Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires

Fast Encapsulation of Microbes into Dissolvable Hydrogel Beads Enables High-Throughput Microbial Single-Cell RNA Sequencing of Clinical Microbiome Samples

Microbial single-cell RNA-seq (mscRNA-seq) can achieve resolution at the cellular level, enhancing the understanding of microbial communities. However, current high-throughput mscRNA-seq methods are limited by multiple centrifugation steps, which can lead to microbial loss and bias. smGel-seq is reported, a high-throughput single-microbe RNA sequencing method for clinical microbiome samples that employs hydrogel beads to encapsulate individual microbes to reduce microbial loss and input requirements. In this method, a novel microchannel array device is implemented for encapsulating single microbe in dissolvable hydrogel beads (smDHBs), along with an optimized automated microfluidic platform to co-encapsulate barcoded beads and smDHBs, enabling high-throughput barcoding of individual microbes. smGel-seq significantly increases the microbial recovery rate in a gut microbiome sample from 8.8% to 91.8%. Furthermore, this method successfully processes clinical microbiome samples with microbial inputs 20 times lower than those required by previous methods. Notably, smGel-seq enables the first mscRNA-seq in a clinical sputum microbiome sample, revealing a specific microbial subpopulation that may play a key role in environmental adaptability, antibiotic resistance, and pathogenicity. These results highlight the compatibility of smGel-seq with clinical microbiome samples and demonstrate its potential for widespread application in diverse clinical and research settings.

Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models

Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.

Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

Functional comparison of metabolic networks across species

Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

A Defined Medium for Cultivation and Exometabolite Profiling of Soil Bacteria

Exometabolomics is an approach to assess how microorganisms alter, or react to their environments through the depletion and production of metabolites. It allows the examination of how soil microbes transform the small molecule metabolites within their environment, which can be used to study resource competition and cross-feeding. This approach is most powerful when used with defined media that enable tracking of all metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse soil bacteria but also is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 108 of the 110 phylogenetically diverse (spanning 36 different families) soil bacterial isolates tested and all of its metabolites were trackable through LC–MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for growing and characterizing diverse microbial isolates and communities.

MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data

MOTIVATION

Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links.

RESULTS

We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products.

AVAILABILITY AND IMPLEMENTATION

MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome

Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host–gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host–microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.

Genome-Inferred Correspondence between Phylogeny and Metabolic Traits in the Wild Drosophila Gut Microbiome

Annotated genome sequences provide valuable insight into the functional capabilities of members of microbial communities. Nevertheless, most studies on the microbiome in animal guts use metagenomic data, hampering the assignment of genes to specific microbial taxa. Here, we make use of the readily culturable bacterial communities in the gut of the fruit fly Drosophila melanogaster to obtain draft genome sequences for 96 isolates from wild flies. These include 81 new de novo assembled genomes, assigned to three orders (Enterobacterales, Lactobacillales, and Rhodospirillales) with 80% of strains identified to species level using average nucleotide identity and phylogenomic reconstruction. Based on annotations by the RAST pipeline, among-isolate variation in metabolic function partitioned strongly by bacterial order, particularly by amino acid metabolism (Rhodospirillales), fermentation, and nucleotide metabolism (Lactobacillales) and arginine, urea, and polyamine metabolism (Enterobacterales). Seven bacterial species, comprising 2-3 species in each order, were well-represented among the isolates and included ≥5 strains, permitting analysis of metabolic functions in the accessory genome (i.e., genes not present in every strain). Overall, the metabolic function in the accessory genome partitioned by bacterial order. Two species, Gluconobacter cerinus (Rhodospirillales) and Lactiplantibacillus plantarum (Lactobacillales) had large accessory genomes, and metabolic functions were dominated by amino acid metabolism (G. cerinus) and carbohydrate metabolism (La. plantarum). The patterns of variation in metabolic capabilities at multiple phylogenetic scales provide the basis for future studies of the ecological and evolutionary processes shaping the diversity of microorganisms associated with natural populations of Drosophila.

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts.

Herold et al. present an integrated meta-omics framework to investigate how mixed microbial communities, such as oleaginous bacterial populations in biological wastewater treatment plants, respond with distinct adaptation strategies to disturbances. They show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity.

A Genomic Toolkit for the Mechanistic Dissection of Intractable Human Gut Bacteria

Despite the remarkable microbial diversity found within humans, our ability to link genes to phenotypes is based upon a handful of model microorganisms. We report a comparative genomics platform for Eggerthella lenta and other Coriobacteriia, a neglected taxon broadly relevant to human health and disease. We uncover extensive genetic and metabolic diversity and validate a tool for mapping phenotypes to genes and sequence variants. We also present a tool for the quantification of strains from metagenomic sequencing data, enabling the identification of genes that predict bacterial fitness. Competitive growth is reproducible under laboratory conditions and attributable to intrinsic growth rates and resource utilization. Unique signatures of in vivo competition in gnotobiotic mice include an adhesin enriched in poor colonizers. Together, these computational and experimental resources represent a strong foundation for the continued mechanistic dissection of the Coriobacteriia and a template that can be applied to study other genetically intractable taxa.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

The functional repertoire contained within the native microbiota of the model nematode Caenorhabditis elegans

The microbiota is generally assumed to have a substantial influence on the biology of multicellular organisms. The exact functional contributions of the microbes are often unclear and cannot be inferred easily from 16S rRNA genotyping, which is commonly used for taxonomic characterization of bacterial associates. In order to bridge this knowledge gap, we here analyzed the metabolic competences of the native microbiota of the model nematode Caenorhabditis elegans. We integrated whole-genome sequences of 77 bacterial microbiota members with metabolic modeling and experimental characterization of bacterial physiology. We found that, as a community, the microbiota can synthesize all essential nutrients for C. elegans. Both metabolic models and experimental analyses revealed that nutrient context can influence how bacteria interact within the microbiota. We identified key bacterial traits that are likely to influence the microbe's ability to colonize C. elegans (i.e., the ability of bacteria for pyruvate fermentation to acetoin) and affect nematode fitness (i.e., bacterial competence for hydroxyproline degradation). Considering that the microbiota is usually neglected in C. elegans research, the resource presented here will help our understanding of this nematode's biology in a more natural context. Our integrative approach moreover provides a novel, general framework to characterize microbiota-mediated functions.

Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

Bifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

A demonstration of unsupervised machine learning in species delimitation

One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now using machine learning, and in evolutionary biology supervised machine learning has recently been used to infer species boundaries. These supervised methods require training data with associated labels. Conversely, unsupervised machine learning (UML) uses inherent data structure and does not require user-specified training labels, potentially providing more objectivity in species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three UML approaches (random forests, variational autoencoders, t-distributed stochastic neighbor embedding) for species delimitation in an arachnid taxon with high population genetic structure (Opiliones, Laniatores, Metanonychus). We find that UML approaches successfully cluster samples according to species-level divergences and not high levels of population structure, while model-based validation methods severely over-split putative species. UML offers intuitive data visualization in two-dimensional space, the ability to accommodate various data types, and has potential in many areas of systematic and evolutionary biology. We argue that machine learning methods are ideally suited for species delimitation and may perform well in many natural systems and across taxa with diverse biological characteristics.

Metabolism, bioenergetics and thermal physiology: influences of the human intestinal microbiota

The micro-organisms which inhabit the human gut (i.e. the intestinal microbiota) influence numerous human biochemical pathways and physiological functions. The present review focuses on two questions, 'Are intestinal microbiota effects measurable and meaningful?' and 'What research methods and variables are influenced by intestinal microbiota effects?'. These questions are considered with respect to doubly labelled water measurements of energy expenditure, heat balance calculations and models, measurements of RMR via indirect calorimetry, and diet-induced energy expenditure. Several lines of evidence suggest that the intestinal microbiota introduces measurement variability and measurement errors which have been overlooked in research studies involving nutrition, bioenergetics, physiology and temperature regulation. Therefore, we recommend that present conceptual models and research techniques be updated via future experiments, to account for the metabolic processes and regulatory influences of the intestinal microbiota.

β-Glucuronidases of opportunistic bacteria are the major contributors to xenobiotic-induced toxicity in the gut

Gut bacterial β-D-glucuronidases (GUSs) catalyze the removal of glucuronic acid from liver-produced β-D-glucuronides. These reactions can have deleterious consequences when they reverse xenobiotic metabolism. The human gut contains hundreds of GUSs of variable sequences and structures. To understand how any particular bacterial GUS(s) contributes to global GUS activity and affects human health, the individual substrate preference(s) must be known. Herein, we report that representative GUSs vary in their ability to produce various xenobiotics from their respective glucuronides. To attempt to explain the distinct substrate preference, we solved the structure of a bacterial GUS complexed with coumarin-3-β-D-glucuronide. Comparisons of this structure with other GUS structures identified differences in loop 3 (or the α2-helix loop) and loop 5 at the aglycone-binding site, where differences in their conformations, hydrophobicities and flexibilities appear to underlie the distinct substrate preference(s) of the GUSs. Additional sequence, structural and functional analysis indicated that several groups of functionally related gut bacterial GUSs exist. Our results pinpoint opportunistic gut bacterial GUSs as those that cause xenobiotic-induced toxicity. We propose a structure-activity relationship that should allow both the prediction of the functional roles of GUSs and the design of selective inhibitors.

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other's growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.

From metagenomic data to personalized in silico microbiotas: predicting dietary supplements for Crohn's disease

Crohn's disease (CD) is associated with an ecological imbalance of the intestinal microbiota, consisting of hundreds of species. The underlying complexity as well as individual differences between patients contributes to the difficulty to define a standardized treatment. Computational modeling can systematically investigate metabolic interactions between gut microbes to unravel mechanistic insights. In this study, we integrated metagenomic data of CD patients and healthy controls with genome-scale metabolic models into personalized in silico microbiotas. We predicted short chain fatty acid (SFCA) levels for patients and controls, which were overall congruent with experimental findings. As an emergent property, low concentrations of SCFA were predicted for CD patients and the SCFA signatures were unique to each patient. Consequently, we suggest personalized dietary treatments that could improve each patient's SCFA levels. The underlying modeling approach could aid clinical practice to find dietary treatment and guide recovery by rationally proposing food aliments.

From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota

An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.

Intestinal Metagenomes and Metabolomes in Healthy Young Males: Inactivity and Hypoxia Generated Negative Physiological Symptoms Precede Microbial Dysbiosis

We explored the metagenomic, metabolomic and trace metal makeup of intestinal microbiota and environment in healthy male participants during the run-in (5 day) and the following three 21-day interventions: normoxic bedrest (NBR), hypoxic bedrest (HBR) and hypoxic ambulation (HAmb) which were carried out within a controlled laboratory environment (circadian rhythm, fluid and dietary intakes, microbial bioburden, oxygen level, exercise). The fraction of inspired O₂ (F_iO₂) and partial pressure of inspired O₂ (P_iO₂) were 0.209 and 133.1 ± 0.3 mmHg for the NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg (~4,000 m simulated altitude) for HBR and HAmb interventions, respectively. Shotgun metagenomes were analyzed at various taxonomic and functional levels, ¹H- and ¹³C -metabolomes were processed using standard quantitative and human expert approaches, whereas metals were assessed using X-ray fluorescence spectrometry. Inactivity and hypoxia resulted in a significant increase in the genus Bacteroides in HBR, in genes coding for proteins involved in iron acquisition and metabolism, cell wall, capsule, virulence, defense and mucin degradation, such as beta-galactosidase (EC3.2.1.23), α-L-fucosidase (EC3.2.1.51), Sialidase (EC3.2.1.18), and α-N-acetylglucosaminidase (EC3.2.1.50). In contrast, the microbial metabolomes, intestinal element and metal profiles, the diversity of bacterial, archaeal and fungal microbial communities were not significantly affected. The observed progressive decrease in defecation frequency and concomitant increase in the electrical conductivity (EC) preceded or took place in absence of significant changes at the taxonomic, functional gene, metabolome and intestinal metal profile levels. The fact that the genus Bacteroides and proteins involved in iron acquisition and metabolism, cell wall, capsule, virulence and mucin degradation were enriched at the end of HBR suggest that both constipation and EC decreased intestinal metal availability leading to modified expression of co-regulated genes in Bacteroides genomes. Bayesian network analysis was used to derive the first hierarchical model of initial inactivity mediated deconditioning steps over time. The PlanHab wash-out period corresponded to a profound life-style change (i.e., reintroduction of exercise) that resulted in stepwise amelioration of the negative physiological symptoms, indicating that exercise apparently prevented the crosstalk between the microbial physiology, mucin degradation and proinflammatory immune activities in the host.

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.

Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.

A clinician's guide to microbiome analysis

Microbiome analysis involves determining the composition and function of a community of microorganisms in a particular location. For the gastroenterologist, this technology opens up a rapidly evolving set of challenges and opportunities for generating novel insights into the health of patients on the basis of microbiota characterizations from intestinal, hepatic or extraintestinal samples. Alterations in gut microbiota composition correlate with intestinal and extraintestinal disease and, although only a few mechanisms are known, the microbiota are still an attractive target for developing biomarkers for disease detection and management as well as potential therapeutic applications. In this Review, we summarize the major decision points confronting new entrants to the field or for those designing new projects in microbiome research. We provide recommendations based on current technology options and our experience of sequencing platform choices. We also offer perspectives on future applications of microbiome research, which we hope convey the promise of this technology for clinical applications.

Comparative Genomic Analysis of the Human Gut Microbiome Reveals a Broad Distribution of Metabolic Pathways for the Degradation of Host-Synthetized Mucin Glycans and Utilization of Mucin-Derived Monosaccharides

The colonic mucus layer is a dynamic and complex structure formed by secreted and transmembrane mucins, which are high-molecular-weight and heavily glycosylated proteins. Colonic mucus consists of a loose outer layer and a dense epithelium-attached layer. The outer layer is inhabited by various representatives of the human gut microbiota (HGM). Glycans of the colonic mucus can be used by the HGM as a source of carbon and energy when dietary fibers are not sufficiently available. Both commensals and pathogens can utilize mucin glycans. Commensals are mostly involved in the cleavage of glycans, while pathogens mostly utilize monosaccharides released by commensals. This HGM-derived degradation of the mucus layer increases pathogen susceptibility and causes many other health disorders. Here, we analyzed 397 individual HGM genomes to identify pathways for the cleavage of host-synthetized mucin glycans to monosaccharides as well as for the catabolism of the derived monosaccharides. Our key results are as follows: (i) Genes for the cleavage of mucin glycans were found in 86% of the analyzed genomes, which significantly higher than a previous estimation. (ii) Genes for the catabolism of derived monosaccharides were found in 89% of the analyzed genomes. (iii) Comparative genomic analysis identified four alternative forms of the monosaccharide-catabolizing enzymes and four alternative forms of monosaccharide transporters. (iv) Eighty-five percent of the analyzed genomes may be involved in potential feeding pathways for the monosaccharides derived from cleaved mucin glycans. (v) The analyzed genomes demonstrated different abilities to degrade known mucin glycans. Generally, the ability to degrade at least one type of mucin glycan was predicted for 81% of the analyzed genomes. (vi) Eighty-two percent of the analyzed genomes can form mutualistic pairs that are able to degrade mucin glycans and are not degradable by any of the paired organisms alone. Taken together, these findings provide further insight into the inter-microbial communications of the HGM as well as into host-HGM interactions.

More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.

BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities

Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena.

In nature, organisms are typically found in near proximity to each other, forming symbiotic relationships. Particularly bacteria are often part of highly organized communities such as biofilms. In this study, we integrate the detailed knowledge about the metabolic capabilities of individual organisms into an individual-based modeling approach for simulating the dynamics of local interactions. We provide a fast and flexible framework, in which established computational models for individual organisms can be simulated in communities. Nutrients can diffuse in an area where cells move, divide, and die. The resulting spatial as well as temporal dynamics and metabolic interactions can be analyzed as well as visualized and subsequently compared to experimental findings. We demonstrate how our approach can be used to gain novel insights on dynamics in single species biofilm formation and multi-species intestinal microbial communities.

Gut microbiota functions: metabolism of nutrients and other food components

The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

Interest in the human microbiome is at an all time high. The number of human microbiome studies is growing exponentially, as are reported associations between microbial communities and disease. However, we have not been able to translate the ever-growing amount of microbiome sequence data into better health. To do this, we need a practical means of transforming a disease-associated microbiome into a health-associated microbiome. This will require a framework that can be used to generate predictions about community dynamics within the microbiome under different conditions, predictions that can be tested and validated. In this review, using the gut microbiome to illustrate, we describe two classes of model that are currently being used to generate predictions about microbial community dynamics: ecological models and metabolic models. We outline the strengths and weaknesses of each approach and discuss the insights into the gut microbiome that have emerged from modeling thus far. We then argue that the two approaches can be combined to yield a community metabolic model, which will supply the framework needed to move from high-throughput omics data to testable predictions about how prebiotic, probiotic, and nutritional interventions affect the microbiome. We are confident that with a suitable model, researchers and clinicians will be able to harness the stream of sequence data and begin designing strategies to make targeted alterations to the microbiome and improve health.

Novel approaches for the taxonomic and metabolic characterization of lactobacilli: Integration of 16S rRNA gene sequencing with MALDI-TOF MS and 1H-NMR

Lactobacilli represent a wide range of bacterial species with several implications for the human host. They play a crucial role in maintaining the ecological equilibrium of different biological niches and are essential for fermented food production and probiotic formulation. Despite the consensus about the ‘health-promoting’ significance of Lactobacillus genus, its genotypic and phenotypic characterization still poses several difficulties. The aim of this study was to assess the integration of different approaches, genotypic (16S rRNA gene sequencing), proteomic (MALDI-TOF MS) and metabolomic (¹H-NMR), for the taxonomic and metabolic characterization of Lactobacillus species. For this purpose we analyzed 40 strains of various origin (intestinal, vaginal, food, probiotics), belonging to different species. The high discriminatory power of MALDI-TOF for species identification was underlined by the excellent agreement with the genotypic analysis. Indeed, MALDI-TOF allowed to correctly identify 39 out of 40 Lactobacillus strains at the species level, with an overall concordance of 97.5%. In the perspective to simplify the MALDI TOF sample preparation, especially for routine practice, we demonstrated the perfect agreement of the colony-picking from agar plates with the protein extraction protocol. ¹H-NMR analysis, applied to both culture supernatants and bacterial lysates, identified a panel of metabolites whose variations in concentration were associated with the taxonomy, but also revealed a high intra-species variability that did not allow a species-level identification. Therefore, despite not suitable for mere taxonomic purposes, metabolomics can be useful to correlate particular biological activities with taxonomy and to understand the mechanisms related to the antimicrobial effect shown by some Lactobacillus species.

Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

Genomic Analysis of the Human Gut Microbiome Suggests Novel Enzymes Involved in Quinone Biosynthesis

Ubiquinone and menaquinone are membrane lipid-soluble carriers of electrons that are essential for cellular respiration. Eukaryotic cells can synthesize ubiquinone but not menaquinone, whereas prokaryotes can synthesize both quinones. So far, most of the human gut microbiome (HGM) studies have been based on metagenomic analysis. Here, we applied an analysis of individual HGM genomes to the identification of ubiquinone and menaquinone biosynthetic pathways. In our opinion, the shift from metagenomics to analysis of individual genomes is a pivotal milestone in investigation of bacterial communities, including the HGM. The key results of this study are as follows. (i) The distribution of the canonical pathways in the HGM genomes was consistent with previous reports and with the distribution of the quinone-dependent reductases for electron acceptors. (ii) The comparative genomics analysis identified four alternative forms of the previously known enzymes for quinone biosynthesis. (iii) Genes for the previously unknown part of the futalosine pathway were identified, and the corresponding biochemical reactions were proposed. We discuss the remaining gaps in the menaquinone and ubiquinone pathways in some of the microbes, which indicate the existence of further alternate genes or routes. Together, these findings provide further insight into the biosynthesis of quinones in bacteria and the physiology of the HGM.