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      土壤中梨火疫病菌實時熒光定量PCR檢測及動態(tài)分析

      2023-09-27 23:31:04乾義柯韓麗麗陳珊珊羅亮魏霜李曉妹牛蒙亮陳衛(wèi)民
      果樹學(xué)報 2023年9期
      關(guān)鍵詞:熒光定量PCR動態(tài)分析土壤

      乾義柯 韓麗麗 陳珊珊 羅亮 魏霜 李曉妹 牛蒙亮 陳衛(wèi)民

      摘? ? 要:【目的】利用實時熒光定量PCR檢測土壤中梨火疫病菌(Erwinia amylovora)濃度,明確梨火疫病菌在土壤中的動態(tài)變化規(guī)律?!痉椒ā?021年3—11月采集庫爾勒市發(fā)病香梨園810份土壤樣品,應(yīng)用所建立的實時熒光定量PCR檢測體系,測定土壤中的梨火疫病菌濃度,同時對梨園發(fā)病率及病情指數(shù)進行調(diào)查。【結(jié)果】土壤中梨火疫病菌濃度值變化趨勢與梨園病情指數(shù)變化趨勢一致,4—5月梨園病情指數(shù)快速升高至最高值,隨著果樹生長期延長,病情指數(shù)逐漸降低。土壤中梨火疫病菌濃度值從4月逐漸升高,6月平均濃度值為914 CFU·g-1,7月平均濃度值最高為965 CFU·g-1,隨后逐漸降低;6月土壤帶菌率為42.2%,濃度值≥103 CFU·g-1的土樣13份;7月土壤帶菌率為44.4%,濃度值≥103 CFU·g-1的土樣26份;6月、7月土壤中梨火疫病菌濃度顯著高于4月、10月、11月,致病風(fēng)險較高?!窘Y(jié)論】6月和7月土壤中梨火疫病菌濃度值最高,主要與4月、5月大量病花病果掉落造成病原菌積累有關(guān)。加強花期病害防治和梨園病殘體清理可有效降低土壤中梨火疫病菌濃度,是降低梨火疫病菌在梨樹根部侵染風(fēng)險的關(guān)鍵點。

      關(guān)鍵詞:梨火疫病菌;土壤;熒光定量PCR;動態(tài)分析

      中圖分類號:S661.2? ? S436.612 文獻標志碼:A 文章編號:1009-9980(2023)09-1839-09

      收稿日期:2022-11-22 接受日期:2023-06-01

      基金項目:國家重點研發(fā)計劃(2022YFC2601500)

      作者簡介:乾義柯,男,高級實驗師,研究方向為植物病理學(xué)。E-mail:qianyike123@126.com

      *通信作者Author for correspondence. E-mail:nfuyuer@163.com; E-mail:chenweiming3998@126.com

      Real-time PCR detection and dynamic analysis of Erwinia amylovora in the soils of pear orchards

      QIAN Yike1, HAN Lili2, CHEN Shanshan1, LUO Liang1, WEI Shuang3, LI Xiaomei4, NIU Mengliang1*, CHEN Weimin2*

      (1Jianghan University, Wuhan 430056, Hubei, China; 2Yili Vocational and Technical College, Yining 835000, Xinjiang, China; 3Guangzhou Customs Technology Center, Guangzhou 510000, Guangdong, China; 4General Biology (Anhui) Co., Ltd., Hefei 230000, Anhui, China)

      Abstract: 【Objective】 The pear fire blight is induced by Erwinia amylovora (E. amylovora). It first appeared in May, 2016 in Huocheng County, Xinjiang Yili, China. It has spread to 14 Xinjiang regions, posing danger to pears, apples, hawthorns, quince, and other fruit trees, particularly in Korla. The disease has been considered an enormous risk to the Xinjiang fruit industry. Since 2019, we have discovered that the main stem of the pear tree exhibits noticeable lesions and bacterial fluids that spread from the root to the stem and ultimately induce the tree to die. Thus, we assessed the E. amylovora concentrations in the soils of the pear orchards and monitor the dynamic variation trend of E. amylovora by real-time fluorescence quantitative PCR in order to get insight into the occurence and control of the pear fire blight. 【Methods】 Six samples were collected from the each point following the random diagonal five-point sampling method. From March to November 2021, we collected 810 soil samples from the diseased pear orchards in Korla, with a sampling depth of 0-20 cm and a sampling volume of around 100 g at every point. The soil was sieved in order to obtain a 2.0 mm fraction, air-dried at room temperature for 2 days and stored in paper bags. The total DNA was extracted from the each soil sample at a dry weight of 0.25 g using the MOBIO Soil Genomic DNA Extraction Kit. 0.25 g dry soil was taken out, and a standard bacterial suspension of 2.0×109 CFU·mL-1 was used for serial dilution of quantitative soil DNA using sterile water. The standard curve was generated using different amounts of standard DNA dilutions, and the concentration of E. amylovora in soil samples was detected by the real-time fluorescence quantitative PCR established by our lab. 【Results】The real time fluorescence quantitative PCR reaction was conducted under conditions of initial 5 min denaturation at 95 ℃, 45 cycles of 95 ℃ for 10 s, 60 ℃ for 30 s. The equation of standard curve was y=-3.458x + 46.033, there was a good linear relationship between the CT value and the logarithm of pathogen concentration. The evaluation standard was based on the CT value and the fluorescence enhancement signal, with a positive result when the CT value was less than 40 and there was a clear fluorescence enhancement signal and a negative result when the CT value was larger than 40 or there was no amplification signal. E. amylovora accounted for a small proportion of total microorganisms in the soils, and the CT value of positive samples ranged from 30 to 40 in general. The carrier rate was 24.2%, 8.9%, 22.2%, 42.2%, 44.4%, 17.8%, 23.3%, 2.2% and 2.2%, and the average concentration of E. amylovora was 515, 82, 428, 914, 965, 277, 408, 15? and 15 CFU·g-1 from March to November respectively. E. amylovora levels were significantly higher in June and July than those in April, October, and November. The highest concentration of E. amylovora in the 810 soil samples was 5.29×104 CFU·g-1, with a CT value of 31.8. 13 soil samples had concentrations greater than 103 CFU·g-1 in June, and 26 soil samples had concentrations greater than 103 CFU·g-1 in July. The results of the study suggested that June and July would have a higher risk of diseases. Since April, the concentration of E. amylovora in the soils had gradually increased with the growing period of pear plants. Because diseased residues such as diseased leaves and fruits were not cleaned in time after the picking season in 2020, the average concentration of E. amylovora in March was higher than those in April and May. The carrier rate and average concentration of E. amylovora in the soils were at their maximum in July and gradually decreased later. Because diseased leftovers such as diseased leaves and fruits were cleaned in time according to prevention and control requirements following the picking period in 2021, the average concentration of E. amylovora was reduced significantly in October and November 2021. In accordance with the survey data, the average concentration and carrier rate of E. amylovora fluctuated with the disease index. The pear orchard disease index was highest in May, whereas the average concentration and carrier rate of E. amylovora were at their maximum in July. The average E. amylovora concentration and carrier rate in the soils had a time lag, due to the sedimentation and accumulation. 【Conclusion】The average concentration and carrier rate of E. amylovora were at their maximum in June and July in the soils of the pear orchards, which had a time lag with the disease index due to the sedimentation and accumulation of the pathogen. The diseased residues should be cleaned out from the orchard in time for effective control of the disease.

      Key words: Erwinia amylovora; Soil; Real-time fluorescence quantitative PCR; Dynamic analysis

      梨火疫病是由解淀粉歐文氏菌(Erwinia amylovora)引起的一種重大國際檢疫性病害,現(xiàn)已擴散分布于世界近50個國家和地區(qū)[1]。2005年以來,與我國毗鄰的日本、韓國、哈薩克斯坦、吉爾吉斯斯坦和俄羅斯等相繼報道發(fā)生[2-4]。2016年5月梨火疫病首次在中國新疆伊犁霍城縣發(fā)生,現(xiàn)已蔓延至新疆14個地州(市),嚴重危害梨、蘋果、山楂、海棠、榅桲、杏等果樹,尤其是在庫爾勒香梨上傳播極為迅速,對新疆乃至全國林果產(chǎn)業(yè)帶來嚴重威脅[5]。梨火疫病菌在果園中主要通過風(fēng)、雨以及果樹修剪傳播,也可以依靠昆蟲和飛鳥遠距離傳播[6]。發(fā)病病株潰瘍斑處的菌膿可隨雨水的沖擊匯集于土壤,成為重要的侵染源。有研究報道用梨火疫病菌菌液灌溉土壤感染梨樹幼苗,證實了土壤中梨火疫病菌從梨樹受傷根部侵染的風(fēng)險性[7]。2020年Ricardo等[8]利用綠色熒光蛋白轉(zhuǎn)化體、落射熒光顯微鏡和激光共聚焦掃描顯微鏡等技術(shù)手段,證實了梨火疫病菌具有感染、定殖和侵入梨樹根部的能力,并在根部和地上部分均能引起典型的火疫病癥狀。因此,對梨園土壤中梨火疫病菌進行早期診斷和動態(tài)監(jiān)測,對于掌握土壤中梨火疫病菌消長規(guī)律、有效控制病害的發(fā)生至關(guān)重要。

      土傳致病菌一般所占土壤中總微生物的比例較低,而且易受其他微生物及土壤復(fù)雜成分的干擾,因此對檢測體系的穩(wěn)定性和靈敏度要求較高[9]。實時熒光定量PCR(quantitative real-time PCR)技術(shù)能進行定性定量的快速檢測,且靈敏度高。錢國梁等[10]建立了梨火疫病菌熒光染料SYBR GreenⅠ檢測方法,對梨枝條浸泡液進行實時熒光PCR檢測,檢測的靈敏度可達24個菌體細胞。尚琳琳等[11]利用4種實時熒光PCR方法分別對從美國進境的326批櫻桃果實中梨火疫病菌進行檢測,表明探針Ams最為靈敏。袁英哲等[12]將疊氮溴化丙啶(PMA)與實時熒光定量PCR技術(shù)相結(jié)合,建立了梨火疫病菌活菌檢測方法。綜合上述研究方法,筆者在本研究中建立土壤中梨火疫病菌的實時熒光定量檢測體系,并應(yīng)用建立的檢測體系對庫爾勒香梨園整個生長期土壤中梨火疫病菌進行動態(tài)監(jiān)測,明確土壤中梨火疫病菌消長分布規(guī)律,為梨火疫病的防治提供技術(shù)支持。

      1 材料和方法

      1.1 材料

      陽性菌株:梨火疫病菌從新疆庫爾勒市香梨園土壤中分離,經(jīng)生化測定、PCR擴增測序分析鑒定為E. amylovora,菌株保存于江漢大學(xué)生命科學(xué)學(xué)院。

      引物及探針:梨火疫病菌熒光PCR檢測[13]用引物探針由武漢天一輝遠基因科技有限公司合成,擴增片段79 bp。

      引物Ams116F:5-TCCCACATACTGTGAATCATCCA-3;

      引物Ams189R:5-GGGTATTTGCGCTAATTTTATTCG-3,

      探針Ams141T:FAM-5-CCAGAATCTGGCCCGCGTATACCG-3-TAMRA。

      培養(yǎng)基:梨火疫病菌平板培養(yǎng)采用營養(yǎng)瓊脂(NA)+5%蔗糖-牛肉浸膏1.0 g·L-1,酵母膏2.0 g·L-1,蛋白胨5.0 g·L-1,氯化鈉5.0 g·L-1,瓊脂粉18.0 g·L-1,蔗糖50.0 g·L-1,pH 7.2。液體培養(yǎng)采用營養(yǎng)肉湯培養(yǎng)基(NB)+5%蔗糖-牛肉浸膏1.0 g·L-1,酵母膏2.0 g·L-1,蛋白胨5.0 g·L-1,氯化鈉5.0 g·L-1,蔗糖50.0 g·L-1,pH 7.2。

      土壤來源:土壤樣品于2021年3—11月定點采集于庫爾勒市包頭湖農(nóng)場梨園、和什力克鄉(xiāng)薩依力克村梨園、阿瓦提農(nóng)場拓普農(nóng)業(yè)開發(fā)進出口股份有限公司梨園。

      主要試劑及儀器:細菌基因組DNA提取試劑盒(TIANGEN,型號:DP302),土壤基因組DNA提取試劑盒(MOBIO DNeasy Power Soil Kit,型號:12888-50),AceQ Universal U+ Probe Master Mix V2(Vazyme);熒光定量PCR儀(型號:Applied Biosystems StepOnePlusTM Real-Time System);紫外分光光度計(Thermo NANo DROP 8000)。

      1.2 方法

      1.2.1? ? 土壤采集及前處理? ? 在庫爾勒地區(qū)選定3個取樣點,取樣地點1為包頭湖農(nóng)場香梨園(土壤類型:黏土;地理坐標:41°41′04′′ N,85°50′12′′ E)、取樣地點2為拓普農(nóng)業(yè)開發(fā)進出口股份有限公司香梨園(土壤類型:沙土;地理坐標:41°36′55′′ N,86°06′42′′ E)、取樣地點3為和什力克鄉(xiāng)薩依力克村香梨園(土壤類型:壤土;地理坐標:41°46′13′′ N,85°52′27′′ E),采集時間2021年3—11月,同時對梨園果樹發(fā)病情況進行調(diào)查統(tǒng)計。在梨園東、西、南、北、中5個方位隨機取樣,每個方位點取6份土樣,取樣深度0~20 cm,取樣量約100 g,盡量避免沙石、枯枝、樹葉等其他雜質(zhì),裝入密封袋備用。土壤樣品過篩(孔徑2 mm),去除沙石、枯枝等雜物,保持樣品均一性。同時采集未發(fā)病健康梨園土壤進行土壤帶菌分離檢測,確認無梨火疫病菌檢出的土壤作陰性對照。采樣梨園信息詳見表1。

      1.2.2? ? 土壤中總DNA提取? ? 參照MOBIO土壤基因組DNA提取試劑盒,每個過篩土樣取干質(zhì)量0.25 g提取總DNA。

      1.2.3? ? 熒光定量PCR反應(yīng)體系與反應(yīng)程序? ? 使用AceQ Universal U+ Probe Master Mix V2(Vazyme)試劑盒進行PCR擴增,每樣3次重復(fù)。反應(yīng)體系20 ?L:2×AceQ qPCR Probe Master Mix V2 10 μL,10 μmol·L-1上下游引物各0.4 ?L,10 μmol·L-1探針0.2 ?L,DNA模板2.0 ?L。擴增條件:預(yù)變性,95 ℃ 5 min;變性,95 ℃ 10 s、退火/延伸,60 ℃ 30 s(45個循環(huán))。

      1.2.4? ? 梨火疫病菌菌懸液制備? ? 梨火疫病陽性菌株劃線于NA+5%蔗糖培養(yǎng)基上,28.5 ℃活化培養(yǎng)48 h,挑單菌落接種于NB+5%蔗糖營養(yǎng)肉湯培養(yǎng)液中,28.5 ℃,150 r·min-1振蕩培養(yǎng)12 h至OD600 1.0~1.2[12]。10 000 r·min-1離心10 min收集病原菌,棄上清液,再用無菌水重懸病原菌配置新鮮菌懸液。用紫外分光光度計在OD600波段下檢測,檢測值1.0對應(yīng)濃度為2.0×109 CFU·mL-1,4 ℃?zhèn)溆谩?/p>

      1.2.5? ? 土壤定量加標檢測? ? 取1 mL濃度為2.0×109 CFU·mL-1的標準菌液10 000 r·min-1離心10 min收集全部菌株于離心管底部,去上清液,添加0.25 g健康土壤渦旋混勻,配置為8.0×109 CFU·g-1的定量土樣。參照MOBIO土壤基因組DNA提取試劑盒,3次重復(fù)提取加標土壤中總DNA,并分別依次6個梯度10倍稀釋,每個梯度稀釋液取2.0 ?L DNA做模板,按照1.2.3體系和程序擴增,獲取檢測樣品Ct值。以病菌濃度的對數(shù)值和檢測樣品的Ct值為橫、縱坐標,建立標準曲線。

      2 結(jié)果與分析

      2.1 標準曲線制作結(jié)果

      土壤加標標準曲線制作結(jié)果顯示,每個梯度重復(fù)檢測3次,重復(fù)性較好。以模板DNA起始濃度對應(yīng)的梨火疫病菌濃度值的對數(shù)值(x)為橫坐標,以Ct值(y)為縱坐標制作熒光定量PCR標準曲線,結(jié)果如圖1所示。標準曲線方程為y=-3.458x+46.033,CT值與病原菌DNA濃度的對數(shù)值呈良好的線性關(guān)系,擴增效率(E)為94.6%,表明所建立的梨火疫病菌熒光定量PCR檢測體系結(jié)果可信度高,擴增效率較理想,可以滿足定量檢測結(jié)果計算標準。

      2.2 檢測體系質(zhì)控對照結(jié)果

      陽性質(zhì)控(PC)為加標土壤樣品,帶菌濃度8.0×106 CFU·g-1;陰性質(zhì)控(NC)為未發(fā)病健康梨園采集的9份土壤樣品,經(jīng)驗證未能分離到梨火疫病菌且PCR檢測為陰性;空白對照(CK)取2.0 ?L無菌水做模板;測試樣品(TC)為發(fā)病梨園隨機采集的10份土壤樣品,經(jīng)驗證均能從樣品中成功分離到梨火疫病菌且巢式PCR檢測為陽性。質(zhì)控對照熒光PCR檢測結(jié)果顯示,空白對照無熒光增強信號;陽性質(zhì)控樣品有明顯的擴增曲線,Ct值在24.2,與預(yù)期結(jié)果相符;9份陰性質(zhì)控樣品中有7份未檢測到熒光信號,2份陰性樣品有微弱熒光信號,但Ct值在40.0以上,有可能受土壤中其他微生物DNA干擾,出現(xiàn)非特異性擴增;10份測試樣品均檢測到不同程度熒光信號,Ct值分布在30~40之間(圖2),根據(jù)標準曲線公式,當(dāng)Ct值為40時,計算濃度值為222 CFU·g-1土壤,該濃度值的土壤致病風(fēng)險較低。為保證檢測結(jié)果的準確性,將Ct值小于40且有明顯熒光增強信號的檢測結(jié)果定為陽性,檢測值可信;Ct值大于40或無擴增信號的檢測結(jié)果定為陰性,按未檢出處理。

      2.3 田間土壤樣品檢測結(jié)果

      2021年3—11月,每月定點采集90份土樣,共計810份土樣DNA進行熒光定量PCR檢測,土壤帶菌平均值為當(dāng)月樣品帶菌總量與土樣總數(shù)之比值,土壤帶菌率即陽性檢出率為當(dāng)月陽性樣品份數(shù)與土樣總數(shù)之比值,結(jié)果見表2。3月土壤帶菌率為24.2%,平均帶菌濃度為515 CFU·g-1;4月土壤帶菌率為8.9%,平均帶菌濃度為82 CFU·g-1;5月土壤帶菌率為22.2%,平均帶菌濃度為428 CFU·g-1;6月土壤帶菌率為42.2%,平均帶菌濃度為914 CFU·g-1土壤;7月土壤帶菌率為44.4%,平均帶菌濃度為965 CFU·g-1;8月土壤帶菌率為17.8%,平均帶菌濃度為277 CFU·g-1;9月土壤帶菌率為23.3%,平均帶菌濃度為409 CFU·g-1;10—11月土壤帶菌率均為2.2%,平均帶菌濃度為15 CFU·g-1。7月梨園果樹生長高峰期土壤帶菌率和平均帶菌濃度均最高,6月與7月的檢測結(jié)果差距較??;10—11月梨園采摘后期土壤帶菌率和平均帶菌濃度均最低。810份土壤樣品中,帶菌濃度最高的土樣采于3月,樣品檢測Ct值為31.8,濃度為5.29×104 CFU·g-1;6月帶菌濃度≥103 CFU·g-1的土樣有13份,7月帶菌濃度≥103 CFU·g-1的土樣有26份,8月帶菌濃度≥103 CFU·g-1的土樣有12份,均處于較高致病風(fēng)險;相較而言,4月、5月、9月土壤帶菌致病風(fēng)險中等,10—11月土壤帶菌致病風(fēng)險較低。

      3—11月,土壤帶菌濃度平均值柱形圖與梨園病情指數(shù)變化曲線結(jié)果見圖3。2021年3月、6月、7月土壤平均帶菌濃度顯著高于4月、10月、11月;4—8月不同梨園土壤平均帶菌濃度誤差較大,3月、10月、11月誤差較小,不同梨園土壤平均帶菌濃度誤差變化較大可能與梨園的病情指數(shù)、土壤類型及梨園管理等多因素有關(guān)。3月、10月、11月梨園果樹處于休眠期,未做發(fā)病率和病情指數(shù)調(diào)查;病情指數(shù)變化曲線顯示,4—5月期間病情指數(shù)快速升高,發(fā)病率顯著提升達到最高值,隨著果樹生長期延長,病情指數(shù)逐漸降低。梨園病情指數(shù)變化趨勢與土壤平均帶菌濃度趨勢整體上基本一致,表現(xiàn)為先升后降,土壤帶菌濃度變化趨勢要晚于梨園病情發(fā)展1個月左右。

      3 討 論

      土壤是梨火疫病菌傳播的主要非生物因子之一,自2016年首次報道梨火疫病菌傳入新疆伊犁地區(qū)以來,該病迅速在南北疆蔓延開來,尤其是庫爾勒地區(qū)果園香梨樹受災(zāi)最為嚴重,2019年之前梨樹病害癥狀從花腐、枝枯到樹干潰瘍,病害癥狀逐漸加重,2019年后在庫爾勒市重度發(fā)病梨園病情調(diào)查中發(fā)現(xiàn),梨樹主干從根部向上出現(xiàn)明顯潰瘍及流膿癥狀,繼而造成整棵梨樹病死??赡茉蛞环矫媾c土壤中不斷積累的梨火疫病菌有關(guān),另一方面與果農(nóng)園間旋耕造成根部傷口加快病原菌侵染有關(guān)。因此,對土壤中梨火疫病菌濃度及變化規(guī)律進行檢測和動態(tài)分析,摸清土壤中病原菌濃度與梨園病情指數(shù)、果樹生長期之間的關(guān)系,對科學(xué)評估不同時期土壤致病風(fēng)險及采取有效防控措施具有重要意義。

      實時熒光定量技術(shù)在土壤中病原菌的定性定量檢測、早期診斷和預(yù)測預(yù)警方面已得到應(yīng)用。張紀利等[14]利用二次實時熒光定量技術(shù)建立了土壤中枯萎病菌和黑脛病菌的快速檢測方法。何子康等[15]針對青枯病菌建立了二次熒光定量PCR方法,用于煙草青枯病的預(yù)警控制。肖姬玲等[16]運用二次熒光定量PCR方法檢測土壤中西瓜枯萎病菌,檢測下限達到每克土壤100個孢子。張海燕等[17]利用熒光染料SYBR Green Ⅰ定量技術(shù)建立了土壤中茄科雷爾氏菌實時熒光定量PCR快速檢測體系,預(yù)測細菌性青枯病發(fā)病風(fēng)險。筆者在本研究中對國內(nèi)外已報道的梨火疫病菌熒光定量PCR檢測方法進行比較分析,選用梨火疫病菌檢測國家標準中推薦的Ams探針法,該探針靈敏度較高,通過優(yōu)化熒光PCR擴增體系,采用45個擴增循環(huán),結(jié)合質(zhì)控對照分析,確認陽性、陰性檢出標準及可信檢測值,建立了土壤中梨火疫病菌實時熒光定量PCR檢測體系,并應(yīng)用建立的檢測體系對庫爾勒香梨園2021年整個生長期土壤中梨火疫病菌進行動態(tài)監(jiān)測。建立的檢測體系在質(zhì)控對照驗證中,陽性測試樣品的Ct值整體偏高,實際樣品檢測的陽性Ct值也均在30以上,說明梨火疫病菌所占土壤中總微生物的比例較低。

      利用熒光定量PCR技術(shù)對土壤中致病菌進行定量檢測及動態(tài)分析,探尋土壤中病原菌濃度與作物發(fā)病率和病情指數(shù)之間的關(guān)系[18-19]、病原菌濃度與作物生長時期的關(guān)系[20]、作物根際土壤微生物與病原菌之間的相關(guān)性[21],以及病原菌在時間和空間分布的規(guī)律[22-23]等,從不同層面為土傳病害的有效防控提供了科學(xué)依據(jù)。為確保采集樣品在時間和空間上具有較好的代表性,采集時間從2021年3—11月,總計810份樣品,每月定點采集90份樣品;采集區(qū)域分布在發(fā)病區(qū)的3個重點梨園按月進行定點取樣;同時對梨園生長期4—9月發(fā)病率和病情指數(shù)進行調(diào)查統(tǒng)計。前期,筆者采用巢式PCR對2020年春季(3月)和秋季(9月)土壤中梨火疫病菌進行檢測,其土壤帶菌率分別為72.8%和91.8%,明顯高于2021年土壤帶菌率。2021年3月土壤帶菌濃度與4月、10月、11月存在顯著性差異,應(yīng)該與2020年采摘期后沒有及時清理梨園落葉、落果等病殘體,造成病原菌大量積累有關(guān)。2021年秋季采摘后,對梨園落葉、落果等病殘體進行了及時清理,10月、11月土壤帶菌率顯著低于2020年秋季。上述結(jié)果表明加強梨園枯枝、落葉及落果等病殘體的清理,能有效降低土壤中梨火疫病菌濃度。4月隨著果樹萌發(fā),生長期氣溫升高、降雨量增加,果樹花腐為主要病害癥狀,到5月病情指數(shù)最高,落花、落果情況最為嚴重,土壤中梨火疫病菌大量積累,應(yīng)該是6—7月土壤中梨火疫病菌濃度顯著升高的重要原因。隨著夏季果樹生長旺盛期到來,果樹抗病能力增強,土壤濕度、光照等條件變化,土壤中梨火疫病菌濃度又出現(xiàn)逐漸降低的趨勢。從全年土壤帶菌濃度、土壤帶菌率及高濃度樣品比例來看,6—7月土壤中梨火疫病菌的致病風(fēng)險概率較高,因此控制梨園花期發(fā)病率,加強落花、落果后期園間清理,降低土壤中梨火疫病菌的沉降積累,是有效降低土壤中梨火疫病菌致病風(fēng)險的重要環(huán)節(jié)。有相關(guān)研究表明不同果樹、作物在不同生長期,根際與非根際土壤細菌多樣性均有較大差異[24-26],而土壤微生物對植物土傳病害的抑制作用是在土壤微生物群體影響下完成的,并不是單一菌群作用的結(jié)果[27],因此對梨園土壤中梨火疫病菌防治研究還需從土壤微生物中各類細菌數(shù)量的具體分析深入探討。

      4 結(jié) 論

      筆者在本研究中對庫爾勒市發(fā)病梨園整個生長期土壤中梨火疫病菌濃度進行定量檢測、動態(tài)監(jiān)測,明確了土壤中梨火疫病菌消長分布規(guī)律及梨園病情指數(shù)與濃度變化的關(guān)系,以及土壤中梨火疫病菌高致病風(fēng)險時期及防控關(guān)鍵環(huán)節(jié),為土壤中梨火疫病菌的早期診斷及梨火疫病的防治提供了技術(shù)支持。

      參考文獻 References:

      [1] MOLINA L,REZZONICO F,D?FAGO G,DUFFY B. Autoinduction in Erwinia amylovora:Evidence of an acyl-homoserine lactone signal in the fire blight pathogen[J]. Journal of Bacteriology,2005,187(9):3206-3213.

      [2] KIM W S,HILDEBRAND M,GEIDER K,JOCK S. Molecular comparison of pathogenic bacteria from pear trees in Japan and the fire blight pathogen Erwinia amylovora[J]. Microbiology,2001,147(11):2951-2959.

      [3] DOOLOTKELDIEVA T,BOBUSHEVA S. Fire blight disease caused by Erwinia amylovora on Rosaceae plants in Kyrgyzstan and biological agents to control this disease[J]. Advances in Microbiology,2016,6(11):831-851.

      [4] JOCK S,WENSING A,PULAWSKA J,DRENOVA N,DREO T,GEIDER K. Molecular analyses of Erwinia amylovora strains isolated in Russia,Poland,Slovenia and Austria describing further spread of fire blight in Europe[J]. Microbiological Research,2013,168(7):447-454.

      [5] 李曉妹,韓麗麗,何亞南,張學(xué)超,陳衛(wèi)民. 20個蘋果品種(類型)對梨火疫病菌的抗病性評價[J]. 植物檢疫,2022,36(4):6-12.

      LI Xiaomei,HAN Lili,HE Yanan,ZHANG Xuechao,CHEN Weimin. Evaluation on the resistance of 20 apple varieties to Erwinia amylovora[J]. Plant Quarantine,2022,36(4):6-12.

      [6] BILLING E. Fireblight Erwinia amylovora and weather:A comparison of warning systems[J]. Annals of Applied Biology,1980,95(3):365-377.

      [7] SANTANDER R D,MARCO-NOALES E,ORDAX M,BIOSCA E G. Erwinia amylovora colonization of host plants inoculated by irrigation[C]//Microorganisms in Industry & Environment-from Scientific & Industrial Research to Consumer Products-the Ⅲ International Conference on Environmental,2015.

      [8] SANTANDER R D,CATAL?-SENENT J F,F(xiàn)IG?S-SEGURA ?,BIOSCA E G. From the roots to the stem:unveiling pear root colonization and infection pathways by Erwinia amylovora[J]. FEMS Microbiology Ecology,2020,96(12):210.

      [9] 王秋君,馬艷,常志州. 土壤團聚體對微生物及土傳病原菌的影響[J]. 江蘇農(nóng)業(yè)學(xué)報,2015,31(4):946-950.

      WANG Qiujun,MA Yan,CHANG Zhizhou. The effect of soil aggregate on soil microorganism and soil borne pathogen[J]. Jiangsu Journal of Agricultural Sciences,2015,31(4):946-950.

      [10] 錢國良,胡白石,盧玲,劉鳳權(quán),許志剛. 梨火疫病菌的實時熒光PCR檢測[J]. 植物病理學(xué)報,2006,36(2):123-128.

      QIAN Guoliang,HU Baishi,LU Ling,LIU Fengquan,XU Zhigang. Detection of Erwinia amylovora by real-time fluorescent PCR[J]. Acta Phytopathologica Sinica,2006,36(2):123-128.

      [11] 尚琳琳,周國梁,仇書紅,陳仲兵,徐殿勝,易建平. 美國進境櫻桃果實中梨火疫病菌的檢測[J]. 植物保護學(xué)報,2010,37(5):441-446.

      SHANG Linlin,ZHOU Guoliang,QIU Shuhong,CHEN Zhongbing,XU Diansheng,YI Jianping. Detection of Erwinia amylovora in cherry fruits imported from USA by PCR[J]. Journal of Plant Protection,2010,37(5):441-446.

      [12] 袁英哲,韓劍,王巖,羅明,包慧芳,張春竹,黃偉. 梨火疫病菌活菌快速定量檢測方法的建立[J]. 果樹學(xué)報,2020,37(9):1425-1433.

      YUAN Yingzhe,HAN Jian,WANG Yan,LUO Ming,BAO Huifang,ZHANG Chunzhu,HUANG Wei. Establishment of rapid quantitative detection of viable Erwinia amylovora[J]. Journal of Fruit Science,2020,37(9):1425-1433.

      [13] PIRC M,RAVNIKAR M,TOMLINSON J,DREO T. Improved fireblight diagnostics using quantitative real-time PCR detection of Erwinia amylovora chromosomal DNA[J]. Plant Pathology,2009,58(5):872-881.

      [14] 張紀利,齊是,欒新博,金亞波,黃崇峻,黎平,韋建玉,顏健. 植煙土壤中枯萎病菌和黑脛病菌的快速檢測方法[J]. 廣東農(nóng)業(yè)科學(xué),2022,49(2):101-108.

      ZHANG Jili,QI Shi,LUAN Xinbo,JIN Yabo,HUANG Chongjun,LI Ping,WEI Jianyu,YAN Jian. Method for rapid detection of Fusarium oxysporum and Phytophthora parasitica var. nicotianae in tabacco planting soil[J]. Guangdong Agricultural Sciences,2022,49(2):101-108.

      [15] 何子康,張紀利,聶錦瑤,齊是,彭瑋瑤,黎平,劉桔,韋建玉,顏健. 土壤中青枯病菌快速檢測方法的建立與應(yīng)用[J]. 中國植保導(dǎo)刊,2022,42(8):5-9.

      HE Zikang,ZHANG Jili,NIE Jinyao,QI Shi,PENG Weiyao,LI Ping,LIU Ju,WEI Jianyu,YAN Jian. Establishment and application of rapid detection method for Ralstonia solanacearum in soil[J]. China Plant Protection,2022,42(8):5-9.

      [16] 肖姬玲,張屹,李基光,朱菲瑩,魏林,梁志懷. 實時熒光定量PCR檢測土壤西瓜枯萎病菌體系的建立[J]. 植物保護學(xué)報,2018,45(4):921-922.

      XIAO Jiling,ZHANG Yi,LI Jiguang,ZHU Feiying,WEI Lin,LIANG Zhihuai. Establishment of real-time PCR system for quantitatively detecting Fusarium oxysporum f. sp. niveum in soil[J]. Journal of Plant Protection,2018,45(4):921-922.

      [17] 張海燕,張小芳,魏蘭芳,李雪,艾瑛,姬廣海. 土壤中茄科雷爾氏菌實時熒光定量PCR快速檢測體系的建立與應(yīng)用[J]. 江蘇農(nóng)業(yè)科學(xué),2017,45(14):17-20.

      ZHANG Haiyan,ZHANG Xiaofang,WEI Lanfang,LI Xue,AI Ying,JI Guanghai. Establishment and application of real-time PCR system for Ralstonia solanacearum in soil[J]. Jiangsu Agricultural Sciences,2017,45(14):17-20.

      [18] 李華. 山東省小麥-玉米輪作田土壤中多種鐮孢菌的周年動態(tài)檢測[D]. 泰安:山東農(nóng)業(yè)大學(xué),2021.

      LI Hua. Annual dynamic detection of Fusarium sp. in soil of wheat-maize rotation field in Shandong province[D]. Taian:Shandong Agricultural University,2021.

      [19] 秦帥. 實時熒光定量PCR技術(shù)用于土壤中煙草青枯病菌的定量檢測及動態(tài)分析[D]. 泰安:山東農(nóng)業(yè)大學(xué),2016.

      QIN Shuai. Quantitative detection and dynamic analysis of Ralstonia solanacearum of tobacco in soil by real-time PCR[D]. Taian:Shandong Agricultural University,2016.

      [20] 徐娜娜. 實時熒光定量PCR技術(shù)用于土壤中小麥紋枯病菌的定量檢測及動態(tài)分析[D]. 泰安:山東農(nóng)業(yè)大學(xué),2014.

      XU Nana. Quantitative detection and dynamic analysis of Rhizoctonia cerealis of wheat in soil by real-time PCR[D]. Taian:Shandong Agricultural University,2014.

      [21] 黨文芳,李雪艷,楊紅梅,楚敏,高雁,曾軍,霍向東,張濤,林青,歐提庫爾,李玉國,婁愷,史應(yīng)武. 新疆棉田根際土壤真菌熒光PCR技術(shù)定量及其時空動態(tài)分析[J]. 新疆農(nóng)業(yè)科學(xué),2019,56(2):317-324.

      DANG Wenfang,LI Xueyan,YANG Hongmei,CHU Min,GAO Yan,ZENG Jun,HUO Xiangdong,ZHANG Tao,LIN Qing,Outikuer,LI Yuguo,LOU Kai,SHI Yingwu. Quantitative and spatio-temporal dynamic analysis of rhizosphere soil fungi by PCR technology in Xinjiang cotton fields[J]. Xinjiang Agricultural Sciences,2019,56(2):317-324.

      [22] 關(guān)格格. 蕓薹根腫菌分子檢測與田間時空動態(tài)分析[D]. 沈陽:沈陽農(nóng)業(yè)大學(xué),2019.

      GUAN Gege. Molecular detection and dynamic analysis of temporal-spatial distribution on Plasmodiophora brassicae[D]. Shenyang:Shenyang Agricultural University,2019.

      [23] 馬丹丹,關(guān)歡歡,李壽如,賈景麗,于秀梅,劉大群,趙偉全. 馬鈴薯瘡痂病菌在植株和田間的分布與動態(tài)分析[J]. 植物病理學(xué)報,2022,52(1):61-67.

      MA Dandan,GUAN Huanhuan,LI Shouru,JIA Jingli,YU Xiumei,LIU Daqun,ZHAO Weiquan. Analysis of Streptomyces scabies distribution in plants and population dynamics in the field[J]. Acta Phytopathologica Sinica,2022,52(1):61-67.

      [24] 肖慧琳,王建萍,楊業(yè)凱,鄭秋玲,徐維華,宮磊,宋志忠,唐美玲,劉萬好. 基于高通量測序技術(shù)的陽光玫瑰不同砧木根際微生物多樣性研究[J]. 果樹學(xué)報,2022,39(9):1639-1648.

      XIAO Huilin,WANG Jianping,YANG Yekai,ZHENG Qiuling,XU Weihua,GONG Lei,SONG Zhizhong,TANG Meiling,LIU Wanhao. Analysis of microbial diversity in rhizosphere soil of Shine Muscat grape on different rootstocks using high-throughput sequencing[J]. Journal of Fruit Science,2022,39(9):1639-1648.

      [25] 邵微,于會麗,張培基,徐國益,喬憲生,高登濤,王志強,田鵬,司鵬. 不同落葉果樹根際微生物群落代謝與組成的差異性研究[J]. 果樹學(xué)報,2020,37(9):1371-1383.

      SHAO Wei,YU Huili,ZHANG Peiji,XU Guoyi,QIAO Xiansheng,GAO Dengtao,WANG Zhiqiang,TIAN Peng,SI Peng. Differences in metabolism and composition of microbial communities in rhizosphere soils with different deciduous fruit trees[J]. Journal of Fruit Science,2020,37(9):1371-1383.

      [26] 吳宇佳,吉清妹,解鈺,雷菲,鄭道君. 西瓜連作土壤細菌種群消長變化[J]. 中國瓜菜,2018,31(11):18-21.

      WU Yujia,JI Qingmei,XIE Yu,LEI Fei,ZHENG Daojun. Soil bacterial population dynamics of continuous watermelon cropping[J]. China Cucurbits and Vegetables,2018,31(11):18-21.

      [27] REN L X,SU S M,YANG X M,XU Y C,HUANG Q W,SHEN Q R. Intercropping with aerobic rice suppressed Fusarium wilt in watermelon[J]. Soil Biology and Biochemistry,2008,40(3):834-844.

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