HTP) platforms are able to collect multi-sensor data with limited effort from researchers [12]
The environmentally controlled platform avoids phenotypic variations caused by genotype–environment interactions and allows the study of a range of plants from model Arabidopsis specimens to field crops [8]
Summary
Food security issues are emerging globally due to climate change, population growth, economic growth in developing countries, and increasing demand for bioenergy [1]. Countries worldwide are developing genetic resources to strengthen intellectual property rights. Identifying beneficial a gene pools from plants with inter- and intra-species variation is key concept in plant breeding. Mutation breeding is a valuable alternative tool for addressing the diversity of problems associated with limited genetic resources [2]. Large mutant resources with various characteristics can be generated through mutagen treatment. Ionizing radiation is the most commonly used to generate useful mutations in plants because of the ease of treatment and high mutation frequency [3,4]
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