元基因组学及其在转化医学中的应用

微生物群落遍布于人体的每个角落,与人共生并对人体健康产生重要和深刻的影响。与人类共生的全部微生物的基因组总和称为“元基因组”或“人类第二基因组”。研究人体微生物群落及相关元基因组数据,对转化医学领域的基础研究和临床应用具有重要的价值。通过对生物医学相关的高通量元基因组数据进行分析,不仅能为基础医学研究向医学临床应用转化提供新思路和新方法,而且具有广阔的应用前景。基于新一代测序技术产生的数据,元基因组分析技术和方法能够弥补以往人体微生物先培养后鉴定方法的缺陷,同时能有效鉴定和分析微生物群落的组成及功能,从而进一步探究和揭示微生物群落与机体生理状态之间的关系,为解决许多医学领域的难题提供了全新的切入角度和思维方法。文章系统介绍了元基因组研究的现状,包括元基因组的方法概念和研究进展,并以元基因组在医学研究中的应用为着眼点,综述了元基因组在转化医学方面的研究进展,进一步阐述了元基因组研究在转化医学应用领域中具有的重要地位。     

Simultaneous virus identification and characterization of severe unexplained pneumonia cases using a metagenomics sequencing technique

Many viruses can cause respiratory diseases in humans. Although great advances have been achieved in methods of diagnosis, it remains challenging to identify pathogens in unexplained pneumonia (UP) cases. In this study, we applied next-generation sequencing (NGS) technology and a metagenomic approach to detect and characterize respiratory viruses in UP cases from Guizhou Province, China. A total of 33 oropharyngeal swabs were obtained from hospitalized UP patients and subjected to NGS. An unbiased metagenomic analysis pipeline identified 13 virus species in 16 samples. Human rhinovirus C was the virus most frequently detected and was identified in seven samples. Human measles virus, adenovirus B 55 and coxsackievirus A10 were also identified. Metagenomic sequencing also provided virus genomic sequences, which enabled genotype characterization and phylogenetic analysis. For cases of multiple infection, metagenomic sequencing afforded information regarding the quantity of each virus in the sample, which could be used to evaluate each viruses’ role in the disease. Our study highlights the potential of metagenomic sequencing for pathogen identification in UP cases.     

Estimating the total genome length of a metagenomic sample using k-mers

Background

Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.

Results

As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses.

Conclusions

We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.

     

Comparison of Boiling and Robotics Automation Method in DNA Extraction for Metagenomic Sequencing of Human Oral Microbes

The rapid improvement of next-generation sequencing performance now enables us to analyze huge sample sets with more than ten thousand specimens. However, DNA extraction can still be a limiting step in such metagenomic approaches. In this study, we analyzed human oral microbes to compare the performance of three DNA extraction methods: PowerSoil (a method widely used in this field), QIAsymphony (a robotics method), and a simple boiling method. Dental plaque was initially collected from three volunteers in the pilot study and then expanded to 12 volunteers in the follow-up study. Bacterial flora was estimated by sequencing the V4 region of 16S rRNA following species-level profiling. Our results indicate that the efficiency of PowerSoil and QIAsymphony was comparable to the boiling method. Therefore, the boiling method may be a promising alternative because of its simplicity, cost effectiveness, and short handling time. Moreover, this method was reliable for estimating bacterial species and could be used in the future to examine the correlation between oral flora and health status. Despite this, differences in the efficiency of DNA extraction for various bacterial species were observed among the three methods. Based on these findings, there is no “gold standard” for DNA extraction. In future, we suggest that the DNA extraction method should be selected on a case-by-case basis considering the aims and specimens of the study.     

Metagenomic Sequencing of an In Vitro-Simulated Microbial Community

Background

Microbial life dominates the earth, but many species are difficult or even impossible to study under laboratory conditions. Sequencing DNA directly from the environment, a technique commonly referred to as metagenomics, is an important tool for cataloging microbial life. This culture-independent approach involves collecting samples that include microbes in them, extracting DNA from the samples, and sequencing the DNA. A sample may contain many different microorganisms, macroorganisms, and even free-floating environmental DNA. A fundamental challenge in metagenomics has been estimating the abundance of organisms in a sample based on the frequency with which the organism''s DNA was observed in reads generated via DNA sequencing.

Methodology/Principal Findings

We created mixtures of ten microbial species for which genome sequences are known. Each mixture contained an equal number of cells of each species. We then extracted DNA from the mixtures, sequenced the DNA, and measured the frequency with which genomic regions from each organism was observed in the sequenced DNA. We found that the observed frequency of reads mapping to each organism did not reflect the equal numbers of cells that were known to be included in each mixture. The relative organism abundances varied significantly depending on the DNA extraction and sequencing protocol utilized.

Conclusions/Significance

We describe a new data resource for measuring the accuracy of metagenomic binning methods, created by in vitro-simulation of a metagenomic community. Our in vitro simulation can be used to complement previous in silico benchmark studies. In constructing a synthetic community and sequencing its metagenome, we encountered several sources of observation bias that likely affect most metagenomic experiments to date and present challenges for comparative metagenomic studies. DNA preparation methods have a particularly profound effect in our study, implying that samples prepared with different protocols are not suitable for comparative metagenomics.     

Inference of Microbial Recombination Rates from Metagenomic Data

Metagenomic sequencing projects from environments dominated by a small number of species produce genome-wide population samples. We present a two-site composite likelihood estimator of the scaled recombination rate, ρ = 2N_ec, that operates on metagenomic assemblies in which each sequenced fragment derives from a different individual. This new estimator properly accounts for sequencing error, as quantified by per-base quality scores, and missing data, as inferred from the placement of reads in a metagenomic assembly. We apply our estimator to data from a sludge metagenome project to demonstrate how this method will elucidate the rates of exchange of genetic material in natural microbial populations. Surprisingly, for a fixed amount of sequencing, this estimator has lower variance than similar methods that operate on more traditional population genetic samples of comparable size. In addition, we can infer variation in recombination rate across the genome because metagenomic projects sample genetic diversity genome-wide, not just at particular loci. The method itself makes no assumption specific to microbial populations, opening the door for application to any mixed population sample where the number of individuals sampled is much greater than the number of fragments sequenced.     

The phylogenetic diversity of metagenomes

Phylogenetic diversity--patterns of phylogenetic relatedness among organisms in ecological communities--provides important insights into the mechanisms underlying community assembly. Studies that measure phylogenetic diversity in microbial communities have primarily been limited to a single marker gene approach, using the small subunit of the rRNA gene (SSU-rRNA) to quantify phylogenetic relationships among microbial taxa. In this study, we present an approach for inferring phylogenetic relationships among microorganisms based on the random metagenomic sequencing of DNA fragments. To overcome challenges caused by the fragmentary nature of metagenomic data, we leveraged fully sequenced bacterial genomes as a scaffold to enable inference of phylogenetic relationships among metagenomic sequences from multiple phylogenetic marker gene families. The resulting metagenomic phylogeny can be used to quantify the phylogenetic diversity of microbial communities based on metagenomic data sets. We applied this method to understand patterns of microbial phylogenetic diversity and community assembly along an oceanic depth gradient, and compared our findings to previous studies of this gradient using SSU-rRNA gene and metagenomic analyses. Bacterial phylogenetic diversity was highest at intermediate depths beneath the ocean surface, whereas taxonomic diversity (diversity measured by binning sequences into taxonomically similar groups) showed no relationship with depth. Phylogenetic diversity estimates based on the SSU-rRNA gene and the multi-gene metagenomic phylogeny were broadly concordant, suggesting that our approach will be applicable to other metagenomic data sets for which corresponding SSU-rRNA gene sequences are unavailable. Our approach opens up the possibility of using metagenomic data to study microbial diversity in a phylogenetic context.     

Identification and Resolution of Microdiversity through Metagenomic Sequencing of Parallel Consortia

To gain a predictive understanding of the interspecies interactions within microbial communities that govern community function, the genomic complement of every member population must be determined. Although metagenomic sequencing has enabled the de novo reconstruction of some microbial genomes from environmental communities, microdiversity confounds current genome reconstruction techniques. To overcome this issue, we performed short-read metagenomic sequencing on parallel consortia, defined as consortia cultivated under the same conditions from the same natural community with overlapping species composition. The differences in species abundance between the two consortia allowed reconstruction of near-complete (at an estimated >85% of gene complement) genome sequences for 17 of the 20 detected member species. Two Halomonas spp. indistinguishable by amplicon analysis were found to be present within the community. In addition, comparison of metagenomic reads against the consensus scaffolds revealed within-species variation for one of the Halomonas populations, one of the Rhodobacteraceae populations, and the Rhizobiales population. Genomic comparison of these representative instances of inter- and intraspecies microdiversity suggests differences in functional potential that may result in the expression of distinct roles in the community. In addition, isolation and complete genome sequence determination of six member species allowed an investigation into the sensitivity and specificity of genome reconstruction processes, demonstrating robustness across a wide range of sequence coverage (9× to 2,700×) within the metagenomic data set.     

宏基因组与宏基因组学

宏基因组学诞生于上世纪90年代,是指不经过微生物培养阶段,采用直接提取环境中总DNA的方法,对微生物基因总和进行研究的一门新学科.宏基因组技术的出现,使得人们对占微生物总体99%以上不可培养微生物的研究成为现实,微生物基因的可探测空间显著增大.总的来说,目前宏基因组技术的应用主要分为两个方面:一方面是筛选功能基因,开发具有所需功能的蛋白;另一方面是通过对宏基因组文库进行分析,探讨在各种环境下微生物间相互作用和微生物与周围环境间相互影响的规律,以便我们能更加客观、全面地认识微生物世界.在宏基因组技术的应用范围被不断扩展的同时,围绕着宏基因组文库的构建和筛选、测序和分析等方面的研究已成为宏基因组学发展的主要推动力,宏基因组技术的进步将不断提升其应用价值.     

Metagenomic analysis of historical herbarium specimens reveals a postmortem microbial community

Advances in DNA extraction and next‐generation sequencing have made a vast number of historical herbarium specimens available for genomic investigation. These specimens contain not only genomic information from the individual plants themselves, but also from associated microorganisms such as bacteria and fungi. These microorganisms may have colonized the living plant (e.g., pathogens or host‐associated commensal taxa) or may result from postmortem colonization that may include decomposition processes or contamination during sample handling. Here we characterize the metagenomic profile from shotgun sequencing data from herbarium specimens of two widespread plant species (Ambrosia artemisiifolia and Arabidopsis thaliana) collected up to 180 years ago. We used blast searching in combination with megan and were able to infer the metagenomic community even from the oldest herbarium sample. Through comparison with contemporary plant collections, we identify three microbial species that are nearly exclusive to herbarium specimens, including the fungus Alternaria alternata, which can comprise up to 7% of the total sequencing reads. This species probably colonizes the herbarium specimens during preparation for mounting or during storage. By removing the probable contaminating taxa, we observe a temporal shift in the metagenomic composition of the invasive weed Am. artemisiifolia. Our findings demonstrate that it is generally possible to use herbarium specimens for metagenomic analyses, but that the results should be treated with caution, as some of the identified species may be herbarium contaminants rather than representing the natural metagenomic community of the host plant.     

Accessing the soil metagenome for studies of microbial diversity

Soil microbial communities contain the highest level of prokaryotic diversity of any environment, and metagenomic approaches involving the extraction of DNA from soil can improve our access to these communities. Most analyses of soil biodiversity and function assume that the DNA extracted represents the microbial community in the soil, but subsequent interpretations are limited by the DNA recovered from the soil. Unfortunately, extraction methods do not provide a uniform and unbiased subsample of metagenomic DNA, and as a consequence, accurate species distributions cannot be determined. Moreover, any bias will propagate errors in estimations of overall microbial diversity and may exclude some microbial classes from study and exploitation. To improve metagenomic approaches, investigate DNA extraction biases, and provide tools for assessing the relative abundances of different groups, we explored the biodiversity of the accessible community DNA by fractioning the metagenomic DNA as a function of (i) vertical soil sampling, (ii) density gradients (cell separation), (iii) cell lysis stringency, and (iv) DNA fragment size distribution. Each fraction had a unique genetic diversity, with different predominant and rare species (based on ribosomal intergenic spacer analysis [RISA] fingerprinting and phylochips). All fractions contributed to the number of bacterial groups uncovered in the metagenome, thus increasing the DNA pool for further applications. Indeed, we were able to access a more genetically diverse proportion of the metagenome (a gain of more than 80% compared to the best single extraction method), limit the predominance of a few genomes, and increase the species richness per sequencing effort. This work stresses the difference between extracted DNA pools and the currently inaccessible complete soil metagenome.     

Improving the genetic representation of rare taxa within complex microbial communities using DNA normalization methods

Complex microbial communities typically contain a large number of low abundance species, which collectively, comprise a considerable proportion of the community. This ‘rare biosphere’ has been speculated to contain keystone species and act as a repository of genomic diversity to facilitate community adaptation. Many environmental microbes are currently resistant to cultivation, and can only be accessed via culture‐independent approaches. To enhance our understanding of the role of the rare biosphere, we aimed to improve their metagenomic representation using DNA normalization methods, and assess normalization success via shotgun DNA sequencing. A synthetic metagenome was constructed from the genomic DNA of five bacterial species, pooled in a defined ratio spanning three orders of magnitude. The synthetic metagenome was fractionated and thermally renatured, allowing the most abundant sequences to hybridize. Double‐stranded DNA was removed either by hydroxyapatite chromatography, or by a duplex‐specific nuclease (DSN). The chromatographic method failed to enrich for the genomes present in low starting abundance, whereas the DSN method resulted in all genomes reaching near equimolar abundance. The representation of the rarest member was increased by approximately 450‐fold. De novo assembly of the normalized metagenome enabled up to 18.0% of genes from the rarest organism to be assembled, in contrast to the un‐normalized sample, where genes were not able to be assembled at the same sequencing depth. This study has demonstrated that the application of normalization methods to metagenomic samples is a powerful tool to enrich for sequences from rare taxa, which will shed further light on their ecological niches.     

Aquatic virus diversity accessed through omic techniques: a route map to function

Viruses are arguably the simplest form of life yet they play a crucial role in regulating planetary processes. From shuttling genes to 'lubricating' microbial loop dynamics, viruses are integral in shaping microbial ecology. In every environment on Earth the role of viruses goes far beyond the simple infect-replicate-kill cycle. Their enormous abundance and seemingly infinite diversity provide the vital clues to the true function of viruses. New 'omic' approaches are now allowing researchers to gain extraordinary insights into virus diversity and inferred function, particularly within aquatic environments. The development of molecular markers and application of techniques including microarrays, metagenomic sequencing and proteomic analysis are now being applied to virus communities. Despite this shift towards culture-independent approaches it has proved difficult to derive useful information about infection strategies since so much of the sequence information has no database matches. Future advances will involve tools such as microarrays to help determine the functionality of unknown genes. Sequence information should be considered as a starting point for asking questions and developing hypotheses about the role of viruses. It is an exciting new era for virus ecology and when used in combination with more traditional approaches, virus genomics will give us access to their ecological function on an unprecedented scale.     

Vaginotypes of the human vaginal microbiome

The human vaginal environment harbours a community of bacteria that plays an important role in maintaining vaginal health and in protecting this environment from various urogenital infections. This bacterial population, also known as vaginal microbiota, has been demonstrated to be dominated by members of the Lactobacillus genus. Several studies employing 16S rRNA gene-based amplicon sequencing have classified the vaginal microbiota into five distinct community state types (CSTs) or vaginotypes. To deepen our understanding of the vaginal microbiota we performed an in-depth meta-analysis of 1312 publicly available datasets concerning healthy vaginal microbiome information obtained by metagenomics sequencing. The analysis confirmed the predominance of taxa belonging to the Lactobacillus genus, followed by members of the genera Gardnerella, Vibrio and Atopobium. Moreover, the statistical robustness offered by this meta-analysis allowed us to disentangle the species-level composition of dominant and accessory taxa constituting each vaginotype and to revisit and refine the previously proposed CST classification. In addition, a functional characterization of the metagenomic datasets revealed particular genetic features associated with each assigned vaginotype.     

A novel retrieval system for nearly complete microbial genomic fragments from soil samples

The construction of a complex genomic library is one of the comprehensive ways to study a complex bacterial community and to access the variety of metabolic pathways present in the rich soil environment. In this report, we developed a new protocol whereby we are able to retrieve nearly complete microbe genomic fragments from soil samples, which are employed to generate a metagenomic library for visualizing the basic scaffolding of the soil microbial community. The use of direct cell lysis within soil-embedded agarose plugs, along with a double-size selection, enabled us to successfully isolate pure and high-molecular weight DNA (0.1-1 Mb) without the need for any further purification. A metagenomic library containing 1.2 Gbp of DNA in total was constructed. Furthermore, analysis of the microbial community structure using 16S rDNA partial sequences found the dominant phylotypes to consist of alpha-Proteobacteria and Actinobacteria, which are similar to those seen in forest and agricultural soils, and numerous uncultured microbes from a wide variety of bacterial taxa as well. In conclusion, this study presents a novel protocol for generating a metagenomic library that carries much larger and diverse DNA fragments from soil bacteria that will be applied for the reconstruction of soil microbial genomes and the discovery of novel habitat-specific pathways.     

Meta-QC-Chain: Comprehensive and Fast Quality Control Method for Metagenomic Data

Next-generation sequencing(NGS) technology has revolutionized and significantly impacted metagenomic research.However,the NGS data usually contains sequencing artifacts such as low-quality reads and contaminating reads,which will significantly compromise downstream analysis.Many quality control(QC) tools have been proposed,however,few of them have been verified to be suitable or efficient for metagenomic data,which are composed of multiple genomes and are more complex than other kinds of NGS data.Here we present a metagenomic data QC method named Meta-QC-Chain.Meta-QC-Chain combines multiple QC functions:technical tests describe input data status and identify potential errors,quality trimming filters poor sequencing-quality bases and reads,and contamination screening identifies higher eukaryotic species,which are considered as contamination for metagenomic data.Most computing processes are optimized based on parallel programming.Testing on an 8-GB real dataset showed that Meta-QC-Chain trimmed low sequencing-quality reads and contaminating reads,and the whole quality control procedure was completed within 20 min.Therefore,Meta-QC-Chain provides a comprehensive,useful and high-performance QC tool for metagenomic data.Meta-QC-Chain is publicly available for free at:http://computationalbioenergy.org/meta-qc-chain.html.     

Sequence capture by hybridization to explore modern and ancient genomic diversity in model and nonmodel organisms

The recent expansion of next-generation sequencing has significantly improved biological research. Nevertheless, deep exploration of genomes or metagenomic samples remains difficult because of the sequencing depth and the associated costs required. Therefore, different partitioning strategies have been developed to sequence informative subsets of studied genomes. Among these strategies, hybridization capture has proven to be an innovative and efficient tool for targeting and enriching specific biomarkers in complex DNA mixtures. It has been successfully applied in numerous areas of biology, such as exome resequencing for the identification of mutations underlying Mendelian or complex diseases and cancers, and its usefulness has been demonstrated in the agronomic field through the linking of genetic variants to agricultural phenotypic traits of interest. Moreover, hybridization capture has provided access to underexplored, but relevant fractions of genomes through its ability to enrich defined targets and their flanking regions. Finally, on the basis of restricted genomic information, this method has also allowed the expansion of knowledge of nonreference species and ancient genomes and provided a better understanding of metagenomic samples. In this review, we present the major advances and discoveries permitted by hybridization capture and highlight the potency of this approach in all areas of biology.     

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.     

Evaluation of mRNA Biomarkers to Identify Risk of Hospital Acquired Infections in Children Admitted to Paediatric Intensive Care Unit

Objectives

Hospital-acquired infections (HAI) are associated with significant mortality and morbidity and prolongation of hospital stay, adding strain on limited hospital resources. Despite stringent infection control practices some children remain at high risk of developing HAI. The development of biomarkers which could identify these patients would be useful. In this study our objective was to evaluate mRNA candidate biomarkers for HAI prediction in a pediatric intensive care unit.

Design

Serial blood samples were collected from patients admitted to pediatric intensive care unit between March and June 2012. Candidate gene expression (IL1B, TNF, IL10, CD3D, BCL2, BID) was quantified using RT-qPCR. Comparisons of relative gene expression between those that did not develop HAI versus those that did were performed using Mann Whitney U-test.

Patients

Exclusion criteria were: age <28 days or ≥16 years, expected length of stay < 24 hours, expected survival < 28 days, end-stage renal disease and end-stage liver disease. Finally, 45 children were included in this study.

Main Results

The overall HAI rate was 30% of which 62% were respiratory infections. Children who developed HAI had a three-fold increase in hospital stay compared to those who did not (27 days versus 9 days, p<0.001). An increased expression of cytokine genes (IL1B and IL10) was observed in patients who developed HAI, as well as a pro-apoptosis pattern (higher expression of BID and lower expression of BCL2). CD3D, a key TCR co-factor was also significantly down-modulated in patients who developed HAI.

Conclusions

To our knowledge, this is the first study of mRNA biomarkers of HAI in the paediatric population. Increased mRNA expressions of anti-inflammatory cytokine and modulation of apoptotic genes suggest the development of immunosuppression in critically ill children. Immune monitoring using a panel of genes may offer a novel stratification tool to identify HAI risk.     

Recovering microbial genomes from metagenomes in hypersaline environments: The Good,the Bad and the Ugly

Current metagenomic tools allow the recovery of microbial genomes directly from the environment. This can be accomplished by binning metagenomic contigs according to their coverage and tetranucleotide frequency, followed by an estimation of the bin quality. The public availability of bioinformatics tools, together with the decreasing cost of next generation sequencing, are democratizing this powerful approach that is spreading from specialized research groups to the general public. Using metagenomes from hypersaline environments, as well as mock metagenomes composed of Archaea and Bacteria frequently found in these systems, we have analyzed the advantages and difficulties of the binning process in these extreme environments to tackle microbial population diversity. These extreme systems harbor relatively low species diversity but high intraspecific diversity, which can compromise metagenome assembly and therefore the whole binning process. The main goal is to compare the output of the binning process with what is previously known from the analyzed samples, based on years of study using different approaches. Several scenarios have been analyzed in detail: (i) a good quality bin from a species highly abundant in the environment; (ii) an intermediate quality bin with incongruences that can be solved by further analyses and manual curation, and (iii) a low-quality bin to investigate the failure to recover a very abundant microbial genome as well as some possible solutions. The latter can be considered the “great metagenomics anomaly” and is mainly due to assembly problems derived from the microdiversity of naturally co-existing populations in nature.