An Invitation to the Marriage of Metagenomics and Metabolomics

Emergent biosynthetic capacity in simple microbial communities

Microbes have an astonishing capacity to transform their environments. Yet, the metabolic capacity of a single species is limited and the vast majority of microorganisms form complex communities and join forces to exhibit capabilities far exceeding those achieved by any single species. Such enhanced metabolic capacities represent a promising route to many medical, environmental, and industrial applications and call for the development of a predictive, systems-level understanding of synergistic microbial capacity. Here we present a comprehensive computational framework, integrating high-quality metabolic models of multiple species, temporal dynamics, and flux variability analysis, to study the metabolic capacity and dynamics of simple two-species microbial ecosystems. We specifically focus on detecting emergent biosynthetic capacity--instances in which a community growing on some medium produces and secretes metabolites that are not secreted by any member species when growing in isolation on that same medium. Using this framework to model a large collection of two-species communities on multiple media, we demonstrate that emergent biosynthetic capacity is highly prevalent. We identify commonly observed emergent metabolites and metabolic reprogramming patterns, characterizing typical mechanisms of emergent capacity. We further find that emergent secretion tends to occur in two waves, the first as soon as the two organisms are introduced, and the second when the medium is depleted and nutrients become limited. Finally, aiming to identify global community determinants of emergent capacity, we find a marked association between the level of emergent biosynthetic capacity and the functional/phylogenetic distance between community members. Specifically, we demonstrate a "Goldilocks" principle, where high levels of emergent capacity are observed when the species comprising the community are functionally neither too close, nor too distant. Taken together, our results demonstrate the potential to design and engineer synthetic communities capable of novel metabolic activities and point to promising future directions in environmental and clinical bioengineering.

Microbiome remodelling leads to inhibition of intestinal farnesoid X receptor signalling and decreased obesity

The antioxidant tempol reduces obesity in mice. Here we show that tempol alters the gut microbiome by preferentially reducing the genus Lactobacillus and its bile salt hydrolase (BSH) activity leading to the accumulation of intestinal tauro-β-muricholic acid (T-β-MCA). T-β-MCA is an farnesoid X receptor (FXR) nuclear receptor antagonist, which is involved in the regulation of bile acid, lipid and glucose metabolism. Its increased levels during tempol treatment inhibit FXR signalling in the intestine. High-fat diet-fed intestine-specific Fxr-null (Fxr(ΔIE)) mice show lower diet-induced obesity, similar to tempol-treated wild-type mice. Further, tempol treatment does not decrease weight gain in Fxr(ΔIE) mice, suggesting that the intestinal FXR mediates the anti-obesity effects of tempol. These studies demonstrate a biochemical link between the microbiome, nuclear receptor signalling and metabolic disorders, and suggest that inhibition of FXR in the intestine could be a target for anti-obesity drugs.

Soil microbial community responses to a decade of warming as revealed by comparative metagenomics

Soil microbial communities are extremely complex, being composed of thousands of low-abundance species (<0.1% of total). How such complex communities respond to natural or human-induced fluctuations, including major perturbations such as global climate change, remains poorly understood, severely limiting our predictive ability for soil ecosystem functioning and resilience. In this study, we compared 12 whole-community shotgun metagenomic data sets from a grassland soil in the Midwestern United States, half representing soil that had undergone infrared warming by 2°C for 10 years, which simulated the effects of climate change, and the other half representing the adjacent soil that received no warming and thus, served as controls. Our analyses revealed that the heated communities showed significant shifts in composition and predicted metabolism, and these shifts were community wide as opposed to being attributable to a few taxa. Key metabolic pathways related to carbon turnover, such as cellulose degradation (∼13%) and CO2 production (∼10%), and to nitrogen cycling, including denitrification (∼12%), were enriched under warming, which was consistent with independent physicochemical measurements. These community shifts were interlinked, in part, with higher primary productivity of the aboveground plant communities stimulated by warming, revealing that most of the additional, plant-derived soil carbon was likely respired by microbial activity. Warming also enriched for a higher abundance of sporulation genes and genomes with higher G+C content. Collectively, our results indicate that microbial communities of temperate grassland soils play important roles in mediating feedback responses to climate change and advance the understanding of the molecular mechanisms of community adaptation to environmental perturbations.

High throughput sequencing methods and analysis for microbiome research

High-throughput sequencing technology is rapidly improving in quality, speed and cost. It is therefore becoming more widely used to study whole communities of prokaryotes in many niches. This review discusses these techniques, including nucleic acid extraction from different environments, sample preparation and high-throughput sequencing platforms. We also discuss commonly used and recently developed bioinformatic tools applied to microbiomes, including analyzing amplicon sequences, metagenome shotgun sequences and metatranscriptome sequences. This field is relatively new and rapidly evolving, thus we hope that this review will provide a baseline for understanding these methods of microbiome analyses. Additionally, we seek to stimulate others to solve the many problems that still exist with the sensitivity, specificity and interpretation of high throughput microbiome sequence analysis.

Isolation of an organic solvent-tolerant lipolytic enzyme from uncultivated microorganism

Although the use of lipases as biocatalysts has frequently been proposed, it is yet scarcely being implemented in industrial processes. This is mainly due to the difficulties associated with the discovery and engineering of new enzymes and the lack of versatile screening methods. In this study, we screened the available strategy from a metagenomic pool for the organic solvent-tolerant lipase with enhanced performance for industrial processes. A novel lipase was identified through functional screening from a metagenomic library of activated sludge in an Escherichia coli system. The gene encoding the lipase from the metagenomic pool, metalip1, was sequenced and cloned by PCR. Metalip1 encoding a polypeptide of 316 amino acids had typical residues essential for lipase such as pentapeptide (GXSXGG) and catalytic triad sequences (Ser160, Asp260, and His291). The deduced amino acid sequence of metalip1 showed high similarity to a putative lipase from Pseudomonas sp. CL-61 (80 %, ABC25547). Metalip1 was expressed in E. coli BL21 (DE3) with a his-tag and purified using a Ni-NTA chelating column and characterized. This enzyme showed high expression level and solubility in the heterologous E. coli host. This enzyme was active over broad organic solvents. Among organic solvents examined, dimethyl formamide was the best organic solvent for metalip1. We showed that function-based strategy is an effective method for fishing out an organic solvent-tolerant lipase from the metagenomic library. Also, it revealed high catalytic turnover rates, which make them a very interesting candidate for industrial application.

Cell metabolomics

Abstract Metabolomics technologies enable the examination and identification of endogenous biochemical reaction products, revealing information on the precise metabolic pathways and processes within a living cell. Metabolism is either directly or indirectly involved with every aspect of cell function, and metabolomics is thus believed to be a reflection of the phenotype of any cell. Metabolomics analysis of cells has many potential applications and advantages compared to currently used methods in the postgenomics era. Cell metabolomics is an emerging field that addresses fundamental biological questions and allows one to observe metabolic phenomena in cells. Cell metabolomics consists of four sequential steps: (a) sample preparation and extraction, (b) metabolic profiles of low-weight metabolites based on MS or NMR spectroscopy techniques, (c) pattern recognition approaches and bioinformatics data analysis, (d) metabolites identification resulting in putative biomarkers and molecular targets. The biomarkers are eventually placed in metabolic networks to provide insight on the cellular biochemical phenomena. This article analyzes the recent developments in use of metabolomics to characterize and interpret the cellular metabolome in a wide range of pathophysiological and clinical contexts, and the putative roles of the endogenous small molecule metabolites in this new frontier of postgenomics biology and systems medicine.

Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities

The human microbiome represents a vastly complex ecosystem that is tightly linked to our development, physiology, and health. Our increased capacity to generate multiple channels of omic data from this system, brought about by recent advances in high throughput molecular technologies, calls for the development of systems-level methods and models that take into account not only the composition of genes and species in a microbiome but also the interactions between these components. Such models should aim to study the microbiome as a community of species whose metabolisms are tightly intertwined with each other and with that of the host, and should be developed with a view towards an integrated, comprehensive, and predictive modeling framework. Here, we review recent work specifically in metabolic modeling of the human microbiome, highlighting both novel methodologies and pressing challenges. We discuss various modeling approaches that lay the foundation for a full-scale predictive model, focusing on models of interactions between microbial species, metagenome-scale models of community-level metabolism, and models of the interaction between the microbiome and the host. Continued development of such models and of their integration into a multi-scale model of the microbiome will lead to a deeper mechanistic understanding of how variation in the microbiome impacts the host, and will promote the discovery of clinically relevant and ecologically relevant insights from the rich trove of data now available.

Functional profiling of the gut microbiome in disease-associated inflammation

The microbial residents of the human gut are a major factor in the development and lifelong maintenance of health. The gut microbiota differs to a large degree from person to person and has an important influence on health and disease due to its interaction with the human immune system. Its overall composition and microbial ecology have been implicated in many autoimmune diseases, and it represents a particularly important area for translational research as a new target for diagnostics and therapeutics in complex inflammatory conditions. Determining the biomolecular mechanisms by which altered microbial communities contribute to human disease will be an important outcome of current functional studies of the human microbiome. In this review, we discuss functional profiling of the human microbiome using metagenomic and metatranscriptomic approaches, focusing on the implications for inflammatory conditions such as inflammatory bowel disease and rheumatoid arthritis. Common themes in gut microbial ecology have emerged among these diverse diseases, but they have not yet been linked to targetable mechanisms such as microbial gene and genome composition, pathway and transcript activity, and metabolism. Combining these microbial activities with host gene, transcript and metabolic information will be necessary to understand how and why these complex interacting systems are altered in disease-associated inflammation.

Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules

The human microbiome plays a key role in human health and is associated with numerous diseases. Metagenomic-based studies are now generating valuable information about the composition of the microbiome in health and in disease, demonstrating nonneutral assembly processes and complex co-occurrence patterns. However, the underlying ecological forces that structure the microbiome are still unclear. Specifically, compositional studies alone with no information about mechanisms of interaction, potential competition, or syntrophy, cannot clearly distinguish habitat-filtering and species assortment assembly processes. To address this challenge, we introduce a computational framework, integrating metagenomic-based compositional data with genome-scale metabolic modeling of species interaction. We use in silico metabolic network models to predict levels of competition and complementarity among 154 microbiome species and compare predicted interaction measures to species co-occurrence. Applying this approach to two large-scale datasets describing the composition of the gut microbiome, we find that species tend to co-occur across individuals more frequently with species with which they strongly compete, suggesting that microbiome assembly is dominated by habitat filtering. Moreover, species' partners and excluders exhibit distinct metabolic interaction levels. Importantly, we show that these trends cannot be explained by phylogeny alone and hold across multiple taxonomic levels. Interestingly, controlling for host health does not change the observed patterns, indicating that the axes along which species are filtered are not fully defined by macroecological host states. The approach presented here lays the foundation for a reverse-ecology framework for addressing key questions concerning the assembly of host-associated communities and for informing clinical efforts to manipulate the microbiome.

Random sampling process leads to overestimation of β-diversity of microbial communities

The site-to-site variability in species composition, known as β-diversity, is crucial to understanding spatiotemporal patterns of species diversity and the mechanisms controlling community composition and structure. However, quantifying β-diversity in microbial ecology using sequencing-based technologies is a great challenge because of a high number of sequencing errors, bias, and poor reproducibility and quantification. Herein, based on general sampling theory, a mathematical framework is first developed for simulating the effects of random sampling processes on quantifying β-diversity when the community size is known or unknown. Also, using an analogous ball example under Poisson sampling with limited sampling efforts, the developed mathematical framework can exactly predict the low reproducibility among technically replicate samples from the same community of a certain species abundance distribution, which provides explicit evidences of random sampling processes as the main factor causing high percentages of technical variations. In addition, the predicted values under Poisson random sampling were highly consistent with the observed low percentages of operational taxonomic unit (OTU) overlap (<30% and <20% for two and three tags, respectively, based on both Jaccard and Bray-Curtis dissimilarity indexes), further supporting the hypothesis that the poor reproducibility among technical replicates is due to the artifacts associated with random sampling processes. Finally, a mathematical framework was developed for predicting sampling efforts to achieve a desired overlap among replicate samples. Our modeling simulations predict that several orders of magnitude more sequencing efforts are needed to achieve desired high technical reproducibility. These results suggest that great caution needs to be taken in quantifying and interpreting β-diversity for microbial community analysis using next-generation sequencing technologies.

Due to the vast diversity and uncultivated status of the majority of microorganisms, microbial detection, characterization, and quantitation are of great challenge. Although large-scale metagenome sequencing technology such as PCR-based amplicon sequencing has revolutionized the studies of microbial communities, it suffers from several inherent drawbacks, such as a high number of sequencing errors, biases, poor quantitation, and very high percentages of technical variations, which could greatly overestimate microbial biodiversity. Based on general sampling theory, this study provided the first explicit evidence to demonstrate the importance of random sampling processes in estimating microbial β-diversity, which has not been adequately recognized and addressed in microbial ecology. Since most ecological studies are involved in random sampling, the conclusions learned from this study should also be applicable to other ecological studies in general. In summary, the results presented in this study should have important implications for examining microbial biodiversity to address both basic theoretical and applied management questions.

Current status and future promise of the human microbiome

The human-associated microbiota is diverse, varies between individuals and body sites, and is important in human health. Microbes in human body play an essential role in immunity, health, and disease. The human microbiome has been studies using the advances of next-generation sequencing and its metagenomic applications. This has allowed investigation of the microbial composition in the human body, and identification of the functional genes expressed by this microbial community. The gut microbes have been found to be the most diverse and constitute the densest cell number in the human microbiota; thus, it has been studied more than other sites. Early results have indicated that the imbalances in gut microbiota are related to numerous disorders, such as inflammatory bowel disease, colorectal cancer, diabetes, and atopy. Clinical therapy involving modulating of the microbiota, such as fecal transplantation, has been applied, and its effects investigated in some diseases. Human microbiome studies form part of human genome projects, and understanding gleaned from studies increase the possibility of various applications including personalized medicine.

Comparison of dimensional reduction methods for detecting and visualizing novel patterns in human and marine microbiome

Using metagenomics to detect the global structure of microbial community remains a significant challenge. The structure of a microbial community and its functions are complicated because of not only the complex interactions among microbes but also their interactions with confounding environmental factors. Recently dimension reduction methods have been employed extensively to investigate the complex structure embedded in metagenomic profiles which summarize the abundance of functional or taxonomic categorizations in metagenomic studies. However, metagenomic profiles are not necessary to meet the "Assumption of Linearity" behind these methods. Therefore it is worth to investigate whether nonlinear methods are appropriate methods which can be utilized in metagenomic analysis. In this paper, we compare the applications of several methods, including two linear methods (Principle component analysis and nonnegative matrix factorization) and a nonlinear manifold learning method--Isomap on visualizing and analyzing metagenomic profiles. These methods are applied and compared on a taxonomic profile from 33 human gut metagenomes and a large-scale Pfam profile which are derived from 45 metagenomes in Global Ocean Sampling expedition. We find that all three methods can discover interesting structures of the taxonomic profile from human gut. Furthermore, Isomap identified a novel nonlinear structure of protein families. The relationships among the identified nonlinear components and environmental factors of global ocean are explored. The results indicate that nonlinear methods could be a complementary technique to current linear methods in analyzing metagenomic dataset.

Computational meta'omics for microbial community studies

Complex microbial communities are an integral part of the Earth's ecosystem and of our bodies in health and disease. In the last two decades, culture-independent approaches have provided new insights into their structure and function, with the exponentially decreasing cost of high-throughput sequencing resulting in broadly available tools for microbial surveys. However, the field remains far from reaching a technological plateau, as both computational techniques and nucleotide sequencing platforms for microbial genomic and transcriptional content continue to improve. Current microbiome analyses are thus starting to adopt multiple and complementary meta'omic approaches, leading to unprecedented opportunities to comprehensively and accurately characterize microbial communities and their interactions with their environments and hosts. This diversity of available assays, analysis methods, and public data is in turn beginning to enable microbiome-based predictive and modeling tools. We thus review here the technological and computational meta'omics approaches that are already available, those that are under active development, their success in biological discovery, and several outstanding challenges.

The intestinal microbiome of the pig

The intestinal microbiome has been the subject of study for many decades because of its importance in the health and well being of animals. The bacterial components of the intestinal microbiome have closely evolved as animals have and in so doing contribute to the overall development and metabolic needs of the animal. The microbiome of the pig has been the subject of many investigations using culture-dependent methods and more recently using culture-independent techniques. A review of the literature is consistent with many of the ecologic principles put forth by Rene Dubos. Animals develop an intestinal microbiome over time and space. During the growth and development of the pig, the microbiome changes in composition in a process known as the microbial succession. There are clear and distinct differences in the composition of the pig intestinal microbiome moving from the proximal end of the intestinal tract to the distal end. The majority (>90%) of the bacteria in the pig intestinal microbiome are from two Phyla: Firmicutes and Bacteroidetes. However, the ileum has a high percentage of bacteria in the phylum Proteobacterium (up to 40%). Perturbations to the microbiome occur in response to many factors including stresses, treatment with antibiotics, and diet.

Acetate-producing bifidobacteria protect the host from enteropathogenic infection via carbohydrate transporters

The human gut harbors a large and diverse community of commensal bacteria. Among them, Bifidobacterium is known to exhibit various probiotic effects including protection of hosts from infectious diseases. We recently discovered that genes encoding an ATP-binding-cassette-type carbohydrate transporter present in certain bifidobacteria contribute to protecting gnotobiotic mice from death induced by enterohemorrhagic Escherichia coli O157:H7. We elucidated the molecular mechanism on lethal infection in mice associated with several bifidobacterial strains by a multi-omics approach combining genomics, transcriptomics and metabolomics. The combined data clearly show that acetate produced by protective bifidobacteria acts in vivo to promote defense functions of the host epithelial cells and thereby protects the host from lethal infection. As demonstrated here, our multi-omics approach provides a powerful strategy for evaluation of host-microbial interactions in the complex gut ecosystem.

Chapter 12: Human microbiome analysis

Humans are essentially sterile during gestation, but during and after birth, every body surface, including the skin, mouth, and gut, becomes host to an enormous variety of microbes, bacterial, archaeal, fungal, and viral. Under normal circumstances, these microbes help us to digest our food and to maintain our immune systems, but dysfunction of the human microbiota has been linked to conditions ranging from inflammatory bowel disease to antibiotic-resistant infections. Modern high-throughput sequencing and bioinformatic tools provide a powerful means of understanding the contribution of the human microbiome to health and its potential as a target for therapeutic interventions. This chapter will first discuss the historical origins of microbiome studies and methods for determining the ecological diversity of a microbial community. Next, it will introduce shotgun sequencing technologies such as metagenomics and metatranscriptomics, the computational challenges and methods associated with these data, and how they enable microbiome analysis. Finally, it will conclude with examples of the functional genomics of the human microbiome and its influences upon health and disease.

Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome

Tongue diagnosis is a unique method in traditional Chinese medicine (TCM). This is the first investigation on the association between traditional tongue diagnosis and the tongue coating microbiome using next-generation sequencing. The study included 19 gastritis patients with a typical white-greasy or yellow-dense tongue coating corresponding to TCM Cold or Hot Syndrome respectively, as well as eight healthy volunteers. An Illumina paired-end, double-barcode 16S rRNA sequencing protocol was designed to profile the tongue-coating microbiome, from which approximately 3.7 million V6 tags for each sample were obtained. We identified 123 and 258 species-level OTUs that were enriched in patients with Cold/Hot Syndromes, respectively, representing "Cold Microbiota" and "Hot Microbiota". We further constructed the tongue microbiota-imbalanced networks associated with Cold/Hot Syndromes. The results reveal an important connection between the tongue-coating microbiome and traditional tongue diagnosis, and illustrate the potential of the tongue-coating microbiome as a novel holistic biomarker for characterizing patient subtypes.

Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective

Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such 'ecosystems biology' approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. This mini review aims to address selected opportunities and challenges of modern metagenomics from a bioinformatics perspective and hopefully will serve as a useful resource for microbial ecologists and bioinformaticians alike.

The human microbiome: ecosystem resilience and health

Given the importance of the microbiome for human health, both the stability and the response to disturbance of this microbial ecosystem are crucial issues. Yet, the current understanding of these factors is insufficient. Early data suggest there is relative stability in the microbiome of adults in the absence of gross perturbation, and that long-term stability of the human indigenous microbial communities is maintained not by inertia but by the action of restorative forces within a dynamic system. After brief exposures to some antibiotics, there is an immediate and substantial perturbation and at least a partial recovery of taxonomic composition. Responses to antibiotics are individualized and are influenced by prior experience with the same antibiotic. These findings suggest that the human microbiome has properties of resilience. Besides serving to reveal critical underlying functional attributes, microbial interactions, and keystone species within the indigenous microbiota, the response to disturbance may have value in predicting future instability and disease and in managing the human microbial ecosystem.

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome

BACKGROUND

We present the Biological Observation Matrix (BIOM, pronounced "biome") format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the "ome-ome") grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses.

FINDINGS

The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages.

CONCLUSIONS

The BIOM file format and the biom-format project are steps toward reducing the "bioinformatics bottleneck" that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.

High throughput sequencing methods for microbiome profiling: application to food animal systems

Analysis of microbial communities using high throughput sequencing methods began in the mid 2000s permitting the production of 1000s to 10,000s of sequence reads per sample and megabases of data per sequence run. This then unprecedented depth of sequencing allowed, for the first time, the discovery of the 'rare biosphere' in environmental samples. The technology was quickly applied to studies in several human subjects. Perhaps these early studies served as a reminder that though the microbes that inhabit mammals are known to outnumber host cells by an order of magnitude or more, most of these are unknown members of our second genome, or microbiome (as coined by Joshua Lederberg), because of our inability to culture them. High throughput methods for microbial 16S ribosomal RNA gene and whole genome shotgun (WGS) sequencing have now begun to reveal the composition and identity of archaeal, bacterial and viral communities at many sites, in and on the human body. Surveys of the microbiota of food production animals have been published in the past few years and future studies should benefit from protocols and tools developed from large-scale human microbiome studies. Nevertheless, production animal-related resources, such as improved host genome assemblies and increased numbers and diversity of host-specific microbial reference genome sequences, will be needed to permit meaningful and robust analysis of 16S rDNA and WGS sequence data.

Droplet microfluidics for high-throughput biological assays

Droplet microfluidics offers significant advantages for performing high-throughput screens and sensitive assays. Droplets allow sample volumes to be significantly reduced, leading to concomitant reductions in cost. Manipulation and measurement at kilohertz speeds enable up to 10(8) samples to be screened in one day. Compartmentalization in droplets increases assay sensitivity by increasing the effective concentration of rare species and decreasing the time required to reach detection thresholds. Droplet microfluidics combines these powerful features to enable currently inaccessible high-throughput screening applications, including single-cell and single-molecule assays.

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.

Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations

Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed.

Human microbiome and HIV/AIDS

Understanding of the human microbiome continues to grow rapidly; however, reports on changes in the microbiome after HIV infection are still limited. This review surveys the progress made in methodology associated with microbiome studies and highlights the remaining challenges to this field. Studies have shown that commensal oral, gut, vaginal, and penile bacteria are vital to the health of the human immune system. Our studies on crosstalk among oral and gastrointestinal soluble innate factors, HIV, and microbes indicated that the oral and gut microbiome was altered in the HIV-positive samples compared to the negative controls. The importance of understanding the bacterial component of HIV/AIDS, and likelihood of "crosstalk" between viral and bacterial pathogens, will help in understanding the role of the microbiome in HIV-infected individuals and facilitate identification of novel antiretroviral factors for use as novel diagnostics, microbicides, or therapeutics against HIV infection.

Diversity and function of the avian gut microbiota

The intestinal microbiota have now been shown to largely affect host health through various functional roles in terms of nutrition, immunity, and other physiological systems. However, the majority of these studies have been carried out in mammalian hosts, which differ in their physiological traits from other taxa. For example, birds possess several unique life history traits, such as hatching from eggs, which may alter the interactions with and transmission of intestinal microbes compared to most mammals. This review covers the diversity of microbial taxa hosted by birds. It also discusses how avian microbial communities strongly influence nutrition, immune function, and processing of toxins in avian hosts, in manners similar to and different from mammalian systems. Finally, areas demanding further research are identified, along with descriptions of existing techniques that could be employed to answer these questions.

Summer workshop in metagenomics: one week plus eight students equals gigabases of cloned DNA

We designed a week-long laboratory workshop in metagenomics for a cohort of undergraduate student researchers. During this course, students learned and utilized molecular biology and microbiology techniques to construct a metagenomic library from Puerto Rican soil. Pre-and postworkshop assessments indicated student learning gains in technical knowledge, skills, and confidence in a research environment. Postworkshop construction of additional libraries demonstrated retention of research techniques by the students.

Toll-like receptor-gut microbiota interactions: perturb at your own risk!

The well-being of the intestine and its host requires that this organ execute its complex function amid colonization by a large and diverse microbial community referred to as the gut microbiota. A myriad of interacting mechanisms of mucosal immunity permit the gut to corral the microbiota in such a way as to maximize the benefits and to minimize the danger of living in close proximity to this large microbial biomass. Toll-like receptors and Nod-like receptors, collectively referred to as pattern recognition receptors (PRRs), recognize a variety of microbial components and, hence, play a central role in governing the interface between host and microbiota. This review examines mechanisms by which PRR-microbiota interactions are regulated so as to allow activation of host defense when necessary while preventing excessive inflammation, which can have a myriad of negative consequences for the host. Analysis of published studies performed in human subjects and a variety of murine disease models reveals the central theme that PRRs play a key role in maintaining a healthful stable relationship between the intestine and its microbiota. In contrast, although select genetic ablations of PRR signaling may protect against some chronic diseases, the overriding theme of studies performed to date is that perturbations of PRR-microbiota interactions are more likely to promote disease states associated with inflammation.

An algorithm for efficient identification of branched metabolic pathways

This article presents a new graph-based algorithm for identifying branched metabolic pathways in multi-genome scale metabolic data. The term branched is used to refer to metabolic pathways between compounds that consist of multiple pathways that interact biochemically. A branched pathway may produce a target compound through a combination of linear pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While branched metabolic pathways predominate in metabolic networks, most previous work has focused on identifying linear metabolic pathways. The ability to automatically identify branched pathways is important in applications that require a deeper understanding of metabolism, such as metabolic engineering and drug target identification. The algorithm presented in this article utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on several well-characterized metabolic pathways that demonstrate that the new merging approach can efficiently find biologically relevant branched metabolic pathways.

The relevance of marine chemical ecology to plankton and ecosystem function: an emerging field

Marine chemical ecology comprises the study of the production and interaction of bioactive molecules affecting organism behavior and function. Here we focus on bioactive compounds and interactions associated with phytoplankton, particularly bloom-forming diatoms, prymnesiophytes and dinoflagellates. Planktonic bioactive metabolites are structurally and functionally diverse and some may have multiple simultaneous functions including roles in chemical defense (antipredator, allelopathic and antibacterial compounds), and/or cell-to-cell signaling (e.g., polyunsaturated aldehydes (PUAs) of diatoms). Among inducible chemical defenses in response to grazing, there is high species-specific variability in the effects on grazers, ranging from severe physical incapacitation and/or death to no apparent physiological response, depending on predator susceptibility and detoxification capability. Most bioactive compounds are present in very low concentrations, in both the producing organism and the surrounding aqueous medium. Furthermore, bioactivity may be subject to synergistic interactions with other natural and anthropogenic environmental toxicants. Most, if not all phycotoxins are classic secondary metabolites, but many other bioactive metabolites are simple molecules derived from primary metabolism (e.g., PUAs in diatoms, dimethylsulfoniopropionate (DMSP) in prymnesiophytes). Producing cells do not seem to suffer physiological impact due to their synthesis. Functional genome sequence data and gene expression analysis will provide insights into regulatory and metabolic pathways in producer organisms, as well as identification of mechanisms of action in target organisms. Understanding chemical ecological responses to environmental triggers and chemically-mediated species interactions will help define crucial chemical and molecular processes that help maintain biodiversity and ecosystem functionality.

Metabolomics and its application to studying metal toxicity

Here we explain the omics approach of metabolomics and how it can be applied to study a physiological response to toxic metal exposure. This review aims to educate the metallomics field to the tool of metabolomics. Metabolomics is becoming an increasingly used tool to compare natural and challenged states of various organisms, from disease states in humans to toxin exposure to environmental systems. This approach is key to understanding and identifying the cellular or biochemical targets of metals and the underlying physiological response. Metabolomics steps are described and overviews of its application to metal toxicity to organisms are given. As this approach is very new there are yet only a small number of total studies and therefore only a brief overview of some metal metabolomics studies is described. A frank critical evaluation of the approach is given to provide newcomers to the method a clear idea of the challenges and the rewards of applying metabolomics to their research.

A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data

Metagenomic studies sequence DNA directly from environmental samples to explore the structure and function of complex microbial and viral communities. Individual, short pieces of sequenced DNA ("reads") are classified into (putative) taxonomic or metabolic groups which are analyzed for patterns across samples. Analysis of such read matrices is at the core of using metagenomic data to make inferences about ecosystem structure and function. Non-negative matrix factorization (NMF) is a numerical technique for approximating high-dimensional data points as positive linear combinations of positive components. It is thus well suited to interpretation of observed samples as combinations of different components. We develop, test and apply an NMF-based framework to analyze metagenomic read matrices. In particular, we introduce a method for choosing NMF degree in the presence of overlap, and apply spectral-reordering techniques to NMF-based similarity matrices to aid visualization. We show that our method can robustly identify the appropriate degree and disentangle overlapping contributions using synthetic data sets. We then examine and discuss the NMF decomposition of a metabolic profile matrix extracted from 39 publicly available metagenomic samples, and identify canonical sample types, including one associated with coral ecosystems, one associated with highly saline ecosystems and others. We also identify specific associations between pathways and canonical environments, and explore how alternative choices of decompositions facilitate analysis of read matrices at a finer scale.

Inferring functional modules of protein families with probabilistic topic models

BACKGROUND

Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.

RESULTS

We describe a Bayesian method, based on a probabilistic topic model, which directly identifies functional modules of protein families. The method explores the co-occurrence patterns of protein families across a collection of sequence samples to infer a probabilistic model of arbitrarily-sized functional modules.

CONCLUSIONS

We show that our method identifies protein modules - some of which correspond to well-known biological processes - that are tightly interconnected with known functional interactions and are different from the interactions identified by pairwise co-occurrence. The modules are not specific to any given organism and may combine different realizations of a protein complex or pathway within different taxa.

The developing intestinal microbiome and its relationship to health and disease in the neonate

The intestinal microbiota normally exists in a commensal and/or symbiotic relationship with the host. In the past few years, emerging technologies derived largely from the Human Genome Project have been applied to evaluating the intestinal microbiota and new discoveries using these techniques have prompted new initiatives such as the Human Microbiome Roadmap designed to evaluate the role of the intestinal microbiome in health and disease. In this review, we wish to focus on some new developments in this area and discuss some of the effects of medical manipulations such as antibiotics, probiotics, prebiotics and C-section versus vaginal delivery on the intestinal microbiota.

Systems biology and the future of medicine

Contemporary views of human disease are based on simple correlation between clinical syndromes and pathological analysis dating from the late 19th century. Although this approach to disease diagnosis, prognosis, and treatment has served the medical establishment and society well for many years, it has serious shortcomings for the modern era of the genomic medicine that stem from its reliance on reductionist principles of experimentation and analysis. Quantitative, holistic systems biology applied to human disease offers a unique approach for diagnosing established disease, defining disease predilection, and developing individualized (personalized) treatment strategies that can take full advantage of modern molecular pathobiology and the comprehensive data sets that are rapidly becoming available for populations and individuals. In this way, systems pathobiology offers the promise of redefining our approach to disease and the field of medicine.

Intestinal microbiota, evolution of the immune system and the bad reputation of pro-inflammatory immunity

The mammalian intestine provides a unique niche for a large community of bacterial symbionts that complements the host in digestive and anabolic pathways, as well as in protection from pathogens. Only a few bacterial phyla have adapted to this predominantly anaerobic environment, but hundreds of different species create an ecosystem that affects many facets of the host's physiology. Recent data show how particular symbionts are involved in the maturation of the immune system, in the intestine and beyond, and how dysbiosis, or alteration of that community, can deregulate immunity and lead to immunopathology. The extensive and dynamic interactions between the symbionts and the immune system are key to homeostasis and health, and require all the blends of so-called regulatory and pro-inflammatory immune reactions. Unfortunately, pro-inflammatory immunity leading to the generation of Th17 cells has been mainly associated with its role in immunopathology. Here we discuss the view that the immune system in general, and type 17 immunity in particular, develop to maintain the equilibrium of the host with its symbionts.

Intervention, integration and translation in obesity research: Genetic, developmental and metaorganismal approaches

Obesity is the focus of multiple lines of inquiry that have -- together and separately -- produced many deep insights into the physiology of weight gain and maintenance. We examine three such streams of research and show how they are oriented to obesity intervention through multilevel integrated approaches. The first research programme is concerned with the genetics and biochemistry of fat production, and it links metabolism, physiology, endocrinology and neurochemistry. The second account of obesity is developmental and draws together epigenetic and environmental explanations that can be embedded in an evolutionary framework. The third line of research focuses on the role of gut microbes in the production of obesity, and how microbial activities interact with host genetics, development and metabolism. These interwoven explanatory strategies are driven by an orientation to intervention, both for experimental and therapeutic outcomes. We connect the integrative and intervention-oriented aspects of obesity research through a discussion of translation, broadening the concept to capture the dynamic, iterative processes of scientific practice and therapy development. This system-oriented analysis of obesity research expands the philosophical scrutiny of contemporary developments in the biosciences and biomedicine, and has the potential to enrich philosophy of science and medicine.

Network medicine: a network-based approach to human disease

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

From Pathways to Genomes and Beyond: The Metabolic Engineering Toolbox and Its Place in Biofuels Production

Concerns about the availability of petroleum-derived fuels and chemicals have led to the exploration of metabolically engineered organisms as novel hosts for biofuels and chemicals production. However, the complexity inherent in metabolic and regulatory networks makes this undertaking a complex task. To address these limitations, metabolic engineering has adapted a wide-variety of tools for altering phenotypes. In this review, we will highlight traditional and recent metabolic engineering tools for optimizing cells including pathway-based, global, and genomics enabled approaches. Specifically, we describe these tools as well as provide demonstrations of their effectiveness in optimizing biofuels production. However, each of these tools provides stepping stones towards the grand goal of biofuels production. Thus, developing methods for largescale cellular optimization and integrative approaches are invaluable for further cell optimization. This review highlights the challenges that still must be met to accomplish this goal.

The colonic microflora and probiotic therapy in health and disease

PURPOSE OF REVIEW

Host-microbe dialogue is involved not only in maintenance of mucosal homeostasis but also in the pathogenesis of several infectious, inflammatory, and neoplastic disorders of the gut. This has led to a resurgence of interest in the colonic microbiota in health and disease. Recent landmark findings are addressed here.

RECENT FINDINGS

Reciprocal signalling between the immune system and the microbiota plays a pivotal role in linking alterations in gut microbiota with risk of metabolic disease in the host, notably insulin resistance, obesity, and chronic low-grade inflammation. Loss of ancestral indigenous organisms consequent upon a modern lifestyle may contribute to an increased frequency of various metabolic and immuno-allergic diseases. The potential to address this underpins the science of pharmabiotics.

SUMMARY

Advances in understanding host-microbe interactions within the gut can inform rational probiotic or pharmabiotic selection criteria. In addition, the gut microbiota may be a repository for drug discovery as well as a therapeutic target.

Metagenomic analyses: past and future trends

Metagenomics has revolutionized microbiology by paving the way for a cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. Metagenomics comprising construction and screening of metagenomic DNA libraries has proven to be a powerful tool to isolate new enzymes and drugs of industrial importance. So far, the majority of the metagenomically exploited habitats comprised temperate environments, such as soil and marine environments. Recently, metagenomes of extreme environments have also been used as sources of novel biocatalysts. The employment of next-generation sequencing techniques for metagenomics resulted in the generation of large sequence data sets derived from various environments, such as soil, the human body, and ocean water. Analyses of these data sets opened a window into the enormous taxonomic and functional diversity of environmental microbial communities. To assess the functional dynamics of microbial communities, metatranscriptomics and metaproteomics have been developed. The combination of DNA-based, mRNA-based, and protein-based analyses of microbial communities present in different environments is a way to elucidate the compositions, functions, and interactions of microbial communities and to link these to environmental processes.

Metagenomic analyses: past and future trends

Metagenomics has revolutionized microbiology by paving the way for a cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. Metagenomics comprising construction and screening of metagenomic DNA libraries has proven to be a powerful tool to isolate new enzymes and drugs of industrial importance. So far, the majority of the metagenomically exploited habitats comprised temperate environments, such as soil and marine environments. Recently, metagenomes of extreme environments have also been used as sources of novel biocatalysts. The employment of next-generation sequencing techniques for metagenomics resulted in the generation of large sequence data sets derived from various environments, such as soil, the human body, and ocean water. Analyses of these data sets opened a window into the enormous taxonomic and functional diversity of environmental microbial communities. To assess the functional dynamics of microbial communities, metatranscriptomics and metaproteomics have been developed. The combination of DNA-based, mRNA-based, and protein-based analyses of microbial communities present in different environments is a way to elucidate the compositions, functions, and interactions of microbial communities and to link these to environmental processes.