Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes. Mol Syst Biol. 2008;4:219. [PMID: 18854818 PMCID: PMC2583082 DOI: 10.1038/msb.2008.56]

Systems biology of host-microbe metabolomics

The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301

For further resources related to this article, please visit the WIREs website.

Conflict of interest: The authors have declared no conflicts of interest for this article.

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

The gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates "germfree" and "ex-germfree" human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial co-metabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism.

The role of intestinal microbiota in murine models of acetaminophen-induced hepatotoxicity

BACKGROUND & AIMS

Variations in intestinal microbiota may influence acetaminophen metabolism. This study aimed to determine whether intestinal microbiota are a source of differential susceptibility to acetaminophen-induced hepatotoxicity.

METHODS

Conventionally housed C3H/HeH (CH) and C3H/HeH germ-free (GF) mice were administered a 200 mg/kg IP dose of acetaminophen. The severity of hepatotoxicity at 8 h was assessed by histology and biochemical indices. A urinary metabolic profile was obtained using (1) H-NMR. Baseline hepatic glutathione content and CYP2E1 expression were quantified. An additional group of C3H/HeJ (LPS-r) mice were assessed to determine the contribution of LPS/TLR4 signalling.

RESULTS

Baseline glutathione levels were significantly reduced (P = 0.03) in GF mice. CYP2E1 mRNA expression and protein levels were not altered. Interindividual variability did not differ between GF and CH groups. No significant differences in the extent of hepatocellular injury (ALT or percentage necrosis) were demonstrated. However, a milder acute liver failure (ALF) phenotype was shown in GF compared with CH mice, with reduced plasma bilirubin and creatinine and increased blood glucose. Differential acetaminophen metabolism was demonstrated. GF mice displayed a higher urinary acetaminophen-sulphate:glucuronide ratio compared with CH (P = 0.01). Urinary analysis showed metabolic differentiation of GF and CH groups at baseline and 8 h (cross-validated anova P = 1 × 10(-22) ). Interruption of TLR4 signalling in LPS-r mice had additional protective effects.

CONCLUSION

Variations in intestinal microbiota do not fully explain differential susceptibility to acetaminophen-induced hepatotoxicity. GF mice experienced some protection from secondary complications following acetaminophen overdose and this may be mediated through reduced TLR4/LPS signalling.

The metabolic responses to hepatitis B virus infection shed new light on pathogenesis and targets for treatment

Chronic infection caused by the hepatitis B virus (HBV), is strongly associated with hepatitis, fatty liver and hepatocellular carcinoma. To investigate the underlying mechanisms, we characterize the metabolic features of host cells infected with the virus using systems biological approach. The results show that HBV replication induces systematic metabolic alterations in host cells. HBV infection up-regulates the biosynthesis of hexosamine and phosphatidylcholine by activating glutamine-fructose-6-phosphate amidotransferase 1 (GFAT1) and choline kinase alpha (CHKA) respectively, which were reported for the first time for HBV infection. Importantly suppressing hexosamine biosynthesis and phosphatidylcholine biosynthesis can inhibit HBV replication and expression. In addition, HBV induces oxidative stress and stimulates central carbon metabolism and nucleotide synthesis. Our results also indicate that HBV associated hepatocellular carcinoma could be attributed to GFAT1 activated hexosamine biosynthesis and CHKA activated phosphatidylcholine biosynthesis. This study provides further insights into the pathogenesis of HBV-induced diseases, and sheds new light on drug target for treating HBV infection.

Profiling of serum bile acids in a healthy Chinese population using UPLC-MS/MS

Bile acids (BAs) are a group of important physiological agents for cholesterol metabolism, intestinal nutrient absorption, and biliary secretion of lipids, toxic metabolites, and xenobiotics. Extensive research in the last two decades has unveiled new functions of BAs as signaling molecules and metabolic regulators that modulate hepatic lipid, glucose, and energy homeostasis through the activation of nuclear receptors and G-protein-coupled receptor signaling in gut-liver metabolic axis involving host-gut microbial co-metabolism. Therefore, investigation of serum BA profiles, in healthy human male and female subjects with a wide range of age and body mass index (BMI), will provide important baseline information on the BA physiology as well as metabolic homeostasis among human subjects that are regulated by two sets of genome, host genome, and symbiotic microbiome. Previous reports on age- or gender-related changes on BA profiles in animals and human showed inconsistent results, and the information acquired from various studies was highly fragmentary. Here we profiled the serum BAs in a large population of healthy participants (n = 502) and examined the impact of age, gender, and BMI on serum BA concentrations and compositions using a targeted metabonomics approach with ultraperformance liquid chromatography triple-quadrupole mass spectrometry. We found that the BA profiles were dependent on gender, age, and BMI among study subjects. The total BAs were significantly higher in males than in females (p < 0.05) and higher in obese females than in lean females (p < 0.05). The difference in BA profiles between male and female subjects was decreased at age of 50-70 years, while the difference in BA profiles between lean and obese increased for subjects aged 50-70 years. The study provides a comprehensive understanding of the BA profiles in healthy subjects and highlights the need to take into account age, gender, and BMI differences when investigating pathophysiological changes of BAs resulting from gastrointestinal diseases.

Monosodium L-Glutamate and Dietary Fat Differently Modify the Composition of the Intestinal Microbiota in Growing Pigs

BACKGROUND

The Chinese have been undergone rapid transition to a high-fat diet-consuming lifestyle, while monosodium L-glutamate (MSG) is widely used as a daily food additive. It has been reported that fat alters the composition of intestinal microbiota. However, little information is available on the effects of oral MSG on intestinal microbiota, and no study was done focusing on the interaction effect of fat and MSG with respect to intestinal microbiota. The present study thus aimed to determine the effects of MSG and/or fat on intestinal microbiota, and also to identify possible interactions between these two nutrients.

METHODS

Four iso-nitrogenous and iso-caloric diets were provided to growing pigs. The microbiota from jejunum, ileum, cecum, and colon were analyzed.

RESULTS

Our results show that both MSG and fat clearly increased the intestinal microbiota diversity. MSG and fat modified the composition of intestinal microbiota, particularly in the colon. Both MSG and fat promoted the colonization of microbes related to energy extraction in gastrointestinal tract via different ways. MSG promoted the colonization of Faecalibacterium prausnitzii and Roseburia, while fat increased the percentage of Prevotella in colon and other intestinal segments.

CONCLUSION

Our results will help to understand how individual or combined dietary changes modify the microbiota composition to prevent obesity.

NMR spectroscopy of biofluids and extracts

Metabonomics-based proton nuclear magnetic resonance ((1)H NMR) spectroscopy is a cross-disciplinary science that overlaps with analytical chemistry, biology, and statistical analysis. Applying (1)H NMR on cell extracts provides a rapid and comprehensive screening of the most abundant metabolites allowing the quantitation of typically 20-70 compounds including amino and organic acids, sugars, amines, nucleosides, phenolic compounds, osmolytes, and lipids produced at sublevel millimolar concentrations. The method is particularly suited for high-throughput analysis (up to 100 samples/24 h), and the powerful structural elucidation of NMR is a great asset for the identification of unknown compounds. This chapter describes procedures for recording metabolite profiles using (1)H NMR, depicts the preprocessing steps leading to data analysis, and presents methods of metabolite identification in spectral profiles of extracts from plants, food, microbes, and mammalian systems.

Dynamic metabonomic and microbiological response of rats to lincomycin exposure: an integrated microbiology and metabonomics analysis

We investigated the changes of gut microbiome, host metabolism and their relationships of lincomycin exposure by microbiological and metabolomics profiling.

Stool phospholipid signature is altered by diet and tumors

Intake of saturated fat is a risk factor for ulcerative colitis (UC) and colon cancer. Changes in the microbiota have been implicated in the development of UC and colon cancer. The host and the microbiota generate metabolites that may contribute to or reflect disease pathogenesis. We used lipid class specific quantitative mass spectrometry to assess the phospholipid (PL) profile (phosphatidylcholine [PC], phosphatidylethanolamine [PE], phosphatidylinositol [PI], phosphatidylserine [PS]) of stool from mice fed a high fat (HFD) or control diet with or without induction of colitis-associated tumors using azoxymethane and dextran sodium sulfate. The microbiota was assessed using qPCR for several bacterial groups. Colitis-associated tumors were associated with reduced bulk PI and PE levels in control diet fed mice compared to untreated mice. Significant decreases in the relative quantities of several PC species were found in colitis-associated tumor bearing mice fed either diet. Statistical analysis of the PL profile revealed distinct clustering by treatment group. Partial least squares regression analysis found that the relative quantities of the PS class profile best predicted bacterial abundance of Clostridium leptum and Prevotella groups. Abundance of selected PL species correlated with bacterial group quantities. Thus, we have described that a HFD and colitis-associated tumors are associated with changes in phospholipids and may reflect host-microbial interactions and disease states.

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

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

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

Alterations of the gut microbiome and metabolome in alcoholic liver disease

Alcohol consumption is one of the leading causes of liver diseases and liver-related death worldwide. The gut is a habitat for billions of microorganisms which promotes metabolism and digestion in their symbiotic relationship with the host. Alterations of gut microbiome by alcohol consumption are referred to bacterial overgrowth, release of bacteria-derived products, and/or changed microbiota equilibrium. Alcohol consumption also perturbs the function of gastrointestinal mucosa and elicits a pathophysiological condition. These adverse effects caused by alcohol may ultimately result in a broad change of gastrointestinal luminal metabolites such as bile acids, short chain fatty acids, and branched chain amino acids. Gut microbiota alterations, metabolic changes produced in a dysbiotic intestinal environment, and the host factors are all critical contributors to the development and progression of alcoholic liver disease. This review summarizes recent findings of how alcohol-induced alterations of gut microbiota and metabolome, and discusses the mechanistic link between gastrointestinal dyshomeostasis and alcoholic liver injury.

Metabolic fingerprint of dimethyl sulfone (DMSO2) in microbial-mammalian co-metabolism

There is growing awareness that intestinal microbiota alters the energy harvesting capacity of the host and regulates metabolism. It has been postulated that intestinal microbiota are able to degrade unabsorbed dietary components and transform xenobiotic compounds. The resulting microbial metabolites derived from the gastrointestinal tract can potentially enter the circulation system, which, in turn, affects host metabolism. Yet, the metabolic capacity of intestinal microbiota and its interaction with mammalian metabolism remains largely unexplored. Here, we review a metabolic pathway that integrates the microbial catabolism of methionine with mammalian metabolism of methanethiol (MT), dimethyl sulfide (DMS), and dimethyl sulfoxide (DMSO), which together provide evidence that supports the microbial origin of dimethyl sulfone (DMSO2) in the human metabolome. Understanding the pathway of DMSO2 co-metabolism expends our knowledge of microbial-derived metabolites and motivates future metabolomics-based studies on ascertaining the metabolic consequences of intestinal microbiota on human health, including detoxification processes and sulfur xenobiotic metabolism.

The cross talk between microbiota and the immune system: metabolites take center stage

The human meta-organism consists of more than 90% of microbial cells. The gastrointestinal tract harbors trillions of commensal microorganisms that influence the development and homeostasis of the host. Alterations in composition and function of the microbiota, termed dysbiosis, have been implicated in a multitude of metabolic and inflammatory diseases in humans. Thus, understanding the molecular underpinnings the cross talk between commensal bacteria and their host during homeostasis and dysbiosis may hold the key to understanding many idiopathic diseases. While most attention has focused on the innate recognition of immune-stimulatory bacterial molecules, such as cell wall components and nucleic acids, we emphasize here the impact of diet-dependent microbial metabolites on the development and function of the immune system.

Distinct signatures of host-microbial meta-metabolome and gut microbiome in two C57BL/6 strains under high-fat diet

A combinatory approach using metabolomics and gut microbiome analysis techniques was performed to unravel the nature and specificity of metabolic profiles related to gut ecology in obesity. This study focused on gut and liver metabolomics of two different mouse strains, the C57BL/6J (C57J) and the C57BL/6N (C57N) fed with high-fat diet (HFD) for 3 weeks, causing diet-induced obesity in C57N, but not in C57J mice. Furthermore, a 16S-ribosomal RNA comparative sequence analysis using 454 pyrosequencing detected significant differences between the microbiome of the two strains on phylum level for Firmicutes, Deferribacteres and Proteobacteria that propose an essential role of the microbiome in obesity susceptibility. Gut microbial and liver metabolomics were followed by a combinatory approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and ultra performance liquid chromatography time of tlight MS/MS with subsequent multivariate statistical analysis, revealing distinctive host and microbial metabolome patterns between the C57J and the C57N strain. Many taurine-conjugated bile acids (TBAs) were significantly elevated in the cecum and decreased in liver samples from the C57J phenotype likely displaying different energy utilization behavior by the bacterial community and the host. Furthermore, several metabolite groups could specifically be associated with the C57N phenotype involving fatty acids, eicosanoids and urobilinoids. The mass differences based metabolite network approach enabled to extend the range of known metabolites to important bile acids (BAs) and novel taurine conjugates specific for both strains. In summary, our study showed clear alterations of the metabolome in the gastrointestinal tract and liver within a HFD-induced obesity mouse model in relation to the host-microbial nutritional adaptation.

Lactobacillus acidophilus NCFM affects vitamin E acetate metabolism and intestinal bile acid signature in monocolonized mice

Monocolonization of germ-free (GF) mice enables the study of specific bacterial species in vivo. Lactobacillus acidophilus NCFM(TM) (NCFM) is a probiotic strain; however, many of the mechanisms behind its health-promoting effect remain unknown. Here, we studied the effects of NCFM on the metabolome of jejunum, cecum, and colon of NCFM monocolonized (MC) and GF mice using liquid chromatography coupled to mass-spectrometry (LC-MS). The study adds to existing evidence that NCFM in vivo affects the bile acid signature of mice, in particular by deconjugation. Furthermore, we confirmed that carbohydrate metabolism is affected by NCFM in the mouse intestine as especially the digestion of oligosaccharides (penta- and tetrasaccharides) was increased in MC mice. Additionally, levels of α-tocopherol acetate (vitamin E acetate) were higher in the intestine of GF mice than in MC mice, suggesting that NCFM affects the vitamin E acetate metabolism. NCFM did not digest vitamin E acetate in vitro, suggesting that direct bacterial metabolism was not the cause of the altered metabolome in vivo. Taken together, our results suggest that NCFM affects intestinal carbohydrate metabolism, bile acid metabolism and vitamin E metabolism, although it remains to be investigated whether this effect is unique to NCFM.

The interplay between the gut microbiota and the immune system

The impact of the gut microbiota on immune homeostasis within the gut and, importantly, also at systemic sites has gained tremendous research interest over the last few years. The intestinal microbiota is an integral component of a fascinating ecosystem that interacts with and benefits its host on several complex levels to achieve a mutualistic relationship. Host-microbial homeostasis involves appropriate immune regulation within the gut mucosa to maintain a healthy gut while preventing uncontrolled immune responses against the beneficial commensal microbiota potentially leading to chronic inflammatory bowel diseases (IBD). Furthermore, recent studies suggest that the microbiota composition might impact on the susceptibility to immune-mediated disorders such as autoimmunity and allergy. Understanding how the microbiota modulates susceptibility to these diseases is an important step toward better prevention or treatment options for such diseases.

Improved metabolic health alters host metabolism in parallel with changes in systemic xeno-metabolites of gut origin

Novel plasma metabolite patterns reflective of improved metabolic health (insulin sensitivity, fitness, reduced body weight) were identified before and after a 14–17 wk weight loss and exercise intervention in sedentary, obese insulin-resistant women. To control for potential confounding effects of diet- or microbiome-derived molecules on the systemic metabolome, sampling was during a tightly-controlled feeding test week paradigm. Pairwise and multivariate analysis revealed intervention- and insulin-sensitivity associated: (1) Changes in plasma xeno-metabolites (“non-self” metabolites of dietary or gut microbial origin) following an oral glucose tolerance test (e.g. higher post-OGTT propane-1,2,3-tricarboxylate [tricarballylic acid]) or in the overnight-fasted state (e.g., lower γ-tocopherol); (2) Increased indices of saturated very long chain fatty acid elongation capacity; (3) Increased post-OGTT α-ketoglutaric acid (α-KG), fasting α-KG inversely correlated with Matsuda index, and altered patterns of malate, pyruvate and glutamine hypothesized to stem from improved mitochondrial efficiency and more robust oxidation of glucose. The results support a working model in which improved metabolic health modifies host metabolism in parallel with altering systemic exposure to xeno-metabolites. This highlights that interpretations regarding the origins of peripheral blood or urinary “signatures” of insulin resistance and metabolic health must consider the potentially important contribution of gut-derived metabolites toward the host's metabolome.

Commensal bacteria at the interface of host metabolism and the immune system

The mammalian gastrointestinal tract, the site of digestion and nutrient absorption, harbors trillions of beneficial commensal microbes from all three domains of life. Commensal bacteria, in particular, are key participants in the digestion of food, and are responsible for the extraction and synthesis of nutrients and other metabolites that are essential for the maintenance of mammalian health. Many of these nutrients and metabolites derived from commensal bacteria have been implicated in the development, homeostasis and function of the immune system, suggesting that commensal bacteria may influence host immunity via nutrient- and metabolite-dependent mechanisms. Here we review the current knowledge of how commensal bacteria regulate the production and bioavailability of immunomodulatory, diet-dependent nutrients and metabolites and discuss how these commensal bacteria-derived products may regulate the development and function of the mammalian immune system.

Lactobacillus acidophilus La5 and Bifidobacterium lactis Bb12 induce different age-related metabolic profiles revealed by 1H-NMR spectroscopy in urine and feces of mice

Age-related dysbioses of intestinal microbiota and decline in the overall metabolic homeostasis are frequently found in the elderly. Probiotic supplementation may represent a way to prevent or reduce the senescence-associated metabolic disorders. The present study evaluated the metabolic impact of Lactobacillus acidophilus La5 and Bifidobacterium lactis Bb12 supplementation in relation to age by analyzing urine and feces metabolic profiles using (1)H-nuclear magnetic resonance spectroscopy and multivariate analysis. Adult (3 mo old) and aged (16 mo old) mice received an oral supplementation of the 2 probiotics (1 × 10(9) colony-forming units/d each) or phosphate buffered saline (control) daily for 30 d. Urine and feces were collected for 48 h before the end of the study. Partial least squares-discriminant analysis showed that the urinary discriminant metabolites for the probiotic treatment included higher dimethylglycine in adult and aged mice, lower sarcosine and nicotinate in adult mice, higher N-methylnicotinamide in adult mice and lower N-methylnicotinamide in aged mice compared with their controls. These results indicate a probiotic-induced modulation of homocysteine and NAD metabolism pathways, which have important implications because these pathways are involved in essential cellular processes that can be altered in senescence. The probiotic supplementation also modified the fecal metabolic profiles, inducing in both adult and aged mice higher 4-hydroxyphenylacetate and lower xylose in treated mice compared with their control mice, whereas valerate was greater in treated adult mice and lower in treated aged mice compared with their controls. The ANOVA simultaneous component analysis on urinary and fecal metabolic profiling showed an age × treatment interaction (P < 0.05), confirming the age-related modulation of the metabolic response to probiotic supplementation. The results suggest that L. acidophilus and B. lactis may prevent or reduce age-related metabolic dysfunction.

Gut microbiota, enteroendocrine functions and metabolism

The gut microbiota affects host metabolism through a number of physiological processes. Emerging evidence suggests that gut microbes interact with the host through several pathways involving enteroendocrine cells (e.g. L cells). The activation of specific G protein coupled receptors expressed on L cells (e.g. GPR41, GPR43, GPR119 and TGR5) triggers the secretion of glucagon-like peptides (GLP-1 and GLP-2) and PYY. These gut peptides are known to control energy homeostasis, glucose metabolism, gut barrier function and metabolic inflammation. Here, we explore how crosstalk between the ligands produced by the gut microbiota (short chain fatty acids, or SCFAs), or produced by the host but influenced by gut microbes (endocannabinoids and bile acids), impact host physiology.

Introduction of a new critical p value correction method for statistical significance analysis of metabonomics data

Nuclear magnetic resonance (NMR) spectroscopy-based metabonomics is of growing importance for discovery of human disease biomarkers. Identification and validation of disease biomarkers using statistical significance analysis (SSA) is critical for translation to clinical practice. SSA is performed by assessing a null hypothesis test using a derivative of the Student's t test, e.g., a Welch's t test. Choosing how to correct the significance level for rejecting null hypotheses in the case of multiple testing to maintain a constant family-wise type I error rate is a common problem in such tests. The multiple testing problem arises because the likelihood of falsely rejecting the null hypothesis, i.e., a false positive, grows as the number of tests applied to the same data set increases. Several methods have been introduced to address this problem. Bonferroni correction (BC) assumes all variables are independent and therefore sacrifices sensitivity for detecting true positives in partially dependent data sets. False discovery rate (FDR) methods are more sensitive than BC but uniformly ascribe highest stringency to lowest p value variables. Here, we introduce standard deviation step down (SDSD), which is more sensitive and appropriate than BC for partially dependent data sets. Sensitivity and type I error rate of SDSD can be adjusted based on the degree of variable dependency. SDSD generates fundamentally different profiles of critical p values compared with FDR methods potentially leading to reduced type II error rates. SDSD is increasingly sensitive for more concentrated metabolites. SDSD is demonstrated using NMR-based metabonomics data collected on three different breast cancer cell line extracts.

High-fat diet alters gut microbiota physiology in mice

The intestinal microbiota is known to regulate host energy homeostasis and can be influenced by high-calorie diets. However, changes affecting the ecosystem at the functional level are still not well characterized. We measured shifts in cecal bacterial communities in mice fed a carbohydrate or high-fat (HF) diet for 12 weeks at the level of the following: (i) diversity and taxa distribution by high-throughput 16S ribosomal RNA gene sequencing; (ii) bulk and single-cell chemical composition by Fourier-transform infrared- (FT-IR) and Raman micro-spectroscopy and (iii) metaproteome and metabolome via high-resolution mass spectrometry. High-fat diet caused shifts in the diversity of dominant gut bacteria and altered the proportion of Ruminococcaceae (decrease) and Rikenellaceae (increase). FT-IR spectroscopy revealed that the impact of the diet on cecal chemical fingerprints is greater than the impact of microbiota composition. Diet-driven changes in biochemical fingerprints of members of the Bacteroidales and Lachnospiraceae were also observed at the level of single cells, indicating that there were distinct differences in cellular composition of dominant phylotypes under different diets. Metaproteome and metabolome analyses based on the occurrence of 1760 bacterial proteins and 86 annotated metabolites revealed distinct HF diet-specific profiles. Alteration of hormonal and anti-microbial networks, bile acid and bilirubin metabolism and shifts towards amino acid and simple sugars metabolism were observed. We conclude that a HF diet markedly affects the gut bacterial ecosystem at the functional level.

The gut microbiota elicits a profound metabolic reorientation in the mouse jejunal mucosa during conventionalisation

OBJECTIVE

Proper interactions between the intestinal mucosa, gut microbiota and nutrient flow are required to establish homoeostasis of the host. Since the proximal part of the small intestine is the first region where these interactions occur, and since most of the nutrient absorption occurs in the jejunum, it is important to understand the dynamics of metabolic responses of the mucosa in this intestinal region.

DESIGN

Germ-free mice aged 8-10 weeks were conventionalised with faecal microbiota, and responses of the jejunal mucosa to bacterial colonisation were followed over a 30-day time course. Combined transcriptome, histology, (1)H NMR metabonomics and microbiota phylogenetic profiling analyses were used.

RESULTS

The jejunal mucosa showed a two-phase response to the colonising microbiota. The acute-phase response, which had already started 1 day after conventionalisation, involved repression of the cell cycle and parts of the basal metabolism. The secondary-phase response, which was consolidated during conventionalisation (days 4-30), was characterised by a metabolic shift from an oxidative energy supply to anabolic metabolism, as inferred from the tissue transcriptome and metabonome changes. Detailed transcriptome analysis identified tissue transcriptional signatures for the dynamic control of the metabolic reorientation in the jejunum. The molecular components identified in the response signatures have known roles in human metabolic disorders, including insulin sensitivity and type 2 diabetes mellitus.

CONCLUSION

This study elucidates the dynamic jejunal response to the microbiota and supports a prominent role for the jejunum in metabolic control, including glucose and energy homoeostasis. The molecular signatures of this process may help to find risk markers in the declining insulin sensitivity seen in human type 2 diabetes mellitus, for instance.

Nutrient-sensing mechanisms in the gut as therapeutic targets for diabetes

The small intestine is traditionally viewed as an organ that mediates nutrient digestion and absorption. This view has recently been revised owing to the ability of the duodenum to sense nutrient influx and trigger negative feedback loops to inhibit glucose production and food intake to maintain metabolic homeostasis. Further, duodenal nutrient-sensing defects are acquired in diabetes and obesity, leading to increased glucose production. In contrast, jejunal nutrient sensing inhibits glucose production and mediates the early antidiabetic effect of bariatric surgery, and gut microbiota composition may alter intestinal nutrient-sensing mechanisms to regain better control of glucose homeostasis in diabetes and obesity in the long term. This perspective highlights nutrient-sensing mechanisms in the gut that regulate glucose homeostasis and the potential of targeting gut nutrient-sensing mechanisms as a therapeutic strategy to lower blood glucose concentrations in diabetes.

Metabolomics approaches for characterizing metabolic interactions between host and its commensal microbes

It is increasingly evident that the gut microbiota is involved in the regulation of multiple mammalian metabolic pathways through a series of interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, brain, and other organs. Correlation of the metabotypes with the gut microbial profiles derived from culture-independent molecular techniques is increasingly useful for deciphering inherent and intimate host-microbe relationships. Real-time analysis of the small molecule metabolites derived from gut microbial-host co-metabolism is essential for understanding the metabolic functions of the gut microbiome and has tremendous implications for personalized healthcare strategies. Metabolomics, an array of analytical techniques that includes high resolution NMR spectroscopy and chromatography-MS in conjunction with chemometrics and bioinformatics tools, enables characterization of the metabolic footprints of mammalian hosts that correlate with the microbial community in the intestinal tract. The metabolomics approach provides important information of a complete spectrum of metabolites produced from the gut microbial-mammalian co-metabolism and is improving our understanding of the molecular mechanisms underlying multilevel host-microbe interactions. In this review, the interactions of gut microbiota with their host are discussed and some examples of NMR- or MS-based metabolomics applications for characterizing the metabolic footprints of gut microbial-host co-metabolism are described. Advances in the metabolomic analysis of bile acids, short-chain fatty acids, and choline metabolism are also summarized.

Omics approaches to study host-microbiota interactions

The intestinal microbiota has profound effects on our physiology and immune system and disturbances in the equilibrium between microbiota and host have been observed in many disorders. Here we discuss the possibilities to further our understanding of how microbiota impacts on human health and disease through the use of large-scale quantifiable tools such as transcriptomics, metagenomics and metabolomics. Reductionist models, including gnotobiotic mouse models have their place in testing hypotheses and elucidating mechanisms by which specific communities or individual species impact on host biology. Network biology approaches can be combined with studies in animal models and cell lines to create iterative cycle of hypotheses and testing, possibly leading to testing in clinical and nutritional intervention studies.

Enhanced green fluorescent protein transgenic expression in vivo is not biologically inert

Enhanced green fluorescent protein (EGFP) is a widely used biological reporter. However, the effects of EGFP expression in vivo are still unclear. To investigate the effects of EGFP transgenic expression in vivo, we employed an NMR-based metabonomics method to analyze the metabonome of EGFP transgenic mice. The results show that the metabonomes of urine, liver, and kidney of the EGFP transgenic mice are different from their wild-type counterparts. The EGFP mice expressed high levels of urinary 3-ureidopropionate, which is due to the down-regulated transcriptional level of β-ureidopropionase. The expression of EGFP in vivo also affects other metabolic pathways, including nucleic acid metabolism, energy utilization, and amino acids catabolism. These findings indicate that EGFP transgenic expression is not as inert as has been considered. Our investigation provides a holistic view on the effect of EGFP expression in vivo, which is useful when EGFP is employed as a functional biological indicator. Our work also highlights the potential of a metabonomics strategy in studying the association between molecular phenotypes and gene function.

A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice

Defining the functional status of host-associated microbial ecosystems has proven challenging owing to the vast number of predicted genes within the microbiome and relatively poor understanding of community dynamics and community-host interaction. Metabolomic approaches, in which a large number of small molecule metabolites can be defined in a biological sample, offer a promising avenue to 'fingerprint' microbiota functional status. Here, we examined the effects of the human gut microbiota on the fecal and urinary metabolome of a humanized (HUM) mouse using an optimized ultra performance liquid chromatography-mass spectrometry-based method. Differences between HUM and conventional mouse urine and fecal metabolomic profiles support host-specific aspects of the microbiota's metabolomic contribution, consistent with distinct microbial compositions. Comparison of microbiota composition and metabolome of mice humanized with different human donors revealed that the vast majority of metabolomic features observed in donor samples are produced in the corresponding HUM mice, and individual-specific features suggest 'personalized' aspects of functionality can be reconstituted in mice. Feeding the mice a defined, custom diet resulted in modification of the metabolite signatures, illustrating that host diet provides an avenue for altering gut microbiota functionality, which in turn can be monitored via metabolomics. Using a defined model microbiota consisting of one or two species, we show that simplified communities can drive major changes in the host metabolomic profile. Our results demonstrate that metabolomics constitutes a powerful avenue for functional characterization of the intestinal microbiota and its interaction with the host.

Alteration of bile acid metabolism in the rat induced by chronic ethanol consumption

Our understanding of the bile acid metabolism is limited by the fact that previous analyses have primarily focused on a selected few circulating bile acids; the bile acid profiles of the liver and gastrointestinal tract pools are rarely investigated. Here, we determined how chronic ethanol consumption altered the bile acids in multiple body compartments (liver, gastrointestinal tract, and serum) of rats. Rats were fed a modified Lieber-DeCarli liquid diet with 38% of calories as ethanol (the amount equivalent of 4-5 drinks in humans). While conjugated bile acids predominated in the liver (98.3%), duodenum (97.8%), and ileum (89.7%), unconjugated bile acids comprised the largest proportion of measured bile acids in serum (81.2%), the cecum (97.7%), and the rectum (97.5%). In particular, taurine-conjugated bile acids were significantly decreased in the liver and gastrointestinal tract of ethanol-treated rats, while unconjugated and glycine-conjugated species increased. Ethanol consumption caused increased expression of genes involved in bile acid biosynthesis, efflux transport, and reduced expression of genes regulating bile acid influx transport in the liver. These results provide an improved understanding of the systemic modulations of bile acid metabolism in mammals through the gut-liver axis.

Gut microbiota composition modifies fecal metabolic profiles in mice

The gut microbiome is known to be extensively involved in human health and disease. In order to reveal the metabolic relationship between host and microbiome, we monitored recovery of the gut microbiota composition and fecal profiles of mice after gentamicin and/or ceftriaxone treatments. This was performed by employing (1)H nuclear magnetic resonance (NMR)-based metabonomics and denaturing gradient gel electrophoresis (DGGE) fingerprint of gut microbiota. The common features of fecal metabolites postantibiotic treatment include decreased levels of short chain fatty acids (SCFAs), amino acids and primary bile acids and increased oligosaccharides, d-pinitol, choline and secondary bile acids (deoxycholic acid). This suggests suppressed bacterial fermentation, protein degradation and enhanced gut microbial modification of bile acids. Barnesiella, Prevotella, and Alistipes levels were shown to decrease as a result of the antibiotic treatment, whereas levels of Bacteroides, Enterococcus and Erysipelotrichaceae incertae sedis, and Mycoplasma increased after gentamicin and ceftriaxone treatment. In addition, there was a strong correlation between fecal profiles and levels of Bacteroides, Barnesiella, Alistipes and Prevotella. The integration of metabonomics and gut microbiota profiling provides important information on the changes of gut microbiota and their impact on fecal profiles during the recovery after antibiotic treatment. The correlation between gut microbiota and fecal metabolites provides important information on the function of bacteria, which in turn could be important in optimizing therapeutic strategies, and developing potential microbiota-based disease preventions and therapeutic interventions.

From meta-omics to causality: experimental models for human microbiome research

Large-scale 'meta-omic' projects are greatly advancing our knowledge of the human microbiome and its specific role in governing health and disease states. A myriad of ongoing studies aim at identifying links between microbial community disequilibria (dysbiosis) and human diseases. However, due to the inherent complexity and heterogeneity of the human microbiome, cross-sectional, case-control and longitudinal studies may not have enough statistical power to allow causation to be deduced from patterns of association between variables in high-resolution omic datasets. Therefore, to move beyond reliance on the empirical method, experiments are critical. For these, robust experimental models are required that allow the systematic manipulation of variables to test the multitude of hypotheses, which arise from high-throughput molecular studies. Particularly promising in this respect are microfluidics-based in vitro co-culture systems, which allow high-throughput first-pass experiments aimed at proving cause-and-effect relationships prior to testing of hypotheses in animal models. This review focuses on widely used in vivo, in vitro, ex vivo and in silico approaches to study host-microbial community interactions. Such systems, either used in isolation or in a combinatory experimental approach, will allow systematic investigations of the impact of microbes on the health and disease of the human host. All the currently available models present pros and cons, which are described and discussed. Moreover, suggestions are made on how to develop future experimental models that not only allow the study of host-microbiota interactions but are also amenable to high-throughput experimentation.

Ketone body metabolism and cardiovascular disease

Ketone bodies are metabolized through evolutionarily conserved pathways that support bioenergetic homeostasis, particularly in brain, heart, and skeletal muscle when carbohydrates are in short supply. The metabolism of ketone bodies interfaces with the tricarboxylic acid cycle, β-oxidation of fatty acids, de novo lipogenesis, sterol biosynthesis, glucose metabolism, the mitochondrial electron transport chain, hormonal signaling, intracellular signal transduction pathways, and the microbiome. Here we review the mechanisms through which ketone bodies are metabolized and how their signals are transmitted. We focus on the roles this metabolic pathway may play in cardiovascular disease states, the bioenergetic benefits of myocardial ketone body oxidation, and prospective interactions among ketone body metabolism, obesity, metabolic syndrome, and atherosclerosis. Ketone body metabolism is noninvasively quantifiable in humans and is responsive to nutritional interventions. Therefore, further investigation of this pathway in disease models and in humans may ultimately yield tailored diagnostic strategies and therapies for specific pathological states.

A community-driven global reconstruction of human metabolism

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

The human gut microbiome and body metabolism: implications for obesity and diabetes

BACKGROUND

Obesity, metabolic syndrome, and type 2 diabetes are major public health challenges. Recently, interest has surged regarding the possible role of the intestinal microbiota as potential novel contributors to the increased prevalence of these 3 disorders.

CONTENT

Recent advances in microbial DNA sequencing technologies have resulted in the widespread application of whole-genome sequencing technologies for metagenomic DNA analysis of complex ecosystems such as the human gut. Current evidence suggests that the gut microbiota affect nutrient acquisition, energy harvest, and a myriad of host metabolic pathways.

CONCLUSION

Advances in the Human Microbiome Project and human metagenomics research will lead the way toward a greater understanding of the importance and role of the gut microbiome in metabolic disorders such as obesity, metabolic syndrome, and diabetes.

Gut microbiota regulates bile acid metabolism by reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist

Bile acids are synthesized from cholesterol in the liver and further metabolized by the gut microbiota into secondary bile acids. Bile acid synthesis is under negative feedback control through activation of the nuclear receptor farnesoid X receptor (FXR) in the ileum and liver. Here we profiled the bile acid composition throughout the enterohepatic system in germ-free (GF) and conventionally raised (CONV-R) mice. We confirmed a dramatic reduction in muricholic acid, but not cholic acid, levels in CONV-R mice. Rederivation of Fxr-deficient mice as GF demonstrated that the gut microbiota regulated expression of fibroblast growth factor 15 in the ileum and cholesterol 7α-hydroxylase (CYP7A1) in the liver by FXR-dependent mechanisms. Importantly, we identified tauro-conjugated beta- and alpha-muricholic acids as FXR antagonists. These studies suggest that the gut microbiota not only regulates secondary bile acid metabolism but also inhibits bile acid synthesis in the liver by alleviating FXR inhibition in the ileum.

Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut

The human gut microbiota consists of ten times more microorganisms than there are cells in our body, processes otherwise indigestible nutrients, and produces important energy precursors, essential amino acids, and vitamins. In this study, we assembled and validated a genome-scale metabolic reconstruction of Bacteroides thetaiotaomicron (iAH991), a prominent representative of the human gut microbiota, consisting of 1488 reactions, 1152 metabolites, and 991 genes. To create a comprehensive metabolic model of host-microbe interactions, we integrated iAH991 with a previously published mouse metabolic reconstruction, which was extended for intestinal transport and absorption reactions. The two metabolic models were linked through a joint compartment, the lumen, allowing metabolite exchange and providing a route for simulating different dietary regimes. The resulting model consists of 7239 reactions, 5164 metabolites, and 2769 genes. We simultaneously modeled growth of mouse and B. thetaiotaomicron on five different diets varying in fat, carbohydrate, and protein content. The integrated model captured mutually beneficial cross-feeding as well as competitive interactions. Furthermore, we identified metabolites that were exchanged between the two organisms, which were compared with published metabolomics data. This analysis resulted for the first time in a comprehensive description of the co-metabolism between a host and its commensal microbe. We also demonstrate in silico that the presence of B. thetaiotaomicron could rescue the growth phenotype of the host with an otherwise lethal enzymopathy and vice versa. This systems approach represents a powerful tool for modeling metabolic interactions between a gut microbe and its host in health and disease.

Effects of probiotics on gut microbiota: mechanisms of intestinal immunomodulation and neuromodulation

Recent explorations of the human gut microbiota suggest that perturbations of microbial communities may increase predisposition to different disease phenotypes. Dietary nutrients may be converted into metabolites by intestinal microbes that serve as biologically active molecules affecting regulatory functions in the host. Probiotics may restore the composition of the gut microbiome and introduce beneficial functions to gut microbial communities, resulting in amelioration or prevention of gut inflammation and other intestinal or systemic disease phenotypes. This review describes how diet and intestinal luminal conversion by gut microbes play a role in shaping the structure and function of intestinal microbial communities. Proposed mechanisms of probiosis include alterations of composition and function of the human gut microbiome, and corresponding effects on immunity and neurobiology.

Sucralose, a synthetic organochlorine sweetener: overview of biological issues

Sucralose is a synthetic organochlorine sweetener (OC) that is a common ingredient in the world's food supply. Sucralose interacts with chemosensors in the alimentary tract that play a role in sweet taste sensation and hormone secretion. In rats, sucralose ingestion was shown to increase the expression of the efflux transporter P-glycoprotein (P-gp) and two cytochrome P-450 (CYP) isozymes in the intestine. P-gp and CYP are key components of the presystemic detoxification system involved in first-pass drug metabolism. The effect of sucralose on first-pass drug metabolism in humans, however, has not yet been determined. In rats, sucralose alters the microbial composition in the gastrointestinal tract (GIT), with relatively greater reduction in beneficial bacteria. Although early studies asserted that sucralose passes through the GIT unchanged, subsequent analysis suggested that some of the ingested sweetener is metabolized in the GIT, as indicated by multiple peaks found in thin-layer radiochromatographic profiles of methanolic fecal extracts after oral sucralose administration. The identity and safety profile of these putative sucralose metabolites are not known at this time. Sucralose and one of its hydrolysis products were found to be mutagenic at elevated concentrations in several testing methods. Cooking with sucralose at high temperatures was reported to generate chloropropanols, a potentially toxic class of compounds. Both human and rodent studies demonstrated that sucralose may alter glucose, insulin, and glucagon-like peptide 1 (GLP-1) levels. Taken together, these findings indicate that sucralose is not a biologically inert compound.

Alteration of bile acid metabolism in pseudo germ-free rats [corrected]

To characterize the impact of gut microbiota on host bile acid metabolism, we investigated the metabolic profiles of oxysterols and bile acids (BAs) in a conventional rat model (SD) (n=5) and its pseudo germ-free (GF) equivalent (n=5). GF rats were developed by the oral administration of bacitracin, neomycin and streptomycin (200 mg/kg, each) twice a day for 6 days. Urinary levels of oxysterols and bile acid metabolites were quantified using gas chromatography-mass spectrometry (GC-MS). The activity levels of enzymes involved in the bile acid metabolic pathway were determined through urinary concentration ratio between product to precursor. Cholic acid (CA) and α-/β-muricholic acid (α-/β-MCA) were significantly elevated at pseudo germ-free condition. An increase of hydroxylase (cholesterol 7α-hydroxylase, oxysterol 7α-hydroxylase and cytochrome P450 scc) and a significant decrease of 7α-dehydroxylase were observed. The urinary concentration ratio of primary bile acids, a marker for hepatotoxicity, increased in pseudo germfree conditions. Therefore, it was found that gut microbiota could play a significant role in the bile acids homeostasis and metabolism.

Gut bacteria-host metabolic interplay during conventionalisation of the mouse germfree colon

The interplay between dietary nutrients, gut microbiota and mammalian host tissues of the gastrointestinal tract is recognised as highly relevant for host health. Combined transcriptome, metabonome and microbial profiling tools were employed to analyse the dynamic responses of germfree mouse colonic mucosa to colonisation by normal mouse microbiota (conventionalisation) at different time-points during 16 days. The colonising microbiota showed a shift from early (days 1 and 2) to later colonisers (days 8 and 16). The dynamic changes in the microbial community were rapidly reflected by the urine metabolic profiles (day 1) and at later stages (day 4 onward) by the colon mucosa transcriptome and metabolic profiles. Correlations of host transcriptomes, metabolite patterns and microbiota composition revealed associations between Bacilli and Proteobacteria, and differential expression of host genes involved in energy and anabolic metabolism. Differential gene expression correlated with scyllo- and myo-inositol, glutamine, glycine and alanine levels in colonic tissues during the time span of conventionalisation. Our combined time-resolved analyses may help to expand the understanding of host-microbe molecular interactions during the microbial establishment.

Molecular analysis of model gut microbiotas by imaging mass spectrometry and nanodesorption electrospray ionization reveals dietary metabolite transformations

The communities constituting our microbiotas are emerging as mediators of the health-disease continuum. However, deciphering the functional impact of microbial communities on host pathophysiology represents a formidable challenge, due to the heterogeneous distribution of chemical and microbial species within the gastrointestinal (GI) tract. Herein, we apply imaging mass spectrometry (IMS) to localize metabolites from the interaction between the host and colonizing microbiota. This approach complements other molecular imaging methodologies in that analytes need not be known a priori, offering the possibility of untargeted analysis. Localized molecules within the GI tract were then identified in situ by surface sampling with nanodesorption electrospray ionization Fourier transform ion cyclotron resonance-mass spectrometry (nanoDESI FTICR-MS). Products from diverse structural classes were identified including cholesterol-derived lipids, glycans, and polar metabolites. Specific chemical transformations performed by the microbiota were validated with bacteria in culture. This study illustrates how untargeted spatial characterization of metabolites can be applied to the molecular dissection of complex biology in situ.

Functional assignment of metagenomic data: challenges and applications

Metagenomic sequencing provides a unique opportunity to explore earth's limitless environments harboring scores of yet unknown and mostly unculturable microbes and other organisms. Functional analysis of the metagenomic data plays a central role in projects aiming to explore the most essential questions in microbiology, namely 'In a given environment, among the microbes present, what are they doing, and how are they doing it?' Toward this goal, several large-scale metagenomic projects have recently been conducted or are currently underway. Functional analysis of metagenomic data mainly suffers from the vast amount of data generated in these projects. The shear amount of data requires much computational time and storage space. These problems are compounded by other factors potentially affecting the functional analysis, including, sample preparation, sequencing method and average genome size of the metagenomic samples. In addition, the read-lengths generated during sequencing influence sequence assembly, gene prediction and subsequently the functional analysis. The level of confidence for functional predictions increases with increasing read-length. Usually, the most reliable functional annotations for metagenomic sequences are achieved using homology-based approaches against publicly available reference sequence databases. Here, we present an overview of the current state of functional analysis of metagenomic sequence data, bottlenecks frequently encountered and possible solutions in light of currently available resources and tools. Finally, we provide some examples of applications from recent metagenomic studies which have been successfully conducted in spite of the known difficulties.