更全的杂志信息网

qPCR和荧光光谱:有毒蓝藻实时定量监测技术的应用分析

更新时间:2009-03-28

藻类暴发可由营养物、温度、光照、水文条件等环境因素以及电导率、盐度、浊度等化学指标的变化引起。由于全球气候的变化,藻类暴发事件的数量在北美(五大湖地区)及亚洲众多地区(中国、东南亚等)正以惊人的速度增涨[1]。蓝藻是藻类暴发的主导物种,会产生藻毒素等致命产物,对人类和水生生态系统造成危害;其中微囊藻毒素不仅会损伤人体的肝脏和神经系统,同时也是一种潜在的致癌物质[2]。目前,关于海洋、咸水及淡水中有毒蓝藻鉴定和监测的文献研究并不鲜见,然而对于稳定可靠的24/7藻类实时监测系统的需求却迫在眉睫。实时监测系统须能在藻类暴发前后实现有毒蓝藻的快速检测和有效鉴定,以防止鱼虾大规模死亡,确保供水安全

自20世纪60年代初期开始,荧光检测技术逐渐开始受到关注。当光被叶绿素分子吸收时,其中一部分吸收的能量会以荧光的形式重新辐射出来,而荧光检测技术能通过识别荧光辐射产生的特征激发光谱来分析光合细菌(如蓝藻)中的叶绿素分子,省时省力,并能收集大量的监测数据。

与此同时,近十年来,定量聚合酶链反应(quantitative polymerase chain reaction,qPCR)技术逐渐得到应用,即通过采用实时聚合酶链反应的分子探针来鉴别水环境系统中特定的蓝藻种类。这一快速便捷的实验室技术目前正在迅速发展,以实现实时的24/7藻类监测。

本文主要对比了传统的光谱分析法和迅速发展的分子学方法——qPCR技术在蓝藻监测方面的应用。首先简要介绍了两种方法的背景,随后对两者的优势进行比较。

“当前,收入水平不断提高和消费结构逐步升级,人们休闲旅游的需求更加旺盛。随着消费观念的转变和带薪休假制度的逐步落实,休闲度假将会成为大众化的消费方式。与此同时,城乡一体化进程不断加快,农村的基础设施和公共服务更加完善,乡村的好山好水好风光更有魅力,城里人到乡村养眼洗肺、解乏去累的愿望更加强烈。”韩长赋分析说,当前休闲农业和乡村旅游发展面临难得的历史机遇。

1 传统荧光光谱分析法

Koskeenniemi等[8]的研究采用特定的PCR引物(SYBR green),同时以结球藻毒素基因亚基(nDaF)为目标,结果表明,波罗的海水样中的基因拷贝数(即蓝藻数量)和结球藻毒素的浓度存在强烈的相关性。该qPCR技术可用于具体研究波罗的海中结球藻的暴发和结球藻毒素的形成。

基于传统光谱法的24/7实时监测系统通常是以探针或固定化系统的形式在现场部署,市面上容易购得相关设备,其成本一般由监测范围的大小而定,通常可负担得起。因此相对而言,基于qPCR技术的24/7实时监测系统成本可能要高得多。

采用两种技术手段,对现场条件下的蓝藻进行24/7实时监测,并对两种方法进行了比较。需要指出的是,尽管实时qPCR技术已有实验室现场原型(field prototype),但目前还没有可商用的qPCR系统来实现对蓝藻的24/7现场实时监测。然而,qPCR技术目前正在迅速发展,且其在蓝藻暴发的前期监测中具有关键作用,因此有必要对两种方法进行比较。

一篇研究报告在综述33项研究(2003年~2015年)的基础上,通过分析微囊藻毒素浓度和qPCR数据之间的相关性,对于qPCR技术能否可靠指示淡水中的蓝藻毒素风险进行了评述[9]。根据相关性分析,其中的22项研究显示为正相关(65%),11项为无相关性(33%)。然而,对于上述相同的研究组来说,在检验微囊藻毒素浓度和叶绿素-a或蓝藻细胞数量之间的相关性时,85%的情况显示为正相关。因此,通过与传统方法的比较,研究者对于采用qPCR技术来评估有毒蓝藻暴发风险的可靠性提出了质疑。

  

图1 四种不同藻类和黄色物质的标准光谱图

 

Fig.1 Normal Spectra of Four Algal Groups and Yellow Substances

2 分子探针——qPCR技术

表1总结了两种方法的关键差别。传统光谱方法通过识别不同光合色素(叶绿素-a、藻青蛋白、藻红蛋白等)特有的激发-发射光谱特征来鉴定不同的藻类,而qPCR技术则通过先进的分子学手段鉴别出不同种类的蓝藻。由于qPCR技术在样品的运送和准备过程中都需要专业生物测定手段的支持,因此参与qPCR实时监测的技术人员所需具备的专业门槛要高于采用传统方法的技术人员。如果是在发展中国家或偏远地区应用qPCR技术,这类在分子生物学领域具备专业能力的技术人员将更难招募。

叶绿素-a是一种光合色素,存在于包括真核生物(藻类)和原核生物(蓝藻)在内的所有浮游植物中,是用于计量浮游植物总生物质的有效且广泛使用的指标。然而蓝藻的色素系统产生的叶绿素-a荧光信号非常微弱。与此同时,蓝藻的荧光发生系统主要位于光系统II,而光系统II主要由藻青蛋白(PC)和藻红蛋白(PE)等藻胆色素构成[3-4]。因此可通过分析藻胆色素荧光光谱携带的大量信息,测算蓝藻的丰度,这就需要对藻胆色素的荧光光谱进行优化。

在小组合作这一模式下,教学评价也是至关重要的一个环节,其将对最终的教学成效产生直接的影响。至此,在这其中,教师也应增强自己的认知程度,不断地强化评价的针对性、目的性、实效性等,使该模式的应用效果得以升华,促使高效课堂得以真正的构建。

尽管采用qPCR技术来评估蓝藻暴发毒性,其结果有时存在争议,但qPCR技术在蓝藻菌群的动力学研究中仍具有重要价值,比如研究有毒菌株相对丰度的时间和空间变化、分析蓝藻暴发的动力学情况及其与环境因素之间的关系等[10-11]。qPCR技术的另一大优势是它能同时检测多个目标基因,称为“多重qPCR”(multiplex qPCR),比如可同时将微囊藻毒素、节球藻毒素、拟柱孢藻毒素以及蛤蚌毒素四种有毒的生物合成基因簇作为目标。对51种毒性和非毒性蓝藻菌株的检测已证实了多重qPCR技术的特异性。采用该方法,成功检测了澳大利亚墨累河中混合蓝藻水华的毒性[12]

3 方法比较

表中:d是子弹的直径;L是子弹的全长;γ是由于子弹抛撒顺序不同引起的邻层角度差;h是由于子弹抛撒顺序不同引起的邻层高度差。

自人类基因组计划(1990年~2003年)开始以来,快速分子生物学工具的开发应用得到了大量关注,它们可用来鉴定存在于敏感生态系统中且对人类具有毒性的特定微生物种类[7]。首次采用实时PCR技术来监测有毒的浮游植物是在2000年。蓝藻专有的基因标记可以以负责生成微囊藻毒素、节球藻毒素、蛤蚌毒素以及拟柱孢藻毒素等毒素的产毒基因为目标。

图1显示了四种不同的藻类和黄色物质(yellow substances,YS)的标准光谱[5]。对于蓝藻色素来说,PC所在的蓝藻组和PE所在的红藻组分别在610 nm和570 nm的波长下检测到了强烈的荧光信号,因此可通过最优激发波长的改变,实现某一特定藻类荧光标记的优化。基于此,在680 nm的发射波长下,采用发光二极管,microLAN系统[6]可分别在570、615 nm和450 nm的选定激发波长下检测PE、PC和叶绿素-a产生的荧光强度。此外,对于水样中一些会干扰检测的其他荧光物质,如溶解性有机物(365 nm)、浊度(710 nm)等,系统也会进行相应的修正。

3)沿填筑好的卵石外侧轮廓面填筑一层500 mm厚的砾石反滤层,砾石反滤层高度与集水廊道侧墙顶面齐平,外侧边坡为1∶1.5,要求砾石粒径d=10~30 mm。

 

表1 传统光谱技术和qPCR技术的比较

 

Tab.1 Comparison of Traditional Spectroscopic and qPCR Methods

  

比较项目传统光谱技术qPCR技术检测对象浮游植物的叶绿素⁃a,藻青蛋白、藻红蛋白等光合色素特定种类蓝藻的目标基因技术人员技能等级中低水平中高水平可用实时监测系统商业化应用模型实验室原型应用地区限制全球范围均可偏远地区仍有难度成本大小中低成本中高成本

在未来3~5年内,我们有望看到传统方法向商用24/7实时流式细胞监测法(flow cytometry monitoring)的发展进化[13];而对于qPCR技术来说,基于“芯片实验室”(lab-on-a-chip)技术的现场原型将使得实时监测系统的商业化应用更具可行性[14-15]

4 结论

采用荧光光谱技术鉴定蓝藻是一项突破性的应用,是实现快速、可靠、24/7实时的水系统蓝藻暴发监测的有力手段。目前传统的光谱学方法已广为人知,且在24/7藻类实时监测系统中广泛应用,与此同时采用分子学技术的qPCR方法在过去十年间也得到了迅速发展。qPCR方法能够单独鉴定一种或同时鉴定多种(4~5种)有毒蓝藻菌种,具有显著优势。在全球气候环境越来越多变的状况下,传统的光谱技术和先进的qPCR技术将继续共同发挥关键作用,为研究人员、监管人员和决策人员在藻类暴发的管理和防治方面提供重要信息和依据。

自然条件不仅影响农村居民点复垦的成本,而且影响复垦土地的利用方向[11]。例如,浅丘、平坝地区农村居民点复垦投资水平一般较丘陵、低山地区要低,有利于大规模开展农村居民点复垦;与此同时,平坝、浅丘地区农村居民点用地复垦腾出的用地大部分可以作为耕地,而中丘、山地地区农村居民点用地复垦腾出的用地主要用于园、林地用途。因此,选取地区地形地貌(虚拟)值为自然条件的评价指标。

参考文献

[1]Wilhelm S W,Farnsley S E,LeCleir G R,et al.The relationships between nutrients,cyanobacterial toxins and the microbial community in Taihu (Lake Tai),China[J].Harmful Algae,2011,10(2):207-215.

[2]USGS.The science of harmful algalblooms[EB/OL].[2016-10-24].https://www.usgs.gov/news/science-harmful-algae-blooms.

[3]Sobiechowska Sasim M,Stoń Egiert J,Kosakowska A.Quantitative analysis of extracted phycobilin pigments in cyanobacteria-An assessment of spectrophotometric and spectrofluorometric methods[J].Journal of Applied Phycology,2014,26(5):2065-2074.

[4]Simis S G H,Huot Y,Babin M,et al.Optimization of variable fluorescence measurements of phytoplankton communities with cyanobacteria[J].Photosynthesis Research,2012,112(1):13-30.

[5]Beutler M.Spectral fluorescence of chlorophyll and phycobilins as an in-situ tool of phytoplankton analysis models,algorithms and instruments[D].Kiel,Germany:Christian-Albrechts-Universität zu Kiel,2003.

[6]MicroLan.ALGControl:Fluorescence monitoring of algae classes and toxic algae[EB/OL].http://www.microlan.nl/monitoring-products/algcontrol-fluorescence-monitoring-algae/.

[7]Genome News Network.Sequencing the genome of Haemophilus influenza Rd[EB/OL].http://www.genomenewsnetwork.org/resources/timeline/1995_Haemophilus.php.

[8]Koskenniemi K,Lyra C,Rajaniemi-Wacklin P,et al.Quantitative real-time PCR detection of toxic Nodularia cyanobacteria in the Baltic Sea[J].Applied and Environmental Microbiology,2007,73(7):2173-2179.

[9]Pacheco A B F,Guedes I A,Azevedo S M F O.Is qPCR a reliable indicator of cyanotoxin risk in freshwater[J].Toxins,2016,8(6):172-172.

[10]Martins A,Vasconcelos V.Use of qPCR for the study of hepatotoxic cyanobacteria population dynamics[J].Archives of Microbiology,2011,193(9):615-615.

[11]Antonella P,Luca G.The quantitative real-time PCR applications in the monitoring of marine harmful algal bloom (HAB)species[J].Environmental Science and Pollution Research,2013,20(10):6851-6862.

[12]Al-Tebrineh J,Merrick C,Ryan D,et al.Community composition,toxigenicity,and environmental conditions during a cyanobacterial bloom occurring along 1 100 kilometers of the Murray River[J].Applied and Environmental Microbiology,2012,78(1):263-272.

[13]Besmer M D,Weissbrodt D G,Kratochvil B E,et al.The feasibility of automated online flow cytometry for in-situ monitoring of microbial dynamics in aquatic ecosystems[J].Frontiers in Microbiology,2014,5(265):265-265.

[14]Weller M G.Immunoassays and biosensors for the detection of cyanobacterial toxins in water[J].Sensors,2013,13(11):15085-15112.

[15]Courtois S,Jary D,Do-Quang Z,et al.Developing a fully-integrated microdevice for the in-situ detection of cyanobacteria and cyanotoxin-producing strains in fresh water samples[C].Busan,Korea:Proceedings of the IWA World Water Congress,2012.

原文

Utilizing Advanced Fluorescence Technology for Real-Time Monitoring of Toxic Cyanobacteria in Algal Blooms

Charles CC Lee1,Joep Appels2,Zhang Xianchao3

(1.University of Newcastle,Singapore; 2.MicroLAN,Netherlands; 3.Zean Inc,China)

Introduction

Algal blooms arelikely caused by environmental factors such as:nutrients; temperature; light; hydrology; and water chemistry (e.g.,pH,conductivity,salinity,turbidity).Due to climate change,algal blooms are increasing at an alarming rate across North America (Great Lakes) and numerous regions in Asia (China,Southeast Asia)[1].Cyanobacteria is the dominant species in algal blooms which produces lethal cyanotoxins harmful to humans and the aquatic ecosystem.Microcystin toxins damages the liver and the nervous system,as well as a potent carcinogen[2].While there is no lack of literature on the identification and monitoring of toxic cyanobacteria in marine,brackish and freshwaters,there is an urgent need for a reliable real-time 24/7 algal monitoring system.This system would allow for a quick detection of toxic cyanobacteria before,during and after algal blooms in order to protect water supplies and prevent massive fish kills.

The analysis of chlorophyll molecules in photosynthetic bacteria (e.g.cyanobacteria) by identification of the unique excitation spectra produced from fluorescence has gained significant interest since the early 1960s.Fluorescence monitoring is extremely attractive as it is quick,not labor intensive,and enables the collection of massive amount of monitoring data.When chlorophyll molecules absorb light,a fraction of the energy absorbed is re-emitted as fluorescence.More recently,in the last decade,molecular probes using a real-time polymerase chain reaction (PCR),also known as quantitative PCR (qPCR) technology were developed to identify specific cyanobacteria species in water systems.This is currently a quick laboratory-based technique that is being rapidly developed experimentally for real-time 24/7 monitoring in the field.

The focusof this paper is to compare the monitoring of cyanobacteria using the traditional spectroscopic method with the rapidly advanced molecular method-qPCR.First a brief background of both methods will be described,followed by a comparison of the advantages of both methods.

Traditional Spectroscopic Method

Chlorophyll-a is a photosynthetic pigment present in all species of phytoplankton,including eukaryotic (algae) and prokaryotic organisms (cyanobacteria) thus it is a reliable and commonly used proxy for total phytoplankton biomass.However,the pigment system of cyanobacteria produces only a weak chlorophyll-a fluorescence signal.The cyanobacteria′s fluorescence system arise mainly from photosystem II.Photosystem II is comprised of pigments called phycobilins - e.g.phycocyanin (PC) and phycoerythrin (PE)[3-4].Therefore,it is important to optimize the fluorescence spectra of phycobilins,carrying a significant amount of spectral information that can be used to assess the abundance of cyanobacteria.

Figure 1 shows the normal spectra of four algal groups and yellow substances (YS)[5].Focusing on the cyanobacteria pigments,a strong signal is found for PC at 610 nm in the “blue group”.For PE,a strong signal is detected at 570 nm in the “red group” (red algae).It is therefore,possible to optimize the fluorescence signature of specific algae by switching on the optimal excitation wavelength.Using light emitting diodes (LED) at selected excitation wavelengths with an emission wavelength of 680nm,the microLan system[6] detects PE,PC and chlorophyll-a at 570,615 and 450 nm,respectively.Additionally,correction can be applied for other fluorescing matters such as dissolved organic matter (365 nm),and turbidity (710 nm) in the water sample that interferes with the measurement.

Molecular Probe-qPCR Method

Since the discovery of the human genome project (1990-2003) there has been an explosive interest in developing rapid molecular biology tools to identify specific microbial species that are toxic to humans and present in sensitive ecosystems[7] .The first applications of real-time PCR to monitor toxic phytoplankton was conducted in the year 2000.Genetic markers,unique to cyanobacteria,target toxin producing genes that are responsible for the synthesis of toxins.These toxins include microcystins,nodularin,saxitoxin,and cylindrospermopsin.

Using specific PCR primers (SYBR green) targeting the nodularin gene subunit (nDaF),Koskeenniemi et al (2007)[8] showed a strong correlation between gene copy numbers (quantity of cyanobacteria) and nodularin concentrations in Baltic seawater samples.This qPCR technique can be used for detailed studies of Nodularin blooms and formation in the Baltic sea.

An extensive research paper reviewing 33 studies (2003-2015) on whether qPCR is a reliable indicator of cyanotoxin risk in freshwater correlated qPCR data with microcystin concentrations[9].The correlation analysis showed the following results:positive correlation for 22 studies (65%),and no correlation for 11 studies (33%).However,for the same set of studies above,when the correlation between the microcystin concentration and chlorophyll-a or number of cyanobacterial cells was tested,it was positive in 85% of the cases.The reviewer therefore questioned the reliability of using qPCR in comparison with traditional methods for the risk assessment of toxic cyanobacterial blooms.

Although the use of qPCR is sometimes questioned for estimating the toxicityof cyanobacterial blooms,it is still considered valuable for the study of cyanobacteria population dynamics.This includes exploring temporal and spatial variations in the relative abundance of toxic strains,and understanding of the dynamics of cyanobacterial blooms and their relationships with environmental factors[10-11].An additional advantage of qPCR is the ability to detect more than one single target gene called multiplex qPCR.Multiplex qPCR therefore can target four toxin biosynthesis gene clusters simultaneously such as microcystin,nodularin,cylindrospermopsin,and saxitoxin.The specificity of the multiplex qPCR was validated by testing 51 toxic and non-toxic cyanobacterial strains.Using this method,the toxigenicity of mixed cyanobacterial blooms in the Murray River (Australia) was successfully tested[12].

Comparisons

The comparison of the two methods is focused on implementation of the technology for 24/7 monitoring of cyanobacteria under real-time field conditions.To our knowledge,whilethere are experimental field prototypes of real-time qPCR,there is currently no commercially available qPCR system deployed to conduct 24/7 cyanobacteria monitoring in the field.However,the qPCR is rapidly advancing and certainly plays a crucial role in the early monitoring of cyanobacteria blooms.Therefore,it is worthwhile to compare the two methods.

Table 1 summarises the key differences between the two methods.While the traditional spectroscopic technology focuses on identification using the unique excitation-emission signature of photosynthetic pigments (chlorophyll-a,phycocyanin,phycoerthrin),qPCR specifically identifies the cyanobacteria species based on advanced molecular techniques.Because the qPCR requires specialized biological assays in both delivery and preparation,the skill sets of the technicians involved in conducting the monitoring would be significantly higher than those required to deploy the traditional method.This specialized skill sets in molecular biology is likely more difficult to find if the qPCR technology is to be deployed in developing economies or in remote areas.

Commercially available 24/7 monitoring systems for cyanobacteria utilizing the traditional spectroscopic method can be readily purchased on the market as deployable probes and stationary systems.Depending on the scale of the implementation,costs for such systems are generally affordable.In comparison,it is likely to be significantly more costly to develop a real-time 24/7 qPCR monitoring system.

 

Tab.1 Comparison of Traditional Spectroscopic and qPCR Methods

  

TypeTraditionalMethodqPCRFocusPhytoplankton,chlorophyll⁃aPhycocyanin,PhycoerythrinSpecificCyanobacteriaspeciesSkilllevelLowtomediumMediumtohighReal⁃timemonitoringCommercialmodelsavailableResearch⁃basedfieldprototypesavailableImplementationWorldwideChallengingforremoteareasCostRelativelyaffordableMediumtohighcost

Moving forward in the next 3-4 years,we will likely see advanced development of thetraditional method into 24/7 commercially available real-time flow cytometry monitoring[13]. For qPCR,field prototypes using lab-on-a-chip technologies will appear feasible to be implemented for real-time monitoring[14-15].

Conclusions

The breakthrough application of fluorescence technologies to the identification of cyanobacteria is a promising tool to transform monitoring of cyanobacteria blooms in water systems to be rapid,and reliable over a 24/7 period.While the traditional spectroscopic method is well-known and utilized extensively in real-time 24/7 monitoring systems,the qPCR method using molecular techniques has advanced rapidly in the last decade.The qPCR′s ability to identify toxic cyanobacteria species individually or a few species (4-5) simultaneously,is certainly a key advantage.Both traditional spectroscopic and advanced qPCR technologies will continue to play centre stage in providing researchers,regulators and policy makers with important information on how to manage and prevent algal blooms in an increasingly uncertain climate change environment.

编辑札记】对藻类的在线预警一直是原水水质管理人员关注的焦点,与传统水厂工艺相比,预警技术仍处于发展期和摸索期,百家初放,各显神通。将qPCR法和荧光光谱法作对比分析,客观论述了两种技术的术有专攻与应用限制,本无优劣之分,但对于不同需求的水库管理,各有用武之地,能帮助我们更好地理解藻类在线预警技术的进展,为水库管理者提供思路。

 
Charles CC Lee
《净水技术》 2018年第03期
《净水技术》2018年第03期文献

服务严谨可靠 7×14小时在线支持 支持宝特邀商家 不满意退款

本站非杂志社官网,上千家国家级期刊、省级期刊、北大核心、南大核心、专业的职称论文发表网站。
职称论文发表、杂志论文发表、期刊征稿、期刊投稿,论文发表指导正规机构。是您首选最可靠,最快速的期刊论文发表网站。
免责声明:本网站部分资源、信息来源于网络,完全免费共享,仅供学习和研究使用,版权和著作权归原作者所有
如有不愿意被转载的情况,请通知我们删除已转载的信息 粤ICP备2023046998号