scholarly journals Warm nights disrupt transcriptome rhythms in field-grown rice panicles

2021 ◽  
Vol 118 (25) ◽  
pp. e2025899118
Author(s):  
Jigar S. Desai ◽  
Lovely Mae F. Lawas ◽  
Ashlee M. Valente ◽  
Adam R. Leman ◽  
Dmitry O. Grinevich ◽  
...  

In rice, a small increase in nighttime temperature reduces grain yield and quality. How warm nighttime temperatures (WNT) produce these detrimental effects is not well understood, especially in field conditions where the typical day-to-night temperature fluctuation exceeds the mild increase in nighttime temperature. We observed genome-wide disruption of gene expression timing during the reproductive phase in field-grown rice panicles acclimated to 2 to 3 °C WNT. Transcripts previously identified as rhythmically expressed with a 24-h period and circadian-regulated transcripts were more sensitive to WNT than were nonrhythmic transcripts. The system-wide perturbations in transcript levels suggest that WNT disrupt the tight temporal coordination between internal molecular events and the environment, resulting in reduced productivity. We identified transcriptional regulators whose predicted targets are enriched for sensitivity to WNT. The affected transcripts and candidate regulators identified through our network analysis explain molecular mechanisms driving sensitivity to WNT and identify candidates that can be targeted to enhance tolerance to WNT.

2019 ◽  
Author(s):  
Jigar S. Desai ◽  
Lovely Mae F. Lawas ◽  
Ashlee M. Valente ◽  
Adam R. Leman ◽  
Dmitry O. Grinevich ◽  
...  

ABSTRACTIn rice, a small increase in nighttime temperatures reduces grain yield and quality. How warm nighttime temperatures (WNT) produce these detrimental effects is not well understood, especially in field conditions where the normal day to night temperature fluctuation exceeds the mild increase in nighttime temperature. We observed genome-wide disruption of gene expression timing during the reproductive phase on field-grown rice panicles acclimated to 2-3°C WNT. Rhythmically expressed transcripts were more sensitive to WNT than non-rhythmic transcripts. The system-wide transcriptional perturbations suggest that WNT disrupts the tight temporal coordination between internal molecular events and the environment resulting in reduced productivity. We identified transcriptional regulators whose predicted targets are enriched for sensitivity to WNT. The affected transcripts and candidate regulators identified through our network analysis explain molecular mechanisms driving sensitivity to WNT and candidates that can be targeted to enhance tolerance to WNT.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Koki Chigira ◽  
Natsuko Kojima ◽  
Masanori Yamasaki ◽  
Kenji Yano ◽  
Shunsuke Adachi ◽  
...  

AbstractLodging can reduce grain yield and quality in cereal crops including rice (Oryza sativa L.). To achieve both high biomass production and lodging resistance, the breeding of new cultivars with strong culms is a promising strategy. However, little is known about the diversity of culm strength in temperate japonica rice and underlying genetic factors. Here, we report a wide variation of culm strength among 135 temperate japonica cultivars, and some landraces having the strongest culms among these cultivars. The genome-wide association study (GWAS) identified 55 quantitative trait loci for culm strength and morphological traits, and revealed several candidate genes. The superior allele of candidate gene for culm thickness, OsRLCK191, was found in many landraces but had not inherited to the modern improved cultivars. Our results suggest that landraces of temperate japonica rice have unutilized superior alleles for contributing future improvements of culm strength and lodging resistance.


2011 ◽  
Vol 37 (10) ◽  
pp. 1809-1818
Author(s):  
Zi-Chang ZHANG ◽  
Hong-Wei LI ◽  
Xue-Ming WANG ◽  
Li-Min YUAN ◽  
Zhi-Qin WANG ◽  
...  

2010 ◽  
Vol 36 (11) ◽  
pp. 1877-1882
Author(s):  
Jiang-Ping REN ◽  
Na WANG ◽  
Xin-Guo WANG ◽  
Yong-Chun LI ◽  
Hong-Bin NIU ◽  
...  

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.


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