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.     

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.     

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.     

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。     

Metagenomic era for biocatalyst identification

Microbial enzymes have many known applications as biocatalysts. However, only a few of them are currently employed for biocatalysis even though an annotated collection of more than 190 billion bases is available in metagenome sequence databases from uncultured and highly diverse microbial populations. This review aims at providing conceptual and technical bases for the translation of metagenome data into both experimental and computational frameworks that facilitates a comprehensive analysis of the biocatalysts diversity space. We will also briefly present the status of the current capabilities that assess and predict catalytic potential of environmental sites and track its diversity and evolution in large-scale biocatalysis process resulting from studies applying metagenomics in association with gene fingerprinting, catabolic arrays and complementary '-omics'.     

Single-cell genomic sequencing using Multiple Displacement Amplification

Single microbial cells can now be sequenced using DNA amplified by the Multiple Displacement Amplification (MDA) reaction. The few femtograms of DNA in a bacterium are amplified into micrograms of high molecular weight DNA suitable for DNA library construction and Sanger sequencing. The MDA-generated DNA also performs well when used directly as template for pyrosequencing by the 454 Life Sciences method. While MDA from single cells loses some of the genomic sequence, this approach will greatly accelerate the pace of sequencing from uncultured microbes. The genetically linked sequences from single cells are also a powerful tool to be used in guiding genomic assembly of shotgun sequences of multiple organisms from environmental DNA extracts (metagenomic sequences).     

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。     

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。     

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     

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.     

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.     

宏基因组克隆技术

环境中约99.8%的微生物不能用常规的微生物学方法培养,这样就使得绝大部分微生物资源的开发利用受到制约,而宏基因组克隆技术的产生则克服了对不可培养微生物研究的困难。到目前为止,通过宏基因组克隆技术已经获得了许多新的抗生素和酶的基因,而且随着该技术的不断完善将会加大有用分子发现的几率和对复杂微生物群落功能的了解。     

宏基因组学挖掘新型生物催化剂的研究进展

微生物蕴藏着大量具有工业应用潜力的生物催化剂。然而,传统培养方法只能从环境中获得不到1%的微生物。宏基因组学是通过提取某一特定环境中的所有微生物基因组DNA、构建基因组文库并对文库进行筛选,寻找和发现新的功能基因的一种方法。它绕过了微生物分离培养过程,成为研究环境样品中不可培养微生物的有力手段。因此,从宏基因组中挖掘新型生物催化剂一直倍受生物学家的关注。以下主要对宏基因组文库的样品来源、DNA提取方法、文库的构建和筛选策略的选择这4个方面的研究状况进行了综述,列举了近年来利用宏基因组技术所获得的新型生物催化剂,并对其今后的研究方向提出了展望。     

Get the most out of your metagenome: computational analysis of environmental sequence data

New advances in sequencing technologies bring random shotgun sequencing of ecosystems within reach of smaller labs, but the complexity of metagenomics data can be overwhelming. Recently, many novel computational tools have been developed to unravel ecosystem properties starting from fragmented sequences. In addition, the so-called 'comparative metagenomics' approaches have allowed the discovery of specific genomic and community adaptations to environmental factors. However, many of the parameters extracted from these data to describe the environment at hand (e.g. genomic features, functional complement, phylogenetic composition) are interdependent and influenced by technical aspects of sample preparation and data treatment, leading to various pitfalls during analysis. To avoid this and complement existing initiatives in data standards, we propose a minimal standard for metagenomics data analysis ('MINIMESS') to be able to take full advantage of the power of comparative metagenomics in understanding microbial life on earth.     

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.     

Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing

Background

The widespread popularity of genomic applications is threatened by the “bioinformatics bottleneck” resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly.

Results

We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers.

Conclusions

Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.     

Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment

Metagenomic studies have revolutionized our understanding of the metabolic potential of uncultured microorganisms in various ecosystems. However, many of these genomic predictions have yet to be experimentally tested, and the functional expression of genomic potential often remains unaddressed. In order to obtain a more thorough understanding of cell physiology, novel techniques capable of testing microbial metabolism under close to in situ conditions must be developed. Here, we provide a benchmark study to demonstrate that bioorthogonal non-canonical amino acid tagging (BONCAT) in combination with fluorescence-activated cell sorting (FACS) and 16S rRNA gene sequencing can be used to identify anabolically active members of a microbial community incubated in the presence of various growth substrates or under changing physicochemical conditions. We applied this approach to a hot spring sediment microbiome from Yellowstone National Park (Wyoming, USA) and identified several microbes that changed their activity levels in response to substrate addition, including uncultured members of the phyla Thaumarchaeota, Acidobacteria, and Fervidibacteria. Because shifts in activity in response to substrate amendment or headspace changes are indicative of microbial preferences for particular growth conditions, results from this and future BONCAT-FACS studies could inform the development of cultivation media to specifically enrich uncultured microbes. Most importantly, BONCAT-FACS is capable of providing information on the physiology of uncultured organisms at as close to in situ conditions as experimentally possible.Subject terms: Environmental microbiology, Microbial communities, Microbial ecology, Archaea