Targeted metagenomics: a high-resolution metagenomics approach for specific gene clusters in complex microbial communities

A major research goal in microbial ecology is to understand the relationship between gene organization and function involved in environmental processes of potential interest. Given that more than an estimated 99% of microorganisms in most environments are not amenable to culturing, methods for culture-independent studies of genes of interest have been developed. The wealth of metagenomic approaches allows environmental microbiologists to directly explore the enormous genetic diversity of microbial communities. However, it is extremely difficult to obtain the appropriate sequencing depth of any particular gene that can entirely represent the complexity of microbial metagenomes and be able to draw meaningful conclusions about these communities. This review presents a summary of the metagenomic approaches that have been useful for collecting more information about specific genes. Specific subsets of metagenomes that focus on sequence analysis were selected in each metagenomic studies. This 'targeted metagenomics' approach will provide extensive insight into the functional, ecological and evolutionary patterns of important genes found in microorganisms from various ecosystems.     

Achievements and new knowledge unraveled by metagenomic approaches

Metagenomics has paved the way for cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. In recent years, significant progress has been made in this research area. A major breakthrough was the improvement and development of high-throughput next-generation sequencing technologies. The application of these technologies resulted in the generation of large datasets derived from various environments such as soil and ocean water. The analyses of these datasets opened a window into the enormous phylogenetic and metabolic diversity of microbial communities living in a variety of ecosystems. In this way, structure, functions, and interactions of microbial communities were elucidated. Metagenomics has proven to be a powerful tool for the recovery of novel biomolecules. In most cases, functional metagenomics comprising construction and screening of complex metagenomic DNA libraries has been applied to isolate new enzymes and drugs of industrial importance. For this purpose, several novel and improved screening strategies that allow efficient screening of large collections of clones harboring metagenomes have been introduced.     

Unravelling core microbial metabolisms in the hypersaline microbial mats of Shark Bay using high-throughput metagenomics

Modern microbial mats are potential analogues of some of Earth''s earliest ecosystems. Excellent examples can be found in Shark Bay, Australia, with mats of various morphologies. To further our understanding of the functional genetic potential of these complex microbial ecosystems, we conducted for the first time shotgun metagenomic analyses. We assembled metagenomic next-generation sequencing data to classify the taxonomic and metabolic potential across diverse morphologies of marine mats in Shark Bay. The microbial community across taxonomic classifications using protein-coding and small subunit rRNA genes directly extracted from the metagenomes suggests that three phyla Proteobacteria, Cyanobacteria and Bacteriodetes dominate all marine mats. However, the microbial community structure between Shark Bay and Highbourne Cay (Bahamas) marine systems appears to be distinct from each other. The metabolic potential (based on SEED subsystem classifications) of the Shark Bay and Highbourne Cay microbial communities were also distinct. Shark Bay metagenomes have a metabolic pathway profile consisting of both heterotrophic and photosynthetic pathways, whereas Highbourne Cay appears to be dominated almost exclusively by photosynthetic pathways. Alternative non-rubisco-based carbon metabolism including reductive TCA cycle and 3-hydroxypropionate/4-hydroxybutyrate pathways is highly represented in Shark Bay metagenomes while not represented in Highbourne Cay microbial mats or any other mat forming ecosystems investigated to date. Potentially novel aspects of nitrogen cycling were also observed, as well as putative heavy metal cycling (arsenic, mercury, copper and cadmium). Finally, archaea are highly represented in Shark Bay and may have critical roles in overall ecosystem function in these modern microbial mats.     

Statistical Approach of Functional Profiling for a Microbial Community

Background

Metagenomics is a relatively new but fast growing field within environmental biology and medical sciences. It enables researchers to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Traditional methods in genomics and microbiology are not efficient in capturing the structure of the microbial community in an environment. Nowadays, high-throughput next-generation sequencing technologies are powerfully driving the metagenomic studies. However, there is an urgent need to develop efficient statistical methods and computational algorithms to rapidly analyze the massive metagenomic short sequencing data and to accurately detect the features/functions present in the microbial community. Although several issues about functions of metagenomes at pathways or subsystems level have been investigated, there is a lack of studies focusing on functional analysis at a low level of a hierarchical functional tree, such as SEED subsystem tree.

Results

A two-step statistical procedure (metaFunction) is proposed to detect all possible functional roles at the low level from a metagenomic sample/community. In the first step a statistical mixture model is proposed at the base of gene codons to estimate the abundances for the candidate functional roles, with sequencing error being considered. As a gene could be involved in multiple biological processes the functional assignment is therefore adjusted by utilizing an error distribution in the second step. The performance of the proposed procedure is evaluated through comprehensive simulation studies. Compared with other existing methods in metagenomic functional analysis the new approach is more accurate in assigning reads to functional roles, and therefore at more general levels. The method is also employed to analyze two real data sets.

Conclusions

metaFunction is a powerful tool in accurate profiling functions in a metagenomic sample.     

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.     

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.     

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.     

Assessment of metagenomic assembly using simulated next generation sequencing data

Due to the complexity of the protocols and a limited knowledge of the nature of microbial communities, simulating metagenomic sequences plays an important role in testing the performance of existing tools and data analysis methods with metagenomic data. We developed metagenomic read simulators with platform-specific (Sanger, pyrosequencing, Illumina) base-error models, and simulated metagenomes of differing community complexities. We first evaluated the effect of rigorous quality control on Illumina data. Although quality filtering removed a large proportion of the data, it greatly improved the accuracy and contig lengths of resulting assemblies. We then compared the quality-trimmed Illumina assemblies to those from Sanger and pyrosequencing. For the simple community (10 genomes) all sequencing technologies assembled a similar amount and accurately represented the expected functional composition. For the more complex community (100 genomes) Illumina produced the best assemblies and more correctly resembled the expected functional composition. For the most complex community (400 genomes) there was very little assembly of reads from any sequencing technology. However, due to the longer read length the Sanger reads still represented the overall functional composition reasonably well. We further examined the effect of scaffolding of contigs using paired-end Illumina reads. It dramatically increased contig lengths of the simple community and yielded minor improvements to the more complex communities. Although the increase in contig length was accompanied by increased chimericity, it resulted in more complete genes and a better characterization of the functional repertoire. The metagenomic simulators developed for this research are freely available.     

Sequencing effort dictates gene discovery in marine microbial metagenomes

Massive metagenomic sequencing combined with gene prediction methods were previously used to compile the gene catalogue of the ocean and host-associated microbes. Global expeditions conducted over the past 15 years have sampled the ocean to build a catalogue of genes from pelagic microbes. Here we undertook a large sequencing effort of a perturbed Red Sea plankton community to uncover that the rate of gene discovery increases continuously with sequencing effort, with no indication that the retrieved 2.83 million non-redundant (complete) genes predicted from the experiment represented a nearly complete inventory of the genes present in the sampled community (i.e., no evidence of saturation). The underlying reason is the Pareto-like distribution of the abundance of genes in the plankton community, resulting in a very long tail of millions of genes present at remarkably low abundances, which can only be retrieved through massive sequencing. Microbial metagenomic projects retrieve a variable number of unique genes per Tera base-pair (Tbp), with a median value of 14.7 million unique genes per Tbp sequenced across projects. The increase in the rate of gene discovery in microbial metagenomes with sequencing effort implies that there is ample room for new gene discovery in further ocean and holobiont sequencing studies.     

The Metagenomic Telescope

Next generation sequencing technologies led to the discovery of numerous new microbe species in diverse environmental samples. Some of the new species contain genes never encountered before. Some of these genes encode proteins with novel functions, and some of these genes encode proteins that perform some well-known function in a novel way. A tool, named the Metagenomic Telescope, is described here that applies artificial intelligence methods, and seems to be capable of identifying new protein functions even in the well-studied model organisms. As a proof-of-principle demonstration of the Metagenomic Telescope, we considered DNA repair enzymes in the present work. First we identified proteins in DNA repair in well–known organisms (i.e., proteins in base excision repair, nucleotide excision repair, mismatch repair and DNA break repair); next we applied multiple alignments and then built hidden Markov profiles for each protein separately, across well–researched organisms; next, using public depositories of metagenomes, originating from extreme environments, we identified DNA repair genes in the samples. While the phylogenetic classification of the metagenomic samples are not typically available, we hypothesized that some very special DNA repair strategies need to be applied in bacteria and Archaea living in those extreme circumstances. It is a difficult task to evaluate the results obtained from mostly unknown species; therefore we applied again the hidden Markov profiling: for the identified DNA repair genes in the extreme metagenomes, we prepared new hidden Markov profiles (for each genes separately, subsequent to a cluster analysis); and we searched for similarities to those profiles in model organisms. We have found well known DNA repair proteins, numerous proteins with unknown functions, and also proteins with known, but different functions in the model organisms.     

Identification and characterization of metagenomic fragments from tidal flat sediment

Phylogenetic surveys based on cultivation-independent methods have revealed that tidal flat sediments are environments with extensive microbial diversity. Since most of prokaryotes in nature cannot be easily cultivated under general laboratory conditions, our knowledge on prokaryotic dwellers in tidal flat sediment is mainly based on the analysis of metagenomes. Microbial community analysis based on the 16S rRNA gene and other phylogenetic markers has been widely used to provide important information on the role of microorganisms, but it is basically an indirect means, compared with direct sequencing of metagenomic DNAs. In this study, we applied a sequence-based metagenomic approach to characterize uncultivated prokaryotes from tidal flat sediment. Two large-insert genomic libraries based on fosmid were constructed from tidal flat metagenomic DNA. A survey based on end-sequencing of selected fosmid clones resulted in the identification of clones containing 274 bacterial and 16 archaeal homologs in which majority were of proteobacterial origins. Two fosmid clones containing large metagenomic DNAs were completely sequenced using the shotgun method. Both DNA inserts contained more than 20 genes encoding putative proteins which implied their ecological roles in tidal flat sediment. Phylogenetic analyses of evolutionary conserved proteins indicate that these clones are not closely related to known prokaryotes whose genome sequence is known, and genes in tidal flat may be subjected to extensive lateral gene transfer, notably between domains Bacteria and Archaea. This is the first report demonstrating that direct sequencing of metagenomic gene library is useful in underpinning the genetic makeup and functional roles of prokaryotes in tidal flat sediments.     

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.     

Reanalyze unassigned reads in Sanger based metagenomic data using conserved gene adjacency

Background  

Investigation of metagenomes provides greater insight into uncultured microbial communities. The improvement in sequencing technology, which yields a large amount of sequence data, has led to major breakthroughs in the field. However, at present, taxonomic binning tools for metagenomes discard 30-40% of Sanger sequencing data due to the stringency of BLAST cut-offs. In an attempt to provide a comprehensive overview of metagenomic data, we re-analyzed the discarded metagenomes by using less stringent cut-offs. Additionally, we introduced a new criterion, namely, the evolutionary conservation of adjacency between neighboring genes. To evaluate the feasibility of our approach, we re-analyzed discarded contigs and singletons from several environments with different levels of complexity. We also compared the consistency between our taxonomic binning and those reported in the original studies.     

Comparative metagenomic,phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients

Terrestrial ecosystems are receiving elevated inputs of nitrogen (N) from anthropogenic sources and understanding how these increases in N availability affect soil microbial communities is critical for predicting the associated effects on belowground ecosystems. We used a suite of approaches to analyze the structure and functional characteristics of soil microbial communities from replicated plots in two long-term N fertilization experiments located in contrasting systems. Pyrosequencing-based analyses of 16S rRNA genes revealed no significant effects of N fertilization on bacterial diversity, but significant effects on community composition at both sites; copiotrophic taxa (including members of the Proteobacteria and Bacteroidetes phyla) typically increased in relative abundance in the high N plots, with oligotrophic taxa (mainly Acidobacteria) exhibiting the opposite pattern. Consistent with the phylogenetic shifts under N fertilization, shotgun metagenomic sequencing revealed increases in the relative abundances of genes associated with DNA/RNA replication, electron transport and protein metabolism, increases that could be resolved even with the shallow shotgun metagenomic sequencing conducted here (average of 75 000 reads per sample). We also observed shifts in the catabolic capabilities of the communities across the N gradients that were significantly correlated with the phylogenetic and metagenomic responses, indicating possible linkages between the structure and functioning of soil microbial communities. Overall, our results suggest that N fertilization may, directly or indirectly, induce a shift in the predominant microbial life-history strategies, favoring a more active, copiotrophic microbial community, a pattern that parallels the often observed replacement of K-selected with r-selected plant species with elevated N.     

Capturing the uncultivated majority

The metagenomic analysis of environmental microbial communities continues to be a rapidly developing area of study. DNA isolation, the first step in capturing the uncultivated majority, has seen many advances in recent years. Protocols have been developed to distinguish DNA from live versus dead cells and to separate extracellular from intracellular DNA. Looking to increase our understanding of the role that members of a microbial community play in ecological processes, several techniques have been developed that are enabling greater in-depth analysis of environmental metagenomes. These include the development of environmental gene tags and the serial analysis of 16S rRNA gene sequence tags. In addition, new screening methods have been designed to select for specific functional genes within metagenomic libraries. Finally, new cultivation methods continue to be developed to improve our ability to capture a greater diversity of microorganisms within the environment.     

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.     

Different techniques reveal the difference of community structure and function of fungi from root and rhizosphere of Salvia miltiorrhiza Bunge

• Fungi have essential functions in plant health and performance. However, the plant-associated functions of many cultured fungi have not been established in detail.
• Here, the fungal species diversity in Salvia miltiorrhiza roots and rhizosphere was assessed for the first time using culturomics and high-throughput sequencing. We present a comprehensive functional metagenomic analysis of these fungi and verified activity of cellulase and chitinase predicted in the metagenomic analysis.
• We first collected and cultured fungi from the root and rhizosphere of S. miltiorrhiza. We found 92 species across 37 families and five phyla, with Ascomycota being dominant. Many rDNA internal transcribed spacer sequences could not be assigned to lower taxonomic levels. There were 19 genera of endophytic fungi and 37 genera of rhizosphere fungi. The culturomics approach had lower taxonomic diversity than high-throughput sequencing, but some fungi were only found in cultures. Structural analyses indicated that the dominant species differed in cultured and non-cultured samples at other levels, apart from the phylum level. Functional analysis mapped 223 carbohydrate enzyme families and 393 pathways in the CAZy and KEGG databases, respectively. The most abundant families were glycoside hydrolases and those involved in carbohydrate metabolism. As predicted by metagenomics, we experimentally verified cellulase and chitinase activity for 29 and 74 fungi, respectively.
• We provide the first evidence of biomass recycling by fungi that are associated with plants. Culturing is essential to reveal the hidden microbial community and critical functions in plant–microbe interactions.