Finding the Needles in the Metagenome Haystack

In the collective genomes (the metagenome) of the microorganisms inhabiting the Earth’s diverse environments is written the history of life on this planet. New molecular tools developed and used for the past 15 years by microbial ecologists are facilitating the extraction, cloning, screening, and sequencing of these genomes. This approach allows microbial ecologists to access and study the full range of microbial diversity, regardless of our ability to culture organisms, and provides an unprecedented access to the breadth of natural products that these genomes encode. However, there is no way that the mere collection of sequences, no matter how expansive, can provide full coverage of the complex world of microbial metagenomes within the foreseeable future. Furthermore, although it is possible to fish out highly informative and useful genes from the sea of gene diversity in the environment, this can be a highly tedious and inefficient procedure. Microbial ecologists must be clever in their pursuit of ecologically relevant, valuable, and niche-defining genomic information within the vast haystack of microbial diversity. In this report, we seek to describe advances and prospects that will help microbial ecologists glean more knowledge from investigations into metagenomes. These include technological advances in sequencing and cloning methodologies, as well as improvements in annotation and comparative sequence analysis. More significant, however, will be ways to focus in on various subsets of the metagenome that may be of particular relevance, either by limiting the target community under study or improving the focus or speed of screening procedures. Lastly, given the cost and infrastructure necessary for large metagenome projects, and the almost inexhaustible amount of data they can produce, trends toward broader use of metagenome data across the research community coupled with the needed investment in bioinformatics infrastructure devoted to metagenomics will no doubt further increase the value of metagenomic studies in various environments.     

Metagenomic mining for microbiologists

Microbial ecologists can now start digging into the accumulating mountains of metagenomic data to uncover the occurrence of functional genes and their correlations to microbial community members. Limitations and biases in DNA extraction and sequencing technologies impact sequence distributions, and therefore, have to be considered. However, when comparing metagenomes from widely differing environments, these fluctuations have a relatively minor role in microbial community discrimination. As a consequence, any functional gene or species distribution pattern can be compared among metagenomes originating from various environments and projects. In particular, global comparisons would help to define ecosystem specificities, such as involvement and response to climate change (for example, carbon and nitrogen cycle), human health risks (eg, presence of pathogen species, toxin genes and viruses) and biodegradation capacities. Although not all scientists have easy access to high-throughput sequencing technologies, they do have access to the sequences that have been deposited in databases, and therefore, can begin to intensively mine these metagenomic data to generate hypotheses that can be validated experimentally. Information about metabolic functions and microbial species compositions can already be compared among metagenomes from different ecosystems. These comparisons add to our understanding about microbial adaptation and the role of specific microbes in different ecosystems. Concurrent with the rapid growth of sequencing technologies, we have entered a new age of microbial ecology, which will enable researchers to experimentally confirm putative relationships between microbial functions and community structures.     

Metagenomic Profiling of Known and Unknown Microbes with MicrobeGPS

Microbial community profiling identifies and quantifies organisms in metagenomic sequencing data using either reference based or unsupervised approaches. However, current reference based profiling methods only report the presence and abundance of single reference genomes that are available in databases. Since only a small fraction of environmental genomes is represented in genomic databases, these approaches entail the risk of false identifications and often suggest a higher precision than justified by the data. Therefore, we developed MicrobeGPS, a novel metagenomic profiling approach that overcomes these limitations. MicrobeGPS is the first method that identifies microbiota in the sample and estimates their genomic distances to known reference genomes. With this strategy, MicrobeGPS identifies organisms down to the strain level and highlights possibly inaccurate identifications when the correct reference genome is missing. We demonstrate on three metagenomic datasets with different origin that our approach successfully avoids misleading interpretation of results and additionally provides more accurate results than current profiling methods. Our results indicate that MicrobeGPS can enable reference based taxonomic profiling of complex and less characterized microbial communities. MicrobeGPS is open source and available from https://sourceforge.net/projects/microbegps/ as source code and binary distribution for Windows and Linux operating systems.     

Global and seasonal variation of marine phosphonate metabolism

Marine microbial communities rely on dissolved organic phosphorus (DOP) remineralisation to meet phosphorus (P) requirements. We extensively surveyed the genomic and metagenomic distribution of genes directing phosphonate biosynthesis, substrate-specific catabolism of 2-aminoethylphosphonate (2-AEP, the most abundant phosphonate in the marine environment), and broad-specificity catabolism of phosphonates by the C-P lyase (including methylphosphonate, a major source of methane). We developed comprehensive enzyme databases by curating publicly available sequences and then screened metagenomes from TARA Oceans and Munida Microbial Observatory Time Series (MOTS) to assess spatial and seasonal variation in phosphonate metabolism pathways. Phosphonate cycling genes were encoded in diverse gene clusters by 35 marine bacterial and archaeal classes. More than 65% of marine phosphonate cycling genes mapped to Proteobacteria with production demonstrating wider taxonomic diversity than catabolism. Hydrolysis of 2-AEP was the dominant phosphonate catabolism strategy, enabling microbes to assimilate carbon and nitrogen alongside P. Genes for broad-specificity catabolism by the C-P lyase were far less widespread, though enriched in the extremely P-deplete environment of the Mediterranean Sea. Phosphonate cycling genes were abundant in marine metagenomes, particularly from the mesopelagic zone and winter sampling dates. Disparity between prevalence of substrate-specific and broad-specificity catabolism may be due to higher resource expenditure from the cell to build and retain the C-P lyase. This study is the most comprehensive metagenomic survey of marine microbial phosphonate cycling to date and provides curated databases for 14 genes involved in phosphonate cycling.Subject terms: Water microbiology, Microbial ecology, Microbial biooceanography, Metagenomics     

Application of tetranucleotide frequencies for the assignment of genomic fragments

A basic problem of the metagenomic approach in microbial ecology is the assignment of genomic fragments to a certain species or taxonomic group, when suitable marker genes are absent. Currently, the (G + C)-content together with phylogenetic information and codon adaptation for functional genes is mostly used to assess the relationship of different fragments. These methods, however, can produce ambiguous results. In order to evaluate sequence-based methods for fragment identification, we extensively compared (G + C)-contents and tetranucleotide usage patterns of 9054 fosmid-sized genomic fragments generated in silico from 118 completely sequenced bacterial genomes (40 982 931 fragment pairs were compared in total). The results of this systematic study show that the discriminatory power of correlations of tetranucleotide-derived z-scores is by far superior to that of differences in (G + C)-content and provides reasonable assignment probabilities when applied to metagenome libraries of small diversity. Using six fully sequenced fosmid inserts from a metagenomic analysis of microbial consortia mediating the anaerobic oxidation of methane (AOM), we demonstrate that discrimination based on tetranucleotide-derived z-score correlations was consistent with corresponding data from 16S ribosomal RNA sequence analysis and allowed us to discriminate between fosmid inserts that were indistinguishable with respect to their (G + C)-contents.     

Reconstructing the Genomic Content of Microbiome Taxa through Shotgun Metagenomic Deconvolution

Metagenomics has transformed our understanding of the microbial world, allowing researchers to bypass the need to isolate and culture individual taxa and to directly characterize both the taxonomic and gene compositions of environmental samples. However, associating the genes found in a metagenomic sample with the specific taxa of origin remains a critical challenge. Existing binning methods, based on nucleotide composition or alignment to reference genomes allow only a coarse-grained classification and rely heavily on the availability of sequenced genomes from closely related taxa. Here, we introduce a novel computational framework, integrating variation in gene abundances across multiple samples with taxonomic abundance data to deconvolve metagenomic samples into taxa-specific gene profiles and to reconstruct the genomic content of community members. This assembly-free method is not bounded by various factors limiting previously described methods of metagenomic binning or metagenomic assembly and represents a fundamentally different approach to metagenomic-based genome reconstruction. An implementation of this framework is available at http://elbo.gs.washington.edu/software.html. We first describe the mathematical foundations of our framework and discuss considerations for implementing its various components. We demonstrate the ability of this framework to accurately deconvolve a set of metagenomic samples and to recover the gene content of individual taxa using synthetic metagenomic samples. We specifically characterize determinants of prediction accuracy and examine the impact of annotation errors on the reconstructed genomes. We finally apply metagenomic deconvolution to samples from the Human Microbiome Project, successfully reconstructing genus-level genomic content of various microbial genera, based solely on variation in gene count. These reconstructed genera are shown to correctly capture genus-specific properties. With the accumulation of metagenomic data, this deconvolution framework provides an essential tool for characterizing microbial taxa never before seen, laying the foundation for addressing fundamental questions concerning the taxa comprising diverse microbial communities.     

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。     

Toward a standards-compliant genomic and metagenomic publication record

Increasingly, we are aware as a community of the growing need to manage the avalanche of genomic and metagenomic data, in addition to related data types like ribosomal RNA and barcode sequences, in a way that tightly integrates contextual data with traditional literature in a machine-readable way. It is for this reason that the Genomic Standards Consortium (GSC) formed in 2005. Here we suggest that we move beyond the development of standards and tackle standards compliance and improved data capture at the level of the scientific publication. We are supported in this goal by the fact that the scientific community is in the midst of a publishing revolution. This revolution is marked by a growing shift away from a traditional dichotomy between "journal articles" and "database entries" and an increasing adoption of hybrid models of collecting and disseminating scientific information. With respect to genomes and metagenomes and related data types, we feel the scientific community would be best served by the immediate launch of a central repository of short, highly structured "Genome Notes" that must be standards compliant. This could be done in the context of an existing journal, but we also suggest the more radical solution of launching a new journal. Such a journal could be designed to cater to a wide range of standards-related content types that are not currently centralized in the published literature. It could also support the demand for centralizing aspects of the "gray literature" (documents developed by institutions or communities) such as the call by the GSC for a central repository of Standard Operating Procedures describing the genomic annotation pipelines of the major sequencing centers. We argue that such an "eJournal," published under the Open Access paradigm by the GSC, could be an attractive publishing forum for a broader range of standardization initiatives within, and beyond, the GSC and thereby fill an unoccupied yet increasingly important niche within the current research landscape.     

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.     

New ecological insights through the Global Lake Ecological Observatory Network (GLEON)

Sensor networks are playing an increasingly important role in ecology. Continual advances in affordable sensors and wireless communication are making the development of automated sensing systems with remote communication (i.e., sensor networks) affordable for many ecological research programs (Porter et al. 2005)[1].     

A metagenomic view of novel microbial and metabolic diversity found within the deep terrestrial biosphere at DeMMO: A microbial observatory in South Dakota,USA

The deep terrestrial subsurface is a large and diverse microbial habitat and vast repository of biomass. However, in relation to its size and physical heterogeneity we have limited understanding of taxonomic and metabolic diversity in this realm. Here we present a detailed metagenomic analysis of samples from the Deep Mine Microbial Observatory (DeMMO) spanning depths from the surface to 1.5 km into the crust. From eight geochemically and spatially distinct fluid samples we reconstructed ~600 partial to near-complete metagenome-assembled genomes (MAGs), representing 50 distinct phyla and including 18 candidate phyla. These novel clades include members of the candidate phyla radiation, two new MAGs from OLB16, a phylum originally identified in DeMMO fluids and for which only one other MAG is currently available, and new MAGs from the Eisenbacteria, Omnitrophota, and Edwardsbacteria. We find that microbes spanning this expansive phylogenetic diversity and physical subsurface space gain a competitive edge by maintaining a wide variety of functional pathways, are often capable of numerous dissimilatory energy metabolisms and poised to take advantage of nutrients as they become available in isolated fracture fluids. Our results support and expand on emerging themes of tight nutrient cycling and genomic plasticity in deep subsurface biosphere taxa.     

Selection in coastal Synechococcus (cyanobacteria) populations evaluated from environmental metagenomes

Environmental metagenomics provides snippets of genomic sequences from all organisms in an environmental sample and are an unprecedented resource of information for investigating microbial population genetics. Current analytical methods, however, are poorly equipped to handle metagenomic data, particularly of short, unlinked sequences. A custom analytical pipeline was developed to calculate dN/dS ratios, a common metric to evaluate the role of selection in the evolution of a gene, from environmental metagenomes sequenced using 454 technology of flow-sorted populations of marine Synechococcus, the dominant cyanobacteria in coastal environments. The large majority of genes (98%) have evolved under purifying selection (dN/dS<1). The metagenome sequence coverage of the reference genomes was not uniform and genes that were highly represented in the environment (i.e. high read coverage) tended to be more evolutionarily conserved. Of the genes that may have evolved under positive selection (dN/dS>1), 77 out of 83 (93%) were hypothetical. Notable among annotated genes, ribosomal protein L35 appears to be under positive selection in one Synechococcus population. Other annotated genes, in particular a possible porin, a large-conductance mechanosensitive channel, an ATP binding component of an ABC transporter, and a homologue of a pilus retraction protein had regions of the gene with elevated dN/dS. With the increasing use of next-generation sequencing in metagenomic investigations of microbial diversity and ecology, analytical methods need to accommodate the peculiarities of these data streams. By developing a means to analyze population diversity data from these environmental metagenomes, we have provided the first insight into the role of selection in the evolution of Synechococcus, a globally significant primary producer.     

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.     

An Improved Methodology to Overcome Key Issues in Human Fecal Metagenomic DNA Extraction

Microbes are ubiquitously distributed in nature, and recent culture-independent studies have highlighted the significance of gut microbiota in human health and disease. Fecal DNA is the primary source for the majority of human gut microbiome studies. However, further improvement is needed to obtain fecal metagenomic DNA with sufficient amount and good quality but low host genomic DNA contamination. In the current study, we demonstrate a quick, robust, unbiased,and cost-effective method for the isolation of high molecular weight(23 kb) metagenomic DNA(260/280 ratio 1.8) with a good yield(55.8 ± 3.8 ng/mg of feces). We also confirm that there is very low human genomic DNA contamination(eubacterial: human genomic DNA marker genes = 2~(27.9):1) in the human feces. The newly-developed method robustly performs for fresh as well as stored fecal samples as demonstrated by 16 S r RNA gene sequencing using 454 FLX+.Moreover, 16 S r RNA gene analysis indicated that compared to other DNA extraction methods tested, the fecal metagenomic DNA isolated with current methodology retains species richnessand does not show microbial diversity biases, which is further confirmed by q PCR with a known quantity of spike-in genomes. Overall, our data highlight a protocol with a balance between quality,amount, user-friendliness, and cost effectiveness for its suitability toward usage for cultureindependent analysis of the human gut microbiome, which provides a robust solution to overcome key issues associated with fecal metagenomic DNA isolation in human gut microbiome studies.     

Genome repository of oil systems: An interactive and searchable database that expands the catalogued diversity of crude oil-associated microbes

Microbial communities ultimately control the fate of petroleum hydrocarbons (PHCs) that enter the natural environment, but the interactions of microbes with PHCs and the environment are highly complex and poorly understood. Genome-resolved metagenomics can help unravel these complex interactions. However, the lack of a comprehensive database that integrates existing genomic/metagenomic data from oil environments with physicochemical parameters known to regulate the fate of PHCs currently limits data analysis and interpretations. Here, we curated a comprehensive, searchable database that documents microbial populations in natural oil ecosystems and oil spills, along with available underlying physicochemical data, geocoded via geographic information system to reveal their geographic distribution patterns. Analysis of the ~2000 metagenome-assembled genomes (MAGs) available in the database revealed strong ecological niche specialization within habitats. Over 95% of the recovered MAGs represented novel taxa underscoring the limited representation of cultured organisms from oil-contaminated and oil reservoir ecosystems. The majority of MAGs linked to oil-contaminated ecosystems were detectable in non-oiled samples from the Gulf of Mexico but not in comparable samples from elsewhere, indicating that the Gulf is primed for oil biodegradation. The repository should facilitate future work toward a predictive understanding of the microbial taxa and their activities that control the fate of oil spills.     

MP3: A Software Tool for the Prediction of Pathogenic Proteins in Genomic and Metagenomic Data

The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51–100 amino acids and Blind B: 30–50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100–150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.