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

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways

Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.

Quantitative analysis of disease-related metabolic dysregulation of human microbiota

The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis.

Modeling interaction networks between host, diet, and bacteria predicts obesogenesis in a mouse model

Host-microbiome interactions are known to have substantial effects on human health, but the diversity of the human microbiome makes it difficult to definitively attribute specific microbiome features to a host phenotype. One approach to overcoming this challenge is to use animal models of host-microbiome interaction, but it must be determined that relevant aspects of host-microbiome interactions are reflected in the animal model. One such experimental validation is an experiment by Ridura et al. In that experiment, transplanting a microbiome from a human into a mouse also conferred the human donor's obesity phenotype. We have aggregated a collection of previously published host-microbiome mouse-model experiments and combined it with thousands of sequenced and annotated bacterial genomes and metametabolomic pathways. Three computational models were generated, each model reflecting an aspect of host-microbiome interactions: 1) Predict the change in microbiome community structure in response to host diet using a community interaction network, 2) Predict metagenomic data from microbiome community structure, and 3) Predict host obesogenesis from modeled microbiome metagenomic data. These computationally validated models were combined into an integrated model of host-microbiome-diet interactions and used to replicate the Ridura experiment in silico. The results of the computational models indicate that network-based models are significantly more predictive than similar but non-network-based models. Network-based models also provide additional insight into the molecular mechanisms of host-microbiome interaction by highlighting metabolites and metabolic pathways proposed to be associated with microbiome-based obesogenesis. While the models generated in this study are likely too specific to the animal models and experimental conditions used to train our models to be of general utility in a broader understanding of obesogenesis, the approach detailed here is expected to be a powerful tool of investigating multiple types of host-microbiome interactions.

Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care

The microbiome, as a community of microorganisms and their structural elements, genomes, metabolites/signal molecules, has been shown to play an important role in human health, with significant beneficial applications for gut health. Skin microbiome has emerged as a new field with high potential to develop disruptive solutions to manage skin health and disease. Despite an incomplete toolbox for skin microbiome analyses, much progress has been made towards functional dissection of microbiomes and host-microbiome interactions. A standardized and robust investigation of the skin microbiome is necessary to provide accurate microbial information and set the base for a successful translation of innovations in the dermo-cosmetic field. This review provides an overview of how the landscape of skin microbiome research has evolved from method development (multi-omics/data-based analytical approaches) to the discovery and development of novel microbiome-derived ingredients. Moreover, it provides a summary of the latest findings on interactions between the microbiomes (gut and skin) and skin health/disease. Solutions derived from these two paths are used to develop novel microbiome-based ingredients or solutions acting on skin homeostasis are proposed. The most promising skin and gut-derived microbiome interventional strategies are presented, along with regulatory, safety, industrial, and technical challenges related to a successful translation of these microbiome-based concepts/technologies in the dermo-cosmetic industry.

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

The microbiome has shown to functionally interact with its host or environment at a metabolic level, however the exact nature of these interactions is not well understood. In addition, metabolic dysregulation caused by the microbiome is believed to contribute to the development of diseases such as inflammatory bowel disease, diabetes mellitus, and obesity. In this manuscript, we introduce a computational framework to integrate microbiome and metabolome data to uncover microbe-metabolite interactions in a data-driven manner. Our model uses neural networks to predict metabolite abundances from microbe abundances. The trained models are then used to derive microbe-metabolite feature scores, which are used for clustering microbes and metabolites into functional modules. These module-based interactions are useful in generating biological insights and facilitating hypothesis generation for the investigation of their roles in various metabolic diseases. The software of our model is made freely available to interested researchers.

Linking Plant Secondary Metabolites and Plant Microbiomes: A Review

Plant secondary metabolites (PSMs) play many roles including defense against pathogens, pests, and herbivores; response to environmental stresses, and mediating organismal interactions. Similarly, plant microbiomes participate in many of the above-mentioned processes directly or indirectly by regulating plant metabolism. Studies have shown that plants can influence their microbiome by secreting various metabolites and, in turn, the microbiome may also impact the metabolome of the host plant. However, not much is known about the communications between the interacting partners to impact their phenotypic changes. In this article, we review the patterns and potential underlying mechanisms of interactions between PSMs and plant microbiomes. We describe the recent developments in analytical approaches and methods in this field. The applications of these new methods and approaches have increased our understanding of the relationships between PSMs and plant microbiomes. Though the current studies have primarily focused on model organisms, the methods and results obtained so far should help future studies of agriculturally important plants and facilitate the development of methods to manipulate PSMs-microbiome interactions with predictive outcomes for sustainable crop productions.

Microbiome analysis and predicted relative metabolomic turnover suggest bacterial heme and selenium metabolism are altered in the gastrointestinal system of zebrafish (Danio rerio) exposed to the organochlorine dieldrin

Dietary exposure to chemicals alters the diversity of microbiome communities and can lead to pathophysiological changes in the gastrointestinal system. The organochlorine pesticide dieldrin is a persistent environmental contaminant that bioaccumulates in fatty tissue of aquatic organisms. The objectives of this study were to determine whether environmentally-relevant doses of dieldrin altered gastrointestinal morphology and the microbiome of zebrafish. Adult zebrafish at ∼4 months of age were fed a measured amount of feed containing either a solvent control or one of two doses of dieldrin (measured at 16, and 163.5 ng/g dry weight) for 4 months. Dieldrin body burden levels in zebrafish after four-month exposure were 0 (control), 11.47 ± 1.13 ng/g (low dose) and 18.32 ± 1.32 ng/g (high dose) wet weight [mean ± std]. Extensive histopathology at the whole organism level revealed that dieldrin exposure did not induce notable tissue pathology, including the gastrointestinal tract. A repeated measure mixed model analysis revealed that, while fish gained weight over time, there were no dieldrin-specific effects on body weight. Fecal content was collected from the gastrointestinal tract of males and 16S rRNA gene sequencing conducted. Dieldrin at a measured feed dose of 16 ng/g reduced the abundance of Firmicutes, a phylum involved in energy resorption. At the level of class, there was a decrease in abundance of Clostridia and Betaproteobacteria, and an increase in Verrucomicrobiae species. We used a computational approach called predicted relative metabolomic turnover (PRMT) to predict how a shift in microbial community composition affects exchange of metabolites. Dieldrin was predicted to affect metabolic turnover of uroporphyrinogen I and coproporphyrinogen I [enzyme]-cysteine, hydrogen selenide, selenite, and methyl-selenic acid in the fish gastrointestinal system. These pathways are related to bacterial heme biosynthesis and selenium metabolism. Our study demonstrates that dietary exposures to dieldrin can alter microbiota composition over 4 months, however the long-term consequences of such impacts are not well understood.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Multi-omic meta-analysis identifies functional signatures of airway microbiome in chronic obstructive pulmonary disease

The interaction between airway microbiome and host in chronic obstructive pulmonary disease (COPD) is poorly understood. Here we used a multi-omic meta-analysis approach to characterize the functional signature of airway microbiome in COPD. We retrieved all public COPD sputum microbiome datasets, totaling 1640 samples from 16S rRNA gene datasets and 26 samples from metagenomic datasets from across the world. We identified microbial taxonomic shifts using random effect meta-analysis and established a global classifier for COPD using 12 microbial genera. We inferred the metabolic potentials for the airway microbiome, established their molecular links to host targets, and explored their effects in a separate meta-analysis on 1340 public human airway transcriptome samples for COPD. 29.6% of differentially expressed human pathways were predicted to be targeted by microbiome metabolism. For inferred metabolite-host interactions, the flux of disease-modifying metabolites as predicted from host transcriptome was generally concordant with their predicted metabolic turnover in microbiome, suggesting a synergistic response between microbiome and host in COPD. The meta-analysis results were further validated by a pilot multi-omic study on 18 COPD patients and 10 controls, in which airway metagenome, metabolome, and host transcriptome were simultaneously characterized. 69.9% of the proposed "microbiome-metabolite-host" interaction links were validated in the independent multi-omic data. Butyrate, homocysteine, and palmitate were the microbial metabolites showing strongest interactions with COPD-associated host genes. Our meta-analysis uncovered functional properties of airway microbiome that interacted with COPD host gene signatures, and demonstrated the possibility of leveraging public multi-omic data to interrogate disease biology.

Multi-Omic Analysis Reveals Different Effects of Sulforaphane on the Microbiome and Metabolome in Old Compared to Young Mice

Dietary factors modulate interactions between the microbiome, metabolome, and immune system. Sulforaphane (SFN) exerts effects on aging, cancer prevention and reducing insulin resistance. This study investigated effects of SFN on the gut microbiome and metabolome in old mouse model compared with young mice. Young (6–8 weeks) and old (21–22 months) male C57BL/6J mice were provided regular rodent chow ± SFN for 2 months. We collected fecal samples before and after SFN administration and profiled the microbiome and metabolome. Multi-omics datasets were analyzed individually and integrated to investigate the relationship between SFN diet, the gut microbiome, and metabolome. The SFN diet restored the gut microbiome in old mice to mimic that in young mice, enriching bacteria known to be associated with an improved intestinal barrier function and the production of anti-inflammatory compounds. The tricarboxylic acid cycle decreased and amino acid metabolism-related pathways increased. Integration of multi-omic datasets revealed SFN diet-induced metabolite biomarkers in old mice associated principally with the genera, Oscillospira, Ruminococcus, and Allobaculum. Collectively, our results support a hypothesis that SFN diet exerts anti-aging effects in part by influencing the gut microbiome and metabolome. Modulating the gut microbiome by SFN may have the potential to promote healthier aging.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Evaluation of Microbiome-Host Relationships in the Zebrafish Gastrointestinal System Reveals Adaptive Immunity Is a Target of Bis(2-ethylhexyl) Phthalate (DEHP) Exposure

To improve physical characteristics of plastics such as flexibility and durability, producers enrich materials with phthalates such as di-2-(ethylhexyl) phthalate (DEHP). DEHP is a high production volume chemical associated with metabolic and immune disruption in animals and humans. To reveal mechanisms implicated in phthalate-related disruption in the gastrointestinal system, male and female zebrafish were fed DEHP (3 ppm) daily for two months. At the transcriptome level, DEHP significantly upregulated gene networks in the intestine associated with helper T cells' (Th1, Th2, and Th17) specific pathways. The activation of gene networks associated with adaptive immunity was linked to the suppression of networks for tight junction, gap junctional intercellular communication, and transmembrane transporters, all of which are precursors for impaired gut integrity and performance. On a class level, DEHP exposure increased Bacteroidia and Gammaproteobacteria and decreased Verrucomicrobiae in both the male and female gastrointestinal system. Further, in males there was a relative increase in Fusobacteriia and Betaproteobacteria and a relative decrease in Saccharibacteria. Predictive algorithms revealed that the functional shift in the microbiome community, and the metabolites they produce, act to modulate intestinal adaptive immunity. This finding suggests that the gut microbiota may contribute to the adverse effects of DEHP on the host by altering metabolites sensed by both intestinal and immune Th cells. Our results suggest that the microbiome-gut-immune axis can be modified by DEHP and emphasize the value of multiomics approaches to study microbiome-host interactions following chemical perturbations.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data

Background

Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live within the host, or from other exposures such as diet or the environment.

Results

We address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps.

Conclusions

AMON empowers researchers to predict origins of metabolites via genomic information and to visualize potential host:microbe interplay. Additionally, the evaluation of enrichment of pathway metabolites of host versus microbial origin gives insight into the metabolic functionality that a microbial community adds to a host:microbe system. Through integrated analysis of microbiome and metabolome data, mechanistic relationships between microbial communities and host phenotypes can be better understood.

Integrated Counts of Carbohydrate-Active Protein Domains as Metabolic Readouts to Distinguish Probiotic Biology and Human Fecal Metagenomes

Bowel microbiota is a "metaorgan" of metabolisms on which quantitative readouts must be performed before interventions can be introduced and evaluated. The study of the effects of probiotic Clostridium butyricum MIYAIRI 588 (CBM588) on intestine transplantees indicated an increased percentage of the "other glycan degradation" pathway in 16S-rRNA-inferred metagenomes. To verify the prediction, a scoring system of carbohydrate metabolisms derived from shotgun metagenomes was developed using hidden Markov models. A significant correlation (R = 0.9, p < 0.015) between both modalities was demonstrated. An independent validation revealed a strong complementarity (R = -0.97, p < 0.002) between the scores and the abundance of "glycogen degradation" in bacteria communities. On applying the system to bacteria genomes, CBM588 had only 1 match and ranked higher than the other 8 bacteria evaluated. The gram-stain properties were significantly correlated to the scores (p < 5 × 10^-4). The distributions of the scored protein domains indicated that CBM588 had a considerably higher (p < 10^-5) proportion of carbohydrate-binding modules than other bacteria, which suggested the superior ability of CBM588 to access carbohydrates as a metabolic driver to the bowel microbiome. These results demonstrated the use of integrated counts of protein domains as a feasible readout for metabolic potential within bacteria genomes and human metagenomes.

Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this 'predictive metabolomic' approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.

The potential of genomics for restoring ecosystems and biodiversity

Billions of hectares of natural ecosystems have been degraded through human actions. The global community has agreed on targets to halt and reverse these declines, and the restoration sector faces the important but arduous task of implementing programmes to meet these objectives. Existing and emerging genomics tools offer the potential to improve the odds of achieving these targets. These tools include population genomics that can improve seed sourcing, meta-omics that can improve assessment and monitoring of restoration outcomes, and genome editing that can generate novel genotypes for restoring challenging environments. We identify barriers to adopting these tools in a restoration context and emphasize that regulatory and ethical frameworks are required to guide their use.

Host and microbiome multi-omics integration: applications and methodologies

The study of the microbial community-the microbiome-associated with a human host is a maturing research field. It is increasingly clear that the composition of the human's microbiome is associated with various diseases such as gastrointestinal diseases, liver diseases and metabolic diseases. Using high-throughput technologies such as next-generation sequencing and mass spectrometry-based metabolomics, we are able to comprehensively sequence the microbiome-the metagenome-and associate these data with the genomic, epigenomics, transcriptomic and metabolic profile of the host. Our review summarises the application of integrating host omics with microbiome as well as the analytical methods and related tools applied in these studies. In addition, potential future directions are discussed.

Gut microbiota, short chain fatty acids, and obesity across the epidemiologic transition: the METS-Microbiome study protocol

BACKGROUND

While some of the variance observed in adiposity and weight change within populations can be accounted for by traditional risk factors, a new factor, the gut microbiota, has recently been associated with obesity. However, the causal mechanisms through which the gut microbiota and its metabolites, short chain fatty acids (SCFAs) influence obesity are unknown, as are the individual obesogenic effects of the individual SCFAs (butyrate, acetate and propionate). This study, METS-Microbiome, proposes to examine the influence of novel risk factors, the gut microbiota and SCFAs, on obesity, adiposity and weight change in an international established cohort spanning the epidemiologic transition.

METHODS

The parent study; Modeling the Epidemiologic Transition Study (METS) is a well-established and ongoing prospective cohort study designed to assess the association between body composition, physical activity, and relative weight, weight gain and cardiometabolic disease risk in five diverse population-based samples in 2500 people of African descent. The cohort has been prospectively followed since 2009. Annual measures of obesity risk factors, including body composition, objectively measured physical activity and dietary intake, components which vary across the spectrum of social and economic development. In our new study; METS-Microbiome, in addition to continuing yearly measures of obesity risk, we will also measure gut microbiota and stool SCFAs in all contactable participants, and follow participants for a further 3 years, thus providing one of the largest gut microbiota population-based studies to date.

DISCUSSION

This new study capitalizes upon an existing, extensively well described cohort of adults of African-origin, with significant variability as a result of the widespread geographic distributions, and therefore variation in the environmental covariate exposures. The METS-Microbiome study will substantially advance the understanding of the role gut microbiota and SCFAs play in the development of obesity and provide novel obesity therapeutic targets targeting SCFAs producing features of the gut microbiota.

TRIAL REGISTRATION

Registered NCT03378765 Date first posted: December 20, 2017.

MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle

The increasing number of metagenomic and genomic sequences has dramatically improved our understanding of microbial diversity, yet our ability to infer metabolic capabilities in such datasets remains challenging. We describe the Multigenomic Entropy Based Score pipeline (MEBS), a software platform designed to evaluate, compare, and infer complex metabolic pathways in large “omic” datasets, including entire biogeochemical cycles. MEBS is open source and available through https://github.com/eead-csic-compbio/metagenome_Pfam_score. To demonstrate its use, we modeled the sulfur cycle by exhaustively curating the molecular and ecological elements involved (compounds, genes, metabolic pathways, and microbial taxa). This information was reduced to a collection of 112 characteristic Pfam protein domains and a list of complete-sequenced sulfur genomes. Using the mathematical framework of relative entropy (H^΄), we quantitatively measured the enrichment of these domains among sulfur genomes. The entropy of each domain was used both to build up a final score that indicates whether a (meta)genomic sample contains the metabolic machinery of interest and to propose marker domains in metagenomic sequences such as DsrC (PF04358). MEBS was benchmarked with a dataset of 2107 non-redundant microbial genomes from RefSeq and 935 metagenomes from MG-RAST. Its performance, reproducibility, and robustness were evaluated using several approaches, including random sampling, linear regression models, receiver operator characteristic plots, and the area under the curve metric (AUC). Our results support the broad applicability of this algorithm to accurately classify (AUC = 0.985) hard-to-culture genomes (e.g., Candidatus Desulforudis audaxviator), previously characterized ones, and metagenomic environments such as hydrothermal vents, or deep-sea sediment. Our benchmark indicates that an entropy-based score can capture the metabolic machinery of interest and can be used to efficiently classify large genomic and metagenomic datasets, including uncultivated/unexplored taxa.

Towards predicting the environmental metabolome from metagenomics with a mechanistic model

The environmental metabolome and metabolic potential of microorganisms are dominant and essential factors shaping microbial community composition. Recent advances in genome annotation and systems biology now allow us to semiautomatically reconstruct genome-scale metabolic models (GSMMs) of microorganisms based on their genome sequence ¹ . Next, growth of these models in a defined metabolic environment can be predicted in silico, mechanistically linking the metabolic fluxes of individual microbial populations to the community dynamics. A major advantage of GSMMs is that no training data is needed, besides information about the metabolic capacity of individual genes (genome annotation) and knowledge of the available environmental metabolites that allow the microorganism to grow. However, the composition of the environment is often not fully determined and remains difficult to measure ² . We hypothesized that the relative abundance of different bacterial species, as measured by metagenomics, can be combined with GSMMs of individual bacteria to reveal the metabolic status of a given biome. Using a newly developed algorithm involving over 1,500 GSMMs of human-associated bacteria, we inferred distinct metabolomes for four human body sites that are consistent with experimental data. Together, we link the metagenome to the metabolome in a mechanistic framework towards predictive microbiome modelling.

Systems biology of the human microbiome

Recent research has shown that the microbiome-a collection of microorganisms, including bacteria, fungi, and viruses, living on and in a host-are of extraordinary importance in human health, even from conception and development in the uterus. Therefore, to further our ability to diagnose disease, to predict treatment outcomes, and to identify novel therapeutics, it is essential to include microbiome and microbial metabolic biomarkers in Systems Biology investigations. In clinical studies or, more precisely, Systems Medicine approaches, we can use the diversity and individual characteristics of the personal microbiome to enhance our resolution for patient stratification. In this review, we explore several Systems Medicine approaches, including Microbiome Wide Association Studies to understand the role of the human microbiome in health and disease, with a focus on 'preventive medicine' or P4 (i.e., personalized, predictive, preventive, participatory) medicine.

Computational Approaches for Integrative Analysis of the Metabolome and Microbiome

The study of the microbiome, the totality of all microbes inhabiting the host or an environmental niche, has experienced exponential growth over the past few years. The microbiome contributes functional genes and metabolites, and is an important factor for maintaining health. In this context, metabolomics is increasingly applied to complement sequencing-based approaches (marker genes or shotgun metagenomics) to enable resolution of microbiome-conferred functionalities associated with health. However, analyzing the resulting multi-omics data remains a significant challenge in current microbiome studies. In this review, we provide an overview of different computational approaches that have been used in recent years for integrative analysis of metabolome and microbiome data, ranging from statistical correlation analysis to metabolic network-based modeling approaches. Throughout the process, we strive to present a unified conceptual framework for multi-omics integration and interpretation, as well as point out potential future directions.

Space-type radiation induces multimodal responses in the mouse gut microbiome and metabolome

BACKGROUND

Space travel is associated with continuous low dose rate exposure to high linear energy transfer (LET) radiation. Pathophysiological manifestations after low dose radiation exposure are strongly influenced by non-cytocidal radiation effects, including changes in the microbiome and host gene expression. Although the importance of the gut microbiome in the maintenance of human health is well established, little is known about the role of radiation in altering the microbiome during deep-space travel.

RESULTS

Using a mouse model for exposure to high LET radiation, we observed substantial changes in the composition and functional potential of the gut microbiome. These were accompanied by changes in the abundance of multiple metabolites, which were related to the enzymatic activity of the predicted metagenome by means of metabolic network modeling. There was a complex dynamic in microbial and metabolic composition at different radiation doses, suggestive of transient, dose-dependent interactions between microbial ecology and signals from the host's cellular damage repair processes. The observed radiation-induced changes in microbiota diversity and composition were analyzed at the functional level. A constitutive change in activity was found for several pathways dominated by microbiome-specific enzymatic reactions like carbohydrate digestion and absorption and lipopolysaccharide biosynthesis, while the activity in other radiation-responsive pathways like phosphatidylinositol signaling could be linked to dose-dependent changes in the abundance of specific taxa.

CONCLUSIONS

The implication of microbiome-mediated pathophysiology after low dose ionizing radiation may be an unappreciated biologic hazard of space travel and deserves experimental validation. This study provides a conceptual and analytical basis of further investigations to increase our understanding of the chronic effects of space radiation on human health, and points to potential new targets for intervention in adverse radiation effects.

Wine microbiome: A dynamic world of microbial interactions

Most fermented products are generated by a mixture of microbes. These microbial consortia perform various biological activities responsible for the nutritional, hygienic, and aromatic qualities of the product. Wine is no exception. Substantial yeast and bacterial biodiversity is observed on grapes, and in both must and wine. The diverse microorganisms present interact throughout the winemaking process. The interactions modulate the hygienic and sensorial properties of the wine. Many studies have been conducted to elucidate the nature of these interactions, with the aim of establishing better control of the two fermentations occurring during wine processing. However, wine is a very complex medium making such studies difficult. In this review, we present the current state of research on microbial interactions in wines. We consider the different kinds of interactions between different microorganisms together with the consequences of these interactions. We underline the major challenges to obtaining a better understanding of how microbes interact. Finally, strategies and methodologies that may help unravel microbe interactions in wine are suggested.

Local Environmental Factors Drive Divergent Grassland Soil Bacterial Communities in the Western Swiss Alps

Mountain ecosystems are characterized by a diverse range of climatic and topographic conditions over short distances and are known to shelter a high biodiversity. Despite important progress, still little is known on bacterial diversity in mountain areas. Here, we investigated soil bacterial biogeography at more than 100 sampling sites randomly stratified across a 700-km² area with 2,200-m elevation gradient in the western Swiss Alps. Bacterial grassland communities were highly diverse, with 12,741 total operational taxonomic units (OTUs) across 100 sites and an average of 2,918 OTUs per site. Bacterial community structure was correlated with local climatic, topographic, and soil physicochemical parameters with high statistical significance. We found pH (correlated with % CaO and % mineral carbon), hydrogen index (correlated with bulk gravimetric water content), and annual average number of frost days during the growing season to be among the groups of the most important environmental drivers of bacterial community structure. In contrast, bacterial community structure was only weakly stratified as a function of elevation. Contrasting patterns were discovered for individual bacterial taxa. Acidobacteria responded both positively and negatively to pH extremes. Various families within the Bacteroidetes responded to available phosphorus levels. Different verrucomicrobial groups responded to electrical conductivity, total organic carbon, water content, and mineral carbon contents. Alpine grassland bacterial communities are thus highly diverse, which is likely due to the large variety of different environmental conditions. These results shed new light on the biodiversity of mountain ecosystems, which were already identified as potentially fragile to anthropogenic influences and climate change.

IMPORTANCE

This article addresses the question of how microbial communities in alpine regions are dependent on local climatic and soil physicochemical variables. We benefit from a unique 700-km² study region in the western Swiss Alps region, which has been exhaustively studied for macro-organismal and fungal ecology, and for topoclimatic modeling of future ecological trends, but without taking into account soil bacterial diversity. Here, we present an in-depth biogeographical characterization of the bacterial community diversity in this alpine region across 100 randomly stratified sites, using 56 environmental variables. Our exhaustive sampling ensured the detection of ecological trends with high statistical robustness. Our data both confirm previously observed general trends and show many new detailed trends for a wide range of bacterial taxonomic groups and environmental parameters.

Local Environmental Factors Drive Divergent Grassland Soil Bacterial Communities in the Western Swiss Alps

Mountain ecosystems are characterized by a diverse range of climatic and topographic conditions over short distances and are known to shelter a high biodiversity. Despite important progress, still little is known on bacterial diversity in mountain areas. Here, we investigated soil bacterial biogeography at more than 100 sampling sites randomly stratified across a 700-km² area with 2,200-m elevation gradient in the western Swiss Alps. Bacterial grassland communities were highly diverse, with 12,741 total operational taxonomic units (OTUs) across 100 sites and an average of 2,918 OTUs per site. Bacterial community structure was correlated with local climatic, topographic, and soil physicochemical parameters with high statistical significance. We found pH (correlated with % CaO and % mineral carbon), hydrogen index (correlated with bulk gravimetric water content), and annual average number of frost days during the growing season to be among the groups of the most important environmental drivers of bacterial community structure. In contrast, bacterial community structure was only weakly stratified as a function of elevation. Contrasting patterns were discovered for individual bacterial taxa. Acidobacteria responded both positively and negatively to pH extremes. Various families within the Bacteroidetes responded to available phosphorus levels. Different verrucomicrobial groups responded to electrical conductivity, total organic carbon, water content, and mineral carbon contents. Alpine grassland bacterial communities are thus highly diverse, which is likely due to the large variety of different environmental conditions. These results shed new light on the biodiversity of mountain ecosystems, which were already identified as potentially fragile to anthropogenic influences and climate change.

IMPORTANCE

This article addresses the question of how microbial communities in alpine regions are dependent on local climatic and soil physicochemical variables. We benefit from a unique 700-km² study region in the western Swiss Alps region, which has been exhaustively studied for macro-organismal and fungal ecology, and for topoclimatic modeling of future ecological trends, but without taking into account soil bacterial diversity. Here, we present an in-depth biogeographical characterization of the bacterial community diversity in this alpine region across 100 randomly stratified sites, using 56 environmental variables. Our exhaustive sampling ensured the detection of ecological trends with high statistical robustness. Our data both confirm previously observed general trends and show many new detailed trends for a wide range of bacterial taxonomic groups and environmental parameters.

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet-a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite "leakage" during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.

Network-based metabolic analysis and microbial community modeling

Network inference is being applied to studies of microbial ecology to visualize and characterize microbial communities. Network representations can allow examination of the underlying organizational structure of a microbial community, and identification of key players or environmental conditions that influence community assembly and stability. Microbial co-association networks provide information on the dynamics of community structure as a function of time or other external variables. Community metabolic networks can provide a mechanistic link between species through identification of metabolite exchanges and species specific resource requirements. When used together, co-association networks and metabolic networks can provide a more in-depth view of the hidden rules that govern the stability and dynamics of microbial communities.

Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root-mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensor systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with 15 transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and jasmonic acid. This multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease.

IMPORTANCE

Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.

Population-reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population-reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population-reaction model. We also show that population-reaction models can be applied to various ecological concepts, such as predator-prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms.

Molecular characterization of the human microbiome from a reproductive perspective

The process of reproduction inherently poses unique microbial challenges because it requires the transfer of gametes from one individual to the other, meanwhile preserving the integrity of the gametes and individuals from harmful microbes during the process. Advances in molecular biology techniques have expanded our understanding of the natural organisms living on and in our bodies, including those inhabiting the reproductive tract. Over the past two decades accumulating evidence has shown that the human microbiome is tightly related to health and disease states involving the different body systems, including the reproductive system. Here we introduce the science involved in the study of the human microbiome. We examine common methods currently used to characterize the human microbiome as an inseparable part of the reproductive system. Finally, we consider a few limitations, clinical implications, and the critical need for additional research in the field of human fertility.

Using metabolomic and transportomic modeling and machine learning to identify putative novel therapeutic targets for antibiotic resistant Pseudomonad infections

Hospital acquired infections sicken or kill tens of thousands of patients every year. These infections are difficult to treat due to a growing prevalence of resistance to many antibiotics. Among these hospital acquired infections, bacteria of the genus Pseudomonas are among the most common opportunistic pathogens. Computational methods for predicting potential novel antimicrobial therapies for hospital acquired Pseudomonad infections, as well as other hospital acquired infectious pathogens, are desperately needed. Using data generated from sequenced Pseudomonad genomes and metabolomic and transportomic computational approaches developed in our laboratory, we present a support vector machine learning method for identifying the most predictive molecular mechanisms that distinguish pathogenic from non-pathogenic Pseudomonads. Predictions were highly accurate, yielding F-scores between 0.84 and 0.98 in leave one out cross validations. These mechanisms are high-value targets for the development of new antimicrobial therapies.

Metabolome of human gut microbiome is predictive of host dysbiosis

Background

Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome.

Results

Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles.

Conclusions

Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0084-3) contains supplementary material, which is available to authorized users.

Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.

Systems-based approaches to unravel multi-species microbial community functioning

Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.

BiomeNet: a Bayesian model for inference of metabolic divergence among microbial communities

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection.

Metagenomic studies of microbial communities yield enormous numbers of gene sequences that have a known enzymatic function, and thus have potential to contribute to community-level metabolic activities. Ecologically divergent microbial communities are presumed to differ in metabolic repertoire and function, but detecting such differences is challenging because the required analytical methodology is complex. Here, we present a novel Bayesian model suitable for this task. Our model, BiomeNet, does not assume that microbiome samples of a certain type are the same; rather, a sample is modeled as a unique mixture of complex metabolic systems referred to as “metabosystems”. The metabosystems are composed of mixtures of subnetworks, where subnetworks are mixtures of reactions related by function. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among IBD patients. We inferred that this metabosystem is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an important antioxidant, possibly contributing to gut inflammation associated with IBD.

Deciphering diversity and ecological function from marine metagenomes

Metagenomic sequencing now represents a common, powerful approach for investigating diversity and functional relationships in marine ecosystems. High-throughput datasets generated from random fragments of environmental DNA can provide a less biased view of organismal abundance (versus PCR-based amplicon sequencing) and enable novel exploration of microbial genomes by recovering genome assemblies from uncultured species, identifying ecological functions, and reconstructing metabolic pathways. This review highlights the current state of knowledge in marine metagenomics, focusing on biological insights gained from recent environmental studies and detailing commonly employed methods for data collection and analysis.

Satellite remote sensing data can be used to model marine microbial metabolite turnover

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km(2)) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes' predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10(-6)) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ∼3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology.

Metabolic pathways for the whole community

Background

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change.

Results

Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools’ performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients.

Conclusions

This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-619) contains supplementary material, which is available to authorized users.

Human and environmental impacts on river sediment microbial communities

Sediment microbial communities are responsible for a majority of the metabolic activity in river and stream ecosystems. Understanding the dynamics in community structure and function across freshwater environments will help us to predict how these ecosystems will change in response to human land-use practices. Here we present a spatiotemporal study of sediments in the Tongue River (Montana, USA), comprising six sites along 134 km of river sampled in both spring and fall for two years. Sequencing of 16S rRNA amplicons and shotgun metagenomes revealed that these sediments are the richest (∼ 65,000 microbial 'species' identified) and most novel (93% of OTUs do not match known microbial diversity) ecosystems analyzed by the Earth Microbiome Project to date, and display more functional diversity than was detected in a recent review of global soil metagenomes. Community structure and functional potential have been significantly altered by anthropogenic drivers, including increased pathogenicity and antibiotic metabolism markers near towns and metabolic signatures of coal and coalbed methane extraction byproducts. The core (OTUs shared across all samples) and the overall microbial community exhibited highly similar structure, and phylogeny was weakly coupled with functional potential. Together, these results suggest that microbial community structure is shaped by environmental drivers and niche filtering, though stochastic assembly processes likely play a role as well. These results indicate that sediment microbial communities are highly complex and sensitive to changes in land use practices.

The microbial nitrogen cycling potential is impacted by polyaromatic hydrocarbon pollution of marine sediments

During hydrocarbon exposure, the composition and functional dynamics of marine microbial communities are altered, favoring bacteria that can utilize this rich carbon source. Initial exposure of high levels of hydrocarbons in aerobic surface sediments can enrich growth of heterotrophic microorganisms having hydrocarbon degradation capacity. As a result, there can be a localized reduction in oxygen potential within the surface layer of marine sediments causing anaerobic zones. We hypothesized that increasing exposure to elevated hydrocarbon concentrations would positively correlate with an increase in denitrification processes and the net accumulation of dinitrogen. This hypothesis was tested by comparing the relative abundance of genes associated with nitrogen metabolism and nitrogen cycling identified in 6 metagenomes from sediments contaminated by polyaromatic hydrocarbons from the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico, and 3 metagenomes from sediments associated with natural oil seeps in the Santa Barbara Channel. An additional 8 metagenomes from uncontaminated sediments from the Gulf of Mexico were analyzed for comparison. We predicted relative changes in metabolite turnover as a function of the differential microbial gene abundances, which showed predicted accumulation of metabolites associated with denitrification processes, including anammox, in the contaminated samples compared to uncontaminated sediments, with the magnitude of this change being positively correlated to the hydrocarbon concentration and exposure duration. These data highlight the potential impact of hydrocarbon inputs on N cycling processes in marine sediments and provide information relevant for system scale models of nitrogen metabolism in affected ecosystems.

Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill

The Deepwater Horizon (DWH) oil spill in the spring of 2010 resulted in an input of ∼4.1 million barrels of oil to the Gulf of Mexico; >22% of this oil is unaccounted for, with unknown environmental consequences. Here we investigated the impact of oil deposition on microbial communities in surface sediments collected at 64 sites by targeted sequencing of 16S rRNA genes, shotgun metagenomic sequencing of 14 of these samples and mineralization experiments using ¹⁴C-labeled model substrates. The 16S rRNA gene data indicated that the most heavily oil-impacted sediments were enriched in an uncultured Gammaproteobacterium and a Colwellia species, both of which were highly similar to sequences in the DWH deep-sea hydrocarbon plume. The primary drivers in structuring the microbial community were nitrogen and hydrocarbons. Annotation of unassembled metagenomic data revealed the most abundant hydrocarbon degradation pathway encoded genes involved in degrading aliphatic and simple aromatics via butane monooxygenase. The activity of key hydrocarbon degradation pathways by sediment microbes was confirmed by determining the mineralization of ¹⁴C-labeled model substrates in the following order: propylene glycol, dodecane, toluene and phenanthrene. Further, analysis of metagenomic sequence data revealed an increase in abundance of genes involved in denitrification pathways in samples that exceeded the Environmental Protection Agency (EPA)'s benchmarks for polycyclic aromatic hydrocarbons (PAHs) compared with those that did not. Importantly, these data demonstrate that the indigenous sediment microbiota contributed an important ecosystem service for remediation of oil in the Gulf. However, PAHs were more recalcitrant to degradation, and their persistence could have deleterious impacts on the sediment ecosystem.