大豆种质资源萌发期耐莠去津鉴定评价及优异种质筛选

室内生物测定是农作物除草剂耐受性鉴定的常用方法,已广泛应用于大豆、棉花等作物对草甘膦、氯嘧磺隆等除草剂的耐受性研究,但莠去津室内生测方法及大豆对莠去津耐受性相关研究鲜见报道。本研究以莠去津对大豆萌发期各单项指标为评价指标,分析莠去津胁迫条件下大豆发芽势、发芽率、发芽指数、活力指数、根长等性状的变化,建立大豆莠去津耐受性室内生测方法,确定3.5 mL L^(-1)是大豆萌发期莠去津耐受性筛选的适宜浓度;对159份大豆种质进行萌发期莠去津耐受性鉴定表明,各指标相对值的变异系数为发芽相对莠去津胁迫率>相对芽长>相对发芽指数>相对活力指数>相对发芽势>相对发芽率;根据T值的聚类分析,将供试种质分为4个莠去津耐性级别,共鉴定出高耐种质9份、中耐种质50份、低耐种质70份和敏感种质30份;黄豆、花色豆、垦丰13、南农99-6等9个种质为莠去津耐受性综合能力优异的种质资源。采用多元逐步回归分析法,建立大豆萌发期耐莠去津评价模型Y=–0.155+0.004X_(2)+0.001X_(3)+0.001X_(4)+0.002X_(5)(P=0.000)。本研究为培育耐莠去津育种的亲本选配、后代选择及大豆耐莠去津相关基因挖掘提供了理论依据、材料和技术支撑。

基于深度学习的大豆豆荚类别识别研究

作物表型调查是作物品种选育过程中的一项关键工作。传统表型调查主要依靠人力,使得表型调查的结果难以达到自动化、高精度、高可靠性的要求。在大豆的表型调查中,对豆荚类别的正确识别是豆荚个数、长度和宽度等表型准确提取的关键和前提。本文针对成熟期大豆豆荚的图片,通过利用深度学习迁移5种不同的网络模型[AlexNet、VggNet(Vgg16,Vgg19)、GoogleNet、ResNet-50],对一粒荚、二粒荚、三粒荚、四粒荚进行识别。为提高训练速度和准确率,本试验微调模型,选择不同的优化器(SGD、Adam)对网络模型进行优化。结果表明,在针对豆荚辨识问题中,Adam的性能优于SGD,而Vgg16网络模型搭配Adam优化器,豆荚类别的测试准确率达到了98.41%,在所选的网络模型中体现了最佳的性能。在十折交叉验证试验中也体现了Vgg16网络模型具有良好的稳定性。因此本研究认为Vgg16网络模型可以应用到实际的豆荚识别中,为进一步实现豆荚表型自动提取提供一条重要的解决途径。

大豆DELLA基因家族鉴定及分析

DELLA基因家族是调控植物与环境互作和植物发育的重要调控因子,可以促进微生物与植物共生关系建立过程中侵染线的形成,是参与赤霉素信号通路的负调控蛋白,在影响植物激素相关基因表达,调节植物与微生物共生固氮和生长发育方面具有重要的作用,但是在大豆中DELLA基因家族的结构特征还没有详细分析。本研究对大豆中DELLA基因家族的基因结构、定位信息、蛋白结构、保守基序分析、系统进化树、顺势作用元件、与拟南芥同源基因的共线性关系和基因表达模式进行了研究。结果发现,大豆基因组中共有7个DELLA基因家族成员,分布在7条不同的染色体上,这些基因都只有一个外显子,一个N端DELLA结构域,并且基序分布相似,证明其基因编码序列结构域高度保守。系统进化树分析表明DELLA家族有三个亚族,其启动子区域含有大量的顺式作用元件,这些元件参与植物激素反应、干旱诱导和光反应。大豆DELLA基因Glyma.11G216500、Glyma.18G040000、Glyma.08G095800和Glyma.05G140400与拟南芥中的AT1G14920、AT1G66350和AT2G01570呈共线性关系;其中Glyma.18G040000和Glyma.11G216500在大豆各个组织中都有表达,而且表达量相对较高。以上研究丰富了我们对大豆DELLA基因家族的理解,为后续大豆DELLA基因的功能研究奠定了基础。

基于共线性分析的大豆种子硬实性QTL定位及候选基因预测

大豆种子硬实度关乎食品与饲料加工。为提高大豆种子硬实度分子标记辅助选择的效率,发现相关候选基因,利用染色体片段代换系(chromosome segment substitution lines,CSSL)对大豆种子硬实性进行了两年的QTL定位研究,结合前人得到的25个种子硬实性QTL,利用MCScanX对整个大豆基因组进行分析,生成共线性区组,评估大豆种子硬实性QTL之间的共线性,确定了位于Gm02片段的中心QTL。利用多个数据库分析hub-QTL区段的基因,锚定了8个与大豆种子硬实性相关的候选基因。从CSSL群体中选择种子硬实性不同的两个品系和轮回亲本,用于随后的实时荧光定量PCR分析,发现候选基因Glyma.02G269400和Glyma.02G269500在CSSL群体中硬实性不同的2个品系及轮回亲本绥农14中的表达差异显著。Glyma.02G262600在绥农14中的相对表达量约是CSSL-136的5倍,而在CSSL-200中表达量中等,推测该基因抑制大豆种皮的形成。

Overexpression of GmBIN2,a soybean glycogen synthase kinase 3 gene,enhances tolerance to salt and drought in transgenic Arabidopsis and soybean hairy roots

Glycogen synthase kinase 3 （GSK3） is a kind of sedne/threonine kinase widely found in eukaryotes. Many plant GSK3 kinases play important roles in regulating stress responses. This study investigated BRASSINOSTEROID-INSENSITIVE 2 （GmBIN2） gene, a member of the GSK3 protein kinase family in soybean and an orthologue of Arabidopsis BIN2/AtSK21. GmBIN2 expression was increased by salt and drought stresses, but was not significantly affected by the ABA treatment. To examine the function of GrnBIN2, transgenic Arabidopsis and transgenic soybean hairy roots were generated. Overexpression of GmBIN2 in Arabidopsis resulted in increased germination rate and root length compared with wild-type plants under salt and mannitol treatments. Overexpression of GmBIN2 increased cellular Ca2~ content and reduced Na~ content, enhancing salt tolerance in transgenic Arabidopsis plants. In the soybean hairy root assay, overexpression of GmBIN2 in transgenic roots also showed significantly higher relative root growth rate than the control when subjected to salt and mannitol treatments. Measurement of physiological indicators, including proline content, superoxide dismutase （SOD） activity, and relative electrical conductivity, supported this conclusion. Furthermore, we also found that GmBIN2 could up-regulate the expression of some stress-related genes in transgenic Arabidopsis and soybean hairy roots. Overall, these results indicated that GmBIN2 improved tolerance to salt and drought in transgenic Arabidopsis and soybean hairy roots.

An Integrated QTL Map of Fungal Disease Resistance in Soybean (Glycine max L. Merr):A Method of Meta-Analysis for Mining R Genes

Diseases caused by fungal pathogens account for approximately 50% of all soybean disease losses around the world. Conflicting results of fungal disease resistance QTLs from different populations often occurred. The objectives of this study were to： （i） evaluate evidence for reported fungal disease resistance QTLs associations in soybean and （ii） extract relatively reliable and useful information from the ＂real＂ QTLs and mine putative genes in soybean. An integrated map of fungal disease resistance QTLs in soybean was established with soymap 2 published in 2004 as a reference map. QTLs of fungal disease resistance developed from each of separate populations in recent 10 years were integrated into a combinative map for gene cloning and marker assisted selection in soybean. 107 QTLs from different maps were integrated and projected to the reference map with the software BioMercator 2.1. A method of meta-analysis was used to narrow down the confidence interval, and 23 ＂real＂ QTLs and their corresponding markers were obtained from 12 linkage groups （LG）, respectively. Two published R genes were found in these ＂real＂ QTLs intervals. Sequences in the ＂real＂ QTLs intervals were predicted by GENSCAN, and these predicted genes were annotated in Goblet. 228 resistance gene analogs （RGAs） in 12 different terms were mined. The results will lay the foundation for a bioinformatics platform combining abundant QTLs, and offer the basis for marker assisted selection and gene cloning in soybean.

Identification of novel soybean oil content-related genes using QTL-based collinearity analysis from the collective soybean genome

Soybean is a global principal source of edible plant oil. As more soybean oil-related quantitative trait loci(QTLs) have been located in the collective genome, it is urgent to establish a classification system for these distributed QTLs. A collinear platform may be useful to characterize and identify relationships among QTLs as well as aid in novel gene discovery. In this study, the collinearity MCScan X algorithm and collective soybean genomic information were used to construct collinearity blocks, to which soybean oil-related QTLs were mapped. The results demonstrated that 666 collinearity blocks were detected in the soybean genome across 20 chromosomes, and 521 collinearity relationships existed in 231 of the 242 effective soybean oil-related QTLs. This included 214 inclusion relationships and 307 intersecting relationships. Among them, the collinearity among QTLs that are related to soybean oil content was shown on a maximum of seven chromosomes and minimum of one chromosome, with the majority of QTLs having collinearity on two chromosomes. Using overlapping hotspot regions in the soybean oil QTLs with collinearity, we mined for novel oil content-related genes. Overall, we identified 23 putatively functional genes associated with oil content in soybean and annotated them using a number of annotation databases. Our findings provide a valuable framework for elucidating evolutionary relationships between soybean oil-related QTLs and lay a foundation for functional marker-assisted breeding relating to soybean oil content.

Meta-Analysis of 100-Seed Weight QTLs in Soybean

100-seed weight is a very complicated quantitative trait of yield. The study of gene mapping for yield trait in soybean is very important for application. However, the mapping result of 100-seed weight was dispersed, the public map should be chosen which was suitable for the published results integrated, and to improve yield. In this research, an integrated map of 100-seed weight QTLs in soybean had been established with soymap2 published in 2004 as a reference map. QTLs of 100-seed weight in soybean were collected in recent 20 yr. With the software BioMercator 2.1, QTLs from their own maps were projected to the reference map. From published papers, 65 QTLs of 100-seed weight were collected and 53 QTLs were integrated, including 17 reductive effect QTLs and 36 additive effect QTLs. 12 clusters of QTLs were found in the integrated map. A method of meta-analysis was used to narrow down the confidence interval, and 6 additive QTLs and 6 reductive QTLs and their corresponding markers were obtained respectively. The minimum confidence interval （C.I.） was shrunk to 1.52 cM. These results would lay the foundation for marker-assisted selection and mapping QTL precisely, as well as QTL gene cloning in soybean.

Identification of candidate genes related to soluble sugar contents in soybean seeds using multiple genetic analyses

Soluble sugar content in seeds is an important quality trait of soybean. In this study, 57 quantitative trait loci(QTLs) related to soluble sugar contents in soybean seeds were collected from databases and published papers. After meta-overview-collinearity integrated analysis to refine QTL intervals, eight consensus QTLs were identified. To further verify the consensus QTLs, a population of chromosome segment substitution lines(CSSLs) was analyzed. Two lines containing fragments covering the regions of consensus QTLs and the recurrent parent were selected: one line showed high soluble sugar contents associated with a consensus QTL fragment, and the other line showed low soluble sugar contents. Transcriptome sequencing was conducted for these two lines at the early, middle, and late stages of seed development, which identified 158, 109 and 329 differentially expressed genes, respectively. Based on the analyses of re-sequencing data of the CSSLs and the consensus QTL region, three candidate genes(Glyma.19 G146800, Glyma.19 G122500, and Glyma.19 G128500) were identified in the genetic fragments introduced from wild soybean. Sequence comparisons between the two CSSL parents SN14 and ZYD00006 revealed a single nucleotide polymorphism(SNP) mutation in the coding sequence of Glyma.19 G122500, causing a nonsynonymous mutation in the amino acid sequence that affected the predicted protein structure. A Kompetitive allele-specific PCR(KASP) marker was developed based on this SNP and used to evaluate the CSSLs. These results lay the foundation for further research to identify genes related to soluble sugar contents in soybean seeds and for future soybean breeding.

An Integrated Quantitative Trait Locus Map of Oil Content in Soybean,Glycine max(L.) Merr.,Generated Using a Meta-Analysis Method for Mining Genes

Soybean is a major cash crop in the world, and its oil content was one of the very important traits. Therefore, the study of gene mapping for oil content in soybean is very important for breeding application. At present, at least 130 QTL loci for soybean oil content have been published; however, the mapping results of oil content were dispersed and a coalescent public map should be established to integrate the published QTLs, and to more efficiently mine genes based on the meta- analysis method of the bioinformatics tools. This study was to construct an integrated map of QTLs for soybean oil content and accelerate the application of bioinformation resource related to oil content improvement in the practice of soybean breeding. We collected information of 130 QTLs reported over the past 20 yr for soybean oil content and used the Software BioMercator 2.1 to project QTLs from their own maps onto a reference map, which was an early-integrated map constructed by Song （2004） for oil-content quantitative trait loci （QTLs） in soybean. Gene mining was performed based on the meta-analysis by running the local ver. GENSCAN and InterProScan. The confidence interval of QTLs was efficaciously narrowed using the meta-analysis method, and 25 consensus QTLs were mapped on the reference map. Using a local version of GENSCAN, 12 805 sequences in the consensus QTL intervals were predicted. With BLAST, these predicted sequences were aligned to gene sequences from the International Protein Index database using InterProScan locally. Thirteen predicted genes were in the class of the geme ontology （GO） accession （0006631）, which were involved in the fatty acid metabolic process. These genes were analyzed using BLAST at the NCBI website to examine whether they were related to oil content. Six genes were found in the oil-synthesis pathway. Twenty-five consensus QTLs and six genes were found in the oil-synthesis pathway. These results would lay the foundation for marker-assisted selection and mapping QTL precisely, and these genes will facilitate the researches on the gene mining of oil synthesis and molecular breeding in soybean.

Characterization of chromosome segment substitution lines reveals candidate genes associated with the nodule number in soybean

Soybean is one of the most important food crops worldwide.Like other legumes,soybean can form symbiotic relationships with Rhizobium species.Nitrogen fixation of soybean via its symbiosis with Rhizobium is pivotal for sustainable agriculture.Type Ⅲ effectors(T3Es)are essential regulators of the establishment of the symbiosis,and nodule number is a feature of nitrogen-affected nodulation.However,genes encoding T3Es at quantitative trait loci(QTLs)related to nodulation have rarely been identified.Chromosome segment substitution lines(CSSLs)have a common genetic background but only a few loci with heterogeneous genetic information;thus,they are suitable materials for identifying candidate genes at a target locus.In this study,a CSSL population was used to identify the QTLs related to nodule number in soybean.Single nucleotide polymorphism(SNP)markers and candidate genes within the QTLs interval were detected,and it was determined which genes showed differential expression between isolines.Four candidate genes(GmCDPK28,GmNAC1,GmbHLH,and GmERF5)linked to the SNPs were identified as being related to nodule traits and pivotal processes and pathways involved in symbiosis establishment.A candidate gene(GmERF5)encoding a transcription factor that may interact directly with the T3E NopAA was identified.The confirmed CSSLs with important segments and candidate genes identified in this study are valuable resources for further studies on the genetic network and T3Es involved in the signaling pathway that is essential for symbiosis establishment.

Meta-analysis of soybean amino acid QTLs and candidate gene mining

The composition and quantity of amino acids influence the protein content and nutritional value of soybeans and also have an important impact upon soybean quality. After integrating and proofreading 140 original QTLs associated with amino acid contentfrom soybase（http：//www.soybase.org/）, 138 QTLs were further analyzed to determine high-confidence QTL regions. Meta-analysis was first carried out using the Bio Mercator ver. 2.1 software, yielding 33 consensus QTLs. The consensus QTL confidence intervals（CIs） ranged from 0.07 to 19.85 Mb. Next, the overview method was used to optimize the CIs, and 57 ＂real＂ QTLs were mapped. Candidate genes in the consensus QTL regions were obtained from Phytozome and were annotated using the Gene Ontology（GO）, Kyoto Encyclopedia of Genes and Genomes（KEGG）, Swissprot, and gene annotation databases. Finally, 16 unpublished candidate genes controlling the content of five types of amino acids were identified with Blast. These results laid the foundation for fine mapping of soybean amino acid-related QTLs and marker-assisted selection.

GmDRR1,a dirigent protein resistant to Phytophthora sojae in Glycine max (L.) Merr.

Soil-borne pathogen Phytophthora sojae is an oomycete that causes devastating damage to soybean yield. To mine original resistant genes in soybean is an effective and environmentally-friend approach controlling the disease. In this study, soybean proteins were extracted from the first trifoliolates infected by predominant P. sojae race 1 and analyzed by twodimensional gel electrophoresis. Nineteen differently-expressed protein spots were detected, and 10 of them were further applied for Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry Assay. One protein containing a dirigent （DIR） domain was identified and belonged to the DIR-b/d family. Therefore, it was named as GmDRR1 （Glycine max Disease Resistance Response 1）. Then, GmDRR1 gene was pathologically confirmed to be involved in the resistant to P. sojae in soybean. GmDRR1-GFP （green fluorescent protein） fusion proteins localized in the cell membrane. qRTPCR results showed GmDRR1 gene expressed differently in P. sojae resistant- and susceptible-soybean cultivars. By the promoter analysis, we found a haplotype H8 was existing in most resistant soybean varieties, while a haplotype H77 was existing in most susceptible soybean varieties. The H77 haplotype had seven SNPs （C to A, G to C, C to A, T to A, T to C, T to C, and T to A） and two single nucleotide insertions. The results supported that the expression difference of GmDRR1 genes between P. sojae resistant- and susceptible-soybean cultivars might depend on the GmDRR1 promoter SNPs. The results suggested that GmDRR1 was a dirigent protein involved in soybean resistant to P. sojae and paved a novel way for investigation of the molecular regulatory mechanism of the defense response to P. sojae in soybean.