Yang Lv

Elucidating the evolutionary trajectories and genetic basis of critical agronomic traits in regional rice germplasm is paramount for discovering elite allelic variations for crop improvement. Here, we systematically characterized a panel of 109 rice accessions from Zhejiang Province through whole-genome resequencing (~10× coverage) coupled with two years of rigorous field phenotypic evaluations. A total of 4,753,071 high-quality genomic variants, including 4,147,316 SNPs, were identified across the genome. Population structure and evolutionary analyses revealed sharp genetic differentiation at the subspecies level, partitioning the panel into distinct indica and japonica clusters accompanied by intricate subpopulation stratification and historical gene flow. Through a joint scanning of the fixation index (Fst) and nucleotide diversity (Pi) ratios, three prominent selective sweep regions (qSS1, qSS10, and qSS12) driving subspecific differentiation were captured on chromosomes 1, 10, and 12. Notably, the qSS12 locus harbors the sucrose transporter gene OsSUT2, indicating that carbohydrate transport and energy metabolism served as core genomic targets driving the indica–japonica divergence. Furthermore, genome-wide association studies (GWAS) successfully mapped 9 significant loci modulating heading date, effective tiller number, and grain size. Subsequent gene-based haplotype analyses within these target intervals pinpointed elite allelic variations in core candidate genes, including OsSPX1 (phosphate homeostasis, 1000-grain weight), Chl9 (chlorophyll synthesis, grain width), and OsCER1 (wax biosynthesis, panicle length). Collectively, this study deciphers the genomic landscape and subspecies differentiation patterns of Zhejiang rice germplasm, providing pivotal molecular targets and invaluable genomic resources for germplasm conservation and precision molecular breeding.

Egyptian rice landraces represent a unique genetic reservoir shaped by arid environments, yet their genomic and transcriptional response to salt stress remains largely unexplored. Here, we integrated genomic, transcriptomic, and population genetic analyses to systematically unravel the mechanisms of salt tolerance in this vital germplasm. Resequencing 56 Egyptian accessions uncovered a treasure trove of genetic variation, including 18,204 novel SNPs. An expanded GWAS on 258 accessions discovered 17 novel loci for salt tolerance. Parallel RNA-Seq analysis of a salt-tolerant-susceptible pair (Giza 176 vs. 9311) under stress delineated a defense network centered on phenylpropanoid and lipid metabolic pathways in the tolerant genotype. The power of our integrated approach was exemplified by the convergent identification of ONAC063, where GWAS loci, transcriptional responsiveness, and haplotype-phenotype association collectively validated its role. Furthermore, selection sweep analysis highlighted 62 candidate genes under divergent selection. Our study not only positions Egyptian rice as a key resource for allele mining but also establishes a robust multi-omics pipeline for bridging genetic diversity with complex traits, accelerating the discovery of functional genes for breeding climate-resilient crops.

Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue and discover cell developmental processes. In this study, we evaluated the effects of sample size (i.e., cell number) on the outcome of single-cell transcriptome analysis by sampling different numbers of cells from a pool of ~57,000 Arabidopsis thaliana root cells integrated from five published studies. Our results indicated that the most significant principal components could be achieved when 20,000–30,000 cells were sampled, a relatively high reliability of cell clustering could be achieved by using ~20,000 cells with little further improvement by using more cells, 96% of the differentially expressed genes could be successfully identified with no more than 20,000 cells, and a relatively stable pseudotime could be estimated in the subsample with 5000 cells. Finally, our results provide a general guide for optimizing sample size to be used in plant scRNA-seq studies.