Systems biology of host-microbe metabolomics

Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses

Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.

Gut Microbiota, Intestinal Morphometric Characteristics, and Gene Expression in Relation to the Growth Performance of Chickens

this study aimed to investigate the growth mechanism in a local breed of chickens by comparing the highest weight (HW) and the lowest weight in their microbiota, histological characteristics, and gene expression. Golden Montazah chickens, an Egyptian breed, were reared until they were 49 days old. All of the birds were fed ad libitum by a starter diet from day 1 until day 21, followed by a grower diet from day 21 to the end of the study. At 49 days old, the forty-eight birds with the heaviest body weight (HW) and the lightest body weight (LW) were chosen. Blood biochemical and histological morphometric parameters, electron microscopy, and intestinal nutrient transporter gene expression were studied in the sampled jejunum. The microbial composition and functions of the content and mucosa in HW and LW chickens were studied using 16S rRNA gene sequencing. The histological morphometric parameters were all more significantly (p < 0.05) increased in the HW chickens than in the LW chickens. Total protein, albumin, and triglycerides in serum were significantly higher (p < 0.05) in the HW chickens than in the LW chickens. The microbiome profile in the gut showed that Microbacterium and Sphingomonas were positively correlated with the body weights. In the local breed, there were significant differences in the intestinal microstructure which could enhance the growth mechanism and body weight. Our findings showed that some microbial components were significantly associated with body weight and their interactions with the host could be inferred to explain why these interactions might alter the host’s metabolic responses. Further investigation into combining bioinformatics with lab experiments in chickens will help us to understand how gut bacteria can change the host’s metabolism by special metabolic features in the gastrointestinal system.

Comparative transmission of bacteria from Artemia salina and Brachionus plicatilis to the cnidarian Nematostella vectensis

The microbial community associated with animals (microbiome) is essential for development, physiology, and health of host organisms. A critical step to understand the assembly of microbiomes is to determine how effectively bacteria colonize and establish within the host. Bacteria commonly colonize hosts through vertical transmission, passively from the environment, or through food consumption. Using the prey feeding method (PFM), we test transmittance of Bacillus velezensis, Pseudoalteromonas spiralis, and Vibrio alginolyticus to Nematostella vectensis using two prey, Artemia salina and Brachionus plicatilis. We compare PFM to a solution uptake method (SUM) to quantify the concentration of bacteria in these host organisms, with plate counts. Larvae had a similar uptake with SUM at 6 h but had greater concentrations at 48 h versus PFM. Juveniles acquired similar concentrations at 6 h for SUM and PFM using B. plicatilis and A. salina. At 2 days, the quantity of bacteria vectored from PFM increased. After 7 days the CFUs decreased 2-fold with B. plicatilis and A. salina relative to the 2-day concentrations, and further decreased after 14 days. Therefore, prey-mediated methods provide greater microbe transplantation than SUM after 24 h, supporting this approach as a more successful inoculation method of individual bacterial species.

Ribosomal RNA-Depletion Provides an Efficient Method for Successful Dual RNA-Seq Expression Profiling of a Marine Sponge Holobiont

Investigations of host-symbiont interactions can benefit enormously from a complete and reliable holobiont gene expression profiling. The most efficient way to acquire holobiont transcriptomes is to perform RNA-Seq on both host and symbionts simultaneously. However, optimal methods for capturing both host and symbiont mRNAs are still under development, particularly when the host is a eukaryote and the symbionts are bacteria or archaea. Traditionally, poly(A)-enriched libraries have been used to capture eukaryotic mRNA, but the ability of this method to adequately capture bacterial mRNAs is unclear because of the short half-life of the bacterial transcripts. Here, we address this gap in knowledge with the aim of helping others to choose an appropriate RNA-Seq approach for analysis of animal host-bacterial symbiont transcriptomes. Specifically, we compared transcriptome bias, depth and coverage achieved by two different mRNA capture and sequencing strategies applied to the marine demosponge Amphimedon queenslandica holobiont. Annotated genomes of the sponge host and the three most abundant bacterial symbionts, which can comprise up to 95% of the adult microbiome, are available. Importantly, this allows for transcriptomes to be accurately mapped to these genomes, and thus quantitatively assessed and compared. The two strategies that we compare here are (i) poly(A) captured mRNA-Seq (Poly(A)-RNA-Seq) and (ii) ribosomal RNA depleted RNA-Seq (rRNA-depleted-RNA-Seq). For the host sponge, we find no significant difference in transcriptomes generated by the two different mRNA capture methods. However, for the symbiont transcriptomes, we confirm the expectation that the rRNA-depleted-RNA-Seq performs much better than the Poly(A)-RNA-Seq. This comparison demonstrates that RNA-Seq by ribosomal RNA depletion is an effective and reliable method to simultaneously capture gene expression in host and symbionts and thus to analyse holobiont transcriptomes.

Liuwei Dihuang Decoction Alleviates Cognitive Dysfunction in Mice With D-Galactose-Induced Aging by Regulating Lipid Metabolism and Oxidative Stress via the Microbiota-Gut-Brain Axis

Background

Aging is an important cause of cognitive dysfunction. Liuwei Dihuang decoction (LW), a commonly applied Chinese medicine formula, is widely used for the treatment of aging-related diseases in China. Previously, LW was confirmed to be effective in prolonging life span and reducing oxidative stress in aged mice. Unfortunately, the underlying mechanism of LW remains unclear. The aim of this study was to interpret the mechanism by which LW alleviates cognitive dysfunction related to aging from the perspective of the microbiota-gut-brain axis.

Method

All C57BL/6 mice (n = 60) were randomly divided into five groups: the control, model, vitamin E (positive control group), low-dose LW and high-dose LW groups (n = 12 in each group). Except for those in the control group, D-galactose was subcutaneously injected into mice in the other groups to induce the aging model. The antiaging effect of LW was evaluated by the water maze test, electron microscopy, 16S rRNA sequencing, combined LC–MS and GC–MS metabolomics, and ELISA.

Results

Liuwei Dihuang decoction ameliorated cognitive dysfunction and hippocampal synaptic ultrastructure damage in aging mice. Moreover, LW decreased Proteobacteria abundance and increased gut microbiota diversity in aging mice. Metabolomic analysis showed that LW treatment was associated with the significantly differential abundance of 14 metabolites, which were mainly enriched in apelin signaling, sphingolipid metabolism, glycerophospholipid and other metabolic pathways. Additionally, LW affected lipid metabolism and oxidative stress in aging mice. Finally, we also found that LW-regulated microbial species such as Proteobacteria and Fibrobacterota had potential relationships with lipid metabolism, oxidative stress and hippocampal metabolites.

Conclusion

In brief, LW improved cognitive function in aging mice by regulating lipid metabolism and oxidative stress through restoration of the homeostasis of the microbiota-gut-brain axis.

Targeting the gut to prevent and counteract metabolic disorders and pathologies during aging

Impairment of gut function is one of the explanatory mechanisms of health status decline in elderly population. These impairments involve a decline in gut digestive physiology, metabolism and immune status, and associated to that, changes in composition and function of the microbiota it harbors. Continuous deteriorations are generally associated with the development of systemic dysregulations and ultimately pathologies that can worsen the initial health status of individuals. All these alterations observed at the gut level can then constitute a wide range of potential targets for development of nutritional strategies that can impact gut tissue or associated microbiota pattern. This can be key, in a preventive manner, to limit gut functionality decline, or in a curative way to help maintaining optimum nutrients bioavailability in a context on increased requirements, as frequently observed in pathological situations. The aim of this review is to give an overview on the alterations that can occur in the gut during aging and lead to the development of altered function in other tissues and organs, ultimately leading to the development of pathologies. Subsequently is discussed how nutritional strategies that target gut tissue and gut microbiota can help to avoid or delay the occurrence of aging-related pathologies.

Therapeutic Effect of Buyang Huanwu Decoction on the Gut Microbiota and Hippocampal Metabolism in a Rat Model of Cerebral Ischemia

Buyang Huanwu decoction (BHD) is a well-known Chinese herbal prescription. It has been widely used in the clinical treatment of cerebral ischemia (CI) in China. However, the mechanism underlying the treatment of CI with BHD remains to be elucidated. In this study, we combined microbiomic and metabolomic strategies to explore the therapeutic effects of BHD on middle cerebral artery occlusion (MCAO) in rats. Our results showed that BHD could effectively improve neurological severity scores and alleviate neuronal damage in rats with MCAO. BHD could also reduce the level of peripheral proinflammatory cytokines and inhibit neuroinflammation. 16S rRNA sequencing showed that BHD could increase the relative abundances of the genera Lactobacillus, Faecalibacterium, Ruminococcaceae_UCG-002, etc., while decreasing the relative abundances of the genera Escherichia-Shigella, Klebsiella, Streptococcus, Coprococcus_2, Enterococcus, etc. Untargeted metabolomic analysis of hippocampal samples showed that 17 significantly differentially abundant metabolites and 9 enriched metabolic pathways were linked with BHD treatment. We also found that the regulatory effects of BHD on metabolites were correlated with the differentially abundant microbial taxa. The predicted function of the gut microbiota and the metabolic pathway enrichment results showed that purine metabolism, glutamatergic synapses, arginine and proline metabolism, and alanine, aspartic acid and glutamate metabolism were involved in the effects of BHD. These pathways may be related to pathological processes such as excitotoxicity, neuroinflammation, and energy metabolism disorder in CI. In summary, these findings suggest that regulation of hippocampal metabolism and of the composition and function of the gut microbiota may be important mechanisms underlying the effect of BHD in the treatment of CI.

Ginsenoside Rb1 Improves Metabolic Disorder in High-Fat Diet-Induced Obese Mice Associated With Modulation of Gut Microbiota

Gut microbiota plays an important role in metabolic homeostasis. Previous studies demonstrated that ginsenoside Rb1 might improve obesity-induced metabolic disorders through regulating glucose and lipid metabolism in the liver and adipose tissues. Due to low bioavailability and enrichment in the intestinal tract of Rb1, we hypothesized that modulation of the gut microbiota might account for its pharmacological effects as well. Here, we show that oral administration of Rb1 significantly decreased serum LDL-c, TG, insulin, and insulin resistance index (HOMA-IR) in mice with a high-fat diet (HFD). Dynamic profiling of the gut microbiota showed that this metabolic improvement was accompanied by restoring of relative abundance of some key bacterial genera. In addition, the free fatty acids profiles in feces were significantly different between the HFD-fed mice with or without Rb1. The content of eight long-chain fatty acids (LCFAs) was significantly increased in mice with Rb1, which was positively correlated with the increase of Akkermansia and Parasuttereller, and negatively correlated with the decrease of Oscillibacter and Intestinimonas. Among these eight increased LCFAs, eicosapentaenoic acid (EPA), octadecenoic acids, and myristic acid were positively correlated with metabolic improvement. Furthermore, the colonic expression of the free fatty acid receptors 4 (Ffar4) gene was significantly upregulated after Rb1 treatment, in response to a notable increase of LCFA in feces. These findings suggested that Rb1 likely modulated the gut microbiota and intestinal free fatty acids profiles, which should be beneficial for the improvement of metabolic disorders in HFD-fed mice. This study provides a novel mechanism of Rb1 for the treatment of metabolic disorders induced by obesity, which may provide a therapeutic avenue for the development of new nutraceutical-based remedies for treating metabolic diseases, such as hyperlipidemia, insulin resistance, and type 2 diabetes.

A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder

Introduction

The integrity of the intestinal epithelium is crucial for human health and is harmed in autism spectrum disorder (ASD). An aberrant gut microbial composition resulting in gut-derived metabolic toxins was found to damage the intestinal epithelium, jeopardizing tissue integrity. These toxins further reach the brain via the gut-brain axis, disrupting the normal function of the brain. A mechanistic understanding of metabolic disturbances in the brain and gut is essential to design effective therapeutics and early intervention to block disease progression. Herein, we present a novel computational framework integrating constraint based tissue specific metabolic (CBM) model and whole-body physiological pharmacokinetics (PBPK) modeling for ASD. Furthermore, the role of gut microbiota, diet, and oxidative stress is analyzed in ASD.

Methods

A representative gut model capturing host-bacteria and bacteria-bacteria interaction was developed using CBM techniques and patient data. Simultaneously, a PBPK model of toxin metabolism was assembled, incorporating multi-scale metabolic information. Furthermore, dynamic flux balance analysis was performed to integrate CBM and PBPK. The effectiveness of a probiotic and dietary intervention to improve autism symptoms was tested on the integrated model.

Results

The model accurately highlighted critical metabolic pathways of the gut and brain that are associated with ASD. These include central carbon, nucleotide, and vitamin metabolism in the host gut, and mitochondrial energy and amino acid metabolisms in the brain. The proposed dietary intervention revealed that a high-fiber diet is more effective than a western diet in reducing toxins produced inside the gut. The addition of probiotic bacteria Lactobacillus acidophilus, Bifidobacterium longum longum, Akkermansia muciniphila, and Prevotella ruminicola to the diet restores gut microbiota balance, thereby lowering oxidative stress in the gut and brain.

Conclusion

The proposed computational framework is novel in its applicability, as demonstrated by the determination of the whole-body distribution of ROS toxins and metabolic association in ASD. In addition, it emphasized the potential for developing novel therapeutic strategies to alleviate autism symptoms. Notably, the presented integrated model validates the importance of combining PBPK modeling with COBRA -specific tissue details for understanding disease pathogenesis.

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.

Environmental Determination of Indigenous Bifidobacteria of the Human Intestine

The environmental determination of indigenous (constantly present) bifidobacteria of the human large intestine is considered in this review. Environmental determination (from the Latin determinere, "I determine") is understood as a set of natural phenomena of a habitat (biotope) that determine the role of indigenous microorganisms in the microbiocenosis. Using the symbiotic approach, an attempt is made to identify the environmental conditions for the habitat of bifidobacteria and their physiological effects in the microsymbiocenosis. The features of indigenous bifidobacteria in terms of their nature have been established: evolutionary-genetic (phylogenetic remoteness, genome conservation, metabolic specialization), biochemical (lysozyme resistance, constitutive acetate production), and physiological (microbial "friend-foe" identification, immunoregulation), which are important in adaptation (persistence) and the provision of mutualistic effects and stability of the bifidoflora in the population.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Associations between the gut microbiome and metabolome in early life

BACKGROUND

The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.

RESULTS

Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.

CONCLUSIONS

Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

Influence of Short-Term Consumption of Hericium erinaceus on Serum Biochemical Markers and the Changes of the Gut Microbiota: A Pilot Study

Hericium erinaceus (H. erinaceus) is widely studied as a medicinal and edible fungus. Recent studies have shown that H. erinaceus has protective effects for diseases, such as inflammatory bowel disease and cancer, which are related to gut microbiota. To investigate the benefits of H. erinaceus intake on gut microbiota and blood indices in adulthood, we recruited 13 healthy adults to consume H. erinaceus powder as a dietary supplement. Blood changes due to H. erinaceus consumption were determined by routine hematological examination and characterized by serum biochemical markers. Microbiota composition was profiled by 16S ribosomal RNA gene sequencing. Results showed that daily H. erinaceus supplementation increased the alpha diversity within the gut microbiota community, upregulated the relative abundance of some short-chain fatty acid (SCFA) producing bacteria (Kineothrix alysoides, Gemmiger formicilis, Fusicatenibacter saccharivorans, Eubacterium rectale, Faecalibacterium prausnitzii), and downregulated some pathobionts (Streptococcus thermophilus, Bacteroides caccae, Romboutsia timonensis). Changes within the gut microbiota were correlated with blood chemical indices including alkaline phosphatase (ALP), low-density lipoprotein (LDL), uric acid (UA), and creatinine (CREA). Thus, we found that the gut microbiota alterations may be part of physiological adaptations to a seven-day H. erinaceus supplementation, potentially influencing beneficial health effects.

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Metabolism is considered to be the core of all cellular activity. Thus, extensive studies of metabolic processes are ongoing in various fields of biology, including cancer research. Cancer cells are known to adapt their metabolism to sustain high proliferation rates and survive in unfavorable environments with low oxygen and nutrient concentrations. Hence, targeting cancer cell metabolism is a promising therapeutic strategy in cancer research. However, cancers consist not only of genetically altered tumor cells but are interwoven with endothelial cells, immune cells and fibroblasts, which together with the extracellular matrix (ECM) constitute the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are linked to poor prognosis in different cancer types, are one important component of the TME. CAFs play a significant role in reprogramming the metabolic landscape of tumor cells, but how, and in what manner, this interaction takes place remains rather unclear. This review aims to highlight the metabolic landscape of tumor cells and CAFs, including their recently identified subtypes, in different tumor types. In addition, we discuss various in vitro and in vivo metabolic techniques as well as different in silico computational tools that can be used to identify and characterize CAF-tumor cell interactions. Finally, we provide our view on how mapping the complex metabolic networks of stromal-tumor metabolism will help in finding novel metabolic targets for cancer treatment.

MODELING INTER-KINGDOM REGULATION OF INFLAMMATORY SIGNALING IN HUMAN INTESTINAL EPITHELIAL CELLS

The human gastrointestinal (GI) tract is colonized by a highly diverse and complex microbial community (i.e., microbiota). The microbiota plays an important role in the development of the immune system, specifically mediating inflammatory responses, however the exact mechanisms are poorly understood. We have developed a mathematical model describing the effect of indole on host inflammatory signaling in HCT-8 human intestinal epithelial cells. In this model, indole modulates transcription factor nuclear factor κ B (NF-κB) and produces the chemokine interleukin-8 (IL-8) through the activation of the aryl hydrocarbon receptor (AhR). Phosphorylated NF-κB exhibits dose and time-dependent responses to indole concentrations and IL-8 production shows a significant down-regulation for 0.1 ng/mL TNF-α stimulation. The model shows agreeable simulation results with the experimental data for IL-8 secretion and normalized NF-κB values. Our results suggest that microbial metabolites such as indole can modulate inflammatory signaling in HTC-8 cells through receptor-mediated processes.

Metabolomics in the study of spontaneous animal diseases

Using analytical chemistry techniques such as nuclear magnetic resonance (NMR) spectroscopy and liquid or gas chromatography-mass spectrometry (LC/GC-MS), metabolomics allows detection of most endogenous and exogenous metabolites in a biological sample. Metabolomics has a wide range of applications, and has been employed in nutrition science, toxicology, environmental studies, and systems biology. Metabolomics is particularly useful in biomedical science, and has been used for diagnostic laboratory testing, identifying targets for drug development, and monitoring drug metabolism, mode of action, and toxicity. Despite its immense potential, metabolomics remains underutilized in the study of spontaneous animal diseases. Our aim was to comprehensively review the existing literature on the use of metabolomics in spontaneous veterinary diseases. Three databases were used to find journal articles that applied metabolomics in veterinary medicine. A screening process was then conducted to eliminate references that did not meet the eligibility criteria; only primary research studies investigating spontaneous animal disease were included; 38 studies met the inclusion criteria. The main techniques used were NMR and MS. All studies detected metabolite alterations in diseased animals compared with non-diseased animals. Metabolomics was mainly used to study diseases of the digestive, reproductive, and musculoskeletal systems. Inflammatory conditions made up the largest proportion of studies when articles were categorized by disease process. Following a comprehensive analysis of the literature on metabolomics in spontaneous veterinary diseases, we concluded that metabolomics, although in its early stages in veterinary research, is a promising tool regarding diagnosis, biomarker discovery, and in uncovering new insights into disease pathophysiology.

Parkinson's disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions

BACKGROUND

Parkinson's disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we addressed this question by an analysis of stool samples from the Luxembourg Parkinson's Study (n = 147 typical PD cases, n = 162 controls).

RESULTS

All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Stool samples from these individuals were first analysed by 16S rRNA gene sequencing. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include the following. Eight genera and seven species changed significantly in their relative abundances between PD patients and healthy controls. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. Particularly, the relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. Furthermore, personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in the predicted secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The predicted microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms.

CONCLUSION

Our results suggest that PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype.

NetMet: A Network-Based Tool for Predicting Metabolic Capacities of Microbial Species and their Interactions

Metabolic conversions allow organisms to produce a set of essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Genomic-based metabolic simulations are being increasingly applied for exploring metabolic capacities, considering different environments and different combinations of microorganisms. NetMet is a web-based tool and a software package for predicting the metabolic performances of microorganisms and their corresponding combinations in user-defined environments. The algorithm takes, as input, lists of (i) species-specific enzymatic reactions (EC numbers), and (ii) relevant metabolic environments. The algorithm generates, as output, lists of (i) compounds that individual species can produce in each given environment, and (ii) compounds that are predicted to be produced through complementary interactions. The tool is demonstrated in two case studies. First, we compared the metabolic capacities of different haplotypes of the obligatory fruit and vegetable pathogen Candidatus Liberibacter solanacearum to those of their culturable taxonomic relative Liberibacter crescens. Second, we demonstrated the potential production of complementary metabolites by pairwise combinations of co-occurring endosymbionts of the plant phloem-feeding whitefly Bemisia tabaci.

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.

Untargeted metabolomic profiling identifies disease-specific signatures in food allergy and asthma

BACKGROUND

Food allergy (FA) affects an increasing proportion of children for reasons that remain obscure. Novel disease biomarkers and curative treatment options are strongly needed.

OBJECTIVE

We sought to apply untargeted metabolomic profiling to identify pathogenic mechanisms and candidate disease biomarkers in patients with FA.

METHODS

Mass spectrometry-based untargeted metabolomic profiling was performed on serum samples of children with either FA alone, asthma alone, or both FA and asthma, as well as healthy pediatric control subjects.

RESULTS

In this pilot study patients with FA exhibited a disease-specific metabolomic signature compared with both control subjects and asthmatic patients. In particular, FA was uniquely associated with a marked decrease in sphingolipid levels, as well as levels of a number of other lipid metabolites, in the face of normal frequencies of circulating natural killer T cells. Specific comparison of patients with FA and asthmatic patients revealed differences in the microbiota-sensitive aromatic amino acid and secondary bile acid metabolism. Children with both FA and asthma exhibited a metabolomic profile that aligned with that of FA alone but not asthma. Among children with FA, the history of severe systemic reactions and the presence of multiple FAs were associated with changes in levels of tryptophan metabolites, eicosanoids, plasmalogens, and fatty acids.

CONCLUSIONS

Children with FA have a disease-specific metabolomic profile that is informative of disease mechanisms and severity and that dominates in the presence of asthma. Lower levels of sphingolipids and ceramides and other metabolomic alterations observed in children with FA might reflect the interplay between an altered microbiota and immune cell subsets in the gut.

Computational Modeling of the Human Microbiome

The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.

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.

Scalable and exhaustive screening of metabolic functions carried out by microbial consortia

Motivation

The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far.

Results

We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria.

Availability and implementation

Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.

Challenges in the construction of knowledge bases for human microbiome-disease associations

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.

Draft genome and description of Merdibacter massiliensis gen.nov., sp. nov., a new bacterium genus isolated from the human ileum

We used phenotypic, genomic and phylogenetic information following the taxono-genomics approach to demonstrate that strain Marseille–P3254, isolated from an ileal sample of a 76-year old woman who underwent upper and lower digestive tract endoscopy for esophagitis and colonic polyp, is representative of a novel bacterial genus within the family Erysipelotrichaceae in the phylum Firmicutes. It is an anaerobic Gram-negative bacterium without catalase and oxidase activities. The genome of strain Marseille–P3254 is 2,468,496-bp long with a 40.1% G + C content. This new bacterium is most closely related to Eubacterium dolichum, with which it shares 90.7% 16S rRNA sequence similarity. In addition, genomic comparison using the digital DNA–DNA hybridization and OrthoANI analyses between the novel organism and the E. dolichum type strain revealed identities of 25.2 and 68.91%, respectively. The major fatty acids were C_16: 0, C_18: 1n9 and C_18: 0. Based on these data, we propose the creation of the new genus Merdibacter gen. nov., with strain Marseille-P3254^T (=CSUR P3254 = DSM 103534) being the type strain of the new species Merdibacter massiliensis gen. nov., sp. nov.

Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

BACKGROUND

The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities.

RESULTS

Therefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life ). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model.

CONCLUSIONS

This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community.

Host-microbe interaction in the gastrointestinal tract

The gastrointestinal tract is a highly complex organ in which multiple dynamic physiological processes are tightly coordinated while interacting with a dense and extremely diverse microbial population. From establishment in early life, through to host-microbe symbiosis in adulthood, the gut microbiota plays a vital role in our development and health. The effect of the microbiota on gut development and physiology is highlighted by anatomical and functional changes in germ-free mice, affecting the gut epithelium, immune system and enteric nervous system. Microbial colonisation promotes competent innate and acquired mucosal immune systems, epithelial renewal, barrier integrity, and mucosal vascularisation and innervation. Interacting or shared signalling pathways across different physiological systems of the gut could explain how all these changes are coordinated during postnatal colonisation, or after the introduction of microbiota into germ-free models. The application of cell-based in-vitro experimental systems and mathematical modelling can shed light on the molecular and signalling pathways which regulate the development and maintenance of homeostasis in the gut and beyond.

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.

Constraint-based modeling in microbial food biotechnology

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

Recon3D enables a three-dimensional view of gene variation in human metabolism

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

The Chemistry of Plant-Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance

Plant roots communicate with microbes in a sophisticated manner through chemical communication within the rhizosphere, thereby leading to biofilm formation of beneficial microbes and, in the case of plant growth-promoting rhizomicrobes/-bacteria (PGPR), resulting in priming of defense, or induced resistance in the plant host. The knowledge of plant-plant and plant-microbe interactions have been greatly extended over recent years; however, the chemical communication leading to priming is far from being well understood. Furthermore, linkage between below- and above-ground plant physiological processes adds to the complexity. In metabolomics studies, the main aim is to profile and annotate all exo- and endo-metabolites in a biological system that drive and participate in physiological processes. Recent advances in this field has enabled researchers to analyze 100s of compounds in one sample over a short time period. Here, from a metabolomics viewpoint, we review the interactions within the rhizosphere and subsequent above-ground 'signalomics', and emphasize the contributions that mass spectrometric-based metabolomic approaches can bring to the study of plant-beneficial - and priming events.

Modeling metabolism of the human gut microbiome

The human gut microbiome plays an important part in human health. The complexity of the microbiome makes it difficult to determine the detailed metabolic functions and cross-talk occurs between the individual species. In silico systems biology studies of the microbiome can help to identify metabolite exchanges among gut microbes. Constraint-based reconstruction and analysis methods use biochemically accurate genome-scale metabolic networks of microorganisms to simulate metabolism between species in a given microbiome and help generate novel hypotheses on microbial interactions. Here, we review metabolic modeling studies that have investigated metabolic functions of the gut microbiome.

Elucidation of complexity and prediction of interactions in microbial communities

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

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.

Chronic Rhinosinusitis and the Evolving Understanding of Microbial Ecology in Chronic Inflammatory Mucosal Disease

Chronic rhinosinusitis (CRS) encompasses a heterogeneous group of debilitating chronic inflammatory sinonasal diseases. Despite considerable research, the etiology of CRS remains poorly understood, and debate on potential roles of microbial communities is unresolved. Modern culture-independent (molecular) techniques have vastly improved our understanding of the microbiology of the human body. Recent studies that better capture the full complexity of the microbial communities associated with CRS reintroduce the possible importance of the microbiota either as a direct driver of disease or as being potentially involved in its exacerbation. This review presents a comprehensive discussion of the current understanding of bacterial, fungal, and viral associations with CRS, with a specific focus on the transition to the new perspective offered in recent years by modern technology in microbiological research. Clinical implications of this new perspective, including the role of antimicrobials, are discussed in depth. While principally framed within the context of CRS, this discussion also provides an analogue for reframing our understanding of many similarly complex and poorly understood chronic inflammatory diseases for which roles of microbes have been suggested but specific mechanisms of disease remain unclear. Finally, further technological advancements on the horizon, and current pressing questions for CRS microbiological research, are considered.

Analysis of Microbial Functions in the Rhizosphere Using a Metabolic-Network Based Framework for Metagenomics Interpretation

Advances in metagenomics enable high resolution description of complex bacterial communities in their natural environments. Consequently, conceptual approaches for community level functional analysis are in high need. Here, we introduce a framework for a metagenomics-based analysis of community functions. Environment-specific gene catalogs, derived from metagenomes, are processed into metabolic-network representation. By applying established ecological conventions, network-edges (metabolic functions) are assigned with taxonomic annotations according to the dominance level of specific groups. Once a function-taxonomy link is established, prediction of the impact of dominant taxa on the overall community performances is assessed by simulating removal or addition of edges (taxa associated functions). This approach is demonstrated on metagenomic data describing the microbial communities from the root environment of two crop plants – wheat and cucumber. Predictions for environment-dependent effects revealed differences between treatments (root vs. soil), corresponding to documented observations. Metabolism of specific plant exudates (e.g., organic acids, flavonoids) was linked with distinct taxonomic groups in simulated root, but not soil, environments. These dependencies point to the impact of these metabolite families as determinants of community structure. Simulations of the activity of pairwise combinations of taxonomic groups (order level) predicted the possible production of complementary metabolites. Complementation profiles allow formulating a possible metabolic role for observed co-occurrence patterns. For example, production of tryptophan-associated metabolites through complementary interactions is unique to the tryptophan-deficient cucumber root environment. Our approach enables formulation of testable predictions for species contribution to community activity and exploration of the functional outcome of structural shifts in complex bacterial communities. Understanding community-level metabolism is an essential step toward the manipulation and optimization of microbial function. Here, we introduce an analysis framework addressing three key challenges of such data: producing quantified links between taxonomy and function; contextualizing discrete functions into communal networks; and simulating environmental impact on community performances. New technologies will soon provide a high-coverage description of biotic and a-biotic aspects of complex microbial communities such as these found in gut and soil. This framework was designed to allow the integration of high-throughput metabolomic and metagenomic data toward tackling the intricate associations between community structure, community function, and metabolic inputs.

Perspectives and Challenges in Microbial Communities Metabolic Modeling

Bacteria have evolved to efficiently interact each other, forming complex entities known as microbial communities. These "super-organisms" play a central role in maintaining the health of their eukaryotic hosts and in the cycling of elements like carbon and nitrogen. However, despite their crucial importance, the mechanisms that influence the functioning of microbial communities and their relationship with environmental perturbations are obscure. The study of microbial communities was boosted by tremendous advances in sequencing technologies, and in particular by the possibility to determine genomic sequences of bacteria directly from environmental samples. Indeed, with the advent of metagenomics, it has become possible to investigate, on a previously unparalleled scale, the taxonomical composition and the functional genetic elements present in a specific community. Notwithstanding, the metagenomic approach per se suffers some limitations, among which the impossibility of modeling molecular-level (e.g., metabolic) interactions occurring between community members, as well as their effects on the overall stability of the entire system. The family of constraint-based methods, such as flux balance analysis, has been fruitfully used to translate genome sequences in predictive, genome-scale modeling platforms. Although these techniques have been initially developed for analyzing single, well-known model organisms, their recent improvements allowed engaging in multi-organism in silico analyses characterized by a considerable predictive capability. In the face of these advances, here we focus on providing an overview of the possibilities and challenges related to the modeling of metabolic interactions within a bacterial community, discussing the feasibility and the perspectives of this kind of analysis in the (near) future.

Impact of environmental factors on alloimmunity and transplant fate

Although gene-environment interactions have been investigated for many years to understand people's susceptibility to autoimmune diseases or cancer, a role for environmental factors in modulating alloimmune responses and transplant outcomes is only now beginning to emerge. New data suggest that diet, hyperlipidemia, pollutants, commensal microbes, and pathogenic infections can all affect T cell activation, differentiation, and the kinetics of graft rejection. These observations reveal opportunities for novel therapeutic interventions to improve graft outcomes as well as for noninvasive biomarker discovery to predict or diagnose graft deterioration before it becomes irreversible. In this Review, we will focus on the impact of these environmental factors on immune function and, when known, on alloimmune function, as well as on transplant fate.

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.

Gut microbiome-related metabolic changes in plasma of antibiotic-treated rats

The intestinal microbiota contributes to the metabolism of its host. Adequate identification of the microbiota's impact on the host plasma metabolites is lacking. As antibiotics have a profound effect on the microbial composition and hence on the mammalian-microbiota co-metabolism, we studied the effects of antibiotics on the "functionality of the microbiome"-defined as the production of metabolites absorbed by the host. This metabolomics study presents insights into the mammalian-microbiome co-metabolism of endogenous metabolites. To identify plasma metabolites related to microbiome changes due to antibiotic treatment, we have applied broad-spectrum antibiotics belonging to the class of aminoglycosides (neomycin, gentamicin), fluoroquinolones (moxifloxacin, levofloxacin) and tetracyclines (doxycycline, tetracycline). These were administered orally for 28 days to male rats including blood sampling for metabolic profiling after 7, 14 and 28 days. Fluoroquinolones and tetracyclines can be absorbed from the gut; whereas, aminoglycosides are poorly absorbed. Hippuric acid, indole-3-acetic acid and glycerol were identified as key metabolites affected by antibiotic treatment, beside changes mainly concerning amino acids and carbohydrates. Inter alia, effects on indole-3-propionic acid were found to be unique for aminoglycosides, and on 3-indoxylsulfate for tetracyclines. For each class of antibiotics, specific metabolome patterns could be established in the MetaMap®Tox data base, which contains metabolome data for more than 550 reference compounds. The results suggest that plasma-based metabolic profiling (metabolomics) could be a suitable tool to investigate the effect of antibiotics on the functionality of the microbiome and to obtain insight into the mammalian-microbiome co-metabolism.

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.