Recruiting Human Microbiome Shotgun Data to Site-Specific Reference Genomes

The human body consists of innumerable multifaceted environments that predispose colonization by a number of distinct microbial communities, which play fundamental roles in human health and disease. In addition to community surveys and shotgun metagenomes that seek to explore the composition and diversity of these microbiomes, there are significant efforts to sequence reference microbial genomes from many body sites of healthy adults. To illustrate the utility of reference genomes when studying more complex metagenomes, we present a reference-based analysis of sequence reads generated from 55 shotgun metagenomes, selected from 5 major body sites, including 16 sub-sites. Interestingly, between 13% and 92% (62.3% average) of these shotgun reads were aligned to a then-complete list of 2780 reference genomes, including 1583 references for the human microbiome. However, no reference genome was universally found in all body sites. For any given metagenome, the body site-specific reference genomes, derived from the same body site as the sample, accounted for an average of 58.8% of the mapped reads. While different body sites did differ in abundant genera, proximal or symmetrical body sites were found to be most similar to one another. The extent of variation observed, both between individuals sampled within the same microenvironment, or at the same site within the same individual over time, calls into question comparative studies across individuals even if sampled at the same body site. This study illustrates the high utility of reference genomes and the need for further site-specific reference microbial genome sequencing, even within the already well-sampled human microbiome.     

Evolutionary analysis of enzymes using Chisel

MOTIVATION: Availability of large volumes of genomic and enzymatic data for taxonomically and phenotypically diverse organisms allows for exploration of the adaptive mechanisms that led to diversification of enzymatic functions. We present Chisel, a computational framework and a pipeline for an automated, high-resolution analysis of evolutionary variations of enzymes. Chisel allows automatic as well as interactive identification, and characterization of enzymatic sequences. Such knowledge can be utilized for comparative genomics, microbial diagnostics, metabolic engineering, drug design and analysis of metagenomes. RESULTS: Chisel is a comprehensive resource that contains 8575 clusters and subsequent computational models specific for 939 distinct enzymatic functions and, when data is sufficient, their taxonomic variations. Application of Chisel to identification of enzymatic sequences in newly sequenced genomes, analysis of organism-specific metabolic networks, 'binning' of metagenomes and other biological problems are presented. We also provide a thorough analysis of Chisel performance with other similar resources and manual annotations on Shewanella oneidensis MR1 genome.     

Strain/species identification in metagenomes using genome-specific markers

Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k-mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25× coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.     

MetaGeniE: Characterizing Human Clinical Samples Using Deep Metagenomic Sequencing

With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies “all against all” relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.     

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.     

Quantitative metagenomic analyses based on average genome size normalization

Over the past quarter-century, microbiologists have used DNA sequence information to aid in the characterization of microbial communities. During the last decade, this has expanded from single genes to microbial community genomics, or metagenomics, in which the gene content of an environment can provide not just a census of the community members but direct information on metabolic capabilities and potential interactions among community members. Here we introduce a method for the quantitative characterization and comparison of microbial communities based on the normalization of metagenomic data by estimating average genome sizes. This normalization can relieve comparative biases introduced by differences in community structure, number of sequencing reads, and sequencing read lengths between different metagenomes. We demonstrate the utility of this approach by comparing metagenomes from two different marine sources using both conventional small-subunit (SSU) rRNA gene analyses and our quantitative method to calculate the proportion of genomes in each sample that are capable of a particular metabolic trait. With both environments, to determine what proportion of each community they make up and how differences in environment affect their abundances, we characterize three different types of autotrophic organisms: aerobic, photosynthetic carbon fixers (the Cyanobacteria); anaerobic, photosynthetic carbon fixers (the Chlorobi); and anaerobic, nonphotosynthetic carbon fixers (the Desulfobacteraceae). These analyses demonstrate how genome proportionality compares to SSU rRNA gene relative abundance and how factors such as average genome size and SSU rRNA gene copy number affect sampling probability and therefore both types of community analysis.     

Metagenomic signatures of 86 microbial and viral metagenomes

Previous studies have shown that dinucleotide abundances capture the majority of variation in genome signatures and are useful for quantifying lateral gene transfer and building molecular phylogenies. Metagenomes contain a mixture of individual genomes, and might be expected to lack compositional signatures. In many metagenomic data sets the majority of sequences have no significant similarities to known sequences and are effectively excluded from subsequent analyses. To circumvent this limitation, di-, tri- and tetranucleotide abundances of 86 microbial and viral metagenomes consisting of short pyrosequencing reads were analysed to provide a method which includes all sequences that can be used in combination with other analysis to increase our knowledge about microbial and viral communities. Both principal component analysis and hierarchical clustering showed definitive groupings of metagenomes drawn from similar environments. Together these analyses showed that dinucleotide composition, as opposed to tri- and tetranucleotides, defines a metagenomic signature which can explain up to 80% of the variance between biomes, which is comparable to that obtained by functional genomics. Metagenomes with anomalous content were also identified using dinucleotide abundances. Subsequent analyses determined that these metagenomes were contaminated with exogenous DNA, suggesting that this approach is a useful metric for quality control. The predictive strength of the dinucleotide composition also opens the possibility of assigning ecological classifications to unknown fragments. Environmental selection may be responsible for this dinucleotide signature through direct selection of specific compositional signals; however, simulations suggest that the environment may select indirectly by promoting the increased abundance of a few dominant taxa.     

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.     

Inclusion of Oxford Nanopore long reads improves all microbial and viral metagenome-assembled genomes from a complex aquifer system

Assembling microbial and viral genomes from metagenomes is a powerful and appealing method to understand structure–function relationships in complex environments. To compare the recovery of genomes from microorganisms and their viruses from groundwater, we generated shotgun metagenomes with Illumina sequencing accompanied by long reads derived from the Oxford Nanopore Technologies (ONT) sequencing platform. Assembly and metagenome-assembled genome (MAG) metrics for both microbes and viruses were determined from an Illumina-only assembly, ONT-only assembly, and a hybrid assembly approach. The hybrid approach recovered 2× more mid to high-quality MAGs compared to the Illumina-only approach and 4× more than the ONT-only approach. A similar number of viral genomes were reconstructed using the hybrid and ONT methods, and both recovered nearly fourfold more viral genomes than the Illumina-only approach. While yielding fewer MAGs, the ONT-only approach generated MAGs with a high probability of containing rRNA genes, 3× higher than either of the other methods. Of the shared MAGs recovered from each method, the ONT-only approach generated the longest and least fragmented MAGs, while the hybrid approach yielded the most complete. This work provides quantitative data to inform a cost–benefit analysis of the decision to supplement shotgun metagenomic projects with long reads towards the goal of recovering genomes from environmentally abundant groups.     

The Human Microbiome Project in 2011 and beyond

The human microbiome comprises the genes and genomes of the microbiota that inhabit the body. We highlight Human Microbiome Project (HMP) resources, including 600 microbial reference genomes, 70 million 16S sequences, 700 metagenomes, and 60 million predicted genes from healthy adult microbiomes. Microbiome studies of specific diseases and future research directions are also discussed.     

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.     

Metabolic reconstruction for metagenomic data and its application to the human microbiome

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.     

MBGD: microbial genome database for comparative analysis

MBGD is a workbench system for comparative analysis of completely sequenced microbial genomes. The central function of MBGD is to create an orthologous gene classification table using precomputed all-against-all similarity relationships among genes in multiple genomes. In MBGD, an automated classification algorithm has been implemented so that users can create their own classification table by specifying a set of organisms and parameters. This feature is especially useful when the user's interest is focused on some taxonomically related organisms. The created classification table is stored into the database and can be explored combining with the data of individual genomes as well as similarity relationships among genomes. Using these data, users can carry out comparative analyses from various points of view, such as phylogenetic pattern analysis, gene order comparison and detailed gene structure comparison. MBGD is accessible at http://mbgd.genome.ad.jp/.     

Metagenomics and biological ontology

Metagenomics is an emerging microbial systems science that is based on the large-scale analysis of the DNA of microbial communities in their natural environments. Studies of metagenomes are revealing the vast scope of biodiversity in a wide range of environments, as well as new functional capacities of individual cells and communities, and the complex evolutionary relationships between them. Our examination of this science focuses on the ontological implications of these studies of metagenomes and metaorganisms, and what they mean for common sense and philosophical understandings of multicellularity, individuality and organism. We show how metagenomics requires us to think in different ways about what human beings are and what their relation to the microbial world is. Metagenomics could also transform the way in which evolutionary processes are understood, with the most basic relationship between cells from both similar and different organisms being far more cooperative and less antagonistic than is widely assumed. In addition to raising fundamental questions about biological ontology, metagenomics generates possibilities for powerful technologies addressed to issues of climate, health and conservation. We conclude with reflections about process-oriented versus entity-oriented analysis in light of current trends towards systems approaches.     

Searching for fat tails in CRISPR-Cas systems: Data analysis and mathematical modeling

Understanding CRISPR-Cas systems—the adaptive defence mechanism that about half of bacterial species and most of archaea use to neutralise viral attacks—is important for explaining the biodiversity observed in the microbial world as well as for editing animal and plant genomes effectively. The CRISPR-Cas system learns from previous viral infections and integrates small pieces from phage genomes called spacers into the microbial genome. The resulting library of spacers collected in CRISPR arrays is then compared with the DNA of potential invaders. One of the most intriguing and least well understood questions about CRISPR-Cas systems is the distribution of spacers across the microbial population. Here, using empirical data, we show that the global distribution of spacer numbers in CRISPR arrays across multiple biomes worldwide typically exhibits scale-invariant power law behaviour, and the standard deviation is greater than the sample mean. We develop a mathematical model of spacer loss and acquisition dynamics which fits observed data from almost four thousand metagenomes well. In analogy to the classical ‘rich-get-richer’ mechanism of power law emergence, the rate of spacer acquisition is proportional to the CRISPR array size, which allows a small proportion of CRISPRs within the population to possess a significant number of spacers. Our study provides an alternative explanation for the rarity of all-resistant super microbes in nature and why proliferation of phages can be highly successful despite the effectiveness of CRISPR-Cas systems.     

Microbiology in the post-genomic era

Genomics has revolutionized every aspect of microbiology. Now, 13 years after the first bacterial genome was sequenced, it is important to pause and consider what has changed in microbiology research as a consequence of genomics. In this article, we review the evolving field of bacterial typing and the genomic technologies that enable comparative analysis of multiple genomes and the metagenomes of complex microbial environments, and address the implications of the genomic era for the future of microbiology.     

Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage

Bacteria in the 16S rRNA clade SAR86 are among the most abundant uncultivated constituents of microbial assemblages in the surface ocean for which little genomic information is currently available. Bioinformatic techniques were used to assemble two nearly complete genomes from marine metagenomes and single-cell sequencing provided two more partial genomes. Recruitment of metagenomic data shows that these SAR86 genomes substantially increase our knowledge of non-photosynthetic bacteria in the surface ocean. Phylogenomic analyses establish SAR86 as a basal and divergent lineage of γ-proteobacteria, and the individual genomes display a temperature-dependent distribution. Modestly sized at 1.25–1.7 Mbp, the SAR86 genomes lack several pathways for amino-acid and vitamin synthesis as well as sulfate reduction, trends commonly observed in other abundant marine microbes. SAR86 appears to be an aerobic chemoheterotroph with the potential for proteorhodopsin-based ATP generation, though the apparent lack of a retinal biosynthesis pathway may require it to scavenge exogenously-derived pigments to utilize proteorhodopsin. The genomes contain an expanded capacity for the degradation of lipids and carbohydrates acquired using a wealth of tonB-dependent outer membrane receptors. Like the abundant planktonic marine bacterial clade SAR11, SAR86 exhibits metabolic streamlining, but also a distinct carbon compound specialization, possibly avoiding competition.     

Iron transporters in marine prokaryotic genomes and metagenomes

In the pelagic environment, iron is a scarce but essential micronutrient. The iron acquisition capabilities of selected marine bacteria have been investigated, but the recent proliferation of marine prokaryotic genomes and metagenomes offers a more comprehensive picture of microbial iron uptake pathways in the ocean. Searching these data sets, we were able to identify uptake mechanisms for Fe(3+), Fe(2+) and iron chelates (e.g. siderophore and haem iron complexes). Transport of iron chelates is accomplished by TonB-dependent transporters (TBDTs). After clustering the TBDTs from marine prokaryotic genomes, we identified TBDT clusters for the transport of hydroxamate and catecholate siderophore iron complexes and haem using gene neighbourhood analysis and co-clustering of TBDTs of known function. The genomes also contained two classes of siderophore biosynthesis genes: NRPS (non-ribosomal peptide synthase) genes and NIS (NRPS Independent Siderophore) genes. The most common iron transporters, in both the genomes and metagenomes, were Fe(3+) ABC transporters. Iron uptake-related TBDTs and siderophore biosynthesis genes were less common in pelagic marine metagenomes relative to the genomic data set, in part because Pelagibacter ubique and Prochlorococcus species, which almost entirely lacked these Fe uptake systems, dominate the metagenomes. Our results are largely consistent with current knowledge of iron speciation in the ocean, but suggest that in certain niches the ability to acquire siderophores and/or haem iron chelates is beneficial.     

An experimental metagenome data management and analysis system

The application of shotgun sequencing to environmental samples has revealed a new universe of microbial community genomes (metagenomes) involving previously uncultured organisms. Metagenome analysis, which is expected to provide a comprehensive picture of the gene functions and metabolic capacity for microbial communities, needs to be conducted in the context of a comprehensive data management and analysis system. We present in this paper IMG/M, an experimental metagenome data management and analysis system that is based on the Integrated Microbial Genomes (IMG) system. IMG/M provides tools and viewers for analyzing both metagenomes and isolate genomes individually or in a comparative context. IMG/M is available at http://img.jgi.doe.gov/m.