Yingpeng Han

QTL underlying the resistance to soybean aphid (Aphis glycines Matsumura) through isoflavone-mediated antibiosis in soybean cultivar ‘Zhongdou 27’

Soybean aphid (Aphis glycines Matsumura) results in severe yield loss of soybean in many soybean-growing countries of the world. A few loci have been previously identified to be associated with the aphid resistance in soybean. However, none of them was via isoflavone-mediated antibiosis process. The aim of the present study was to conduct genetic analysis of aphid resistance and to identify quantitative trait loci (QTL) underlying aphid resistance in a Chinese soybean cultivar with high isoflavone content. One hundred and thirty F5:6 derived recombinant inbred lines from the ‘Zhongdou 27’xa0×xa0‘Jiunong 20’ cross were used. Two QTL were directly associated with resistance to aphid as measured by aphid damage index. qRa_1, close to Satt470 on soybean linkage group (LG) A2 (chromosome 8), was consistently detected for 3- and 4-week ratings and explained a large portion of phenotypic variations ranging from 25 to 35%. qRa_2, close to Satt144 of LG F (chromosome 13), was detected for 3- and 4-week ratings and could explain 7 and 11% of the phenotypic variation, respectively. These two QTL were highly associated with high isoflavone content and both positive alleles were derived from ‘Zhongdou 27’, a cultivar with higher isoflavone content. The results revealed that higher individual or total isoflavones contents in soybean lines could protect soybean against aphid attack. These two QTL detected jointly provide potential for marker-assisted selection to improve the resistance of soybean cultivars to aphid along with the increase of isoflavone content.

Transgenic expression of ThIPK2 gene in soybean improves stress tolerance, oleic acid content and seed size

Inositol polyphosphate kinase (IPK; EC 2.7.1.151) participates in inositol metabolism, calcium signaling, stress response, gene transcription and other physiological and biochemical processes. In the present work, ThIPK2 (an inositol polyphosphate kinase from Thellungiella halophila) was transferred into soybean through an Agrobacterium tumefaciens-mediated transformation. Transgenic plants with ThIPK2 gene displayed water deficit-, salt- and oxidative-tolerance compared to non-transformed controls. Furthermore, the expression of ThIPK2 altered the ratio of fatty acid components in soybean seeds, resulting in an increase of oleic acid (C18:1). Also seed size was increased in transgenic plants. These findings might be available to improve soybean seed quality and soybean yield.

Dynamic QTL analysis of linolenic acid content in different developmental stages of soybean seed

Linolenic acid (LN) in soybean (Glycine max L. Merr.) seed mainly contributes to the undesirable odors and flavors commonly associated with poor oil quality. LN deposition at various stages of soybean seed development had not been reported by 2010. The objects of this study were (1) to identify and measure quantitative trait loci (QTL) underlying LN content and (2) to estimate the QTL effects expressed from earlier seed developmental stages to drying seed of soybean. One hundred and twenty-five F5:8 and F5:9 recombinant inbred lines derived from the cross of soybean cultivars ‘Hefeng 25’ and ‘Dongnong L5’ were used for the identification of QTL underlying LN content from the 37xa0day (D) to 86D stages after flowering, at Harbin in 2008 and 2009. QTLxa0×xa0Environment interactions (QE) effects were evaluated using a mixed genetic model (Zhu in J Zhejiang Univ (Natural Science) 33:327–335, 1999). Twelve unconditional QTL and 12 conditional QTL associated with LN content were identified at different developmental stages. Most of the QTL explained <10% of phenotypic variation of LN content. Unconditional QTL QLNF-1, QLNC2-1, QLND1b-1, QLNA2-1 and QLNH-1 influenced LN content across different development stages and environments. Conditional QTL QLNF-1, QLNC2-1 and QLNH-1 were identified in multiple developmental stages and environments. Conditional and unconditional QTL clustered in neighboring intervals on linkage groups A2, C2 and D1b. Ten QTL with conditional additive main effects (a) and/or conditional additivexa0×xa0environment interaction effects (ae) at specific developmental stage were identified on nine linkage groups. Of them, six QTL only possessed additive main effects and seven QTL had significant ae effects in different developmental stages. A total of 13 epistatic pairwise QTL were identified by conditional mapping in different developmental stages. Two pairs of QTL only showed aa effects and five pairs of QTL only showed aae effects at different developmental stages. QTL with aa effects, as well as their environmental interaction effects, appeared to vary at different developmental stages.

SSR- and SNP-related QTL underlying linolenic acid and other fatty acid contents in soybean seeds across multiple environments

Soybean fatty acids (FAs) are major sources of vegetable oil in the world. The FA composition of soybean is associated with the quality and nutritional value of its oil and food products. The polyunsaturated FAs, particularly linolenic acid (LN), are prone to oxidation by lipoxygenase isozymes and negatively affect the flavor and shelf-life of soybean products. The improvement of FA composition and the increase of oxidative stability has been a major goal of soybean breeding for decades. The objective of the present study was to identify quantitative trait loci (QTL) associated with the low LN and other FA contents in six environments. One hundred and twenty-five recombinant inbred lines (RILs) of F5:7, F5:8 and F5:9 generations derived by the single-seed-descent method from the cross of Hefeng 25 [LN 6.20%; linoleic acid (LI) 53.06%; oleic acid (OL) 19.75%; palmitic acid (PA) 12.16%; stearic acid (ST) 4.97%] and Dongnong L-5 (LN 2.53%; LI 59.30%; OL 24.24%; PA 10.0%; ST 3.99%) were used in this study. A total of 112 simple sequence repeat markers were used to construct a genetic linkage map. Six QTL associated with LN content, four QTL associated with LI content, four QTL associated with OL content, four QTL associated with PA content and one QTL associated with ST content were identified, and mapped onto different linkage groups (LGs). The QTL detected here explained 2.53–37.30% of phenotypic variation for individual FA in different environments or individual FA means. Of them, the beneficial alleles of QLNB2_1 (close to Satt726), QLNB2_2 (close to Fad3a-4) and QLND1b_1 (close to Satt701), which were associated with low LN content across various environments and LN means, were derived from Dongnong L-5. These three QTL might have great potential value in marker-assisted selection for low LN content in soybean seed.

QTL underlying resistance to two HG types of Heterodera glycines found in soybean cultivar 'L-10'.

BackgroundResistance of soybean (Glycine max L. Merr.) cultivars to populations of cyst nematode (SCN; Heterodera glycines I.) was complicated by the diversity of HG Types (biotypes), the multigenic nature of resistance and the temperature dependence of resistance to biotypes. The objective here was to identify QTL for broad-spectrum resistance to SCN and examine the transcript abundances of some genes within the QTL.ResultsA Total of 140 F5 derived F7 recombinant inbred lines (RILs) were advanced by single-seed-descent from a cross between L-10 (a soybean cultivar broadly resistant to SCN) and Heinong 37 (a SCN susceptible cultivar). Associated QTL were identified by WinQTL2.1. QTL Qscn3-1 on linkage group (LG) E, Qscn3-2 on LG G, Qscn3-3 on LG J and Qscn14-1 on LG O were associated with SCN resistance in both year data (2007 and 2008). Qscn14-2 on LG O was identified to be associated with SCN resistance in 2007. Qscn14-3 on LG D2 was identified to be associated with SCN resistance in 2008. Qscn14-4 on LG J was identified to be associated with SCN resistance in 2008. The Qscn3-2 on LG G was linked to Satt309 (less than 4 cM), and explained 19.7% and 23.4% of the phenotypic variation in 2007 and 2008 respectively. Qscn3-3 was less than 5 cM from Satt244 on LG J, and explained 19.3% and 17.95% of the phenotypic variations in 2007 and 2008 respectively. Qscn14-4 could explain 12.6% of the phenotypic variation for the SCN race 14 resistance in 2008 and was located in the same region as Qscn3-3. The total phenotypic variation explained by Qscn3-2 and Qscn3-3 together was 39.0% and 41.3% in 2007 and 2008, respectively. Further, the flanking markers Satt275, Satt309, Sat_350 and Satt244 were used for the selection of resistant lines to SCN race 3, and the accuracy of selection was about 73% in this RIL population. Four genes in the predicted resistance gene cluster of LG J (chromosome 16) were successfully cloned by RT-PCR. The transcript encoded by the gene Glyma16g30760.1 was abundant in the SCN resistant cultivar L-10 but absent in susceptible cultivar Heinong 37. Further, the abundance was higher in root than in leaf for L-10. Therefore, the gene was a strong candidate to underlie part of the resistance to SCN.ConclusionsSatt275, Satt309, Sat_305 and Satt244, which were tightly linked to the major QTL for resistance to SCN on LG G and J, would be candidates for marker-assisted selection of lines resistant to the SCN race 3. Among the six RLK genes, Glyma16g30760.1 was found to accumulate transcripts in the SCN resistance cultivar L-10 but not in Heinong 37. The transcript abundance was higher in root than in leaf for L-10.

GmGBP1, a homolog of human ski interacting protein in soybean, regulates flowering and stress tolerance in Arabidopsis

BackgroundSKIP is a transcription cofactor in many eukaryotes. It can regulate plant stress tolerance in rice and Arabidopsis. But the homolog of SKIP protein in soybean has been not reported up to now.ResultsIn this study, the expression patterns of soybean GAMYB binding protein gene (GmGBP1) encoding a homolog of SKIP protein were analyzed in soybean under abiotic stresses and different day lengths. The expression of GmGBP1 was induced by polyethyleneglycol 6000, NaCl, gibberellin, abscisic acid and heat stress. GmGBP1 had transcriptional activity in C-terminal. GmGBP1 could interact with R2R3 domain of GmGAMYB1 in SKIP domain to take part in gibberellin flowering pathway. In long-day (16 h-light) condition, transgenic Arabidopsis with the ectopic overexpression of GmGBP1 exhibited earlier flowering and less number of rosette leaves; Suppression of AtSKIP in Arabidopsis resulted in growth arrest, flowering delay and down-regulation of many flowering-related genes (CONSTANS, FLOWERING LOCUS T, LEAFY); Arabidopsis myb33 mutant plants with ectopic overexpression of GmGBP1 showed the same flowering phenotype with wild type. In short-day (8 h-light) condition, transgenic Arabidopsis plants with GmGBP1 flowered later and showed a higher level of FLOWERING LOCUS C compared with wild type. When treated with abiotic stresses, transgenic Arabidopsis with the ectopic overexpression of GmGBP1 enhanced the tolerances to heat and drought stresses but reduced the tolerance to high salinity, and affected the expressions of several stress-related genes.ConclusionsIn Arabidopsis, GmGBP1 might positively regulate the flowering time by affecting CONSTANS, FLOWERING LOCUS T, LEAFY and GAMYB directly or indirectly in photoperiodic and gibberellin pathways in LDs, but GmGBP1 might represse flowering by affecting FLOWERING LOCUS C and SHORT VEGETATIVE PHASE in autonomous pathway in SDs. GmGBP1 might regulate the activity of ROS-eliminating to improve the resistance to heat and drought but reduce the high-salinity tolerance.

In silico Detection of Novel MicroRNAs Genes in Soybean Genome

The importance of microRNAs (miRNAs) at the post-transcriptional regulation level has recently been recognized in both animals and plants. In this study, the simple and most effective method of comparative genomic approach was used. First known plants miRNAs BLAST against the soybean genome, and then the located candidates were searched for novel miRNAs by RNA folding method in the vicinity (±400 nt) of the candidates. The results showed that a total of 521 novel soybean miRNA genes, including 236 mature miRNAs, were identified. All these mature miRNAs were grouped into 58 families, of which 21 of them were novel family in soybean. The upstream 2 000 nt of potential pre-miRNAs was used for promoter prediction, in order to investigate prediction of miRNAs and detect transcript unit and clustering. In this study, miRNA genes less tend to be present as clusters in soybean. Only 9 clusters, containing 21 miRNA genes (accounted for 4.0% of the total), were observed as part of polycistronic transcripts. Detailed analysis of sequence characteristics of novel miRNAs in soybean and all previous known plants miRNAs, were carried out. These results of this study provide a reference point for further study on miRNAs identification in plants, and improve the understanding of genome in soybean.

Genomic Analysis of MicroRNA Promoters and Their Cis-Acting Elements in Soybean

MicroRNAs (miRNAs) are derived from distinct loci in the genome and play crucial roles in RNA-mediated gene silencing mechanisms that regulate cellular processes during development and stress responses of plants. The miRNAs are approximately 21 nucleotides long and code for the complementary strand to a larger genic mRNA. They are often found within the complementary primary transcript (pri-miRNAs). In the past few years, a growing number of soybean miRNAs have been discovered, however, little is known about the transcriptional regulation of these miRNAs. In this study, promoters and cis-acting elements of soybean miRNAs were analyzed using the genomic data for the first time. A total of 82 miRNAs were located among 122 loci in genome, some were present as double or multiple copies. Five clusters that included ten miRNAs were found in genome, and only one cluster share the same promoter. A total of 191 promoters from 122 loci of the soybean miRNA sequences were found and further analyzed. The results indicated that the conserved soybean miRNA genes had a greater proportion of promoters than that of non-conserved ones, and the distribution of the transcript start sites (TSSs) and TATA-boxes found had different motif styles between conserved and non-conserved miRNA genes. Furthermore, the cis-acting elements 5′ of the TSSs were analyzed to obtain potential function and spatiotemporal expression pattern of miRNAs. The data obtained here may lead to the identification of specific sequences upstream of pre-miRNAs and the functional annotation of miRNAs in soybean.

Novel representation of RNA secondary structure used to improve prediction algorithms

We propose a novel representation of RNA secondary structure for a quick comparison of different structures. Secondary structure was viewed as a set of stems and each stem was represented by two values according to its position. Using this representation, we improved the comparative sequence analysis method results and the minimum free-energy model. In the comparative sequence analysis method, a novel algorithm independent of multiple sequence alignment was developed to improve performance. When dealing with a single-RNA sequence, the minimum free-energy model is improved by combining it with RNA class information. Secondary structure prediction experiments were done on tRNA and RNAse P RNA; sensitivity and specificity were both improved. Furthermore, software programs were developed for non-commercial use.

Impact of epistasis and QTL × environmental interaction on the mass filling rate during seed development of soybean.

Seed filling rate of soybean has been shown to be a dynamic process in different developmental stages affected by both genotype and environment. The objective of the present study was to determine additive, epistatic and quantitative trait loci (QTLs) × environment interaction (QE) effects of the QTL underlying a seed filling rate of soybean. One hundred and forty-three recombinant inbred lines (RILs) derived from the cross of Charleston and Dongnong 594 were used with 2 years of field data (2004 and 2005). Eleven QTLs with significantly unconditional and conditional additive (a) effect and/or additive × environment interaction (ae) effect at different filling stages were identified. Of them six QTLs showed positive a effects and four QTLs had negative a effects on the seed filling rate during seed development. aa and aae effects of 12 pairs of QTLs were identified by unconditional mapping from the initial stage to the final stage. Thirteen pairs of QTLs underlying the seed filling rate with aa and aae effects were identified by conditional mapping. QTLs with aa and aae (additive × additive × environment) effects appeared to vary at different filling stages. Our results demonstrated that the mass filling rate in soybean seed were under genetic and environmental control.