禹桃兵 石琪晗 年 海 連騰祥

澇害對不同大豆品種根際微生物群落結(jié)構(gòu)特征的影響

禹桃兵 石琪晗 年 海*連騰祥*

華南農(nóng)業(yè)大學(xué)農(nóng)學(xué)院/ 國家大豆改良中心廣東分中心, 廣東廣州 510642

淹水影響不同大豆品種根際微生物群落組成, 不同基因型大豆植株耐澇性差異較大。本研究選取耐澇(waterlogging-tolerant, W-T)基因型大豆齊黃34和澇害敏感(waterlogging-sensitive, W-S)基因型大豆冀豆17為材料, 采用熒光定量PCR、Illumina MiSeq高通量測序技術(shù), 分析了不同淹水時間下2個基因型根際細(xì)菌多樣性、群落組成和網(wǎng)絡(luò)特征。結(jié)果表明, 耐澇基因型大豆的生物量和細(xì)菌豐度明顯高于澇害敏感基因型大豆。主坐標(biāo)分析(PCoA)表明, 耐澇基因型與敏感基因型大豆微生物群落組成的差異隨淹水時間的增加而變化(< 0.05)。在淹水條件下, 耐澇基因型大豆富集了屬和屬以及OTU274 ()和OTU2334 ()等物種, 這些細(xì)菌的富集可能與耐澇性有關(guān), 本研究提供了大豆根際微生物抗?jié)碀摿Φ淖C據(jù)。

大豆; 耐澇; 根際微生物; 16S rRNA; 網(wǎng)絡(luò)分析

未來極端氣候事件的發(fā)生頻率將會大幅度增加,其中包含了干旱、暴雨和隨之而來的澇災(zāi)[1-2]。在美國、加拿大、巴西、法國、中國和日本等一些糧食生產(chǎn)關(guān)鍵地區(qū)受到極端氣候事件的影響尤為顯著[3-5]。大豆(L.)是對澇害適應(yīng)性較差的作物, 當(dāng)大豆遭受澇害時, 土壤中的氧氣含量非常低, 造成了根部呼吸作用減弱進(jìn)而導(dǎo)致嚴(yán)重的營養(yǎng)供給危機(jī)[6-8]。此外, 澇害脅迫還會干擾作物的生理功能, 包括葉片光合作用減少、葉、莖和根的氣孔封閉以及生長抑制, 從而導(dǎo)致產(chǎn)量下降[9-10]。因此, 我們應(yīng)該盡可能多的理解關(guān)于作物耐澇的機(jī)制, 包括土壤微生物在緩解澇害中的作用[11-12]。

目前已有多種方法用于提高作物對澇害脅迫的適應(yīng)能力, 包括傳統(tǒng)的植物育種、作物的基因工程和植物相關(guān)微生物組的管理和應(yīng)用[13-14]。作物的植物育種和基因工程可以幫助植物更好地適應(yīng)脅迫環(huán)境[15]。然而, 許多耐脅迫基因型的研究并未考慮土壤環(huán)境的生物和非生物因素, 特別是微生物對植物的脅迫響應(yīng)所產(chǎn)生的耐性作用[16-18]。已有研究表明, 根系相關(guān)微生物不僅直接受到澇害的影響, 同時也間接受到植物對脅迫的響應(yīng)的影響[19]。作物受到澇害脅迫, 導(dǎo)致地下碳輸入數(shù)量及質(zhì)量發(fā)生變化[9,20], 進(jìn)而影響了根際微生物組[21]。不同植物種類對土壤過程和反饋的特征存在差異, 且不同植物基因型在根際會招募不同的微生物組, 因此土壤微生物群落在介導(dǎo)植物對澇害脅迫的反應(yīng)中發(fā)揮重要作用[22-23]。

近年來, 越來越多的研究關(guān)注于植物微生物組介導(dǎo)的抵抗脅迫研究[24]。例如, 含1-氨基環(huán)丙烷-1-羧酸脫氨酶的有益細(xì)菌可以降低脅迫誘導(dǎo)的乙烯含量[25-26], 進(jìn)而保護(hù)植物免受淹水[27]、干旱[28]和高鹽[29]等脅迫的有害影響。耐性品種可以通過募集特定的微生物來抵抗外界脅迫[30-31]。我們基于不同耐鋁大豆根際微生物對鋁毒的響應(yīng)機(jī)制研究發(fā)現(xiàn), 耐鋁大豆會招募耐鋁的細(xì)菌和, 可以有效幫助耐鋁大豆減輕Al的毒性[31]。然而關(guān)于大豆?jié)碁?zāi)的研究, 多數(shù)集中關(guān)注遺傳和栽培措施的改進(jìn), 而關(guān)于不同基因型大豆的根際微生物如何響應(yīng)澇害的相關(guān)研究還未見報道。

基于此, 本研究選取耐澇基因型大豆齊黃34和澇害敏感基因型大豆冀豆17為材料, 通過評估不同淹水時長下耐澇和澇害敏感基因型大豆根際細(xì)菌多樣性及群落結(jié)構(gòu), 來解析澇害程度對不同大豆品種根際微生物的影響差異。本研究強(qiáng)調(diào)了根際細(xì)菌群落作為潛在育種目標(biāo)的可能性, 以生產(chǎn)對澇害脅迫更耐受的作物。

1 材料與方法

1.1 供試材料

1.1.1 供試土壤 供試土壤為酸性土, 采自廣東省英德市(113°42′E, 24°28′N), 其包含全碳10.30 g kg-1、全氮0.33 g kg-1、全鉀12.15 g kg-1、速效氮143.5 mg kg-1、速效磷1.33 mg kg-1、速效鉀115.9 mg kg-1, pH 4.75。

1.1.2 供試大豆 盆栽試驗(yàn)供試大豆(L.)包括澇害敏感型大豆: W-S (冀豆17-JD17)[32]; 耐澇大豆: W-T (齊黃34-QH34)[33]。冀豆17由河北省農(nóng)林科學(xué)院糧油作物研究所張孟臣研究員提供, 齊黃34由山東省農(nóng)業(yè)科學(xué)院作物研究所徐冉研究員提供。

1.2 大豆土培盆栽試驗(yàn)

本試驗(yàn)在廣東省廣州市華南農(nóng)業(yè)大學(xué)農(nóng)學(xué)院進(jìn)行, 采用土培盆栽的方式, 設(shè)置2個大豆品種(JD17和QH34), 1個無淹水對照, 2個淹水時長處理, 共6個處理, 每個處理設(shè)置6個重復(fù)。2個淹水時長為1 d和5 d。試驗(yàn)前將風(fēng)干土壤過2 mm的篩, 去除雜質(zhì)備用。采用規(guī)格相同的花盆(外徑13.8 cm, 內(nèi)徑13.0 cm, 底徑10.4 cm, 高12.2 cm), 每盆裝風(fēng)干土約2.5 kg。在播種前2～3 d, 保持土壤含水量為田間持水量的50%～60%。播種時, 每盆播8粒外形一致、壯實(shí)飽滿的大豆種子, 生長過程在可控條件的玻璃房中進(jìn)行(白天溫度為28～32℃, 夜間溫度為16～20℃), 出苗6 d后定苗至3株大豆, 出苗期再移至自然光線良好充足的室外繼續(xù)生長, 期間按時管理和除草。生長期間的土壤含水量控制在田間持水量的80%左右。

播種大豆后待其生長至花期, 進(jìn)行淹水處理。取與種植用花盆等量的相同規(guī)格的花盆作為隔水用花盆。將塑料膜裁剪成合適大小的方片, 緊貼著隔水用花盆內(nèi)壁覆蓋花盆, 用以阻隔水分, 防止水分從花盆底下流失, 保證淹水處理效果。塑料膜鋪好后需要高出花盆1～2 cm。確認(rèn)無破孔漏洞后, 在種植用花盆外能直接套入外層隔水用花盆, 兩花盆之間能緊密貼合。添加水量至淹沒土壤表面以上2 cm, 每日2次定時定量加水, 觀察并記錄生長情況, 分別于水淹后1 d、5 d時戳破花盆低漏洞處的塑料膜恢復(fù)排水, 觀察并記錄。

1.3 土壤樣品的收集

不同處理的大豆分別在淹水后1 d和5 d采用“抖根法”采集根際土壤[34]。試驗(yàn)總共6個處理, 每個處理設(shè)置6個重復(fù), 共采集36個樣品。由于淹水脅迫下, 土壤含水量高于最大田間持水量, 土壤黏結(jié)成塊狀, 實(shí)際操作時, 需要托起瀝去部分水分, 輕輕撇掉與大豆根系結(jié)合松散的土壤, 然后取下與大豆根系緊密附著的土壤作為根際土壤樣品, 保存于?80℃冰箱。隨后將所有土壤樣品送深圳華大基因科技服務(wù)有限公司, 完成土壤微生物總DNA的提取、PCR擴(kuò)增以及測序數(shù)據(jù)的初步處理。

1.4 DNA提取和qPCR擴(kuò)增

利用Fast kit DNA試劑盒提取根際土壤總DNA, 提取的DNA在去離子水中稀釋, 并儲存于-20℃冰箱中保存?zhèn)溆?。使用具?2 nt barcode引物338F (5′-ACTCCTACGGGAGGCAGCAG-3′)和806R (5′- GGACTACHVGGGTWTCTAAT-3′)擴(kuò)增16S rRNA基因的V3–V4高變區(qū)[35]。qPCR反應(yīng)體系包括22.5 μL的PCR SuperMix、1.0 μL正向引物和1.0 μL反向引物、10 ng模板DNA, ddH2O補(bǔ)充至25 μL。PCR擴(kuò)增體系95℃變性10 min; 95℃ 15 s, 60℃ 10 s, 72℃ 10 s, 28個循環(huán); 72℃延伸10 min[36]。

1.5 數(shù)據(jù)分析

使用QIIME v1.19.1處理原始序列數(shù)據(jù)[37]。刪除小于200 bp和平均質(zhì)量分?jǐn)?shù)低于20的序列[38]。通過運(yùn)行UCHIME算法檢測潛在的嵌合序列[39]。用CD-HIT按97%相似性對序列進(jìn)行OTU聚類[40]。使用RDP對數(shù)據(jù)庫執(zhí)行OTU代表序列的分類歸屬[41-42]。所有樣本均隨機(jī)重采樣至相同序列深度(每個樣本29,169個序列), 以減少測序深度對處理效果的影響。在QIIME v1.19.1中計算根際細(xì)菌Alpha-多樣性(Chao1和Shannon指數(shù))。并且基于Bray-Curtis距離矩陣進(jìn)行主坐標(biāo)分析(PCoA)用于分析不同處理之間微生物群落組成的相似性[43]。利用非參數(shù)多元方差分析(Adonis)計算微生物群落結(jié)構(gòu)的差異。采用檢驗(yàn)(Student’s-test)對相對豐度在前100的屬進(jìn)行差異性分析, 并利用Duncan’s法進(jìn)行多重比較。Venn分析用于統(tǒng)計不同處理樣本中所共有和獨(dú)有的細(xì)菌屬的數(shù)量。采用GenStat統(tǒng)計軟件進(jìn)行大豆生物量、細(xì)菌豐度和多樣性指數(shù)的單因素方差分析(ANOVA), 并利用Duncan’s法來進(jìn)行多重比較, 顯著性水平均設(shè)為0.05。

利用共現(xiàn)網(wǎng)絡(luò)評價相對豐度大于0.2%的細(xì)菌OTUs之間的關(guān)系。利用R包psych[44]計算Spearman相關(guān)系數(shù), 得到OTU之間的成對相關(guān), 網(wǎng)絡(luò)中包含> 0.8和< 0.05的相關(guān)。利用Gephi v.0.9.2為每個植物基因型和淹水處理構(gòu)建了共現(xiàn)網(wǎng)絡(luò)可視化[45-46]。計算網(wǎng)絡(luò)的拓?fù)湫再|(zhì), 以闡明不同基因型和淹水處理的群落結(jié)構(gòu)差異。

2 結(jié)果與分析

2.1 大豆生物量、根際細(xì)菌豐度及多樣性

2種基因型在不淹水和淹水1 d處理中生物量無顯著差異, 在淹水5 d時, 耐澇基因型大豆生物量顯著高于澇害敏感大豆(圖1-a)。淹水降低了大豆根際微生物細(xì)菌的基因拷貝數(shù), 淹水5 d處理中耐澇基因型的細(xì)菌基因拷貝數(shù)明顯高于敏感基因型(圖1-b,<0.05)。由不同淹水處理根際土壤細(xì)菌的多樣性指數(shù)(圖1-c, d)表明, 淹水處理沒有對2個基因型大豆根際微生物多樣性產(chǎn)生影響。

2.2 不同處理根際細(xì)菌的群落結(jié)構(gòu)

對36個土壤樣品測序共獲得了1,304,142條有效序列, 每個樣品19,852～37,180條, 平均35,603條, 在97%相似度下聚類得到5538個OTU。在6個處理中只檢測到41個門, 其中變形菌門(Proteobacteria)、酸桿菌門(Acidobacteria)、綠灣菌門(Chloroflexi)、浮霉菌門(Planctomycetes)和疣微菌門(Verrucomicrobia)為優(yōu)勢菌門, 其相對豐度分別為31.0%～33.7%、14.8%～17.7%、4.6%～8.7%、7.0%～ 8.3%、6.1%～7.0% (圖2)。隨著淹水時長的增加, 耐澇基因型大豆根際中的變形菌門(Proteobacteria)、酸桿菌門(Acidobacteria)的相對豐度增加, 但是綠彎菌門(Chloroflexi)、浮霉菌門(Planctomycetes)、疣微菌門(Verrucomicrobia)的相對豐度隨著淹水時間的增加而減少。隨著淹水時長的增加, 澇害敏感基因型大豆中的酸桿菌門(Acidobacteria)相對豐度隨著淹水時長的增加而增加, 浮霉菌門(Planctomycetes)的相對豐度隨淹水時間增加而減少(圖2)。在屬水平上, 在無淹水處理、淹水1 d和淹水5 d處理中, 耐澇和敏感基因型大豆根際分別有9、13和12個屬顯著不同。在無淹水對照處理中, 有5個屬的相對豐度在耐澇基因型大豆中更高(圖3-a, b)。然而, 在淹水1 d和5 d處理下, 各有4個屬的相對豐度在耐澇基因型中更高, 其中和在2個處理中的耐澇害基因型中共同增高(圖3-c)。

為評價不同澇害處理下耐澇和敏感基因型大豆OTU相對豐度的差異, 建立了負(fù)二項(xiàng)分布的廣義線性模型。結(jié)果表明, 相對于耐澇不淹水處理, 耐澇淹水處理1 d和5 d的根際土壤樣本中顯著富集的OTU分別有150個和290個(圖4-a, b); 相對于敏感不淹水處理, 敏感淹水1 d和5 d處理的根際土壤樣本中顯著富集的OTU分別有100個和230個(圖4-c, d)。通過Venn分析表明, 18個OTU僅在淹水1 d和5 d處理中富集在耐澇基因型大豆根際土壤中(圖4-e)。其中OTU218 ()、OTU938 ()、OTU274 ()、OTU2334 ()、OTU1337 ()分別在2個大豆基因型淹水處理下存在顯著差異, 淹水5 d處理中, OTU274 ()、OTU2334 ()在耐澇基因型中的相對豐度較高(圖5)。

不同字母表示相同處理下2個品種間差異顯著(< 0.05)。W-TCK; 耐澇品種淹水0 d; W-SCK: 澇害敏感品種淹水0 d; W-T1D: 耐澇品種淹水1 d; W-S1D: 澇害敏感品種淹水1 d; W-T5D: 耐澇品種淹水5 d; W-S5D: 澇害敏感品種淹水5 d。

Boxes superscripted by different letters indicate significant differences between two varieties under the same treatment (< 0.05). W-TCK: waterlogging-tolerant variety without waterlogging; W-SCK: waterlogging-sensitive variety without waterlogging; W-T1D: waterlogging-tolerant variety under waterlogging for one day; W-S1D: waterlogging-sensitive variety under waterlogging for one day; W-T5D: waterlogging-tolerant variety under waterlogging for five days; W-S5D: waterlogging-sensitive variety under waterlogging for five days.

處理同圖1。Treatments are the same as those given in Fig. 1.

誤差條表示6個重復(fù)樣本的標(biāo)準(zhǔn)差。采用Benjamini-Hochberg法校正值(< 0.05)。處理同圖1。

The error bars show the calculated standard variation of six replicates. Corrected-values were calculated by the Benjamini-Hochberg false discovery rate approach at (< 0.05). Treatments are the same as those given in Fig. 1.

a: 耐澇品種淹水1 d和不淹水的對比分析; b: 耐澇品種淹水5 d和不淹水的對比分析; c: 澇害敏感品種淹水1 d和不淹水的對比分析; d: 澇害敏感品種淹水5 d和不淹水的對比分析; e: 不同淹水處理下根際土壤細(xì)菌在OTU水平的Venn分析。處理同圖1。

a: comparison analysis of W-T variety under waterlogging for one day and no waterlogging; b: comparison analysis of W-T variety under waterlogging for five days and no waterlogging; c: comparison analysis of W-S variety under waterlogging for one day and no waterlogging; d: comparison analysis of W-S variety under waterlogging for five days and no waterlogging; e: Venn analysis of rhizosphere soil bacteria at genus level under different waterlogging time. Treatments are the same as those given in Fig. 1.

W-T: 耐澇基因型大豆; W-S: 澇害敏感基因型大豆。不同字母表示相同處理下2個品種間差異顯著(< 0.05)。處理同圖1。

W-T: waterlog-tolerant soybean genotypes; W-S: waterlog-sensitive soybean genotypes. Bars superscripted with different letters indicate significant differences between the two varieties under the same treatment at< 0.05. Treatments are the same as those given in Fig. 1.

2.3 根際土壤細(xì)菌群落的結(jié)構(gòu)

PCoA結(jié)果顯示(圖6), X和Y軸共解釋了65.2%的群落變異, 并且6個處理分成3個大組, 其中不同淹水處理的根際細(xì)菌群落聚集在一起, 說明淹水處理是改變根際微生物群落結(jié)構(gòu)的主要因素。此外, 不同處理下2種基因型的根際細(xì)菌群落結(jié)構(gòu)呈現(xiàn)顯著差異, 而且這個差異隨著淹水時長的增加也發(fā)生了變化(圖6和表1)。

處理同圖1。Treatments are the same as those given in Fig. 1.

表1 通過變異多變量方差分析(PERMANOVA)評估大豆基因型對根際細(xì)菌群落結(jié)構(gòu)的影響

處理同圖1。**表示1%顯著水平;*表示5%顯著水平。

Treatments are the same as those given in Fig. 1.**Significant at the 1% probability level;*Significant at the 5% probability level.

2.4 細(xì)菌群落的關(guān)聯(lián)網(wǎng)絡(luò)分析

網(wǎng)絡(luò)關(guān)聯(lián)分析表明, 在不同淹水處理下, 耐澇和敏感基因型大豆根際土壤之間的網(wǎng)絡(luò)結(jié)構(gòu)存在顯著差異(圖7和表2)。隨著澇害時間的增加, 耐澇基因型的正相關(guān)數(shù)、圖密度和平均加權(quán)度增加, 而在敏感基因型中網(wǎng)絡(luò)的正相關(guān)數(shù)在淹水處理下更高。隨著淹水時長的增加, 耐澇和敏感基因型的平均加權(quán)度在淹水1 d和5 d處理下分別比不淹水有所增高。淹水5 d處理下, 耐澇基因型的正相關(guān)和負(fù)相關(guān)鏈接數(shù)都高于澇害敏感基因型。隨著淹水時長的增加, 耐澇基因型網(wǎng)絡(luò)變得更復(fù)雜, 值得注意的是敏感基因型中的網(wǎng)絡(luò)也變得比不淹水時復(fù)雜。

3 討論

3.1 不同淹水時長對根際土壤微生物多樣性的影響

本研究旨在揭示澇害脅迫對耐澇基因型大豆和澇害敏感基因型大豆根際細(xì)菌的影響。當(dāng)大豆遭受長時間淹水處理后, 細(xì)菌豐度都出現(xiàn)降低趨勢, 且耐澇基因型高于澇害敏感型。該結(jié)果與Azarbad等[47]的研究結(jié)果一致, 他們發(fā)現(xiàn)澇害急速降低了小麥根際土壤細(xì)菌的豐度。然而該結(jié)果與郭太忠等[48]的研究結(jié)果相反, 他們發(fā)現(xiàn)澇害增加了玉米根際細(xì)菌的數(shù)量。造成這一不同的原因可能有2個: (1) 2個研究利用的方法不同, 郭太忠等[48]利用的是平板計數(shù)法, 而該研究利用的是熒光定量PCR的方法。(2) 作物種類的不同可能也會造成相反的結(jié)果。該研究中, 耐澇品種的根際擁有更多的細(xì)菌數(shù)量, 這也與Azarbad等[47]的研究結(jié)果相似, 澇害對不同作物基因型的根際細(xì)菌影響差異顯著。這可能是因?yàn)橥寥涝谘退畻l件下, 氧氣大量減少, 好氧細(xì)菌不易存活[49-50], 此外, 淹水脅迫下耐澇基因型和敏感基因型根部不定根數(shù)量、通氣組織強(qiáng)弱程度、根瘤數(shù)量、電導(dǎo)率和丙二醛含量等參數(shù)也可能造成耐澇品種擁有更多的細(xì)菌數(shù)量[47,51-54]。最后, 澇害脅迫強(qiáng)烈影響大豆植物的初級和次級代謝, 且大多數(shù)改變的化合物都參與碳和氮的代謝以及苯丙烷途徑, 且這些改變在耐澇品種和澇害敏感品種中的差異是不一樣的,其可能也是造成耐澇品種擁有更多的細(xì)菌數(shù)量的重要原因之一[55]。

a: 耐澇品種淹水0 d時根際細(xì)菌網(wǎng)絡(luò)結(jié)構(gòu); b: 澇害敏感品種淹水0 d時根際細(xì)菌網(wǎng)絡(luò)結(jié)構(gòu); c: 耐澇品種淹水1 d時根際細(xì)菌網(wǎng)絡(luò)結(jié)構(gòu); d: 澇害敏感品種淹水1 d時根際細(xì)菌網(wǎng)絡(luò)結(jié)構(gòu); e: 耐澇品種淹水5 d時根際細(xì)菌網(wǎng)絡(luò)結(jié)構(gòu); f: 澇害敏感品種淹水5 d時根際細(xì)菌網(wǎng)絡(luò)結(jié)構(gòu)。圖上不同的顏色節(jié)點(diǎn)表示不同的門。紅色連接線表示2個節(jié)點(diǎn)正相關(guān), 藍(lán)色連接線表示2個節(jié)點(diǎn)負(fù)相關(guān)。處理同圖1。

a: co-occurrence network of the rhizosphere bacterial community of W-T variety without waterlogging; b: co-occurrence network of the rhizosphere bacterial community of W-S variety without waterlogging; c: co-occurrence network of the rhizosphere bacterial community of W-T variety under waterlogging for one day; d: co-occurrence network of the rhizosphere bacterial community of W-S variety under waterlogging for one day; e: co-occurrence network of the rhizosphere bacterial community of W-T variety under waterlogging for five days; f: co-occurrence network of the rhizosphere bacterial community of W-S variety under waterlogging for five days. Different color nodes represent different phyla. The red connection line indicates positive correlation between two nodes, and the blue connection line indicates negative correlation between two nodes. Treatments are the same as those given in Fig. 1.

表2 根際細(xì)菌網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)

處理同圖1。Treatments are the same as those given in Fig. 1.

3.2 不同淹水時長對根際土壤微生物群落結(jié)構(gòu)的影響

基于PCoA分析結(jié)果發(fā)現(xiàn), 澇害顯著影響了大豆根際細(xì)菌群落結(jié)構(gòu)(圖6)。這與前人研究的結(jié)果一致, 澇害會對作物的根際土壤微生物群落結(jié)構(gòu)產(chǎn)生影響[56]。澇害可以對根際微生物造成直接的影響, 當(dāng)澇害導(dǎo)致厭氧時, 對環(huán)境極為敏感的土壤微生物呼吸速率和活動減少, 進(jìn)而群落結(jié)構(gòu)發(fā)生變化[57-62]。此外, 作物對澇害脅迫的響應(yīng)也間接影響著根際微生物群落結(jié)構(gòu)[12]。已有研究表明, 當(dāng)作物受到澇害脅迫會影響地下碳輸入的變化[19-20], 進(jìn)而影響了根際微生物組[63]。

本研究還發(fā)現(xiàn), 澇害顯著改變了不同大豆基因型的根際微生物群落結(jié)構(gòu)(表1)。這是因?yàn)椴煌闹参锘蛐晚憫?yīng)澇害脅迫的機(jī)制是不同的, 比如, 與澇害敏感大豆相比, 耐澇大豆在淹水后, 其根系生長、根系損傷以及氣孔的速率表現(xiàn)出更高的適應(yīng)性[9,48]。此外, 淹水對大豆植株根部的一級代謝和二級代謝均有較大影響。大多數(shù)改變的化合物涉及碳和氮代謝以及苯丙醇途徑, 而澇害敏感大豆和耐澇大豆之間的反應(yīng)是不同的[64]。以上的不同都會對根際微生物產(chǎn)生直接的影響, 造成了澇害條件下2個品種根際微生物群落結(jié)構(gòu)的顯著差異。

基于屬水平上的分析我們發(fā)現(xiàn), 在淹水時長為1 d和5 d時, 耐澇基因型的細(xì)菌屬和顯著高于敏感基因型。其中, 細(xì)菌屬屬于放線菌門, 而前人研究表明, 放線菌能夠定殖在植物上促進(jìn)根瘤細(xì)菌的分化, 還能夠促進(jìn)土壤中的鐵離子的吸收, 增加植物抗性[65-66]。屬于浮酶狀菌綱, 浮酶狀菌綱的一些屬可以在厭氧條件下處于厭氧氨氧化過程, 而且還參與碳循環(huán)[67-69]。因此, 上述和在耐澇處理下相對豐度的增加, 說明耐澇品種可能通過促進(jìn)作物對碳氮元素及微量離子元素的吸收, 進(jìn)而增加大豆對澇害的抵抗力。然而, 在2個基因型中差異表達(dá)OTU與耐澇性的相關(guān)是通過統(tǒng)計方式實(shí)現(xiàn)的, 因此其在耐澇過程中具體的功能和作用還需要進(jìn)一步進(jìn)行驗(yàn)證。

基于OTU水平上的分析發(fā)現(xiàn), 淹水處理5 d時, 耐澇基因型大豆富集了OTU274 ()和OTU2334 () (圖5)。是厭氧異養(yǎng)促生菌, 這些屬的一些種具有可以定植在根系周圍產(chǎn)生丙酸、分解纖維素的功能, 可能會幫助植物獲取更多的營養(yǎng)物質(zhì)適應(yīng)脅迫[70-71]。有研究報道在土壤中的硫酸鹽還原和碳循環(huán)中起著關(guān)鍵作用[72], 然而該物種在耐澇害中的作用還沒有報道。

3.3 不同淹水時長對不同大豆基因型細(xì)菌關(guān)聯(lián)網(wǎng)絡(luò)的影響

在農(nóng)田環(huán)境中, 微生物物種是不可能單獨(dú)生存的, 而是與其他自然界物種形成復(fù)雜的生態(tài)網(wǎng)絡(luò)[73]。網(wǎng)絡(luò)分析的結(jié)構(gòu)特性不僅可以揭示物種之間的復(fù)雜關(guān)系, 還可以表征生態(tài)網(wǎng)絡(luò)結(jié)構(gòu)的穩(wěn)定性[62,74]。本研究進(jìn)行了關(guān)聯(lián)網(wǎng)絡(luò)分析, 以獲得對細(xì)菌群落組成的更全面了解, 隨著耐澇基因型的淹水時長的增加, 正相關(guān)數(shù)、圖密度和平均加權(quán)度增加, 表明耐澇基因型的網(wǎng)絡(luò)被復(fù)雜化, 而敏感基因型中觀察到淹水處理也使得網(wǎng)絡(luò)較為復(fù)雜。但是在水淹5 d處理下, 耐澇基因型的網(wǎng)絡(luò)比敏感基因型更加復(fù)雜化, 耐澇基因型的正相關(guān)數(shù)、負(fù)相關(guān)數(shù)、平均度都高于澇害敏感基因型。這和我們的前期研究結(jié)果相似, 即非生物脅迫下, 抗性品種的網(wǎng)絡(luò)結(jié)構(gòu)更為復(fù)雜[75]。前人研究表明, 高度連接的微生物群可以激發(fā)植物免疫系統(tǒng), 加速激活對外界的防御[76-78]。表明澇害使耐澇基因型的網(wǎng)絡(luò)復(fù)雜化, 從而提高大豆抵御澇害脅迫的能力[78-79]。

4 結(jié)論

不同淹水處理對不同基因型大豆的土壤根際細(xì)菌群落產(chǎn)生影響。隨著淹水時長的增加, 2個基因型大豆的細(xì)菌豐度逐漸降低, 且在淹水時長為5 d的時候也出現(xiàn)了顯著差異, 耐澇基因型的細(xì)菌豐度高于澇害敏感型基因的細(xì)菌豐度。耐澇基因型大豆在淹水處理到達(dá)5 d后富集了屬和屬以及OTU274 ()和OTU2334 (), 這些物種的富集可能與耐澇相關(guān), 但其在耐澇過程中具體的功能和作用還需要進(jìn)一步進(jìn)行驗(yàn)證。此外, 澇害脅迫復(fù)雜化耐澇基因型大豆的網(wǎng)絡(luò)結(jié)構(gòu), 這可能提高土壤細(xì)菌群落抵御其他生物和非生物因素脅迫的能力。

[1] Loreti E, Van V H, Perata P. Plant responses to flooding stress., 2016, 33: 64–71.

[2] Duggan B L, Domitruk D R, Fowler D B. Yield component variation in winter grown under drought stress., 2000, 80: 739–745.

[3] Bagci S A, Ekiz H, Yilmaz A, Cakmak I. Effects of zinc deficiency and drought on grain yield of field-grown wheat cultivars in Central Anatolia., 2007, 193: 198–206.

[4] Jiang D, Fan X, Dai T, Cao W. Nitrogen fertilizer rate and post anthesis waterlogging effects on carbohydrate and nitrogen dynamics in wheat., 2008, 304: 301–314.

[5] Yang M, Wangr F. Effects of tea and fungus intercropping on soil microbial community of tea., 2010, 11: 13–16.

[6] Fukao T, Bailey S J. Submergence tolerance conferred byis mediated by SLR1 and SLRL1 restriction of gibberellin res-ponses in rice., 2008, 105: 16814– 16819.

[7] Setter T L, Waters I. Review of prospects for germplasm improvement for waterlogging tolerance in wheat barley and oats., 2003, 253: 1–34.

[8] Herzog M, Striker G G, Colmer T D, Pedersen O. Mechanisms of waterlogging tolerance in wheat: a review of root and shoot physiology., 2016, 39: 1068–1086.

[9] Grayston S, Wang J, Campbell C D S, Edwards A C. Selective influence of plant species on microbial diversity in the rhizosphere., 1998, 30: 369–378.

[10] Sairam R K, Dharmar K, Chinnusamy V, Meena R C. Waterlogging-induced increase in sugar mobilization, fermentation, and related gene expression in the roots of mung bean ()., 2009, 166: 602–616.

[11] Ngumbi E, Kloepper J. Bacterial-mediated drought tolerance: current and future prospects., 2016, 105: 109–125.

[12] Nguyen L T T, Osanai Y, Lai K, Anderson I C, Bange M P, Tissue D T, Singh B K. Responses of the soil microbial community to nitrogen fertilizer regimes and historical exposure to extreme weather events: flooding or prolonged-drought., 2018, 118: 227–236.

[13] Fleury D, Jefffferies S, Kuchel H, Langridge P. Genetic and genomic tools to improve drought tolerance in wheat., 2010, 61: 3211–3222.

[14] Quiza L, St-Arnaud M, Yergeau E. Harnessing phytomicrobiome signaling for rhizosphere microbiome engineering., 2015, 6: 507–516.

[15] Coleman D D, Tringe S G. Building the crops of tomorrow: advantages of symbiont-based approaches to improving abiotic stress tolerance., 2014, 5: 283–295.

[16] Budak H, Akpinar B A, Unver T, Turktas M. Proteome changes in wild and modern wheat leaves upon drought stress by two-dimensional electrophoresis and nanoLC-ESI-MS/MS., 2013, 83: 89–103.

[17] Swamy B P M, Kumar A. Genomics-based precision breeding approaches to improve drought tolerance in rice., 2013, 31: 1308–1318.

[18] Waterer D, Benning N T, Wu G, Luo X, Liu X, Gusta M. Evaluation of abiotic stress tolerance of genetically modified potatoes (cv. Desiree)., 2010, 25: 527–540.

[19] Sanaullah M, Blagodatskaya E, Chabbi A, Rumpel C, Kuzyakov Y. Drought effects on microbial biomass and enzyme activities in the rhizosphere of grasses depend on plant community composition., 2011, 48: 38–44.

[20] Canarini A, Dijkstra F. Dry-rewetting cycles regulate wheat carbon rhizodeposition, stabilization and nitrogen cycling., 2015, 81: 195–203.

[21] Grayston S J, Wang S, Campbell C D, Edwards A C. Selective influence of plant species on microbial diversity in the rhizosphere., 1998, 30: 369–378.

[22] Lau J A, Lennon J T. Rapid responses of soil microorganisms improve plant fitness in novel environments., 2012, 35: 14058–14062.

[23] Kaisermann A, Vries F T, Grifths R I, Bardgett R D. Legacy effects of drought on plant-soil feedbacks and plant-plant interactions., 2017, 215: 1413–1424.

[24] Castrillo G, Teilxeira P, Paredes S, Theresa F L, Laura L, Meghan E, Feltcher O M, Finkel N W, Breakfield P M, Corbin D J, Javier P A, Jeffery L. Root microbiota drive direct integration of phosphate stress and immunity., 2017, 543: 513–518.

[25] Saleem M, Arshad M, Hussain S, Bhatti A S. Perspective of plant growth promoting rhizobacteria (PGPR) containing ACC deaminase in stress agriculture., 2007, 34: 635–648.

[26] Barnawal D, Bharti N, Maji D, Chanotiya C S, Kalra A. 1-Aminocyclopropane-1-carboxylic acid (ACC) deaminase-containing rhizobacteria protectplants during waterlogging stress via reduced ethylene generation., 2012, 58: 227–235.

[27] Grichko V P, Glick B R. Flooding tolerance of transgenic tomato plants expressing the bacterial enzyme ACC deaminase controlled by the 35S,or PRB-1promoter.2001, 39: 19–25.

[28] Zahir Z A, Munir A, Asghar H N, Shaharoona B, M Arshad. Effectiveness of rhizobacteria containing ACC-deaminase for growth promotion of pea () under drought conditions., 2007, 18: 958–963.

[29] Mayak S, Tirosh T, Glick B R. Plant growth-promoting bacteria confer resistance in tomato plants to salt stress., 2004, 42: 565–572.

[30] Lebeis S L, Paredes S H, Lundberg D S, Breakfield N, Gehring J, McDonald M, Malfatti S T, Tijana G, Corbin D, Susannah G, Jeffery L. Salicylic acid modulates colonization of the root mic-robiome by specific bacterial taxa., 2015, 349: 860–864.

[31] Lian T X, Ma Q B, Shi Q H, Cai Z D, Zhang Y F, Cheng Y B, Nian H. High aluminum stress drives different rhizosphere soil enzyme activities and bacterial community structure between aluminum-tolerant and aluminum-sensitive soybean genotypes., 2019, 440: 409–425.

[32] 趙青松, 閆龍, 劉兵強(qiáng), 邸銳, 史曉蕾, 趙雙進(jìn), 張孟臣, 楊春燕. 高產(chǎn)廣適優(yōu)質(zhì)大豆品種冀豆17. 大豆科學(xué), 2015, 34: 736–739.

Zhao Q S, Yan L, Liu B Q, Di R, Shi X L, Zhao S J, Zhang M C, Yang C Y. High yield wide adaptability and high quality soybean variety Jidou 17., 2015, 34: 736–739 (in Chinese with English abstract).

[33] 徐冉, 王彩潔, 張禮鳳, 李偉, 戴海英, 張軍. 高產(chǎn)優(yōu)質(zhì)多抗廣適大豆新品種齊黃34的選育. 山東農(nóng)業(yè)科學(xué), 2013, 45(3): 107–108.

Xu R, Wang C J, Zhang L F, Li W, Dai H Y, Zhang J. Breeding of a new soybean variety Qihuang 34 with high yield, high quality and multi resistance., 2013, 45(3): 107–108 (in Chinese with English abstract).

[34] Castrillo G, Teilxeira P, Paredes S, Law T, Lorenzo L, Feltcher M, Finkel O, Breakfield N, Mieczkowski P, Jones C, Paz J, Dangl J. Root microbiota drive direct integration of phosphate stress and immunity., 2017, 543: 513–518.

[35] Caporaso J G, Kuczynski J, Stombaugh J, Bittinger K, Bushman F D, Costello E K, Noah F, Antonio G P, Julia K G, Jeffrey I G, Gavin A H, Scott T K, Dan K, Jeremy E K. QIIME allows analysis of high-throughput community sequencing data., 2010, 7: 335–336.

[36] Magoc T, Salzberg S L. FLASH: fast length adjustment of short reads to improve genome assemblies., 2011, 27: 2957–2963.

[37] Edgar R, Haas B, Clemente J, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection., 2011, 27: 2194–2200.

[38] Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences., 2006, 22: 1658–1659.

[39] Abarenkov K, Nilsson R H, Larsson K H, Alexander I J, Eberhardt U, Erland S, Klaus H, Rasmus K, Ellen L, Taina P, Robin S, Andy F S, Taylor L, Bj?rn M U, Trude V. The UNITE database for molecular identification of fungi recent updates and future perspectives., 2010, 186: 281–285.

[40] Wang Q, Garrity G M, Tiedje J M, Cole J R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy., 2007, 73: 5261–5267.

[41] Muyzer G, Waal E D, Uitterlinden A G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction amplified genes coding for 16S rRNA., 1993, 59: 695–700.

[42] Li J, Lin J, Pei C, Kaitao L, Thomas C J, Guang D T. Variation of soil bacterial communities along a chronosequence of Eucalyptus plantation., 2018, 6: 5648–5657.

[43] Revelle W. Procedures for personality and psychological research., 2017, 7: 136–142.

[44] Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks., 2009, 361–362.

[45] Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks., 2014, 5: 219–232.

[46] Agler M T, Ruhe J, Kroll S, Morhenn C, Kim S T, Weigel D, Eric M K. Microbial hub taxa link host and abiotic factors to plant microbiome variation., 2016, 14: e1002352.

[47] AzarbadH, Constant P, GiardL C, Bainard L D, Yergeau E. Water stress history and wheat genotype modulate rhizosphere microbial response to drought., 2018, 126: 228–236.

[48] 郭太忠, 袁劉正, 趙月強(qiáng), 柳家友, 谷川. 漬澇對玉米產(chǎn)量和根際土壤微生物的影響. 湖北農(nóng)業(yè)科學(xué), 2014, 53: 505–507.

Guo T Z, Yuan L Z, Zhao Y Q, Liu J Y, Gu C. Effects of waterlogging on maize yield and the rhizosphere soil microorganism., 2014, 53: 505–507 (in Chinese with English abstract).

[49] 曾成城, 陳錦平, 魏虹, 劉媛, 馬文超, 王婷, 周翠. 水淹生境下秋華柳對Cd污染土壤微生物數(shù)量及酶活性的影響. 生態(tài)學(xué)報, 2017, 37: 4327–4334.

Zeng C C, Chen J P, Wei H, Liu Y, Mao W C, Wang T, Zhou C. Effects ofon soil microorganisms and enzymatic activity in contaminated soils under flooding conditions., 2017, 37: 4327–4334 (in Chinese with English abstract).

[50] 趙可夫. 植物對水澇脅迫的適應(yīng). 生物學(xué)通報, 2003, 38(12): 11–14.

Zhao K F. Adaptation of plants to waterlogging stress., 2003, 38(12): 11–14 (in Chinese with English abstract).

[51] Went F W. Effect of root system on tomato stem growth., 1943, 18: 51–65.

[52] 倪君蒂, 李振國. 淹水對大豆生長的影響. 大豆科學(xué), 2000, 19: 42–48.

Ni J D, Li Z G. Effects of flooding on soybean growth., 2000, 19: 42–48 (in Chinese).

[53] 宋曉慧, 滕占林, 簫長亮, 李冬梅, 李文濱, 張代平. 淹水脅迫對不同耐澇性大豆品種苗期根部形態(tài)及葉部生理指標(biāo)的影響. 大豆科學(xué), 2014, 32: 130–132.

Song X H, Teng Z L, Xiao C L, Li D M, Li W B, Zhang D P. Effect of waterlogging on root morphology and foliar physiological indexes of soybean varieties., 2014, 32: 130–132 (in Chinese with English abstract).

[54] Constant P, Chowdhury S P, Hesse L, Pratscher J, Conrad R. Genome data mining and soil survey for the novel group 5 [NiFe]-hydrogenase to explore the diversity and ecological importance of presumptive high affinity H2-oxidizing bacteria., 2011, 77: 6027–6035.

[55] Duarte C I, Mertz H L M, Aurelian D S, Nepomuceno A, Moraes L, Alexandra C, Marcolino G J, Richter C, Colnago L A. Flooded soybean metabolomic analysis reveals important primary and secondary metabolites involved in the hypoxia stress response and tolerance., 2018, 153: 176–187.

[56] Greening C, Biswas A, Carere C R, Jackson C J, Taylor M C, Stott M B, Cook G M, Morales S E. Genomic and metagenomic surveys of hydrogenase distribution indicate H is a widely uti-lised energy source for microbial growth and survival., 2016, 10: 761–777.

[57] Evans S E, Wallenstein M D. Soil microbial community response to drying and rewetting stress: does historical precipitation regime matter., 2012, 109: 101–116.

[58] Preece C, Pe?uelas J. Rhizodeposition under drought and consequences for soil communities and ecosystem resilience., 2016, 409: 1–17.

[59] Unger I M, Kennedy A C, Muzika R M. Flooding effects on soil microbial communities., 2009, 42: 1–8.

[60] Fierer N, Schimel J, Holden P. Variations in microbial community composition through two soil depth profiles., 2003, 35: 167–176.

[61] Moyano F E, Manzoni S, Chenu C. Responses of soil heterotrophic respiration to moisture availability: an exploration of processes and models., 2013, 59: 72–85.

[62] Meisner A, Leizeaga A, Rousk J, B??th E. Partial drying accelerates bacterial growth recovery to rewetting., 2017, 112: 269–276.

[63] Fuchslueger L, Bahn M, Fritz K, Hasibeder R, Richter A. Experimental drought reduces the transfer of recently fixed plant carbon to soil microbes and alters the bacterial community composition in a mountain meadow., 2014, 201: 916–927.

[64] Kozlowski T T. Flooding and plant growth., 1994, 91: 107.

[65] Coutinho I D, Baker J M, Ward J L, Beale M H, Creste S, Cavalheiro A. Metabolite profiling of sugarcane genotypes and identification of flavonoid glycosides and phenolic acids., 2016, 64: 4198–4206.

[66] Tokala R K, Strap J L, Jung C M, Jung D L, Crawford M S, Lee A, Deobald J, Franklin B. Novel plant-microbe rhizosphere interaction involvingWYEC108 and the pea plant ()., 2002, 68: 2161–2171.

[67] Yamanaka K, Oikawa H, Ogawa H, Hideaki T, Shohei S, Teruhiko B, Kenji U. Desferrioxamine E produced bystimulates growth and development of., 2005, 151: 2899–2905.

[68] 黃佩蓓, 焦念志, 馮浩, 舒青龍. 海洋浮霉?fàn)罹鄻有耘c生態(tài)學(xué)功能研究進(jìn)展. 微生物學(xué)報, 2014, 41: 1891–1902.

Huang P B, Jiao N Z, Feng H, Shu Q L. Research progress on Planctomycetes’ diversity and ecological function in marine environments., 2014, 41: 1891–1902 (in Chinese with English abstract).

[69] Gloeckner F O, Bauer M, Teeling H, Lombardot T, Ludwig W, Gade D, Beck A, Borzym K, Heitmann K, Rabus R, Schlesner H, Amann R, Reinhardt R. Complete genome sequence of the marine planctomycetesp. Strain 1., 2003, 103: 292–310.

[70] Strobel G. Harnessing endophytes for industrial microbiology., 2006, 9: 240–244.

[71] Somers E, Vanderleyden J, Srinivasan M. Rhizosphere bacterial signalling: a love parade beneath our feet., 2004, 30: 205–240.

[72] Zhang Y H P, Lynd L R. Cellulose utilization by Clostridium thermocellum: bioenergetics and hydrolysis product assimilation., 2005, 56: 168–176.

[73] Pester M, Bittner N, Deevong P, Wagner M, Loy A. A ‘rare biosphere’ microorganism contributes to sulfate reduction in a peatland., 2010, 4: 1591–1602.

[74] Freilich S, Kreimer A, Meilijson I, Gophna U, Sharan R, Ruppin E. The large-scale organization of the bacterial network of ecological co-occurrence interactions., 2010, 38: 3857–3868.

[75] Shi Q, Liu Y, Shi A, Cai Z, Nian H, Martin H, Lian T. Rhizosphere soil fungal communities of aluminum-tolerant and -sensitive soybean genotypes respond differently to aluminum stress in an acid soil., 2020, 11: 1177.

[76] Xiao X, Liang Y, Zhou S, Zhuang S, Sun B. Fungal community reveals less dispersal limitation and potentially more connected network than that of bacteria in bamboo forest soils., 2018, 27: 550–563.

[77] Jones J D G, Dang J L. The plant immune system., 2006, 444: 323–329.

[78] Peter N D, John P R. Plant immunity: towards an integrated view of plant-pathogen interactions., 2010, 11: 539–548.

[79] Van der Ent S, Van Hulten M, Pozo M J, Czechowski T, Udvardi M K, Pieterse C M J, Ton J. Priming of plant innate immunity by rhizobacteria and β-aminobutyric acid: differences and similarities in regulation., 2009, 183: 419–431.

Effects of waterlogging on rhizosphere microorganisms communities of different soybean varieties

YU Tao-Bing, SHI Qi-Han, NIAN-Hai*, and LIAN Teng-Xiang*

College of Agriculture, South China Agricultural University / Guangdong Subcenter of National Soybean Improvement Center, Guangzhou 510642,Guangdong, China

Waterlogging affects the composition of rhizosphere microbial community of different soybean varieties. The tolerance of soybean plant with different genotypes to waterlogging is quite different. In this study, waterlogging tolerant soybean genotype (Qihuang 34) and waterlogging sensitive soybean genotype (Jidou 17) were selected. The bacterial diversity, community composition, and network characteristics in the rhizosphere of the two genotypes under different waterlogging time were analyzed via fluorescence quantitative qPCR and Illumina Miseq high-throughput sequencing. The results showed that the biomass of waterlogging tolerant genotype and bacterial abundance in its rhizosphere were significantly higher than those for waterlogging sensitive genotype. The PCoA analysis showed that the difference in microbial community composition between waterlogging tolerant and sensitive soybean genotypes changed with waterlogging time (< 0.05). Under the condition of waterlogging,and, OTU274 () and OTU2334 () enriched in the rhizosphere of the waterlogging tolerant genotype. The enrichment of these bacteria might be related to waterlogging tolerance. This study provides evidence of the microbial potential in the rhizosphere of soybean against waterlogging.

soybean; waterlogging tolerance; rhizosphere microorganism; 16S rRNA; network analysis

10.3724/SP.J.1006.2021.04137

本研究由國家重點(diǎn)研發(fā)計劃項(xiàng)目“大田經(jīng)濟(jì)作物優(yōu)質(zhì)豐產(chǎn)的生理基礎(chǔ)與調(diào)控”(2018YFD1000900)資助。

This study was supported by the National Key Research and Development Program of China “Physiological Basis and Agronomic Management for High-quality and High-yield of Field Cash Crops” (2018YFD1000900).

連騰祥, E-mail: liantx@scau.edu.cn; 年海, E-mail: hnian@scau.edu.cn

E-mail: 277885643@qq.com

2020-06-23;

2020-09-13;

2020-09-22.

URL: https://kns.cnki.net/kcms/detail/11.1809.S.20200922.1143.006.html