Increased grain yield will be critical to meet the growing demand for food, and could be achieved by delaying crop senescence. Here, via quantitative trait locus (QTL) mapping, we uncover the genetic basis underlying distinct life cycles and senescence patterns of two rice subspecies, indica and japonica. Promoter variations in the Stay-Green (OsSGR) gene encoding the chlorophyll-degrading Mg⁺⁺-dechelatase were found to trigger higher and earlier induction of OsSGR in indica, which accelerated senescence of indica rice cultivars. The indica-type promoter is present in a progenitor subspecies O. nivara and thus was acquired early during the evolution of rapid cycling trait in rice subspecies. Japonica OsSGR alleles introgressed into indica-type cultivars in Korean rice fields lead to delayed senescence, with increased grain yield and enhanced photosynthetic competence. Taken together, these data establish that naturally occurring OsSGR promoter and related lifespan variations can be exploited in breeding programs to augment rice yield.

Resveratrol has gained widespread scientific attention due to its ability to significantly extend the lifespan of yeast. However, research on the efficacy of resveratrol on lifespan extension has yielded mixed results in animal studies, making resveratrol a contentious subject. In our previous work, we reported that transgenic resveratrol rice DJ526 showed unusual health benefits beyond expectations. In this work, we established a callus culture of resveratrol rice DJ526, which contains 180 times more resveratrol than the grain, and found that resveratrol rice callus significantly extended the median lifespan of Drosophila melanogaster by up to 50% compared to the control. The resveratrol rice callus also ameliorated age-dependent symptoms, including locomotive deterioration, body weight gain, eye degeneration, and neurodegeneration of D. melanogaster with age progression. Considering that resveratrol is the most preferred antiaging compound due to its superior safety and proven mechanism against many serious adult diseases, the outstanding efficacy of resveratrol on the longevity of wild-type animals could cast a light on the development of antiaging therapeutic agents.

Ilchan Song, Young Koung Lee, Jin Wook Kim, Seung-Won Lee, Se Ra Park, Hae Kyung Lee, Soyeon Oh, Kinarm Ko, Mi Kyung Kim, Soon Ju Park, Dae Heon Kim, Moon-Soo Kim, Do Sun Kim, and Kisung Ko. Mol. Cells 2021;44:770-9. https://doi.org/10.14348/molcells.2021.2002

The control of flowering time is a major contributing factor to the improvement of crop yield by optimizing plant growth in a crop cycle. Genetic variants that determine flowering time can provide insights into optimizing flowering time for higher yields and other beneficial traits in tomato crops. Here, we examined a collection of flowering time variants to assess their effects on biomass and total tomato yields. Five late flowering (lf), thirteen large plant (lp), and seven floral homeotic (fh) mutants were identified as flowering time variants that could be rearranged according to leaf production in the primary shoot meristem (PSM). A flowering time continuum of mutants was translated into a positive continuum of biomass yield with more leaves, branches, and floral organs. The flowering time continuum showed an optimal curve of fruit yield, indicating a certain late flowering time as optimal for fruit yield, with the yield gradually decreasing in both directions with earlier or later flowering times. We isolated lf1, lf10, lp22, and fh13 as high-yielding genotypes with optimal flowering time, showing a new balance between the vegetative and flowering phases of tomato. Additionally, lp8, fh8, and fh15 produced extremely high biomass in leaves, axillary shoots, and floral organs due to late flowering in shoot apices with additional production of floral organs and lateral shoot. Our new late-flowering variants provide new genetic resources that can be used to optimize crop yield by fine-tuning flowering time, and future molecular studies could be conducted by revisiting our yield model.