基因芯片及其在环境微生物研究中的应用

基因芯片因其具有高密度、高灵敏度、快速 (实时 )检测、经济、自动化和低背景水平等特点 ,而广泛应用于不同的研究领域。目前 ,应用于环境微生物研究的基因芯片主要有功能基因芯片 (FGAs)、系统发育的寡核苷酸芯片 (POAs)和群落基因组芯片 (CGAs)。综述了基因芯片在环境微生物研究中的应用 ,包括自然环境中微生物的基因表达分析、比较基因组分析和混合微生物群落的分析等。讨论了基因芯片面临的挑战和前景展望     

用于分析微生物种类组成的微生物生态学研究方法

对环境中微生物群落进行分析对于理解该环境中各类微生物的功能具有重要意义,因此日益受到重视。用于环境中微生物群落分析的方法很多,将简要介绍基于PCR的研究方法,原位杂交技术,基因芯片技术,宏蛋白质组学技术等研究方法的原理、应用及其优缺点。     

微生物分子生态学研究方法的新进展

环境中微生物的群落结构及多样性和微生物的功能及代谢机理是微生物生态学的研究热点,长期以来,由于受到研究技术的限制,对微生物的群落结构和多样性的认识还不全面,微生物的功能及代谢机理方面了解也很少.随着高通量测序、基因芯片等新技术的不断更新,微生物分子生态学的研究方法和研究途径也在不断变化.高通量测序技术改变了微生物多样性、宏基因组学和宏转录组学的研究方法,GeoChip高密度覆盖海量已知功能的基因探针于单张芯片,能快速确定微生物和已知功能基因的存在与否.总结和比较了目前最新的研究手段,并归纳了这些方法的适用性和优缺点.     

生物冶金过程中的关键微生物及其碳循环代谢途径研究

【目的】深入研究极端酸性环境中微生物的碳循环过程。【方法】应用16S r RNA高通量测序和功能基因芯片技术对德兴铜矿中浸矿堆(LH)和积液池(LS)两个子系统中的微生物群落结构组成和功能基因组成进行分析;并运用PICRUSt功能基因预测的方法对群落功能进行预测。【结果】功能基因芯片和功能预测分析都表明碳循环基因在子系统间存在显著差异(P0.05),且碳固定相关的卡尔文循环、还原性三羧酸循环等基因以及碳降解相关的己聚糖和纤维素等基因在LS系统中都要明显高于LH系统。碳循环功能基因在子系统之间的差异与环境条件相关,其中TON、Ca、ES、Fe3+和P作用显著。【结论】在极端酸性环境中,环境条件的差异会对微生物群落碳循环功能基因产生筛选作用,参与碳循环的微生物的种类和相对丰度都发生变化,最终改变了群落碳循环模式。     

功能基因在反硝化菌群生态学研究中的应用

具有反硝化功能的微生物分布非常广泛,与系统分化无关,因此16SrRNA不适合分析环境中的反硝化群落。目前,利用功能基因分析环境样品中的反硝化群落成为研究热点。本文介绍了反硝化过程的分子生物学基础,比较了环境样品中反硝化菌群落结构的分子生物学手段,简述了目前环境样品中反硝化菌群落研究的主要内容,并探讨了该领域研究的不足及展望。虽然到目前为止,群落结构和功能的关系还未建立,但反硝化菌群落的研究必将为应用反硝化菌解决环境问题提供基础数据。     

中国传统酿造食品微生物生态学及其研究策略

中国传统酿造食品的生产通常是在开放度高、周围环境条件粗放、微生物群落结构时空差异显著批次间变化规律类似的微生态环境中进行的.其过程具有生产原料成分多样、参与的微生物种类多、代谢反应复杂、工艺繁复以及产品组分复杂等特征,从而导致科学阐释其过程代谢机理存在着巨大的挑战.基因组学、转录组学等组学技术以及基于现代检测技术的代谢组学方法的发展和应用,为应用微生态学方法研究其过程的微生物群落结构与功能、群落内部以及与环境的交互作用规律提供了有力的支撑.本文结合本研究团队前期研究工作,综述了近年来传统酿造食品微生物生态学的研究进展,从系统解析酿造微生物群落结构与代谢功能入手,解析微生物群落及个体功能并实现理性调控的传统酿造微生物群落研究策略,包括把握微生物群落三个要素:结构、功能、调控;解析好两个互动:酿造群落内的交互作用,群落与外部环境的交互作用;抓住一个关键:群落中的关键核心功能微生物(群).在上述基础上努力实现传统食品酿造过程中的微生物功能可控、酿造过程可控和产品品质可控的三个目标.     

变性梯度凝胶电泳在环境微生物生态学中的应用

PCR-变性梯度凝胶电泳(PCR-DGGE)具有可靠性强、重复性好、方便快捷等优点,已被广泛应用于环境生态学中微生物群落多样性、动态性分析和功能细菌的跟踪。本文综述了PCR-DGGE技术的基本原理,不同DNA提取方法的比较,不同PCR方式的比较及其在环境生态学中研究微生物群落多样性、环境中微生物群落变化的动态监测、硝化菌-反硝化菌和硫酸还原菌(SRB)的动态分析和监测等领域中的应用,并对该技术自身存在的局限性和应用前景进行了评价。     

基因芯片技术检测细菌耐药性的研究进展

基因芯片技术是将无数预先设计好的寡核苷酸、cDNA、基因组 (Genomic)DNA在芯片上做成点阵 ,与样品中同源核酸分子杂交 ,对样品的序列信息进行高效的解读和分析 ,大规模获取相关生物信息。该技术应用领域主要有表达谱分析、基因突变及多态性分析、疾病诊断和预测、DNA测序、药物筛选、检测筛选耐药基因、微生物菌种鉴定及致病机制研究等。着重介绍了基因芯片技术检测细菌耐药性方面的国外研究进展。基因芯片可以大量、快捷地检测出细菌耐药性菌株以及引起细菌耐药性的基因的突变 ,由于其在检测中的高效率 ,因此要优越于传统的细菌学检测技术。基因芯片技术在细菌耐药性检测中有着巨大的应用价值 ,具有广阔的应用前景。     

基因芯片在海洋微藻研究中的应用前景

基因芯片通常指DNA芯片,其基本原理是将大量的寡核苷酸分子固定于支持物上,然后与标记的样品进行杂交,通过检测杂交信号从而获取结果。多年来,我国在医药疾病研究、微生物检测等领域已成功研制出多种基因芯片,取得了骄人的成绩,但在对海洋微藻研究的应用上成果较少。现着力从实际应用的角度阐述基因芯片及其在海洋微藻研究中的应用。     

基于功能基因的微生物碳循环分子生态学研究进展

碳循环是生态系统中重要的生物地球化学元素循环之一。微生物参与碳固定、甲烷代谢、碳降解等多个重要的碳循环过程,深入了解微生物群落在碳循环过程中的功能和作用,有助于获悉微生物对全球气候变化的响应、适应和反馈机制,这也是微生物生态学研究的关键问题之一。传统的研究多集中于微生物分离培养技术,无法覆盖绝大部分未培养微生物,并且无法深入解析碳循环过程中微生物群落的结构和功能,宏基因组学技术的出现克服了这些缺陷,成为研究微生物群落结构和功能的有效手段。本文对目前宏基因组学的主要技术——定量PCR、DNA分子指纹图谱、基因芯片、克隆文库和高通量测序等技术进行了简要介绍,着重介绍了参与碳固定、甲烷生成和氧化、碳降解等主要碳循环过程的关键功能基因的研究现状,最后对碳循环过程中微生物宏基因组学研究的未来发展进行了总结与展望。     

Microarrays for bacterial detection and microbial community analysis

Several types of microarrays have recently been developed and evaluated for bacterial detection and microbial community analysis. These studies demonstrated that specific, sensitive and quantitative detection could be obtained with both functional gene arrays and community genome arrays. Although single-base mismatch can be differentiated with phylogenetic oligonucleotide arrays, reliable specific detection at the single-base level is still problematic. Microarray-based hybridization approaches are also useful for defining genome diversity and bacterial relatedness. However, more rigorous and systematic assessment and development are needed to realize the full potential of microarrays for microbial detection and community analysis.     

Diagnostic microbial microarrays in soil ecology

Soil microbial communities are responsible for important physiological and metabolic processes. In the last decade soil microorganisms have been frequently analysed by cultivation-independent techniques because only a minority of the natural microbial communities are accessible by cultivation. Cultivation-independent community analyses have revolutionized our understanding of soil microbial diversity and population dynamics. Nevertheless, many methods are still laborious and time-consuming, and high-throughput methods have to be applied in order to understand population shifts at a finer level and to be better able to link microbial diversity with ecosystems functioning. Microbial diagnostic microarrays (MDMs) represent a powerful tool for the parallel, high-throughput identification of many microorganisms. Three categories of MDMs have been defined based on the nature of the probe and target molecules used: phylogenetic oligonucleotide microarrays with short oligonucleotides against a phylogenetic marker gene; functional gene arrays containing probes targeting genes encoding specific functions; and community genome arrays employing whole genomes as probes. In this review, important methodological developments relevant to the application of the different types of diagnostic microarrays in soil ecology will be addressed and new approaches, needs and future directions will be identified, which might lead to a better insight into the functional activities of soil microbial communities.     

Microarray-based analysis of subnanogram quantities of microbial community DNAs by using whole-community genome amplification

Microarray technology provides the opportunity to identify thousands of microbial genes or populations simultaneously, but low microbial biomass often prevents application of this technology to many natural microbial communities. We developed a whole-community genome amplification-assisted microarray detection approach based on multiple displacement amplification. The representativeness of amplification was evaluated using several types of microarrays and quantitative indexes. Representative detection of individual genes or genomes was obtained with 1 to 100 ng DNA from individual or mixed genomes, in equal or unequal abundance, and with 1 to 500 ng community DNAs from groundwater. Lower concentrations of DNA (as low as 10 fg) could be detected, but the lower template concentrations affected the representativeness of amplification. Robust quantitative detection was also observed by significant linear relationships between signal intensities and initial DNA concentrations ranging from (i) 0.04 to 125 ng (r2 = 0.65 to 0.99) for DNA from pure cultures as detected by whole-genome open reading frame arrays, (ii) 0.1 to 1,000 ng (r2 = 0.91) for genomic DNA using community genome arrays, and (iii) 0.01 to 250 ng (r2 = 0.96 to 0.98) for community DNAs from ethanol-amended groundwater using 50-mer functional gene arrays. This method allowed us to investigate the oligotrophic microbial communities in groundwater contaminated with uranium and other metals. The results indicated that microorganisms containing genes involved in contaminant degradation and immobilization are present in these communities, that their spatial distribution is heterogeneous, and that microbial diversity is greatly reduced in the highly contaminated environment.     

Microarray-Based Analysis of Subnanogram Quantities of Microbial Community DNAs by Using Whole-Community Genome Amplification

Microarray technology provides the opportunity to identify thousands of microbial genes or populations simultaneously, but low microbial biomass often prevents application of this technology to many natural microbial communities. We developed a whole-community genome amplification-assisted microarray detection approach based on multiple displacement amplification. The representativeness of amplification was evaluated using several types of microarrays and quantitative indexes. Representative detection of individual genes or genomes was obtained with 1 to 100 ng DNA from individual or mixed genomes, in equal or unequal abundance, and with 1 to 500 ng community DNAs from groundwater. Lower concentrations of DNA (as low as 10 fg) could be detected, but the lower template concentrations affected the representativeness of amplification. Robust quantitative detection was also observed by significant linear relationships between signal intensities and initial DNA concentrations ranging from (i) 0.04 to 125 ng (r² = 0.65 to 0.99) for DNA from pure cultures as detected by whole-genome open reading frame arrays, (ii) 0.1 to 1,000 ng (r² = 0.91) for genomic DNA using community genome arrays, and (iii) 0.01 to 250 ng (r² = 0.96 to 0.98) for community DNAs from ethanol-amended groundwater using 50-mer functional gene arrays. This method allowed us to investigate the oligotrophic microbial communities in groundwater contaminated with uranium and other metals. The results indicated that microorganisms containing genes involved in contaminant degradation and immobilization are present in these communities, that their spatial distribution is heterogeneous, and that microbial diversity is greatly reduced in the highly contaminated environment.     

Linking microbial community structure with function: fluorescence in situ hybridization-microautoradiography and isotope arrays

The ecophysiology of microorganisms has been at the heart of microbial ecology since its early days, but only during the past decade have methods become available for cultivation-independent, direct identification of microorganisms in complex communities and for the simultaneous investigation of their activity and substrate uptake patterns. The combination of fluorescence in situ hybridization (FISH) and microautoradiography (MAR) is currently the most widely applied tool for revealing physiological properties of microorganisms in their natural environment with single-cell resolution. For example, this technique has been used in wastewater treatment and marine systems to describe the functional properties of newly discovered species, and to identify microorganisms responsible for key physiological processes. Recently, the scope of FISH-MAR was extended by rendering it quantitative and by combining it with microelectrode measurements or stable isotope probing. Isotope arrays have also been developed that exploit the parallel detection offered by DNA microarrays to measure incorporation of labelled substrate into the rRNA of many community members in a single experiment.     

Evaluation of 50-mer oligonucleotide arrays for detecting microbial populations in environmental samples

Microarrays fabricated with oligonucleotides longer than 40 bp have been introduced for monitoring whole genome expression but have not been evaluated with environmental samples. To determine the potential of this type of microarray for environmental studies, a 50-mer oligonucleotide microarray was constructed using 763 genes involved in nitrogen cycling: nitrite reductase (nirS and nirK), ammonia monooxygenase (amoA), nitrogenase (nifH), methane monooxygenase (pmoA), and sulfite reductase (dsrAB) from public databases and our own sequence collections. The comparison of the sequences from pure cultures indicated that the developed microarrays could provide species-level resolution for analyzing microorganisms involved in nitrification, denitrification, nitrogen fixation, methane oxidation, and sulfite reduction. Sensitivity tests suggested that the 50-mer oligonucleotide arrays could detect dominant populations in the environments, although sensitivity still needs to be improved. A significant quantitative relationship was also obtained with a mixture of DNAs from eight different bacteria. These results suggest that the 50-mer oligonucleotide array can be used as a specific and quantitative parallel tool for the detection of microbial populations in environmental samples.     

Microarrays for pathogen detection and analysis

DNA microarrays have emerged as a viable platform for detection of pathogenic organisms in clinical and environmental samples. These microbial detection arrays occupy a middle ground between low cost, narrowly focused assays such as multiplex PCR and more expensive, broad-spectrum technologies like high-throughput sequencing. While pathogen detection arrays have been used primarily in a research context, several groups are aggressively working to develop arrays for clinical diagnostics, food safety testing, environmental monitoring and biodefense. Statistical algorithms that can analyze data from microbial detection arrays and provide easily interpretable results are absolutely required in order for these efforts to succeed. In this article, we will review the most promising array designs and analysis algorithms that have been developed to date, comparing their strengths and weaknesses for pathogen detection and discovery.     

Validation of a more sensitive method for using spotted oligonucleotide DNA microarrays for functional genomics studies on bacterial communities

Spotted oligonucleotide microarrays potentially offer a wide scope of applications for microbial ecology, especially as they improve the flexibility of design and the specificity of detection compared to PCR product based microarrays. Sensitivity, however, was expected to be problematic, as studies with the more sensitive PCR-based cDNA microarrays indicate that only genes from populations contributing to more than 5% of the community DNA can be detected. We evaluated several parameters to increase sensitivity and then tested applicability for bacterial functional genomics. The optimal parameters were the use of 5'-C6-amino-modified 70-mers printed on CMT-GAPS II substrates at a 40 micro M concentration combined with the use of Tyramide Signal Amplification labelling. This protocol allowed detection of single copy genes belonging to an organism contributing to 1% or more of the total community. To demonstrate its application, we detected the specific aromatic oxygenase genes in a soil community degrading polychlorinated biphenyls (PCBs). This increase in sensitivity is important if oligonucleotide microarrays are to be used for simultaneous monitoring of a range of functions performed by different microorganisms in the environment.