Project description:Global climate change and environmental pollution have imposed severe challenges on global food security by exacerbating abiotic stresses such as salinity. Rice, in particular, is susceptible to high soil salinity which not only restricts rice growth but also limits its yield and tolerance to other stress conditions. Thus, efforts have been made in the recent years to develop multi-stress-tolerant rice varieties. A notable example is the successful introgression of Submergence 1 (Sub1), Anaerobic Germination 1 (AG1), and Pi9 QTLs into Ciherang, a widely cultivated Indonesian rice variety, resulting in the development of CSA-Pi9 line exhibiting improved tolerance to submergence, salinity, and blast disease. In this study, we conducted a comprehensive proteomic investigation to elucidate the molecular basis of salinity tolerance of Dongjin (DJ), CSA, and CSA-Pi9 rice varieties using data-independent acquisition mass spectrometry (DIA-MS). Basal node tissues were collected from following salinity stress treatment, leading to the identification of 9,350 proteins, among which 3,016 were differentially modulated in response to salinity stress. Hierarchical clustering grouped these proteins into seven distinct clusters, reflecting diverse regulatory patterns across genotypes. Notably, 234 and 275 proteins were specifically upregulated in the CSA and CSA-Pi9 varieties, respectively. Functional enrichment analysis revealed that these proteins are predominantly involved in key metabolic pathways, including the tricarboxylic acid (TCA) cycle, mitochondrial electron transport, ion transport including Na⁺/K⁺ channels and H⁺-ATPases, cellular signaling, structural organization, and protein biosynthesis. Collectively, our findings provide proteome-wide insights into the complex regulatory networks that underlie salinity tolerance in rice, offering potential targets for future crop improvement strategies.
Project description:This dataset consists of in situ HiC-seq data from a human oesophageal adenocarcinoma cell line (OE19). In total, the dataset includes 2 biological replicated samples. The Hi-C sample and library preparations were generated using Arima-HiC Kit (A510008, ARIMA Genomics) and Arima Library Prep module (A303011, ARIMA Genomics), respectively.