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Browsing by Author "Obata, Toshihiro"

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    Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice
    Bi, Ye; Yassue, Rafael Massahiro; Paul, Puneet; Dhatt, Balpreet Kaur; Sandhu, Jaspreet; Do, Phuc Thi; Walia, Harkamal; Obata, Toshihiro; Morota, Gota (Oxford University Press, 2023-05)
    The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature (HNT) conditions of genotypes, and metabolites and single-nucleotide polymorphisms (SNPs) to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and HNT conditions with high accuracy using random forest or extreme gradient boosting. Best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and HNT conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to SNPs, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of HNT responses and regression modeling of grain-size-related phenotypes in rice.
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    Vision, challenges and opportunities for a Plant Cell Atlas
    Jha, Suryatapa Ghosh; Borowsky, Alexander T.; Cole, Benjamin J.; Fahlgren, Noah; Farmer, Andrew; Huang, Shao-Shan Carol; Karia, Purva; Libault, Marc; Provart, Nicholas J.; Rice, Selena L.; Saura-Sanchez, Maite; Agarwal, Pinky; Ahkami, Amir H.; Anderton, Christopher R.; Briggs, Steven P.; Brophy, Jennifer An; Denolf, Peter; Di Costanzo, Luigi F.; Exposito-Alonso, Moises; Giacomello, Stefania; Gomez-Cano, Fabio; Kaufmann, Kerstin; Ko, Dae Kwani; Kumar, Sagar; Malkovskiy, Andrey; Nakayama, Naomi; Obata, Toshihiro; Otegui, Marisa S.; Palfalvi, Gergo; Quezada-Rodriguez, Elsa H.; Singh, Rajveer; Uhrig, R. Glen; Waese, Jamie; Van Wijk, Klaas; Wright, R. Clay; Ehrhardt, David W.; Birnbaum, Kenneth D.; Rhee, Seung Y. (eLife, 2021-09-07)
    With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.