MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets

Enabled by rapid advances in sequencing technology, metagenomic studies aim to characterize entire communities of microbes bypassing the need for culturing individual bacterial members. One major goal of metagenomic studies is to identify specific functional adaptations of microbial communities to their habitats. The functional profile and the abundances for a sample can be estimated by mapping metagenomic sequences to the global metabolic network consisting of thousands of molecular reactions. Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic datasets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge. First, we introduce a scoring function for an arbitrary subnetwork and find the max-weight subnetwork in the global network by a greedy search algorithm. Then we compute two p values (p abund and p struct ) using nonparametric approaches to answer two different statistical questions: (1) is this subnetwork differentically abundant? (2) What is the probability of finding such good subnetworks by chance given the data and network structure? Finally, significant metabolic subnetworks are discovered based on these two p values. In order to validate our methods, we have designed a simulated metabolic pathways dataset and show that MetaPath outperforms other commonly used approaches. We also demonstrate the power of our methods in analyzing two publicly available metagenomic datasets, and show that the subnetworks identified by MetaPath provide valuable insights into the biological activities of the microbiome. We have introduced a statistical method for finding significant metabolic subnetworks from metagenomic datasets. Compared with previous methods, results from MetaPath are more robust against noise in the data, and have significantly higher sensitivity and specificity (when tested on simulated datasets). When applied to two publicly available metagenomic datasets, the output of MetaPath is consistent with previous observations and also provides several new insights into the metabolic activity of the gut microbiome. The software is freely available at http://metapath.cbcb.umd.edu .https://doi.org/10.1186/1753-6561-5-S2-S

A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes

A common biological pathway reconstruction approach—as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences—starts with the identification of protein functions or families (e.g., KO families for the KEGG database and the FIG families for the SEED database) in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath (Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database—as compared to the naïve mapping approach—eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities

Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset

We present an approach in which the semantics of an XML language is defined by means of a transformation from an XML document model (an XML schema) to an application specific model. The application specific model implements the intended behavior of documents written in the language. A transformation is specified in a model transformation language used in the Model Driven Architecture (MDA) approach for software development. Our approach provides a better separation of three concerns found in XML applications: syntax, syntax processing logic and intended meaning of the syntax. It frees the developer of low-level syntactical details and improves the adaptability and reusability of XML applications. Declarative transformation rules and the explicit application model provide a finer control over the application parts affected by adaptations. Transformation rules and the application model for an XML language may be composed with the corresponding rules and application models defined for other XML languages. In that way we achieve reuse and composition of XML applications

Glutamate mediated metabolic neutralization mitigates propionate toxicity in intracellular Mycobacterium tuberculosis

Metabolic networks in biological systems are interconnected, such that malfunctioning parts can be corrected by other parts within the network, a process termed adaptive metabolism. Unlike Bacillus Calmette-Guérin (BCG), Mycobacterium tuberculosis (Mtb) better manages its intracellular lifestyle by executing adaptive metabolism. Here, we used metabolomics and identified glutamate synthase (GltB/D) that converts glutamine to glutamate (Q → E) as a metabolic effort used to neutralize cytoplasmic pH that is acidified while consuming host propionate carbon through the methylcitrate cycle (MCC). Methylisocitrate lyase, the last step of the MCC, is intrinsically downregulated in BCG, leading to obstruction of carbon flux toward central carbon metabolism, accumulation of MCC intermediates, and interference with GltB/D mediated neutralizing activity against propionate toxicity. Indeed, vitamin B12 mediated bypass MCC and additional supplement of glutamate led to selectively correct the phenotypic attenuation in BCG and restore the adaptive capacity of BCG to the similar level of Mtb phenotype. Collectively, a defective crosstalk between MCC and Q → E contributes to attenuation of intracellular BCG. Furthermore, GltB/D inhibition enhances the level of propionate toxicity in Mtb. Thus, these findings revealed a new adaptive metabolism and propose GltB/D as a synergistic target to improve the antimicrobial outcomes of MCC inhibition in Mtb

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

Bacterial Niche-Specific Genome Expansion Is Coupled with Highly Frequent Gene Disruptions in Deep-Sea Sediments

The complexity and dynamics of microbial metagenomes may be evaluated by genome size, gene duplication and the disruption rate between lineages. In this study, we pyrosequenced the metagenomes of microbes obtained from the brine and sediment of a deep-sea brine pool in the Red Sea to explore the possible genomic adaptations of the microbes in response to environmental changes. The microbes from the brine and sediments (both surface and deep layers) of the Atlantis II Deep brine pool had similar communities whereas the effective genome size varied from 7.4 Mb in the brine to more than 9 Mb in the sediment. This genome expansion in the sediment samples was due to gene duplication as evidenced by enrichment of the homologs. The duplicated genes were highly disrupted, on average by 47.6% and 70% for the surface and deep layers of the Atlantis II Deep sediment samples, respectively. The disruptive effects appeared to be mainly due to point mutations and frameshifts. In contrast, the homologs from the Atlantis II Deep brine sample were highly conserved and they maintained relatively small copy numbers. Likely, the adaptation of the microbes in the sediments was coupled with pseudogenizations and possibly functional diversifications of the paralogs in the expanded genomes. The maintenance of the pseudogenes in the large genomes is discussed

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

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/human​n. 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.National Institutes of Health (U.S.) (U54HG004968