Weibo Xie

Systematic sequence analysis and identification of tissue-specific or stress-responsive genes of NAC transcription factor family in rice

NAM, ATAF, and CUC (NAC) transcription factors comprise a large plant-specific gene family and a few members of this family have been characterized for their roles in plant growth, development, and stress tolerance. In this study, systematic sequence analysis revealed 140 putative NAC or NAC-like genes (ONAC) in rice. Phylogenetic analysis suggested that NAC family can be divided into five groups (I–V). Among them, all the published development-related genes fell into group I, and all the published stress-related NAC genes fell into the group III (namely stress-responsive NAC genes, SNAC). Distinct compositions of the putative motifs were revealed on the basis of NAC protein sequences in rice. Most members contained a complete NAC DNA-binding domain and a variable transcriptional regulation domain. Sequence analysis, together with the organization of putative motifs, indicated distinct structures and potential diverse functions of NAC family in rice. Yeast one-hybrid analysis confirmed that 12 NAC proteins representing different motif compositions can bind the NAC core DNA-binding site. Real-time polymerase chain reaction (PCR) analysis revealed 12 genes with different tissue-specific (such as callus, root, stamen, or immature endosperm) expression patterns, suggesting that these genes may play crucial regulatory roles during growth and development of rice. The expression levels of this family were also checked under various abiotic stresses including drought, salinity, and low temperature. A preliminary check based on our microarray data suggested that more than 40 genes of this family were responsive to drought and/or salt stresses. Among them, 20 genes were further investigated for their stress responsiveness in detail by real-time PCR analysis. Most of these stress-responsive genes belonged to the group III (SNAC). Considering the fact that a very limited number of genes of the NAC family have been characterized, our data provide a very useful reference for functional analysis of this family in rice.

A dynamic gene expression atlas covering the entire life cycle of rice

Growth and development of a plant are controlled by programmed expression of suits of genes at the appropriate time, tissue and abundance. Although genomic resources have been developed rapidly in recent years in rice, a model plant for cereal genome research, data of gene expression profiling are still insufficient to relate the developmental processes to transcriptomes, leaving a large gap between the genome sequence and phenotype. In this study, we generated genome-wide expression data by hybridizing 190 Affymetrix GeneChip Rice Genome Arrays with RNA from 39 tissues collected throughout the life cycle of the rice plant from two varieties, Zhenshan 97 and Minghui 63. Analyses of the global transcriptomes revealed many interesting features of dynamic patterns of gene expression across the tissues and stages. In total, 38 793 probe sets were detected as expressed and 69% of the expressed transcripts showed significantly variable expression levels among tissues/organs. We found that similarity of transcriptomes among organs corresponded well to their developmental relatedness. About 5.2% of the expressed transcripts showed tissue-specific expression in one or both varieties and 22.7% of the transcripts exhibited constitutive expression including 19 genes with high and stable expression in all the tissues. This dataset provided a versatile resource for plant genomic research, which can be used for associating the transcriptomes to the developmental processes, understanding the regulatory network of these processes, tracing the expression profile of individual genes and identifying reference genes for quantitative expression analyses.

Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing

Bar-coded multiplexed sequencing approaches based on new-generation sequencing technologies provide capacity to sequence a mapping population in a single sequencing run. However, such approaches usually generate low-coverage and error-prone sequences for each line in a population. Thus, it is a significant challenge to genotype individual lines in a population for linkage map construction based on low-coverage sequences without the availability of high-quality genotype data of the parental lines. In this paper, we report a method for constructing ultrahigh-density linkage maps composed of high-quality single-nucleotide polymorphisms (SNPs) based on low-coverage sequences of recombinant inbred lines. First, all potential SNPs were identified to obtain drafts of parental genotypes using a maximum parsimonious inference of recombination, making maximum use of SNP information found in the entire population. Second, high-quality SNPs were identified by filtering out low-quality ones by permutations involving resampling of windows of SNPs followed by Bayesian inference. Third, lines in the mapping population were genotyped using the high-quality SNPs assisted by a hidden Markov model. With 0.05× genome sequence per line, an ultrahigh-density linkage map composed of bins of high-quality SNPs using 238 recombinant inbred lines derived from a cross between two rice varieties was constructed. Using this map, a quantitative trait locus for grain width (GW5) was localized to its presumed genomic region in a bin of 200 kb, confirming the accuracy and quality of the map. This method is generally applicable in genetic map construction with low-coverage sequence data.

Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism

Plant metabolites are important to world food security in terms of maintaining sustainable yield and providing food with enriched phytonutrients. Here we report comprehensive profiling of 840 metabolites and a further metabolic genome-wide association study based on ∼6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. We observed substantial heterogeneity in the natural variation of metabolites and their underlying genetic architectures among different subspecies of rice. Data mining identified 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof of concept, we functionally identified or annotated five candidate genes influencing metabolic traits. Our study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.

Gains in QTL Detection Using an Ultra-High Density SNP Map Based on Population Sequencing Relative to Traditional RFLP/SSR Markers

Huge efforts have been invested in the last two decades to dissect the genetic bases of complex traits including yields of many crop plants, through quantitative trait locus (QTL) analyses. However, almost all the studies were based on linkage maps constructed using low-throughput molecular markers, e.g. restriction fragment length polymorphisms (RFLPs) and simple sequence repeats (SSRs), thus are mostly of low density and not able to provide precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, we constructed an ultra-high density genetic map based on high quality single nucleotide polymorphisms (SNPs) from low-coverage sequences of a recombinant inbred line (RIL) population of rice, generated using new sequencing technology. The quality of the map was assessed by validating the positions of several cloned genes including GS3 and GW5/qSW5, two major QTLs for grain length and grain width respectively, and OsC1, a qualitative trait locus for pigmentation. In all the cases the loci could be precisely resolved to the bins where the genes are located, indicating high quality and accuracy of the map. The SNP map was used to perform QTL analysis for yield and three yield-component traits, number of tillers per plant, number of grains per panicle and grain weight, using data from field trials conducted over years, in comparison to QTL mapping based on RFLPs/SSRs. The SNP map detected more QTLs especially for grain weight, with precise map locations, demonstrating advantages in detecting power and resolution relative to the RFLP/SSR map. Thus this study provided an example for ultra-high density map construction using sequencing technology. Moreover, the results obtained are helpful for understanding the genetic bases of the yield traits and for fine mapping and cloning of QTLs.

Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice

Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

Genetic composition of yield heterosis in an elite rice hybrid

Heterosis refers to the superior performance of hybrids relative to the parents. Utilization of heterosis has contributed tremendously to the increased productivity in many crops for decades. Although there have been a range of studies on various aspects of heterosis, the key to understanding the biological mechanisms of heterotic performance in crop hybrids is the genetic basis, much of which is still uncharacterized. In this study, we dissected the genetic composition of yield and yield component traits using data of replicated field trials of an “immortalized F2” population derived from an elite rice hybrid. On the basis of an ultrahigh-density SNP bin map constructed with population sequencing, we calculated single-locus and epistatic genetic effects in the whole genome and identified components pertaining to heterosis of the hybrid. The results showed that the relative contributions of the genetic components varied with traits. Overdominance/pseudo-overdominance is the most important contributor to heterosis of yield, number of grains per panicle, and grain weight. Dominance × dominance interaction is important for heterosis of tillers per plant and grain weight and has roles in yield and grain number. Single-locus dominance has relatively small contributions in all of the traits. The results suggest that cumulative effects of these components may adequately explain the genetic basis of heterosis in the hybrid.

Chalk5 encodes a vacuolar H+-translocating pyrophosphatase influencing grain chalkiness in rice

Grain chalkiness is a highly undesirable quality trait in the marketing and consumption of rice grain. However, the molecular basis of this trait is poorly understood. Here we show that a major quantitative trait locus (QTL), Chalk5, influences grain chalkiness, which also affects head rice yield and many other quality traits. Chalk5 encodes a vacuolar H+-translocating pyrophosphatase (V-PPase) with inorganic pyrophosphate (PPi) hydrolysis and H+-translocation activity. Elevated expression of Chalk5 increases the chalkiness of the endosperm, putatively by disturbing the pH homeostasis of the endomembrane trafficking system in developing seeds, which affects the biogenesis of protein bodies and is coupled with a great increase in small vesicle-like structures, thus forming air spaces among endosperm storage substances and resulting in chalky grain. Our results indicate that two consensus nucleotide polymorphisms in the Chalk5 promoter in rice varieties might partly account for the differences in Chalk5 mRNA levels that contribute to natural variation in grain chalkiness.

A High-Density SNP Genotyping Array for Rice Biology and Molecular Breeding

A high-density single nucleotide polymorphism (SNP) array is critically important for geneticists and molecular breeders. With the accumulation of huge amounts of genomic re-sequencing data and available technologies for accurate SNP detection, it is possible to design high-density and high-quality rice SNP arrays. Here we report the development of a high-density rice SNP array and its utility. SNP probes were designed by screening more than 10 000 000 SNP loci extracted from the re-sequencing data of 801 rice varieties and an array named RiceSNP50 was produced on the Illumina Infinium platform. The array contained 51 478 evenly distributed markers, 68% of which were within genic regions. Several hundred rice plants with parent/F1 relationships were used to generate a high-quality cluster file for accurate SNP calling. Application tests showed that this array had high genotyping accuracy, and could be used for different objectives. For example, a core collection of elite rice varieties was clustered with fine resolution. Genome-wide association studies (GWAS) analysis correctly identified a characterized QTL. Further, this array was successfully used for variety verification and trait introgression. As an accurate high-throughput genotyping tool, RiceSNP50 will play an important role in both functional genomics studies and molecular breeding.

Natural variation in Ghd7.1 plays an important role in grain yield and adaptation in rice

Natural variation in Ghd7.1 plays an important role in grain yield and adaptation in rice