轉(zhuǎn)錄組結(jié)合區(qū)域關(guān)聯(lián)分析挖掘油菜含油量積累的候選基因

曹 松 姚 敏 任 睿 賈 元 向星汝 李 文 何 昕 劉忠松 官春云 錢論文 熊興華

轉(zhuǎn)錄組結(jié)合區(qū)域關(guān)聯(lián)分析挖掘油菜含油量積累的候選基因

曹 松 姚 敏 任 睿 賈 元 向星汝 李 文 何 昕 劉忠松 官春云 錢論文*熊興華*

湖南農(nóng)業(yè)大學(xué)農(nóng)學(xué)院, 湖南長(zhǎng)沙 410128

油菜(L.)是中國(guó)食用植物油的主要來(lái)源, 提高種子含油量是增加菜籽油供應(yīng)最為有效的方法。本研究利用4個(gè)油菜自交系授粉后25 d、35 d、45 d的種子轉(zhuǎn)錄組數(shù)據(jù)分析, 篩選出43個(gè)與油脂合成相關(guān)基因, 其中33個(gè)基因持續(xù)上調(diào)表達(dá), 10個(gè)基因持續(xù)下調(diào)表達(dá), 主要基因包括、、和等。同時(shí), 結(jié)合50份半冬性甘藍(lán)型油菜重測(cè)序數(shù)據(jù), 檢測(cè)到與含油量顯著相關(guān)3個(gè)SNP、9個(gè)SNP分別定位到A10和A05, 其中-A10_Hap1對(duì)應(yīng)材料含油量顯著高于Hap2,A05_Hap1對(duì)應(yīng)材料含油量顯著高于Hap3。此外, 利用WGCNA構(gòu)建基因共表達(dá)網(wǎng)絡(luò)發(fā)現(xiàn),與通過(guò)3個(gè)轉(zhuǎn)錄因子LEC1、HMGB3、HTA11間接相連, 形成了潛在調(diào)控的分子網(wǎng)絡(luò), 影響種子油脂積累。這些結(jié)果有利于我們開發(fā)單體型功能標(biāo)記進(jìn)一步提高油菜籽含油量。

甘藍(lán)型油菜; 含油量; 轉(zhuǎn)錄組分析; 共表達(dá)分析; 區(qū)域關(guān)聯(lián)分析

油菜是異源四倍體油料植物(AACC, 2=38), 大約在7500年前由2個(gè)二倍體植物(AA, 2=20)和(CC, 2=18)雜交, 通過(guò)自然加倍而成。在全球范圍內(nèi), 它是植物油的第三大來(lái)源, 約占人類消費(fèi)植物油總量的15%。油菜含油量一般在35%～50%之間[1], 極少數(shù)品種的含油量能夠達(dá)到60%以上[2]。王漢中[3]研究表明, 含油量增加1%, 相當(dāng)于種子產(chǎn)量增加了2.3%。因此, 提高油菜含油量對(duì)油菜產(chǎn)業(yè)的發(fā)展具有重要意義。

油菜的含油量主要受母體效應(yīng)、胚胎基因效應(yīng)、花粉直感、細(xì)胞質(zhì)效應(yīng)以及相應(yīng)的基因-環(huán)境相互作用效應(yīng)控制, 符合加-顯-上位遺傳模型, 基于加性和顯性遺傳, 具有較高的廣義遺傳力[4-6]。含油量在種子發(fā)育過(guò)程中具有明顯的動(dòng)態(tài)變化, 與植物光合作用、種子發(fā)育、油脂合成轉(zhuǎn)運(yùn)、油脂積累降解等多種生物途徑密切相關(guān), 形成了多基因調(diào)控網(wǎng)絡(luò)[7-9]。種子油脂生物合成分為脂肪酸合成和三酰基甘油合成2個(gè)階段, 主要以三酰基甘油的形式儲(chǔ)存, 占種子質(zhì)量的60%[10]。三酰基甘油的合成需要許多亞細(xì)胞結(jié)構(gòu)和多種途徑的相互作用, 并且整個(gè)過(guò)程涉及許多酶基因[11-12]。關(guān)鍵酶編碼基因在油菜油脂的合成中起著重要作用, 其對(duì)油菜SOC的影響主要取決于基因的表達(dá)和酶活性的調(diào)控, 參與這一過(guò)程的基因主要催化脂肪酸鏈和三酰基甘油的合成, 包括、、、、和[13-15], 任何一種酶的表達(dá)或調(diào)控都會(huì)影響SOC。關(guān)鍵轉(zhuǎn)錄因子也會(huì)影響油菜中的SOC, 主要調(diào)節(jié)種子發(fā)育和種子油脂積累。有研究表明, 包括WRI1、LEC1、LEC2、FUS3和ABI3在內(nèi)的轉(zhuǎn)錄因子可以通過(guò)激活或抑制與種子油脂合成相關(guān)的基因的表達(dá)來(lái)增加種子油含量[16-20]。此外, miRNA也可能參與油脂代謝[21]。

基于高通量測(cè)序平臺(tái)的轉(zhuǎn)錄組測(cè)序技術(shù)能夠全面獲得物種特定組織或器官的轉(zhuǎn)錄本信息, 從而進(jìn)行基因表達(dá)水平研究、新轉(zhuǎn)錄本發(fā)現(xiàn)研究、轉(zhuǎn)錄本結(jié)構(gòu)變異研究等。Xu等[22]使用RNA-Seq對(duì)2個(gè)不同含油量的品種在3個(gè)發(fā)育階段的油菜角果轉(zhuǎn)錄組進(jìn)行分析, 鑒定了與油脂代謝相關(guān)的14個(gè)候選基因; Shah等[23]在不同發(fā)育階段和溫度條件下對(duì)半冬性雙單倍體寧油7號(hào)進(jìn)行了RNA測(cè)序(RzNA-seq), 鑒定出36個(gè)與開花時(shí)間、種子產(chǎn)量或兩者相關(guān)的基因; Zhao等[24]利用2個(gè)高低含油量油菜品種不同發(fā)育時(shí)期種子轉(zhuǎn)錄組數(shù)據(jù), 得到了18個(gè)與含油量相關(guān)的候選基因。

全基因組關(guān)聯(lián)研究(Genome-Wide Association Studies, GWAS)被用作檢測(cè)QTL/基因的替代方法, 并提供了一種高分辨率的圖譜, 以識(shí)別目標(biāo)性狀的特定單核苷酸多態(tài)(Single Nucleotide Polymorphism, SNP), 從序列數(shù)據(jù)中生成的廉價(jià)、高密度的SNP標(biāo)記目前可用來(lái)評(píng)估甘藍(lán)型油菜的全基因組分析。He等[25]利用60k蕓苔屬Illumina Infinium SNP對(duì)327種質(zhì)材料進(jìn)行了分支數(shù)目的GWAS研究, 結(jié)果表明, 4個(gè)SNP位點(diǎn)與分枝數(shù)顯著相關(guān); Gajardo等[26]利用4025個(gè)SNP對(duì)89份油菜材料的硫苷和纖維素含量進(jìn)行GWAS分析, 鑒定出17個(gè)SNP與種子硫苷含量相關(guān), 5個(gè)SNP與纖維素含量顯著相關(guān); Liu等[27]利用Brassica 60K SNP對(duì)521份甘藍(lán)型油菜種子含油量進(jìn)行全基因組關(guān)聯(lián)研究, 鑒定出50個(gè)與種子含油量顯著相關(guān)的位點(diǎn), 這些位點(diǎn)可以解釋約80%的表型變異, 其中29個(gè)位點(diǎn)未被報(bào)道; Xiao等[28]對(duì)588份油菜自交系進(jìn)行高通量基因組重測(cè)序獲得的385,692個(gè)SNP, 并對(duì)含油量進(jìn)行全基因組關(guān)聯(lián)分析,共鑒定出17個(gè)與含油量顯著相關(guān)的位點(diǎn); Qu等[29]利用11,368個(gè)SNP標(biāo)記, 對(duì)520份材料進(jìn)行了油脂組成的全基因組關(guān)聯(lián)研究, 最終預(yù)測(cè)24個(gè)參與油脂生物合成的候選基因; Zhao等[30]利用2,340,881個(gè)SNP對(duì)204份油菜材料進(jìn)行GWAS分析, 鑒定出16個(gè)與油菜油含量顯著相關(guān)的位點(diǎn)。因此, 全基因組關(guān)聯(lián)分析在挖掘數(shù)量性狀基因和檢測(cè)功能基因有利變異位點(diǎn)具有獨(dú)特的優(yōu)勢(shì)。

本研究以長(zhǎng)沙地區(qū)4個(gè)甘藍(lán)型油菜品種為研究對(duì)象, 利用轉(zhuǎn)錄組分析種子發(fā)育過(guò)程中基因的動(dòng)態(tài)表達(dá), 篩選出影響含油量的候選基因集。結(jié)合區(qū)域關(guān)聯(lián)分析鑒定A10、A05基因結(jié)構(gòu)變異影響了含油量積累。共表達(dá)網(wǎng)絡(luò)分析表明,A10、A05、LEC1、HMGB3與HTA11形成分子網(wǎng)絡(luò), 共同對(duì)含油量的積累進(jìn)行調(diào)控。這些結(jié)果能用于開發(fā)功能標(biāo)記來(lái)選育高含油量品種。

1 材料與方法

1.1 試驗(yàn)材料

轉(zhuǎn)錄組分析選用甘藍(lán)型油菜品種(系)為試驗(yàn)材料, 分別為CS115、CS136、XY015和XY777。4個(gè)試驗(yàn)材料在湖南長(zhǎng)沙湖南農(nóng)業(yè)大學(xué)耘園基地進(jìn)行秋播。在油菜現(xiàn)蕾時(shí), 每個(gè)材料選取5株形態(tài)、長(zhǎng)勢(shì)一致的植株, 標(biāo)記出主花序并套袋自交, 在標(biāo)記后第25、35及45天從每個(gè)標(biāo)記單株取5個(gè)角果, 混合后將角果皮與種子在冰浴條件下盡快分離, 分離后的種子置于-80℃的超低溫冰箱中保存, 以備提取RNA進(jìn)行轉(zhuǎn)錄組測(cè)序。

區(qū)域關(guān)聯(lián)分析選用50份中國(guó)半冬油菜材料, 來(lái)自于中國(guó)西南大學(xué)重慶油菜工程中心, 這些材料代表亞洲油菜的多樣性。這些材料種植在德國(guó)大田和溫室, 調(diào)查并整理含油量表型。2013年(Field_2013)和2014年(Field_2014)分別在Gross Gerau (49.94°N, 8.50°E)和Rauischholzhausen (50.76°N, 8.88°E)進(jìn)行了無(wú)重復(fù)完全隨機(jī)設(shè)計(jì)的大田試驗(yàn)。每株系都種植3行, 每行25株, 每行株間5 cm, 行間25 cm。采集各小區(qū)3株自花授粉種子, 測(cè)定種子含油量。2012年, 在德國(guó)尤斯圖斯-李比希大學(xué)(Justus Liebig University Giessen, GH_2012)溫室進(jìn)行種植, 每個(gè)株系種植5株材料, 3株自交系種子用于測(cè)定含油量。用近紅外光譜法測(cè)定了50份甘藍(lán)型油菜種子含油量。

1.2 數(shù)據(jù)分析

1.2.1 轉(zhuǎn)錄組數(shù)據(jù)分析 CS115、CS136、XY015和XY777作為轉(zhuǎn)錄組分析的樣品, 取發(fā)育25 d、35 d、45 d的油菜角果, 摘下后立即在液氮中冷凍并儲(chǔ)存在-80℃直至RNA提取。采用天根生化科技有限公司的植物總RNA提取試劑盒DP432提取油菜種子的RNA。隨后分別用NanoPhotometer分光光度計(jì)(IMPLEN, 美國(guó))和Qubit3.0 Flurometer (Life Technologies, 美國(guó))檢測(cè)RNA純度和濃度。然后使用Agilent 2100 RNA Nano 6000 Assay Kit (Agilent Technologies, 美國(guó))評(píng)估RNA的完整性。樣品總RNA質(zhì)量合格后, 對(duì)質(zhì)量良好、完整性合格的RNA進(jìn)行進(jìn)一步處理, 清洗含poly-A mRNA片段, 合成雙鏈cDNA, 通過(guò)聚合酶鏈反應(yīng)(Polymerase Chain Reaction, PCR)進(jìn)行擴(kuò)增。cDNA測(cè)序文庫(kù)有效濃度大于10 nmol L–1的樣本被認(rèn)為可以進(jìn)行測(cè)序。測(cè)序采用Illumina HiSeq X測(cè)序平臺(tái), 測(cè)序策略為PE150 (對(duì)端2× 150 bp模式), 對(duì)原始數(shù)據(jù)采取各種質(zhì)量控制措施, 獲得高質(zhì)量數(shù)據(jù)。所有隨后的分析都是基于高質(zhì)量數(shù)據(jù)。使用HISAT2將高質(zhì)量reads與甘藍(lán)型油菜參考基因組進(jìn)行比對(duì)。使用HTSeq v0.6.1計(jì)算每個(gè)基因的reads數(shù)。然后, 根據(jù)基因的長(zhǎng)度和reads計(jì)數(shù), 計(jì)算每個(gè)基因的轉(zhuǎn)錄本每千堿基每百萬(wàn)reads的片段。

1.2.2 基因篩選和表達(dá)分析、GO功能富集分析

利用R軟件中DEseq2程序包進(jìn)行基因表達(dá)分析。基因篩選標(biāo)準(zhǔn)為: |log2(Fold Change)| > 1和< 0.05。將篩選的基因比對(duì)到擬南芥基因組數(shù)據(jù)庫(kù), 篩選與含油量相關(guān)基因, 用R軟件heatmap包將候選基因集表達(dá)量進(jìn)行可視化處理。采用TBtools軟件進(jìn)行基因的GO富集分析, 對(duì)每個(gè)有代表性的GOterm計(jì)算值,值小于0.05時(shí)GOterm被認(rèn)為是顯著富集的。GO功能顯著性富集分析分別包括基因的分子功能(molecular function)、細(xì)胞組分(cellular component)、生物過(guò)程(biological process)。

1.2.3 重測(cè)序分析 對(duì)來(lái)自西南大學(xué)的50份多世代(自花授粉至少5代)半冬性甘藍(lán)型油菜種質(zhì)資源進(jìn)行測(cè)序。采用十六烷基三甲基溴化銨(CTAB)的方法[31]從幼葉中提取基因組DNA。使用Illumina HiSeq 4000儀器(Illumina Co., Inc., 美國(guó))構(gòu)建DNA文庫(kù)。生物標(biāo)記技術(shù)公司(中國(guó)北京)完成文庫(kù)的準(zhǔn)備和測(cè)序。利用Burrows-Wheeler Aligner的MEM算法[32]將50個(gè)中國(guó)油菜自選系篩選后的reads定位到油菜參考基因組(https://www.genoscope.cns.fr/bras-sicanapus/)。用GATK對(duì)每個(gè)樣品進(jìn)行SNP調(diào)用(https://www.broadinstitute.org/gatk/guide/best-practices. php), 使用Beagle軟件包[33]去除缺失率≥0.6的SNP。最后選用532,005個(gè)等位基因頻率(MAF) ≥0.05、雜合率≥0.25的優(yōu)質(zhì)SNP標(biāo)記, 用于油菜種子含油量的區(qū)域關(guān)聯(lián)分析。

1.2.4 含油量的區(qū)域關(guān)聯(lián)分析 50份中國(guó)半冬性甘藍(lán)型油菜的全基因組重測(cè)序在Illumina HiSeq 4000平臺(tái)完成, 測(cè)序共產(chǎn)生了28.6億對(duì)reads, 每個(gè)材料的平均測(cè)序深度大約為5倍, 基因組覆蓋率達(dá)到85%。50份中國(guó)半冬性甘藍(lán)型油菜, 共檢測(cè)320萬(wàn)個(gè)SNP位點(diǎn)。通過(guò)嚴(yán)格的過(guò)濾后, 533,245個(gè)高質(zhì)量SNP標(biāo)記被用種子含油量關(guān)聯(lián)分析。利用TASSEL 5.0軟件[34]和Q + K (Population Structure and Kinship)混合線性模型型(Mixed Linear Model, MLM)[35]對(duì)標(biāo)記-性狀關(guān)聯(lián)進(jìn)行檢測(cè)。R軟件包QQman[36]被用于繪制曼哈頓圖和Quantile-Quantile散點(diǎn)圖。FDR (False Discovery Rate)值的計(jì)算是利用R語(yǔ)言中的-value軟件包(http://www.Bioconductor.org/packages/ release/bioc/qvalue.html), 最終確定顯著水平的值(≤1×10–4)。

1.2.5 共表達(dá)網(wǎng)絡(luò)分析 12份(4份甘藍(lán)型油菜3個(gè)不同時(shí)期)中國(guó)半冬性甘藍(lán)型油菜種質(zhì)的轉(zhuǎn)錄組中獲得基因表達(dá)數(shù)據(jù)。使用軟件R中的R包“WGCNA”[37]構(gòu)建共表達(dá)網(wǎng)絡(luò), 將權(quán)重參數(shù)的截止值設(shè)為0.2。輸出文件后在Cytoscape 3.6版軟件[38]中進(jìn)行基因模塊和關(guān)鍵基因網(wǎng)絡(luò)的可視化。

2 結(jié)果與分析

2.1 油菜角果含油量表型統(tǒng)計(jì)和分析

對(duì)種植在長(zhǎng)沙的4個(gè)油菜品種進(jìn)行含油量表型統(tǒng)計(jì)和分析發(fā)現(xiàn), CS115、CS136、XY015和XY777成熟種子含油量平均值±標(biāo)準(zhǔn)差分別為48.84±1.21 (%)、49.50±1.30 (%)、40.39±1.28 (%)、33.32±1.67 (%), 變異系數(shù)分別為2.32%、2.62%、3.16%、5.02%, 4個(gè)品種含油量具有差異性(表1)。

2.2 差異表達(dá)基因的統(tǒng)計(jì)

對(duì)所有基因進(jìn)行表達(dá)量分析發(fā)現(xiàn), 授粉后25～35 d, CS115、CS136、XY015和XY777分別有5513、7191、5537、5213個(gè)基因上調(diào)表達(dá), 6166、8275、5107、5319個(gè)基因下調(diào)表達(dá); 授粉后35～45 d, CS115、CS136、XY015和XY777分別有3900、4633、3655、3475個(gè)基因上調(diào)表達(dá), 4347、5871、4145、4186個(gè)基因下調(diào)表達(dá)(圖1-a)。統(tǒng)計(jì)4個(gè)品種授粉后25～45 d持續(xù)上調(diào)或下調(diào)的表達(dá)基因, 得到383個(gè)持續(xù)上調(diào)或下調(diào)表達(dá)基因, 其中持續(xù)上調(diào)表達(dá)基因292個(gè), 持續(xù)下調(diào)表達(dá)基因91個(gè)(圖1-b, c)。

表1 長(zhǎng)沙地區(qū)4個(gè)油菜品種成熟種子含油量統(tǒng)計(jì)表

圖1 持續(xù)上調(diào)或下調(diào)表達(dá)基因篩選統(tǒng)計(jì)

(a) 長(zhǎng)沙地區(qū)4個(gè)油菜品種, 25～35 d、35～45 d持續(xù)上調(diào)或下調(diào)基因數(shù)量統(tǒng)計(jì)圖。(b) 4個(gè)品種持續(xù)上調(diào)基因維恩圖。(c) 4個(gè)品種持續(xù)下調(diào)基因維恩圖。

(a) the number of continuously up-regulated or down-regulated genes of four rapeseed varieties in Changsha area during 25–35 days and 35–45 days periods. (b) Venn diagrams of four varieties consistently up-regulated genes. (c) Venn diagrams of consistently down-regulated genes in four varieties.

2.3 基因通路富集分析

將篩選出的383個(gè)基因注釋到GO數(shù)據(jù)庫(kù)中, 進(jìn)行通路富集分析(圖2)發(fā)現(xiàn), 大量基因在生化過(guò)程中參與油脂合成(GO:0033993), 部分差異基因與細(xì)胞組分中油脂小滴有關(guān)(GO:0005811), 說(shuō)明初步篩選的基因功能與油脂合成具有關(guān)聯(lián)。

2.4 基因表達(dá)分析

將383個(gè)基因在油菜Darmor-bzh參考基因組v.4.2中進(jìn)行比對(duì), 得到其基因序列后在擬南芥基因組數(shù)據(jù)庫(kù)中確認(rèn)基因功能, 得到43個(gè)與含油量相關(guān)基因。其中有33個(gè)基因上調(diào)表達(dá), 動(dòng)態(tài)分析顯示授粉后25～45 d表達(dá)量持續(xù)升高, 尤其授粉后25～35 d表達(dá)量迅速升高, 包括-A09、-A02、C02、A04和A10,A05表達(dá)量授粉后35～ 45 d迅速升高; 10個(gè)基因下調(diào)表達(dá), 動(dòng)態(tài)分析顯示在授粉后25～45 d表達(dá)量呈現(xiàn)下降趨勢(shì), 尤其授粉后35～45 d表達(dá)量迅速下降, 包括-A07-C07等含油量積累的主要轉(zhuǎn)錄調(diào)控因子(圖3)。

圓圈大小表示基因數(shù)量, 熱圖表示–log10(-value)的值。

The size of the circle represents the number of genes, and the heat map represents the value of –log10(-value).

2.5 區(qū)域關(guān)聯(lián)分析和單倍型分析

利用區(qū)域關(guān)聯(lián)分析進(jìn)一步解析這43個(gè)差異表達(dá)基因。含油量的區(qū)域關(guān)聯(lián)分析采用Q+K模型, 基于-log10()≥4的顯著截點(diǎn), 檢測(cè)到2個(gè)顯著的單倍型A10 (1,969,139～1,969,452 bp,2=0.9993), A05 (4,414,567～ 4,416,061 bp,2=0.9985)與含油量顯著相關(guān), 進(jìn)一步分析鑒定到2個(gè)候選基因(,-A10)、(,-A05)。3個(gè)SNP位于-A10啟動(dòng)子區(qū)(A10: 1969139; A10: 1969444; A10: 1969452,= 1.35×10–5) (圖4-b)。在-A10基因區(qū)檢測(cè)到2個(gè)單倍型(Hap)等位基因(圖4-c), 對(duì)比分析這2個(gè)單倍型等位基因所對(duì)應(yīng)的含油量表型發(fā)現(xiàn),-A10_Hap1對(duì)應(yīng)材料的含油量顯著高于-A10_Hap2對(duì)應(yīng)材料的含油量(檢驗(yàn)-雙樣本等方差假設(shè); 圖4-d)。

在A05染色體的4,413,939～4,416,132 bp, 發(fā)現(xiàn)基因-A05 ()與種子含油量顯著相關(guān)(圖5-a)。9個(gè)SNP位于-A05基因區(qū)內(nèi), 1個(gè)SNP (A05: 4414567;=1.42×10–4)在基因外顯子區(qū), 與種子含油量顯著相關(guān)(圖5-b)。在- A05中鑒定出3個(gè)單倍型等位基因(圖5-c), Hap1對(duì)應(yīng)材料的含油量顯著高于Hap3對(duì)應(yīng)材料的含油量(檢驗(yàn)-雙樣本等方差假設(shè); 圖5-d)。

圖3 熱圖顯示與含油量相關(guān)差異表達(dá)基因的表達(dá)量(DEGs)

RNA-seq數(shù)據(jù)的表達(dá)值進(jìn)行了log10(fpkm+1)轉(zhuǎn)換, 并顯示為填充塊, 從藍(lán)色到黃色, 再到紅色。

The expression values for RNA-seq data were log10(fpkm+1) transformed and displayed as filled blocks, from blue to yellow to red.

圖4 50個(gè)重測(cè)序材料中單體型(1,944,128～1,994,025 bp)區(qū)域含油量關(guān)聯(lián)分析

(a) 單體型(1,944,128～1,994,025 bp;2=0.99)區(qū)域的含油量關(guān)聯(lián)分析。藍(lán)色實(shí)線表示全基因組顯著性的閾值值為1.0×10–4。(b)和(c) 3個(gè)SNP (Chr.A10: 1,969,139; Chr.A10: 1,969,444; Chr.A10: 1,969,452,=1.35×10–5)與含油量顯著相關(guān), 并定位在-A10基因啟動(dòng)子區(qū)域。熱圖顯示這些SNPs存在強(qiáng)的連鎖不平衡。在-A10單倍型區(qū)檢測(cè)到2個(gè)單倍型等位基因。(d) 比較分析2個(gè)單體型等位基因?qū)?yīng)材料的含油量。單體型等位基因在群體中的頻率大于0.01將被用于此分析。箱型圖顯示-A10_Hap1等位基因?qū)?yīng)的材料含油量顯著高于-A10_Hap2對(duì)應(yīng)的材料含油量。*、**、***分別表示在0.05、0.01和0.001概率水平差異顯著。

(a) Haplotype (1,944,128–1,994,025 bp;2=0.99) oil content correlation analysis. The solid blue line indicates a threshold-value of 1.0×10–4for genome-wide significance. (b)–(c) three SNPs (Chr. A10: 1,969,139; Chr. A10: 1,969,444; Chr. A10: 1,969,452,= 1.35×10–5) was significantly correlated with oil content and was localized in the promoter region of-A10 gene. Heat maps show a strong linkage imbalance in these SNPs. Two haplotype alleles were detected in the-A10 haplotype region. (d) Comparative analysis of the oil content of the materials corresponding to the two haplotype alleles. Haplotype alleles with frequencies greater than 0.01 in the population will be used for this analysis. The box pattern shows that the material corresponding to the-A10_Hap1 allele has a higher oil content than that of-A10_Hap2. *, **, and *** mean significant difference at the 0.05, 0.01, and 0.001 probability levels, respectively.

2.6 共表達(dá)網(wǎng)絡(luò)分析

為進(jìn)一步分析A10和-A05基因功能聯(lián)系, 本研究利用12份甘藍(lán)型油菜種子轉(zhuǎn)錄組數(shù)據(jù)構(gòu)建了共表達(dá)網(wǎng)絡(luò)。該分析產(chǎn)生了4個(gè)基因模塊, 每個(gè)模塊在輸出中用不同的顏色表示(圖6-a)。模塊包含候選基因已被WGCNA識(shí)別,-A10和-A05都位于與含油量相關(guān)的綠松石模塊中(圖6-c)。在功能注釋的基礎(chǔ)上, 將共表達(dá)網(wǎng)絡(luò)基因進(jìn)行功能分類, 共有113個(gè)基因位于亞網(wǎng)絡(luò)中, 與油脂生物合成過(guò)程、油脂轉(zhuǎn)運(yùn)、油脂氧化、光合作用、碳水化合物代謝過(guò)程、轉(zhuǎn)錄因子相關(guān)基因分別有30、15、11、12、18、27個(gè)(圖6-d)。與直接相連, 另外與通過(guò)3個(gè)轉(zhuǎn)錄因子LEC1、HMGB3、HTA11間接相連,- A10、-A05和眾多影響油脂代謝基因形成了種子油脂積累潛在調(diào)控的分子網(wǎng)絡(luò)。

圖5 50個(gè)重測(cè)序材料中單體型(4,389,567～4,439,432 bp)區(qū)域的含油量關(guān)聯(lián)分析

(a) 單體型(4,389,567～4,439,432 bp;2=0.99)區(qū)域的含油量關(guān)聯(lián)分析。藍(lán)色實(shí)線表示全基因組顯著性的閾值值為1.0×10–4。(b)和(c) 9個(gè)SNP (A05: 4414567;=1.42×10–4)與含油量顯著相關(guān), 并定位在-A05基因區(qū)域。熱圖顯示這些SNPs存在強(qiáng)的連鎖不平衡。在-A05單倍型區(qū)檢測(cè)到3個(gè)單倍型等位基因。(d) 比較分析3個(gè)單體型等位基因?qū)?yīng)材料的含油量。單體型等位基因在群體中的頻率大于0.01將被用于此分析。箱型圖顯示-A05_Hap1等位基因?qū)?yīng)的材料有較高的含油量。*、**、***分別表示在0.05、0.01和0.001水平差異顯著。

(a) Haplotype (4,389,567–4,439,432 bp;2=0.99) oil content correlation analysis. The solid blue line indicates a threshold-value of 1.0×10–4for genome-wide significance. (b)–(c) nine SNPs (A05: 4,414,567;= 1.42×10–4) was significantly correlated with oil content and was localized in the-A05 gene region. Heat maps show a strong linkage imbalance in these SNPs. Three haplotype alleles were detected in the haplotype region of-A05. (d) Comparative analysis of the oil content of the materials corresponding to the three haplotype alleles. Haplotype alleles with frequencies greater than 0.01 in the population will be used for this analysis. The box pattern shows that the material corresponding to the-A05_Hap1 allele has a higher oil content. *, **, and *** mean significant difference at the 0.05, 0.01, and 0.001 probability levels, respectively.

3 討論

高含油量油菜的選育一直是油菜育種的主要目標(biāo)之一, 然而, 油菜含油量是一個(gè)復(fù)雜的數(shù)量性狀, 受多種遺傳和環(huán)境因素的調(diào)控[39]。現(xiàn)有的高通量基因分型技術(shù)促進(jìn)了GWAS對(duì)復(fù)雜性狀的解析。最近有研究者對(duì)505、370和204份具有高密度SNP的不同油菜品系進(jìn)行了GWAS和轉(zhuǎn)錄組數(shù)據(jù)分析, 分別得到27、7和18個(gè)SNP與種子含油量顯著相關(guān), 篩選出重要候選基因[40-42]。本研究利用轉(zhuǎn)錄組數(shù)據(jù)初步篩選到383個(gè)差異表達(dá)基因, 比對(duì)擬南芥基因功能數(shù)據(jù)庫(kù), 其中有43個(gè)與含油量相關(guān)差異表達(dá)基因, 包括-A09、-A02、- C02、-A04、-A10、-A07、-C07和-A05等重要的油脂合成代謝相關(guān)基因。緊接著對(duì)這43個(gè)基因進(jìn)行區(qū)域關(guān)聯(lián)分析和單倍型分析發(fā)現(xiàn),-A10和-A05基因與含油量顯著相關(guān), 其中檢測(cè)到2個(gè)優(yōu)良的單體型等位基因(-A10_Hap1和-A05_ Hap1)。在擬南芥中,是5個(gè)編碼油體蛋白的基因之一, 擬南芥突變會(huì)導(dǎo)致成熟種子含油量低于對(duì)照組[43]。ABI5能夠調(diào)控編碼TAG (三酰基甘油)生物合成的(二酰基甘油酰基轉(zhuǎn)移酶)表達(dá), 進(jìn)而影響擬南芥種子含油量[44]; ABI5還能激活WRI1-1 (編碼激活油脂生物合成基因表達(dá))的轉(zhuǎn)錄, 擬南芥WRI1-1超表達(dá)株系種子含油量得到提高[45]。此外, 在共表達(dá)基因網(wǎng)絡(luò)中,與通過(guò)3個(gè)轉(zhuǎn)錄因子LEC1、HMGB3、HTA11間接相連。有研究表明ABI3調(diào)節(jié)的表達(dá)[46], 并且ABI3與ABI5聯(lián)系密切, 能夠增強(qiáng)彼此表達(dá)[47], 所以ABI5可能參與的表達(dá)調(diào)控, 結(jié)合共表達(dá)網(wǎng)絡(luò)分析和前人研究, 本研究推測(cè)參與等基因組成的潛在分子網(wǎng)絡(luò)調(diào)控含油量的積累。這些結(jié)果有利于我們開發(fā)單體型功能標(biāo)記, 進(jìn)一步提高油菜含油量。

圖6 共表達(dá)網(wǎng)絡(luò)分析

(a) 模塊系統(tǒng)樹圖。(b) 模塊與含油量相關(guān)性。(c) 模塊中基因數(shù)目對(duì)比。(d) 基因網(wǎng)絡(luò)圖。八邊形紅色節(jié)點(diǎn)代表候選基因, 根據(jù)功能標(biāo)注, 共表達(dá)網(wǎng)絡(luò)分為油脂生物合成過(guò)程(紅色節(jié)點(diǎn))、油脂轉(zhuǎn)運(yùn)(紫色節(jié)點(diǎn))、油脂氧化(橙色節(jié)點(diǎn))、光合作用(綠色節(jié)點(diǎn))和碳水化合物代謝過(guò)程(灰色節(jié)點(diǎn))。

(a): dendrogram of module system. (b): correlation between modules and oil content. (c): comparison of gene numbers in modules. (d): gene network diagram. Octagonal red nodes represent candidate genes, and according to functional labeling, co-expression networks are divided into: lipid/fatty acid biosynthesis (red nodes), lipid transport (purple nodes), lipid/fatty acid oxidation (orange nodes), photosynthesis (green nodes), and carbohydrate metabolism (gray nodes).

4 結(jié)論

利用轉(zhuǎn)錄組數(shù)據(jù)篩選到43個(gè)與含油量相關(guān)的持續(xù)上調(diào)或下調(diào)表達(dá)基因, 包括、、和等。結(jié)合區(qū)域關(guān)聯(lián)分析, 檢測(cè)到43個(gè)基因中的和與含油量存在顯著關(guān)系, 并鑒定到-A10_Hap1和-A05_Hap1單倍型等位基因?qū)?yīng)材料的含油量較高。另外, 結(jié)合共表達(dá)網(wǎng)絡(luò)分析揭示與通過(guò)3個(gè)轉(zhuǎn)錄因子LEC1、HMGB3、HTA11間接相連形成潛在的網(wǎng)絡(luò)影響含油量積累。本研究結(jié)果可為油菜含油量改良奠定基礎(chǔ), 為進(jìn)一步提高含油量提供理論依據(jù)。

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A combination of genome-wide association and transcriptome analysis reveal candidate genes affecting seed oil accumulation in

CAO Song, YAO Min, REN Rui, JIA Yuan, XIANG Xing-Ru, LI Wen, HE Xin, LIU Zhong-Song, GUAN Chun-Yun, QIAN Lun-Wen*, and XIONG Xing-Hua*

College of Agronomy, Hunan Agricultural University, Changsha 410128, Hunan, China

Rapeseed (L.) is the main source of edible vegetable oil in China, and increasing seed oil content is the most effective way to increase the supply of rapeseed oil. In this study, 43 genes related to lipid synthesis were selected by analyzing the seed transcriptome data of 4 rapeseed inbred lines 25, 35, and 45 days after pollination. Among them, 33 genes were continuously up-expressed and 10 genes were continuously down-expressed from 25 to 45 days of seed development. The main genes included,,, and. At the same time, combined with the resequencing data of 50 semi-winter, 3 SNPs and 9 SNPs significantly related to oil content were detected to-A10 and-A05, respectively, and the oil content of-A10_Hap1 was significantly higher than Hap2. The oil content of-A05_Hap1 was significantly higher than Hap3. In addition, WGCNA was used to construct gene networks, and we found thatandwere indirectly connected through three transcription factors LEC1, HMGB3, and HTA11, which together formed a molecular network involved in the potential regulation of seed oil accumulation. The results of this study provide valuable insights for the development of haplotype functional markers, aiming to further enhance oil content in.

; oil content; transcriptome analysis; coexpression analysis; regional association analysis

10.3724/SP.J.1006.2024.34152

本研究由湖南省杰出青年科學(xué)基金項(xiàng)目(2022JJ10027), 湖南省教育廳科學(xué)研究重點(diǎn)項(xiàng)目(21A0135)和國(guó)家科技重大專項(xiàng)項(xiàng)目(2022ZD04009)資助。

This study was supported by the Science Foundation for Distinguished Youth Scholars of Hunan Province, China (2022JJ10027), the Research Foundation of Education Bureau of Hunan Province, China (21A0135), and the National Key Science and Technology Project (2022ZD04009).

熊興華, E-mail: xiongene@hunau.edu.cn; 錢論文, E-mail: qianlunwen@163.com

E-mail: c18890046232@163.com

2023-09-08;

2024-01-12;

2024-01-25.

URL: https://link.cnki.net/urlid/11.1809.S.20240125.0905.002

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).