QTL Analysis Of Fat Content In Rice And Molecular Cloning Of Diacylglycerol Acyltransferase 2 Type

With the stability of rice production and the improvement of people's life quality, rice quality improvement has become one of the most important goals in rice breeding programs. Fat is one of the most important nutrition quality traits of rice. Fat content and eating quality are positively correlated. High fat content is one of the characters of high rice quality, which could improve the aroma, polish and taste of cooked rice. The increased fat content could significantly improve the rice eating quality when it is difficult to improve the other traits such as amylase content, gel consistency and gelatinization temperature. So far, the genetic studies on fat had been carried out mainly by classical quantitative genetic methods and QTLs had been identified using single population and single environment. To some extent, the limited genetic information of fat restricted the improvement for fat by molecular method and the molecular design breeding for rice eating and nutrition quality. To better elucidate genetic basis and improve rice nutrition quality by marker-assisted selection and gene engineering procedure, the following studies were carried out:1. The analysis of QTL and the stability across three environments underlying fat content by BILsUsing a population of 98 BC1F9 lines (Backcross Inbred Lines, BIL) from a backcross of Nipponbare (japonica)/Kasalath (indica) // Nipponbare, QTL and the stability of QTL underlying fat content across three environments were analyzed. Altogether 7 QTLs were identified in three environments and mapped on 2, 3, 4, 5 and 7 rice chromosomes, which accounted for 5.48-23.17% of phenotypic variance. None QTL could be detected in three environment, two QTLs could be detected in two environments, and the others could only be identified in one environment. Among of them a major QTL, denoted as qFC-7, with positive effect contributed by Nipponbare allele in 2003Nanjing, could explain 23.17% of phenotypic variance. However, it could only accound for 7.50% of phenotypic variance in 2004Nanjing with the positive effect coutributed by Kasalath allele. qFC-5-1 could stably express in 2003Nanjing and 2003Hainan with the positive effect contributed by Nipponbare allele. The percentage of phenotype variation explained by the QTL detected in one environment was ranged from 5.48% to 10.3%.2. QTL detection underlying fat content during the development of grain and in different environment by RIL.QTL underlying fat content during the development of grain and in different environment were analyzed using a population of 71 recombination inbred lines (RIL) derived from Asominori and IR24. Seventeen QTLs were identified to be distributed on 8 of 12 rice chromosomes, which accounted for 7.2-17.38% of phenotypic variance. Alleles from Asominori and IR24 can both have positive effects. Two QTLs, denoted as qFC-1-2 and qFC-11-3, were detected in two environments, and other QTLs were only identified in one environment. Based on the observation of the four grain development stages at Nanjing in 2005, a total of 12 QTLs were detected, which explained 7.2-14.19% of phenotypic variance. One QTL, denoted as qFC-11-3, was detected in two stages and others were detected in one stage. At mature stage of Nanjing in 2005 and 2004, and Hainan in 2003, 10 QTLs with the percentage of phenotypic variation explained (PVE) 9.36-17.38% were identified. The results indicated that the expression of QTL controlling fat content was environment dependent.3. Dynamic QTL analysis of fat content and fat index during grain filling stage of riceA RIL population of 71 lines from Asominori/IR24 was used to analyze the developmental behavior of fat content (FC) and fat index (FI) by unconditional and conditional QTL mapping methods. It was indicated that 11 unconditional QTLs and 10 conditional QTLs controlling FC, and 11 unconditional QTLs and 8 conditional QTLs controlling FC were identified in four filling time of rice. Many QTLs controlling FC and FI identified at the early stages were undetectable at the final stage and more QTLs were identified at 7DAF and 14DAF than at 21DAF and 28DAF. Some QTLs could be detected at two stages and most QTLs could only be detected at one stage. The present study suggested that accumulation of fat of rice was governed by time-dependent gene expression, the method of dynamic QTL analysis chould detect more genetice information than the traditional QTL method, which may be useful for elucidating genetic basis of fat accumulation and improving rice quality.4. Cloning of Acyl CoA:diacyIgycerol acyltransferase and analysis of transcript in different tissuesTriacylglycerol (TAG) is the major form of storage lipid, which represents the most efficient storage form of energy in plant seed. Acyl CoA:diacylgycerol acyltransferase (EC 2.3.1.20;DGAT) drives the final and only committed step in the formation of oils by catalysing the acylation of the sn-3 position of the sn-1,2-diacylglycerol (DAG) to yield triacylglycerol(TAG). A novel full-length cDNA, designated OsDGAT2, has been isolated by the combination of bioinformatics and PCR based approaches. A homology tree of DGAT from various species including mammalian, fungus and plant revealed that OsDGAT had a closer evolutionary relationship with plant of tung tree. Meanwhile, the putative peptide of OsDGAT2 includes two important putative conserved domains: diacylglycerol acyltransferase (DAGAT) and phosphate acyltransferases (PlsC). Furthermore, the corresponding genomic clone was isolated and sequenced, and it was composed of 9 exons and 8 introns. RT-PCR analysis showed that the OsDGAT2 transcripts were detected in all tissues including root, stems, matured leaves, old leaves, seedling and grain in different development stage and the abundance of OsDGAT2 transcripts was detected markedly in stems, seedling, developing seeds and matured seeds.5. Four calibration models were established to analyze quantitatively fat content by NIRSThe chemical analysis of fat content is time and cost consuming and could result in poor reproduction between replications. Near infrared spectroscopy (NIRS) can solve those problems by providing a rapid, nondestructive and quantitative analysis. Based on the NIRS technique and partial least squares (PLS) algorithm, four calibration models were established to analyze quantitatively fat content in brown rice grain and flour, milled rice grain and flour with 248 representative samples. The determination coefficients (R2) of these calibration models were 0.79, 0.84, 0.89 and 0.91, respectively; with the corresponding root mean square errors 0.16%, 0.14%, 0.09% and 0.08%. The R2 were 0.73, 0.81, 0.81 and 0.89 with the corresponding root mean square errors 0.17%, 0.15%, 0.12% and 0.09%, respectively in cross validation. And the R2 were 0.62, 0.80, 0.81 and 0.87, respectively, with the root mean square errors 0.25%, 0.31%, 0.28% and 0.30% in external validation. These results indicate that the method of NIRS has relatively high accuracy in the prediction of rice fat content. The four calibration models established in the present study should be useful for quality improvement in rice breeding.