| Rice is one of the three major food crops in the world for more than half of the world's population. As the rapid growth of the population, the current food production can't meet the needs of human beings, thus increasing rice yield is the key problem to be solved for rice breeders. Rice yield is mainly controlled by three factors:grain number per panicle, effective panicle and thousand-grain weight. So identifying and utilizing of these yield-related genes will promote the development of high-yield rice varieties to solve the food crisis from the rapid growing population. In this study, on the one hand, we analysed the genetic variation of Gnla, which influences rice grain yield through controlling grain number per panicle, in different varieties representing a diverse range of grain number per panicle from 19 countries. We performed correlation analysis between different alleles of GN1A and spikelet number per panicle to find the prior allele, which may facilitate the development of high-yield rice in future breeding programs. On the other hand, we found a big panicle variety IL1880 in the introgress lines constructed from the cross between 9311 and Oryza longistaminlata. Using map-based cloning, we identified the functional gene which regulates spikelet number per panicle in IL1880, and preliminary explored its regulatory mechanism. The main results are summarized as follows:(1) The Gnla sequences were cloned and sequenced from the 175 cultivar lines and 21 wild rice accessions, and the average number of nucleotide differences per site between any two DNA sequences from the sample population (71) was used to estimate the polymorphisms between O. sativa and O. rufipogon. We found that the ? value of O. rufipogon was higher than that of O. sativa, which indicated that the Gnla alleles were more diverse in the wild rice accessions than in the cultivars. Based on the divergence of protein sequence of each allele, we found 14 variations in the cultivars, which were renamed as alleles API to AP14. As alleles AP3, AP8 and AP9 represented the highest frequency, we performed phenotypic analysis among these three alleles, and found that AP9 was more prevalent than AP3 and AP8 in cultivars because it produced more spikelets per panicle.(2) To understand the general status of the GN1A alleles in the Oryza genus, we further investigated the distribution of the GN1A alleles in the wild rice O. rufipogon, and found AP1, AP3 and AP8 were all detected in O. rufipogon, which indicated these three alleles originated from O. rufipogon. Then we constructed a phylogenetic network for alleles of GN1A in O. sativa and O. rufipogon based on the amino acid sequences. Using this network, we proposed a schematic diagram to describe the possible evolutionary pattern of the main alleles. Besides, we investigated the alleles in the pedigree of Guichao 2, a high grain-yield cultivar in China, as a model to test how artificial selection influences the distribution of the GN1A alleles. We found that when another allele (AP2 in Qingnong'ai) was introduced in this pedigree, the excellent AP9 allele was still selected in the offspring, implying there was a strong artificial selection of GN1A in this pedigree. At last, we analyzed the regional distribution of the GNIA alleles in the cultivars to explore the relationship between the evolutionary pattern and geographic distribution of the GNIA alleles. We found that these alleles showed a strong distinctive geographic character:AP3 was primarily located in Brazil, AP8 was mainly concentrated in Indonesia and the Philippines, and AP9 was mainly in China.(3) We analysed the agronomic traits between 9311 and IL1880 (Introgress Line 1880), and found that IL1880 harbored stronger culm, more branches including primary branches and secondary branches, more spikelets per panicle, but lower seed-setting ratio and thousand-grain weight, and there was no significance difference in yield per plant between 9311 and IL1880. Using mut-map sequencing, we cross-mapped the functional gene which controlled spikelets per panicle in the long arm of chromosome 8, so this gene was named GRAIN NUMBER 8.1(GN8.1).(4) Then we constructed BC3F2 population and narrowed GN8.1 into the 84.5-kb region between markers RM23419 and INS-9, in which eight open reading frames (ORFs) were predicted, and two of which were retrotransposons. Then we validated this candidate region in BC3F3 population, and found there was a tightly linkage between the candidate region and primary branch number per panicle, so we concluded GN8.1 affected rice yield mainly through controlling rice primary branches per panicle. Additionaly, we screened a better plant architecture with more primary branches and spikelets per panicle. Excluding the GN8.1 region of chromosome 8 in this material, there were two another chromosome segments from IL1880 in chromosome 3 and 4, so we named this material as chromosome segments substitution line 1(CSSL1).(5) DNA sequencing analysis showed that there were no differences among these six functional genes' coding sequences between 9311 and CSSL1. Then we performed qRT-PCR to analyse the expression level of these six genes, and found that the expression level of Os08g39890 was much higher than the other five genes in both 9311 and CSSL1. Besides, only Os08g39890 showed higher expression level nearly 3.5 fold in CSSL1 than 9311, so Os08g39890 was the candidate gene for GN8.1. Os08g39890 encoded SPL14 transcriptional factor, and it was reported Os08g39890 shaped the ideal plant architecture through regulating expression level of TB1 and DEP1, thus this gene was also called as IPA1(Ideal Plant Architecture 1). To further determine whether OsSPL14 underlies the GN8.1, we perform confirmation tests by generating overexpression lines and RNA interference lines, and the transgenic experiment is going on.(6) We investigated promoter sequence of OsSPL14, but there was only one single nucleotide polymorphism (SNP) between 9311 and CSSL1, so we speculated that epigenetic modification including DNA methylation and histone modification might affect the expression level of OsSPL14. To confirm these hypotheses, we performed DNA methylation analysis in OsSPL14 promoter between 9311 and CSSL1, and found that the cytosine methylation from -900 bp to -600 bp of OsSPL14 promoter in 9311 was obviously higher than that in CSSL1. Additionaly, we investigated different histone modification (including histone methylation and acetylation) in young panicle between 9311 and CSSL1, the results showed that the enrichment of H3K27 di-methylation and H3K27 tri-methylation in 9311 were higher than that in CSSL1, besides we confirmed this result in young panicle between 9311 and CSSL1 by fluorescent immunochemistry stain. Chromatin immuno precipitation-PCR (CHIP-PCR) showed that higher enrichment of H3K27me2 and H3K27me3 in promoter and gene body of OsSPL14 in 9311 resulted in lower expression level compared to CSSL1. In a word, we insisted that GN8.1, which was an epi-allele of OsSPL14, was regulated by epigenetic modification including DNA methylation and histone modification, resulted in the different primary branch number and spikelet number per panicle between 9311 and CSSL1. |
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