High-resolution metagenomics targets specific functional types in complex microbial communities

Most microbes in the biosphere remain unculturable. Whole genome shotgun (WGS) sequencing of environmental DNA (metagenomics) can be used to study the genetic and metabolic properties of natural microbial communities. However, in communities of high complexity, metagenomics fails to link specific microbes to specific ecological functions. To overcome this limitation, we developed a method to target microbial subpopulations by labeling DNA through stable isotope probing (SIP), followed by WGS sequencing. Metagenome analysis of microbes from Lake Washington in Seattle that oxidize single-carbon (C1) compounds shows specific sequence enrichments in response to different C1 substrates, revealing the ecological roles of individual phylotypes. We also demonstrate the utility of our approach by extracting a nearly complete genome of a novel methylotroph, Methylotenera mobilis, reconstructing its metabolism and conducting genome-wide analyses. This high-resolution, targeted metagenomics approach may be applicable to a wide variety of ecosystems.     

Metagenomic applications in environmental monitoring and bioremediation

With the rapid advances in sequencing technology, the cost of sequencing has dramatically dropped and the scale of sequencing projects has increased accordingly. This has provided the opportunity for the routine use of sequencing techniques in the monitoring of environmental microbes. While metagenomic applications have been routinely applied to better understand the ecology and diversity of microbes, their use in environmental monitoring and bioremediation is increasingly common. In this review we seek to provide an overview of some of the metagenomic techniques used in environmental systems biology, addressing their application and limitation. We will also provide several recent examples of the application of metagenomics to bioremediation. We discuss examples where microbial communities have been used to predict the presence and extent of contamination, examples of how metagenomics can be used to characterize the process of natural attenuation by unculturable microbes, as well as examples detailing the use of metagenomics to understand the impact of biostimulation on microbial communities.     

Single-cell metagenomics: challenges and applications

With the development of high throughput sequencing and single-cell genomics technologies, many uncultured bacterial communities have been dissected by combining these two techniques. Especially, by simultaneously leveraging of single-cell genomics and metagenomics, researchers can greatly improve the efficiency and accuracy of obtaining whole genome information from complex microbial communities, which not only allow us to identify microbes but also link function to species, identify subspecies variations, study host-virus interactions and etc. Here, we review recent developments and the challenges need to be addressed in single-cell metagenomics, including potential contamination, uneven sequence coverage, sequence chimera, genome assembly and annotation. With the development of sequencing and computational methods, single-cell metagenomics will undoubtedly broaden its application in various microbiome studies.     

微生物组大数据管理与分析

高通量技术的迅猛发展促使微生物生态学研究获得了重大突破,掀起了元基因组学(Metagenomics)研究的热潮。元基因组学通常被定义为对未培养的环境样本中微生物群体的DNA序列分析。随着微生物组学数据的日益剧增,微生物大数据的高效管理与分析越来越受到研究者的关注。如何从海量的微生物组数据中挖掘出具有科研价值的数据信息并应用于实际问题成为当前的研究热点。目前已有很多计算生物学程序工具及数据库用于元基因组数据的分析与管理。本文主要综述了随着高通量测序技术的进步,国际上主要的微生物组计划及微生物组数据平台,如人类微生物组项目(human microbiome project,HMP)、地球微生物组项目(earth microbiome project,EMP)、欧盟的肠道微生物组计划(metagenomics of human intestinal tract,MetaHIT)、MG-RAST、i Microbe、整合微生物组(integration microbial genomes,IMG)以及EBI Metagenomics等;介绍了微生物数据分析的主要流程与工具;提出了建设多源异构的微生物生态数据管理与分析系统的必要性。     

Bioinformatics for whole-genome shotgun sequencing of microbial communities

The application of whole-genome shotgun sequencing to microbial communities represents a major development in metagenomics, the study of uncultured microbes via the tools of modern genomic analysis. In the past year, whole-genome shotgun sequencing projects of prokaryotic communities from an acid mine biofilm, the Sargasso Sea, Minnesota farm soil, three deep-sea whale falls, and deep-sea sediments have been reported, adding to previously published work on viral communities from marine and fecal samples. The interpretation of this new kind of data poses a wide variety of exciting and difficult bioinformatics problems. The aim of this review is to introduce the bioinformatics community to this emerging field by surveying existing techniques and promising new approaches for several of the most interesting of these computational problems.     

Metagenomics as a new technological tool to gain scientific knowledge

Metagenomics (also Environmental Genomics, Ecogenomics or Community Genomics) is an emerging approach to studying microbial communities in the environment. This relatively new technique enables studies of organisms that are not easily cultured in a laboratory, thus differing from traditional microbiology that relies almost entirely on cultured organisms. Metagenomics technology thus holds the premise of new depths of understanding of microbes and, importantly, is a new tool for addressing biotechnological problems, without tedious cultivation efforts. DNA sequencing technology has already made a significant breakthrough, and generation of gigabase-pairs of microbial DNA sequences is not posing a challenge any longer. However, conceptual advances in microbial science will not only rely on the availability of innovative sequencing platforms, but also on sequence-independent tools for getting an insight into the functioning of microbial communities. This is an important issue, as we know that even the best annotations of genomes and metagenomes only create hypotheses of the functionality and substrate spectra of encoded proteins which require experimental testing by classical disciplines such as physiology and biochemistry. In this review, we address the following question, how to take advantage of, and how can we improve the, metagenomic technology for accommodating the needs of microbial biologists and enzymologists?     

From genomics to metagenomics

Next-generation sequencing has changed metagenomics. However, sequencing DNA is no longer the bottleneck, rather, the bottleneck is computational analysis and also interpretation. Computational cost is the obvious issue, as is tool limitations, considering most of the tools we routinely use have been built for clonal genomics or are being adapted to microbial communities. The current trend in metagenomics analysis is toward reducing computational costs through improved algorithms and through analysis strategies. Data sharing and interoperability between tools are critical, since computation for metagenomic datasets is very high.     

Phylogenetic and functional metagenomic profiling for assessing microbial biodiversity in environmental monitoring

Decisions guiding environmental management need to be based on a broad and comprehensive understanding of the biodiversity and functional capability within ecosystems. Microbes are of particular importance since they drive biogeochemical cycles, being both producers and decomposers. Their quick and direct responses to changes in environmental conditions modulate the ecosystem accordingly, thus providing a sensitive readout. Here we have used direct sequencing of total DNA from water samples to compare the microbial communities of two distinct coastal regions exposed to different anthropogenic pressures: the highly polluted Port of Genoa and the protected area of Montecristo Island in the Mediterranean Sea. Analysis of the metagenomes revealed significant differences in both microbial diversity and abundance between the two areas, reflecting their distinct ecological habitats and anthropogenic stress conditions. Our results indicate that the combination of next generation sequencing (NGS) technologies and bioinformatics tools presents a new approach to monitor the diversity and the ecological status of aquatic ecosystems. Integration of metagenomics into environmental monitoring campaigns should enable the impact of the anthropogenic pressure on microbial biodiversity in various ecosystems to be better assessed and also predicted.     

Signal Processing for Metagenomics: Extracting Information from the Soup

Traditionally, studies in microbial genomics have focused on single-genomes from cultured species, thereby limiting their focus to the small percentage of species that can be cultured outside their natural environment. Fortunately, recent advances in high-throughput sequencing and computational analyses have ushered in the new field of metagenomics, which aims to decode the genomes of microbes from natural communities without the need for cultivation. Although metagenomic studies have shed a great deal of insight into bacterial diversity and coding capacity, several computational challenges remain due to the massive size and complexity of metagenomic sequence data. Current tools and techniques are reviewed in this paper which address challenges in 1) genomic fragment annotation, 2) phylogenetic reconstruction, 3) functional classification of samples, and 4) interpreting complementary metaproteomics and metametabolomics data. Also surveyed are important applications of metagenomic studies, including microbial forensics and the roles of microbial communities in shaping human health and soil ecology.     

农牧交错带草地土壤剖面微生物总量、多样性和互作网络的垂直分布特征

【背景】草地土壤微生物是维持草地生态系统功能和稳定的关键要素之一,探寻微生物在土壤剖面的垂直分布特征和构建规律对于理解其在草地生态系统的作用至关重要。【目的】在80cm深的土壤剖面内,全面分析微生物的总量、多样性和物种间的相互作用网络表现出的垂直分布特征。【方法】基于内蒙古农牧交错带上典型草原土壤中原核微生物的定量和高通量测序数据,比较微生物的总量和多样性,使用分子生态网络方法(molecular ecological network approach,MENA)探究微生物相互作用网络的垂直变化。【结果】原核生物的总量和多样性随深度增加而逐渐降低,且群落结构变异沿土壤剖面逐渐增大。网络结构在表层最为复杂,微生物物种间联系紧密;随着深度的增加,微生物间紧密的关联会逐渐变稀疏,网络结构变得简单。此外,酸杆菌是当地土壤生态系统中的优势种群以及网络核心微生物物种,可能对土壤生态服务功能的稳定发挥具有重要的作用。【结论】原核微生物的总量、多样性和物种间的互作网络都表现出高度一致的垂直变化规律,即总量、多样性与深度成负相关,且其群落结构变异会逐渐扩大,同时微生物网络相关性会减弱。这些结果为我们提供了微生物动态变化的重要见解,对典型农牧交错带草地的生态保护具有一定的参考价值。     

Functional metagenomics: a tool to gain knowledge for agronomic and veterinary sciences

The increased global demand for food production has motivated agroindustries to increase their own levels of production. Scientific efforts have contributed to improving these production systems, aiding to solve problems and establishing novel conceptual views and sustainable alternatives to cope with the increasing demand. Although microorganisms are key players in biological systems and may drive certain desired responses toward food production, little is known about the microbial communities that constitute the microbiomes associated with agricultural and veterinary activities. Understanding the diversity, structure and in situ interactions of microbes, together with how these interactions occur within microbial communities and with respect to their environments (including hosts), constitutes a major challenge with an enormous relevance for agriculture and biotechnology. The emergence of high-throughput sequencing technologies, together with novel and more accessible bioinformatics tools, has allowed researchers to learn more about the functional potential and functional activity of these microbial communities. These tools constitute a relevant approach for understanding the metabolic processes that can occur or are currently occurring in a given system and for implementing novel strategies focused on solving production problems or improving sustainability. Several ‘omics’ sciences and their applications in agriculture are discussed in this review, and the usage of functional metagenomics is proposed to achieve substantial advances for food agroindustries and veterinary sciences.     

From bacterial genomics to metagenomics: concept,tools and recent advances

In the last 20 years, the applications of genomics tools have completely transformed the field of microbial research. This has primarily happened due to revolution in sequencing technologies that have become available today. This review therefore, first describes the discoveries, upgradation and automation of sequencing techniques in a chronological order, followed by a brief discussion on microbial genomics. Some of the recently sequenced bacterial genomes are described to explain how complete genome data is now being used to derive interesting findings. Apart from the genomics of individual microbes, the study of unculturable microbiota from different environments is increasingly gaining importance. The second section is thus dedicated to the concept of metagenomics describing environmental DNA isolation, metagenomic library construction and screening methods to look for novel and potentially important genes, enzymes and biomolecules. It also deals with the pioneering studies in the area of metagenomics that are offering new insights into the previously unappreciated microbial world. The authors have contributed equally to the work     

环境微生物群落结构与功能多样性研究方法

微生物群落的结构及群落内种间相互作用是影响其生态功能的决定性因素。尽管微生物群落是地球生物化学循环的主要驱动者,但是由于传统的微生物培养方法只能分离约1%10%的环境微生物,对复杂的环境微生物群落结构和功能多样性了解甚少。元基因组学、单细胞分析和群落遗传学等方法的出现,及其与微生物学的交叉融合,使得人们能够从微生物群落组成、物种功能、种间相互作用和预测模型等方面分析微生物群落。重点综述了元基因组学、单细胞分析和群落遗传学等方法及其在环境微生物群落结构和功能多样性中的应用进展。     

未培养微生物的研究与微生物分子生态学的发展*

近年来现代分子技术和基因组学逐渐渗透到有关生命科学的整个领域,也为微生物生态学提供了新的研究方法和机遇。16S rRNA基因序列分析、DNA-DNA杂交、核酸指纹图谱以及宏基因组学等分子技术检查自然环境中的微生物,可以克服传统纯培养技术的不足,是一条探知未培养微生物、寻找新基因及其产物的新途径,开启了我们认识微生物多样性和获得新资源的大门。     

Ecology theory disentangles microbial dichotomies

Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-specific, sometimes precluding clear-cut results interpretation. We can unravel microbial dichotomies as life history strategies by combining ecology theory with Monod curves, a laboratory mathematical tool of bacterial physiology that relates the specific growth rate of a microbe with the concentration of a limiting nutrient. Fitting of Monod curves provides quantities that directly correspond to key parameters in ecological theories addressing species coexistence and diversity, such as r/K selection theory, resource competition and community structure theory and the CSR triangle of life strategies. The resulting model allows us to reconcile the copiotrophic/oligotrophic and fast/slow-growing dichotomies as different subsamples of a life history strategy triangle that also includes r/K strategists. We also used the number of known carbon sources together with community structure theory to partially explain the diversity of heterotrophic microbes observed in metagenomics experiments. In sum, we propose a theoretical framework for the study of natural microbial communities that unifies several existing proposals. Its application would require the integration of metagenomics, metametabolomics, Monod curves and carbon source data.     

DNA sequencing,genomes and genetic markers of microbes on fruits and vegetables

The development of DNA sequencing technology has provided an effective method for studying foodborne and phytopathogenic microorganisms on fruits and vegetables (F & V). DNA sequencing has successfully proceeded through three generations, including the tens of operating platforms. These advances have significantly promoted microbial whole-genome sequencing (WGS) and DNA polymorphism research. Based on genomic and regional polymorphisms, genetic markers have been widely obtained. These molecular markers are used as targets for PCR or chip analyses to detect microbes at the genetic level. Furthermore, metagenomic analyses conducted by sequencing the hypervariable regions of ribosomal DNA (rDNA) have revealed comprehensive microbial communities in various studies on F & V. This review highlights the basic principles of three generations of DNA sequencing, and summarizes the WGS studies of and available DNA markers for major bacterial foodborne pathogens and phytopathogenic fungi found on F & V. In addition, rDNA sequencing-based bacterial and fungal metagenomics are summarized under three topics. These findings deepen the understanding of DNA sequencing and its application in studies of foodborne and phytopathogenic microbes and shed light on strategies for the monitoring of F & V microbes and quality control.     

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Background

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

Results

Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16?S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).

Conclusions

The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.     

Advancing analytical algorithms and pipelines for billions of microbial sequences

The vast number of microbial sequences resulting from sequencing efforts using new technologies require us to re-assess currently available analysis methodologies and tools. Here we describe trends in the development and distribution of software for analyzing microbial sequence data. We then focus on one widely used set of methods, dimensionality reduction techniques, which allow users to summarize and compare these vast datasets. We conclude by emphasizing the utility of formal software engineering methods for the development of computational biology tools, and the need for new algorithms for comparing microbial communities. Such large-scale comparisons will allow us to fulfill the dream of rapid integration and comparison of microbial sequence data sets, in a replicable analytical environment, in order to describe the microbial world we inhabit.