农业土壤微生物基因与群落多样性研究进展

介绍了群落基因组多样性、结构多样性与功能多样性相互关系的研究方法 ,重点论述了近年来农业土壤微生物群落遗传、结构与功能多样性的研究进展。同时总结了耕作措施和养分管理对农业土壤微生物群落多样性的影响 ,提出微生物序列分析、比较基因组学和微生物芯片技术与传统研究技术结合将有助于对微生物群落结构与功能和生物与环境因素对土壤微生物群落影响的深刻理解     

MAR-FISH技术及其在环境微生物群落与功能研究中的应用

对复杂环境中微生物群落结构和功能的研究是微生物生态学的重要任务。尽管现代分子生物学技术已经成功地用于解析环境中微生物的群落结构, 但是这些方法并不能提供微生物的原位生理学信息。而一种新的方法, 微观放射自显影和荧光原位杂交集成技术(MAR-FISH)则能够同时在单细胞水平上, 检测复杂环境中微生物的系统发育信息及其生理特性。本文总结了MAR-FISH方法的原理, 实验步骤及其在环境微生物群落与功能研究中的应用。     

微生物分子生态学发展历史及研究现状

微生物群落多样性是微生物生态学和环境学研究的重点之一。分子生物学方法应用于微生物群落结构分析使得对环境样品中占大多数的不可培养微生物的研究成为了可能。由于功能上高度保守,序列上的不同位置具有不同的变异速率,核糖体RNA（rRNA）是目前在微生物分子生态学上最为有用以及应用最广泛的分子标记,通过rRNA序列比对,可以分析不同分类水平的系统发育关系。元基因组学研究方法通过对环境样品中的各种微生物群落的总的基因组进行分析,充分展示了环境微生物代谢途径,极大地扩展了对微生物的认识。快速发展的高通量测序极大地促进了各项微生物生态学技术的发展,带来了新的突破。     

基于宏组学方法认识微生物群落及其功能

进入后基因组学时代,测序技术飞速发展,测序成本明显下降,形成了涵盖宏基因组学、宏转录组学和宏蛋白质组学的宏组学技术,推动了对微生物群落的多样性、结构及潜在基因功能方面的深入研究。最近随着整合的宏组学技术的提出及应用,全面系统分析微生物群落动态变化及其代谢功能已成为可能,这将成为微生物生态学研究的新趋势。本文综述了宏组学在研究海洋湖泊、深海热泉、人体肠道、牛瘤胃生境、森林土壤与堆肥生境等环境中微生物群落的结构和功能方面的最新进展与成功应用案例。     

元蛋白质组学在微生物功能研究中的应用

后基因组时代,仅依靠基因组方法来研究原位微生物群落的功能已远远不够,在这种背景下元蛋白质组学研究逐渐兴起。应用元蛋白质组学技术可大规模研究原位微生物群落的蛋白质表达,分析生态系统中微生物的功能,寻找新的功能基因和代谢通路,为微生物群体的基因和功能多样性研究提供数据。同时,还可鉴定与微生物功能相关的蛋白质,这些蛋白质未来可以作为生物标记物为环境可持续发展铺路。综述了元蛋白质组学的发展概况及其在微生物功能研究中的重大作用,强调了元蛋白质组学方法在分析新功能基因及其相关基因,揭示微生物多样性与微生物群体功能之间的关系等方面起到的作用,并对其应用前景进行了展望。     

微生物单细胞基因组技术及其在环境微生物研究中的应用

单细胞及单细胞基因组学研究是近年生命科学研究的热点之一,微生物单细胞基因组学研究是继微生物元基因组学(又称宏基因组学,Metagenomics)之后新发展起来的,可有效获取环境中大量无法培养的微生物遗传信息的技术。微生物单细胞基因组技术包括单细胞获取、全基因组扩增、全基因组测序以及数据分析等步骤,目前该技术在环境微生物研究中的应用主要集中于探索未被元基因组技术或其它常规技术探测到的新型功能基因,或是对环境中物种丰度极小的未培养微生物的发现,以及对微生物细胞生命进化过程的研究等。本文对微生物单细胞基因组技术中单细胞获取和全基因组扩增所涉及到的不同方法以及应用此技术对环境微生物取得的主要研究进展进行综述。     

海岸带沉积物中氮循环功能微生物多样性

海岸带生境类型多样,环境梯度明显,是研究微生物多样性、群落结构与功能关系及调控机制的天然实验场.沉积物是海岸带环境中营养盐再生与转化发生的重要场所,其中多种微生物类群在氮素循环过程中扮演重要角色.本文重点介绍海岸带沉积物中固氮菌、氨氧化菌、厌氧氨氧化菌、反硝化与硝酸盐铵化微生物的基于16SrRNA基因的物种多样性和基于关键酶基因nifH、amoA、narG、nirS、nirK、nosZ、nrfA、hzo、hzs等的功能多样性;总结了在海岸带特有生境(如河口、潮间带、海草藻床、红树林、盐沼、珊瑚礁、浅海等)及污染胁迫、生物扰动等条件下各功能类群的群落组成特征及时空变化规律,并提出今后需要重点关注新的培养技术和方法的开发,以进一步提高微生物的可培养性,将单细胞基因组测序与分析技术、DNA和RNA结合起来研究,以全面了解氮循环微生物多样性、参与介导硝酸盐铵化过程的微生物多样性等方面.     

活性污泥微生物群落宏组学研究进展

活性污泥是全球最常用的废水生物处理人工生态系统,微生物是驱动其污染净化能力的关键。活性污泥微生物群落所有物种与基因(简称"微生物组")的研究先后经历了"显微镜观察和纯菌培养分离"(1915)、"PCR扩增-测序"(1994)和"高通量测序-宏组学分析"(2006)三个重要阶段的发展变迁。相应地,我们对活性污泥微生物组的认知经历了从最早对微型动物(如钟虫和轮虫)及其他微生物的形貌观察和纯种培养鉴定到今天对整个微生物组的全局多样性认识的飞跃。近13年来,基于高通量测序的宏组学方法被广泛应用于揭示活性污泥微生物群落组成结构和功能,我们现在充分意识到活性污泥微生物组蕴藏着大量不可培养新物种和基因多样性,驱动着各类污染物的降解与转化。目前,特异性分子标记基因的扩增子测序技术已经被广泛应用于揭示城市和工业废水处理活性污泥微生物组和典型功能种群(如硝化细菌和聚磷菌)的时空多样性和群落构建机制,进而为未来实现活性污泥微生物组功能的精准调控奠定理论基础。宏基因组学研究在群落、种群和个体基因组水平全面解析了活性污泥微生物组驱动的碳、氮、磷元素循环过程,以及有机微污染物的生物降解和转化机理。将来活性污泥微生物组学研究需要在"标准化的组学分析方法和绝对定量""高通量培养组学""高通量功能基因组学"和"多组学方法的结合及多种方法并用"4个方面取得实现精准生态基因组学所需的技术突破,以最大限度发掘活性污泥微生物组在污水处理与资源回收领域的生态学与工程学价值。     

植被对土壤微生物群落结构的影响

研究了不同土壤及覆盖其上的植被与土壤微生物群落结构和多样性的关系．植被使土壤中的微生物种类更丰富,群落多样性更高．表层土壤微生物群落中没有明显的优势种群,种间竞争作用较弱．并介绍了研究土壤微生物群落的分子生物学方法．     

生态基因组学研究进展

生态基因组学是一个整合生态学、分子遗传学和进化基因组学的新兴交叉学科。生态基因组学将基因组学的研究手段和方法引入生态学领域,通过将群体基因组学、转录组学、蛋白质组学等手段与方法将个体、种群及群落、生态系统不同层次的生态学相互作用整合起来,确定在生态学响应及相互作用中具有重要意义的关键的基因和遗传途径,阐明这些基因及遗传途径变异的程度及其生态和进化后果的特征,从基因水平探索有机体响应天然环境(包括生物与非生物的环境因子)的遗传学机制。生态基因组学的研究对象可以分为模式生物与非模式生物两大类。拟南芥、酿酒酵母等模式生物在生态基因组学领域发挥了重要作用。随着越来越多基因组学技术的开发与完善,越来越多的非模式生物生态基因组学的研究将为生态学的发展提供重要的理论与实践依据。生态基因组学最核心的方法包括寻找序列变异、研究基因差异表达和分析基因功能等方法。生态基因组学已广泛渗透到生态学的相关领域中,将会在生物对环境的响应、物种间的相互作用、进化生态学、全球变化生态学、入侵生态学、群落生态学等研究领域发挥更大的作用。     

The microbial gene diversity along an elevation gradient of the Tibetan grassland

Tibet is one of the most threatened regions by climate warming, thus understanding how its microbial communities function may be of high importance for predicting microbial responses to climate changes. Here, we report a study to profile soil microbial structural genes, which infers functional roles of microbial communities, along four sites/elevations of a Tibetan mountainous grassland, aiming to explore the potential microbial responses to climate changes via a strategy of space-for-time substitution. Using a microarray-based metagenomics tool named GeoChip 4.0, we showed that microbial communities were distinct for most but not all of the sites. Substantial variations were apparent in stress, N and C-cycling genes, but they were in line with the functional roles of these genes. Cold shock genes were more abundant at higher elevations. Also, gdh converting ammonium into urea was more abundant at higher elevations, whereas ureC converting urea into ammonium was less abundant, which was consistent with soil ammonium contents. Significant correlations were observed between N-cycling genes (ureC, gdh and amoA) and nitrous oxide flux, suggesting that they contributed to community metabolism. Lastly, we found by Canonical correspondence analysis, Mantel tests and the similarity tests that soil pH, temperature, NH₄⁺–N and vegetation diversity accounted for the majority (81.4%) of microbial community variations, suggesting that these four attributes were major factors affecting soil microbial communities. On the basis of these observations, we predict that climate changes in the Tibetan grasslands are very likely to change soil microbial community functional structure, with particular impacts on microbial N-cycling genes and consequently microbe-mediated soil N dynamics.     

Marine Metagenome as A Resource for Novel Enzymes

More than 99% of identified prokaryotes, including many from the marine environment,cannot be cultured in the laboratory. This lack of capability restricts our knowledge of microbial genetics and community ecology. Metagenomics, the culture-independent cloning of environmental DNAs that are isolated directly from an environmental sample, has already provided a wealth of information about the uncultured microbial world. It has also facilitated the discovery of novel biocatalysts by allowing researchers to probe directly into a huge diversity of enzymes within natural microbial communities. Recent advances in these studies have led to a great interest in recruiting microbial enzymes for the development of environmentally-friendly industry. Although the metagenomics approach has many limitations, it is expected to provide not only scientific insights but also economic benefits, especially in industry. This review highlights the importance of metagenomics in mining microbial lipases, as an example, by using high-throughput techniques. In addition, we discuss challenges in the metagenomics as an important part of bioinformatics analysis in big data.     

Metagenomics for studying unculturable microorganisms: cutting the Gordian knot

More than 99% of prokaryotes in the environment cannot be cultured in the laboratory, a phenomenon that limits our understanding of microbial physiology, genetics, and community ecology. One way around this problem is metagenomics, the culture-independent cloning and analysis of microbial DNA extracted directly from an environmental sample. Recent advances in shotgun sequencing and computational methods for genome assembly have advanced the field of metagenomics to provide glimpses into the life of uncultured microorganisms.     

Trait-based life-history strategies explain succession scenario for complex bacterial communities under varying disturbance

Trait-based approaches are increasingly gaining importance in community ecology, as a way of finding general rules for the mechanisms driving changes in community structure and function under the influence of perturbations. Frameworks for life-history strategies have been successfully applied to describe changes in plant and animal communities upon disturbance. To evaluate their applicability to complex bacterial communities, we operated replicated wastewater treatment bioreactors for 35 days and subjected them to eight different disturbance frequencies of a toxic pollutant (3-chloroaniline), starting with a mixed inoculum from a full-scale treatment plant. Relevant ecosystem functions were tracked and microbial communities assessed through metagenomics and 16S rRNA gene sequencing. Combining a series of ordination, statistical and network analysis methods, we associated different life-history strategies with microbial communities across the disturbance range. These strategies were evaluated using tradeoffs in community function and genotypic potential, and changes in bacterial genus composition. We further compared our findings with other ecological studies and adopted a semi-quantitative competitors, stress-tolerants, ruderals (CSR) classification. The framework reduces complex data sets of microbial traits, functions and taxa into ecologically meaningful components to help understand the system response to disturbance and hence represents a promising tool for managing microbial communities.     

土壤微生物群落构建理论与时空演变特征

土壤微生物作为陆地生态系统的重要组成部分,直接或间接地参与几乎所有的土壤生态过程,在物质循环、能量转换以及污染物降解等过程中都发挥着重要作用。对土壤微生物时空演变规律及其形成机制的研究,不仅是微生物演变和进化的基础科学问题,也是预测微生物及其所介导的生态功能对环境条件变化响应、适应和反馈的理论依据。讨论了土壤微生物群落的定义、测度方法和指标,认为群落是联系动植物宏观生态学与微生物生态学的基础,群落构建机制是宏观和微观生态学都需要研究的核心科学问题;从生态学的群落构建理论出发,阐述了包括生态位理论/中性理论、过程理论和多样性-稳定性理论在土壤微生物时空演变研究中的应用,以及微生物群落在时间和空间上的分布特征及其尺度效应;确立了以微生物群落构建理论为基础、不同时空尺度下土壤微生物群落演变特征为主要内容的微生物演变研究的基本框架。     

An invitation to the marriage of metagenomics and metabolomics

Metagenomics seeks to characterize the composition of microbial communities, their operations, and their dynamically coevolving relationships with the habitats they occupy without having to culture community members. Uniting metagenomics with analyses of the products of microbial community metabolism (metabolomics) will shed light on how microbial communities function in a variety of environments, including the human body.     

Interspecies bacterial interactions in biofilms

Interactions among bacterial populations can have a profound influence on the structure and physiology of microbial communities. Interspecies microbial interactions begin to influence a biofilm during the initial stages of formation, bacterial attachment and surface colonization, and continue to influence the structure and physiology of the biofilm as it develops. Although the majority of research on bacterial interactions has utilized planktonic communities, the characteristics of biofilm growth (cell positions that are relatively stable and local areas of hindered diffusion) suggest that interspecies interactions may be more significant in biofilms.     

Microbial Communities Can Be Described by Metabolic Structure: A General Framework and Application to a Seasonally Variable,Depth-Stratified Microbial Community from the Coastal West Antarctic Peninsula

Taxonomic marker gene studies, such as the 16S rRNA gene, have been used to successfully explore microbial diversity in a variety of marine, terrestrial, and host environments. For some of these environments long term sampling programs are beginning to build a historical record of microbial community structure. Although these 16S rRNA gene datasets do not intrinsically provide information on microbial metabolism or ecosystem function, this information can be developed by identifying metabolisms associated with related, phenotyped strains. Here we introduce the concept of metabolic inference; the systematic prediction of metabolism from phylogeny, and describe a complete pipeline for predicting the metabolic pathways likely to be found in a collection of 16S rRNA gene phylotypes. This framework includes a mechanism for assigning confidence to each metabolic inference that is based on a novel method for evaluating genomic plasticity. We applied this framework to 16S rRNA gene libraries from the West Antarctic Peninsula marine environment, including surface and deep summer samples and surface winter samples. Using statistical methods commonly applied to community ecology data we found that metabolic structure differed between summer surface and winter and deep samples, comparable to an analysis of community structure by 16S rRNA gene phylotypes. While taxonomic variance between samples was primarily driven by low abundance taxa, metabolic variance was attributable to both high and low abundance pathways. This suggests that clades with a high degree of functional redundancy can occupy distinct adjacent niches. Overall our findings demonstrate that inferred metabolism can be used in place of taxonomy to describe the structure of microbial communities. Coupling metabolic inference with targeted metagenomics and an improved collection of completed genomes could be a powerful way to analyze microbial communities in a high-throughput manner that provides direct access to metabolic and ecosystem function.