Nutritional influences on the gut microbiota and the consequences for gastrointestinal health

The human colonic microbiota degrades dietary substrates that are indigestible in the upper GIT (gastrointestinal tract), releasing bacterial metabolites, some of which are important for gut health. Advances in molecular biology techniques have facilitated detailed analyses of the composition of the bacterial community resident in the lower GIT. Such analyses have indicated that more than 500 different bacterial species colonize an individual, and that, although there is much functional consistency in the resident bacterial groups, there is considerable inter-individual variation at the species/strain level. The bacterial community develops during early childhood until it reaches an adult-like composition. Whereas colonization and host factors influence the species composition, dietary factors also have an important impact, with specific bacterial groups changing in response to specific dietary interventions. Since bacterial species have different metabolic activities, specific diets have various consequences for health, dependent on the effect exerted on the bacterial population.     

Nutritional metabonomics: applications and perspectives

Nowadays, nutrition focuses on improving health of individuals through diet. Current nutritional research aims at health promotion, disease prevention, and performance improvement. Modern analytical platforms allow the simultaneous measurement of multiple metabolites providing new insights in the understanding of the functionalities of cells and whole organisms. Metabonomics, "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modifications", provides a systems approach to understanding global metabolic regulations of organisms. This concept has arisen from various applications of NMR and MS spectroscopies to study the multicomponent metabolic composition of biological fluids, cells, and tissues. The generated metabolic profiles are processed by multivariate statistics to maximize the recovery of information to be correlated with well-determined stimuli such as dietary intervention or with any phenotypic data or diet habits. Metabonomics is thus uniquely suited to assess metabolic responses to deficiencies or excesses of nutrients and bioactive components. Furthermore, metabonomics is used to characterize the metabolic phenotype of individuals integrating genetic polymorphism, metabolic interactions with commensal and symbiotic partners such as gut microflora, as well as environmental and behavioral factors including dietary preferences. This paper reports several experimental key aspects in nutritional metabonomics, reviews its applications employing targeted and holistic approach analysis for the study of the metabolic responses following dietary interventions. It also reports the assessment of intra- and inter-individual variability in animal and human populations. The potentialities of nutritional metabonomics for the discovery of new biomarkers and the characterization of metabolic phenotypes are discussed in a context of their possible utilizations for personalized nutrition to provide health maintenance at the individual level.     

Assessing individual metabolic responsiveness to a lipid challenge using a targeted metabolomic approach

The development of assessment techniques with immediate clinical applicability is a priority for resolving the growing epidemic in metabolic disease. Many imbalances in diet-dependent metabolism are not detectable in the fasted state. Resolving the high inter-individual variability in response to diet requires the development of techniques that can detect metabolic dysfunction at the level of the individual. The intra- and inter-individual variation in lipid metabolism in response to a standardized test meal was determined. Following an overnight fast on three different days, three healthy subjects consumed a test meal containing 40% of their daily calories. Plasma samples were collected at fasting, and 1, 3, 6, and 8 h after the test meal. Plasma fatty acid (FA) concentrations within separated lipid classes and lipoprotein fractions were measured at each time point. The intra-individual variation within each subject across three days was lower than the inter-individual differences among the three subjects for over 50% of metabolites in the triacylglycerol (TG), FA, and phosphatidylcholine (PC) lipid classes at 6 h, and for 25–50% of metabolites across lipid classes at 0, 1, 3, and 8 h. The consistency of response within individuals was visualized by principal component analysis (PCA) and confirmed by ANOVA. Three representative metabolites that discriminated among the three individuals in the apolipoprotein B (ApoB) fraction, TG16:1n7, TG18:2n6, and PC18:3n3, are discussed in detail. The postprandial responses of individuals were unique within metabolites that were individual discriminators (ID) of metabolic phenotype. This study shows that the targeted metabolomic measurement of individual metabolic phenotype in response to a specially formulated lipid challenge is possible even without lead-in periods, dietary and lifestyle control, or intervention over a 3-month period in healthy free-living individuals.     

Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500?ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6?h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0320-5) contains supplementary material, which is available to authorized users.     

Going with the grain: Fiber,cognition, and the microbiota-gut-brain-axis

Healthy dietary intake has been acknowledged for decades as one of the main contributors to health. More recently, the field of nutritional psychiatry has progressed our understanding regarding the importance of nutrition in supporting mental health and cognitive function. Thereby, individual nutrients, including omega-3 fatty acids and polyphenols, have been recognized to be key drivers in this relationship. With the progress in appreciating the influence of dietary fiber on health, increasingly research is focusing on deciphering its role in brain processes. However, while the importance of dietary fiber in gastrointestinal and metabolic health is well established, leading to the development of associated health claims, the evidence is not conclusive enough to support similar claims regarding cognitive function. Albeit the increasing knowledge of the impact of dietary fiber on mental health, only a few human studies have begun to shed light onto the underexplored connection between dietary fiber and cognition. Moreover, the microbiota-gut-brain axis has emerged as a key conduit for the effects of nutrition on the brain, especially fibers, that are acted on by specific bacteria to produce a variety of health-promoting metabolites. These metabolites (including short chain fatty acids) as well as the vagus nerve, the immune system, gut hormones, or the kynurenine pathway have been proposed as underlying mechanisms of the microbiota-brain crosstalk. In this minireview, we summarize the evidence available from human studies on the association between dietary fiber intake and cognitive function. We provide an overview of potential underlying mechanisms and discuss remaining questions that need to be answered in future studies. While this field is moving at a fast pace and holds promise for future important discoveries, especially data from human cohorts are required to further our understanding and drive the development of public health recommendations regarding dietary fiber in brain health.     

Human metabolic phenotypes link directly to specific dietary preferences in healthy individuals

Individual human health is determined by a complex interplay between genes, environment, diet, lifestyle, and symbiotic gut microbial activity. Here, we demonstrate a new "nutrimetabonomic" approach in which spectroscopically generated metabolic phenotypes are correlated with behavioral/psychological dietary preference, namely, "chocolate desiring" or "chocolate indifferent". Urinary and plasma metabolic phenotypes are characterized by differential metabolic biomarkers, measured using 1H NMR spectroscopy, including the postprandial lipoprotein profile and gut microbial co-metabolism. These data suggest that specific dietary preferences can influence basal metabolic state and gut microbiome activity that in turn may have long-term health consequences to the host. Nutrimetabonomics appears as a promising approach for the classification of dietary responses in populations and personalized nutritional management.     

Genetic variation in insulin‐induced kinase signaling

Individual differences in sensitivity to insulin contribute to disease susceptibility including diabetes and metabolic syndrome. Cellular responses to insulin are well studied. However, which steps in these response pathways differ across individuals remains largely unknown. Such knowledge is needed to guide more precise therapeutic interventions. Here, we studied insulin response and found extensive individual variation in the activation of key signaling factors, including ERK whose induction differs by more than 20‐fold among our subjects. This variation in kinase activity is propagated to differences in downstream gene expression response to insulin. By genetic analysis, we identified cis‐acting DNA variants that influence signaling response, which in turn affects downstream changes in gene expression and cellular phenotypes, such as protein translation and cell proliferation. These findings show that polymorphic differences in signal transduction contribute to individual variation in insulin response, and suggest kinase modulators as promising therapeutics for diseases characterized by insulin resistance.     

PhIP metabolites in human urine after consumption of well-cooked chicken

We devised an assay to quantify the metabolites of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) in human urine following a single exposure to well-cooked meat. Our method uses LC/MS/MS to detect four metabolites and four deuterated internal standard peaks in a single chromatographic run. N2-OH-PhIP-N2-glucuronide was the most abundant urinary metabolite excreted by the 12 individuals who participated in our study. N2-PhIP glucuronide was the second most abundant metabolite for 8 of the 12 volunteers. The stability of PhIP metabolism over time was studied in three of the volunteers who repeated the assay eight times over a 2.5 year-period. PhIP metabolite excretion varied in each subject over time, although the rate of excretion was more constant. Our results suggest that quantifying PhIP metabolites should make future studies of individual susceptibility and dietary interventions possible.     

Metabolic profiling of strawberry (Fragaria x ananassa Duch.) during fruit development and maturation

Strawberry (Fragaria × ananassa Duch), a fruit of economic and nutritional importance, is also a model species for fleshy fruits and genomics in Rosaceae. Strawberry fruit quality at different harvest stages is a function of the fruit's metabolite content, which results from physiological changes during fruit growth and ripening. In order to investigate strawberry fruit development, untargeted (GC-MS) and targeted (HPLC) metabolic profiling analyses were conducted. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to explore the non-polar and polar metabolite profiles from fruit samples at seven developmental stages. Different cluster patterns and a broad range of metabolites that exerted influence on cluster formation of metabolite profiles were observed. Significant changes in metabolite levels were found in both fruits turning red and fruits over-ripening in comparison with red-ripening fruits. The levels of free amino acids decreased gradually before the red-ripening stage, but increased significantly in the over-ripening stage. Metabolite correlation and network analysis revealed the interdependencies of individual metabolites and metabolic pathways. Activities of several metabolic pathways, including ester biosynthesis, the tricarboxylic acid cycle, the shikimate pathway, and amino acid metabolism, shifted during fruit growth and ripening. These results not only confirmed published metabolic data but also revealed new insights into strawberry fruit composition and metabolite changes, thus demonstrating the value of metabolomics as a functional genomics tool in characterizing the mechanism of fruit quality formation, a key developmental stage in most economically important fruit crops.     

Analyses of short-chain fatty acids and exhaled breath volatiles in dietary intervention trials for metabolic diseases

As an alternative to pharmacological treatment to diseases, lifestyle interventions, such as dietary changes and physical activities, can help maintain healthy metabolic conditions. Recently, the emerging analyses of volatile organic compounds (VOCs) from breath and short-chain fatty acids (SCFAs) from plasma/feces have been considered as useful tools for the diagnosis and mechanistic understanding of metabolic diseases. Furthermore, diet-induced changes of SCFAs in individuals with diagnosed metabolic abnormalities have been correlated with the composition changes of the gut microbiome. More interestingly, the analysis of exhaled breath (breathomics) has gained attention as a useful technique to measure the human VOC profile altered as a result of dietary interventions. In this mini-review, we examined recent clinical trials that performed promising dietary interventions, SCFAs analysis in plasma/feces, and VOC profile analysis in exhaling breath to understand the relationship between dietary intervention and metabolic health.     

Metabolic Determinants of Weight Gain in Humans

One of the fundamental challenges in obesity research is to identify subjects prone to weight gain so that obesity and its comorbidities can be promptly prevented or treated. The principles of thermodynamics as applied to human body energetics demonstrate that susceptibility to weight gain varies among individuals as a result of interindividual differences in energy expenditure and energy intake, two factors that counterbalance one another and determine daily energy balance and, ultimately, body weight change. This review focuses on the variability among individuals in human metabolism that determines weight change. Conflicting results have been reported about the role of interindividual differences in energy metabolism during energy balance in relation to future weight change. However, recent studies have shown that metabolic responses to acute, short‐term dietary interventions that create energy imbalance, such as low‐protein overfeeding or fasting for 24 hours, may reveal the underlying metabolic phenotype that determines the degree of resistance to diet‐induced weight loss or the propensity to spontaneous weight gain over time. Metabolically “thrifty” individuals, characterized by a predilection for saving energy in settings of undernutrition and dietary protein restriction, display a minimal increase in plasma fibroblast growth factor 21 concentrations in response to a low‐protein overfeeding diet and tend to gain more weight over time compared with metabolically “spendthrift” individuals. Similarly, interindividual variability in the causal relationship between energy expenditure and energy intake (“energy sensing”) and in the metabolic response to cold exposure (e.g., brown adipose tissue activation) seems, to some extent, to be indicative of individual propensity to weight gain. Thus, an increased understanding and the clinical characterization of phenotypic differences in energy metabolism among individuals (metabolic profile) may lead to new strategies to prevent weight gain or improve weight‐loss interventions by targeted therapies on the basis of metabolic phenotype and susceptibility to obesity in individual persons.     

Interpreting Metabolomic Profiles using Unbiased Pathway Models

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for “active modules”—regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.     

Metabolic profiling of transgenic tomato plants overexpressing hexokinase reveals that the influence of hexose phosphorylation diminishes during fruit development

We have conducted a comprehensive metabolic profiling on tomato (Lycopersicon esculentum) leaf and developing fruit tissue using a recently established gas chromatography-mass spectrometry profiling protocol alongside conventional spectrophotometric and liquid chromatographic methodologies. Applying a combination of these techniques, we were able to identify in excess of 70 small-M(r) metabolites and to catalogue the metabolite composition of developing tomato fruit. In addition to comparing differences in metabolite content between source and sink tissues of the tomato plant and after the change in metabolite pool sizes through fruit development, we have assessed the influence of hexose phosphorylation through fruit development by analyzing transgenic plants constitutively overexpressing Arabidopsis hexokinase AtHXK1. Analysis of the total hexokinase activity in developing fruits revealed that both wild-type and transgenic fruits exhibit decreasing hexokinase activity with development but that the relative activity of the transgenic lines with respect to wild type increases with development. Conversely, both point-by-point and principal component analyses suggest that the metabolic phenotype of these lines becomes less distinct from wild type during development. In summary, the data presented in this paper demonstrate that the influence of hexose phosphorylation diminishes during fruit development and highlights the importance of greater temporal resolution of metabolism.     

Expression of human amyloid precursor protein in the skeletal muscles of Drosophila results in age- and activity-dependent muscle weakness

Background

Pigs are widely used as models for human physiological changes in intervention studies, because of the close resemblance between human and porcine physiology and the high degree of experimental control when using an animal model. Cloned animals have, in principle, identical genotypes and possibly also phenotypes and this offer an extra level of experimental control which could possibly make them a desirable tool for intervention studies. Therefore, in the present study, we address how phenotype and phenotypic variation is affected by cloning, through comparison of cloned pigs and normal outbred pigs.

Results

The metabolic phenotype of cloned pigs (n = 5) was for the first time elucidated by nuclear magnetic resonance (NMR)-based metabolomic analysis of multiple bio-fluids including plasma, bile and urine. The metabolic phenotype of the cloned pigs was compared with normal outbred pigs (n = 6) by multivariate data analysis, which revealed differences in the metabolic phenotypes. Plasma lactate was higher for cloned vs control pigs, while multiple metabolites were altered in the bile. However a lower inter-individual variability for cloned pigs compared with control pigs could not be established.

Conclusions

From the present study we conclude that cloned and normal outbred pigs are phenotypically different. However, it cannot be concluded that the use of cloned animals will reduce the inter-individual variation in intervention studies, though this is based on a limited number of animals.     

Validation of a urine metabolome fingerprint in dog for phenotypic classification

Selective breeding of dogs over hundreds of years has inadvertently resulted in breed-specific propensities to particular diseases. Furthermore, it has likely induced more subtle affects on the physiology of certain breeds and moved them from their evolutionary optima. In the absence of obvious disease phenotypes such subtle changes could have yet unrecognised breed-specific implications for health and well-being. Here we have applied NMR metabolomics as a discovery-driven approach to identify the impact of breed on the urinary profile of dog and to determine if non-disease-related breed differences can be identified. Multiple urines were collected non-invasively over a two-week period from seven neutered male Labrador retrievers and miniature Schnauzers. Following NMR analyses by 1-dimensional ¹H and 2-dimensional ¹H J-resolved (JRES) spectroscopy, principal component analysis revealed that the metabolic variability within each individual is relatively small compared to inter-individual variability, and that some separation between breeds was evident. A supervised model, using partial least squares discriminant analysis (PLS-DA) with class based upon breed, was trained using the JRES data. The model predicted correctly the breed of seven additional urines, yielding a model sensitivity and specificity of 100%. Several significant metabolic differences between the breeds were identified. A second model was developed using PLS-DA with class based upon individual dogs, which again achieved high classification accuracy for the test set. Overall, this confirms that canine urine is information-rich and that breed is a major determinant of urinary metabolic fingerprints. In the future this may enable a more accurate development of specific nutritional care for an individual or breed.     

Individual variation behind the evolution of cooperation

Life on Earth has two remarkable properties. The first is variation: even apart from the vast number of extant species, there are considerable differences between individuals within a single species. The second property is cooperation. It is surprising that until recently the interactions between these two properties have rarely been addressed from an evolutionary point of view. Here, I concentrate on how inter-individual differences influence the evolution of cooperation. First, I deal with cases where individuality is maintained by random processes like mutation or phenotypic noise. Second, I examine when differences in state cause differences in behaviour. Finally, I investigate the effects of individual role specialization. Variation can be important in several ways. Increased random variation can change the expectation about cooperativeness of future partners, altering behaviour in a current relationship. Differences in state may serve as a book-keeping mechanism that is necessary for the evolution of reciprocity. If the cost of cooperation can depend on state then strategic regulation of state makes it possible to coerce partners to cooperate. If conditions force individuals to specialize, cooperation becomes more valuable. My review of theoretical models suggests that variation plays an important role in the evolution of cooperation.     

Variations in concentrations of bacterial metabolites, enzyme activities, moisture, pH and bacterial composition between and within individuals in faeces of seven healthy adults

Variations between and within individuals, and correlations between concentrations of bacterial metabolites, including putrefactive products, ammonia and short chain fatty acids (SCFAs), enzyme activities, moisture and pH, as well as bacterial composition, were studied in faecal samples from seven healthy adults over a period of 7 months. Large variations, both between and within individuals, were observed in concentrations of putrefactive products. Although values for ammonia, SCFAs, enzyme activities, moisture and pH were generally variable, significant person-to-person differences were observed.
While ranges of log viable counts of the predominant bacteria such as eubacteria, bifidobacteria and bacteroides in each subject remained between 0·2 and 1·3, those of enterobacteria, streptococci (including enterococci) and lecithinase-negative clostridia varied between 0·4 and 3·0. Levels of bifidobacteria, enterobacteria, streptococci and total aerobic bacteria showed inter-individual variations. Correlations were found among certain of the parameters: moisture correlated negatively with p -cresol ( r = -0·707), pH ( r = -0–671) and β-glucosidase activity (GS) ( r = -0·608), and positively with acetic acid ( r = 0·621), while negative correlations were observed in pH with acetic and butyric acids ( r = -0·690 and -0·623, respectively).
No significant correlations were found between bacterial compositions, and other faecal factors such as pH, moisture, metabolic enzyme activities and concentrations of putrefactive products.     

Metabolic engineering of flavonoids in tomato (Solanum lycopersicum): the potential for metabolomics

Flavonoids comprise a large and diverse group of polyphenolic plant secondary metabolites. In plants, flavonoids play important roles in many biological processes such as pigmentation of flowers, fruits and vegetables, plant-pathogen interactions, fertility and protection against UV light. Being natural plant compounds, flavonoids are an integral part of the human diet and there is increasing evidence that dietary polyphenols are likely candidates for the observed beneficial effects of a diet rich in fruits and vegetables on the prevention of several chronic diseases. Within the plant kingdom, and even within a single plant species, there is a large variation in the levels and composition of flavonoids. This variation is often due to specific mutations in flavonoid-related genes leading to quantitative and qualitative differences in metabolic profiles. The use of such specific flavonoid mutants with easily scorable, visible phenotypes has led to the isolation and characterisation of many structural and regulatory genes involved in the flavonoid biosynthetic pathway from different plant species. These genes have been used to engineer the flavonoid biosynthetic pathway in both model and crop plant species, not only from a fundamental perspective, but also in order to alter important agronomic traits, such as flower and fruit colour, resistance, nutritional value. This review describes the advances made in engineering the flavonoid pathway in tomato (Solanum lycopersicum). Three different approaches will be described; (I) Increasing endogenous tomato flavonoids using structural or regulatory genes; (II) Blocking specific steps in the flavonoid pathway by RNA interference strategies; and (III) Production of novel tomato flavonoids by introducing novel branches of the flavonoid pathway. Metabolite profiling is an essential tool to analyse the effects of pathway engineering approaches, not only to analyse the effect on the flavonoid composition itself, but also on other related or unrelated metabolic pathways. Metabolomics will therefore play an increasingly important role in revealing a more complete picture of metabolic perturbation and will provide additional novel insights into the effect of the introduced genes and the role of flavonoids in plant physiology and development.     

城市人群血清营养水平指标与膳食习惯关系的分析

目的:分析血清营养状态指标与膳食习惯的关联关系与意义。方法:采用横断面调查的数据,用标准问卷询问1694人相关信息,内容包括人口学资料、自报疾病现患史、膳食习惯、健康自评、认知功能信息;同时采集被调查人群血样本;测定血红蛋白、白蛋白、叶酸和维生素B12水平。采用R 3.2.5和SPSS 20.0软件进行相关和显著性差异分析。结果:人体血红蛋白、白蛋白、叶酸和维生素B12水平与被调查个体进食水果和鸡蛋习惯有密切关联;天天进食水果、鸡蛋的个体上述指标水平明显高于不进食此类食物的个体,且血红蛋白、白蛋白水平随增龄下降程度减低;天天进食水果习惯的个体同时具有天天进食蛋、奶、菜习惯;有进食水果习惯的人群表现为受教育程度高;自评健康状态好和相对自报患病率低等特点。结论:合理膳食习惯可保持较高的血营养状态指标水平;且延缓相关指标水平随增龄下降的程度。天天进食水果习惯可被视为合理膳食的指标,为膳食评估提供简易、可行、有效的指标。