MR metabolomics of fecal extracts: applications in the study of bowel diseases

Characterization of physical and chemical properties of dietary fiber from grain bran and its regulation of gut microbiota and metabolite to prevent colitis

Grain bran dietary fiber (DF) has the effect of promoting intestinal health and is worth being studied. In the present study, the physicochemical properties and prevention effect of DF on ulcerative colitis (UC) were investigated. The results showed that the optimal extraction conditions were determined as α-amylase (350 U/g, 70 °C, pH 7.0, 2.5 h) and papain (100 U/g, 60 °C, pH 7.0, 1.5 h), resulting in a yield of 83.81% for DF. Moreover, DF exhibited unique physicochemical properties contributing to its preventive effects, as evidenced by its ability to mitigate symptoms such as hematochezia, immune inflammation, and impaired intestinal barrier in UC mice. The underlying mechanism can be attributed to the regulation of phenylalanine, tyrosine and tryptophan biosynthesis pathway and maintenance of intestinal microbial homeostasis. Therefore, our study suggests that grain bran DF holds potential for the prevention of UC, providing a basis for the development and utilization of grain bran.

Gastrointestinal Characteristics of Constipation from the Perspectives of Microbiome and Metabolome

BACKGROUND

Constipation is one of the most common gastrointestinal complaints. Yet, the underlying mechanisms of constipation remain to be explored deeply. Integration of microbiome and metabolome is powerful and promising to demonstrate characteristics of constipation.

AIM OF STUDY

This study aimed to characterize intestinal microbiome and metabolome of constipation. In addition, this study revealed the correlations among behaviors, intestinal microbiota, and metabolites interrupted by constipation.

METHODS

Firstly, the constipation model was successfully applied. At the macro level, the ability of learning, memory, locomotor activity, and the defecation index of rats with constipation-like phenotype were characterized. At the micro-level, 16S rRNA sequencing was applied to analyze the intestinal microbiota in rats with constipation-like phenotype. 1H nuclear magnetic resonance (NMR)-based metabolomics was employed to investigate the metabolic phenotype of constipation. In addition, we constructed a correlation network, intuitively showing the correlations among behaviors, intestinal microbiota, and metabolites.

RESULTS

Constipation significantly attenuated the locomotor activity, memory recognition, and frequency of defecation of rats, while increased the time of defecation. Constipation significantly changed the diversity of intestinal microbial communities, which correspondingly involved in 5 functional pathways. Besides, 28 fecal metabolites were found to be associated with constipation, among which 14 metabolites were further screened that can be used to diagnose constipation. On top of this, associated networks intuitively showed the correlations among behaviors, intestinal microbiota, and metabolites.

CONCLUSIONS

The current findings are significant in terms of not only laying a foundation for understanding characteristics of constipation, but also providing accurate diagnosis and treatments of constipation clinically.

Temporal stability of fecal metabolomic profiles in irritable bowel syndrome

BACKGROUND

The potential of the fecal metabolome to serve as a biomarker for irritable bowel syndrome (IBS) depends on its stability over time. Therefore, this study aimed to determine the temporal dynamics of the fecal metabolome, and the potential relationship with stool consistency, in patients with IBS and healthy subjects.

METHODS

Fecal samples were collected in two cohorts comprising patients with IBS and healthy subjects. For Cohort A, fecal samples collected during 5 consecutive days were analyzed by gas chromatography-tandem mass spectrometry (GC-MS/MS). For Cohort B, liquid chromatography-MS (LC-MS) was used to analyze fecal samples collected at week 0 (healthy and IBS) and at week 4 (patients only). Stool consistency was determined by the Bristol Stool Form scale.

KEY RESULTS

Fecal samples were collected from Cohort A (seven healthy subjects and eight IBS patients), and Cohort B (seven healthy subjects and 11 IBS patients). The fecal metabolome of IBS patients was stable short-term (Cohort A, 5 days and within the same day) and long-term (Cohort B, 4 weeks). A similar trend was observed over 5 days in the healthy subjects of Cohort A. The metabolome dissimilarity was larger between than within participants over time in both healthy subjects and IBS patients. Further analyses showed that patients had greater range of stool forms (types) than healthy subjects, with no apparent influence on metabolomic dynamics.

CONCLUSION & INFERENCES

The fecal metabolome is stable over time within IBS patients as well as healthy subjects. This supports the concept of a stable fecal metabolome in IBS despite fluctuations in stool consistency, and the use of single timepoint sampling to further explore how the fecal metabolome is related to IBS pathogenesis.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Fecal 1H-NMR Metabolomics: A Comparison of Sample Preparation Methods for NMR and Novel in Silico Baseline Correction

Analysis of enteric microbiota function indirectly through the fecal metabolome has the potential to be an informative diagnostic tool. However, metabolomic analysis of feces is hampered by high concentrations of macromolecules such as proteins, fats, and fiber in samples. Three methods—ultrafiltration (UF), Bligh–Dyer (BD), and no extraction (samples added directly to buffer, vortexed, and centrifuged)—were tested on multiple rat (n = 10) and chicken (n = 8) fecal samples to ascertain whether the methods worked equally well across species and individuals. An in silico baseline correction method was evaluated to determine if an algorithm could produce spectra similar to those obtained via UF. For both rat and chicken feces, UF removed all macromolecules and produced no baseline distortion among samples. By contrast, the BD and no extraction methods did not remove all the macromolecules and produced baseline distortions. The application of in silico baseline correction produced spectra comparable to UF spectra. In the case of no extraction, more intense peaks were produced. This suggests that baseline correction may be a cost-effective method for metabolomic analyses of fecal samples and an alternative to UF. UF was the most versatile and efficient extraction method; however, BD and no extraction followed by baseline correction can produce comparable results.

Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells).

Robust Microbial Markers for Non-Invasive Inflammatory Bowel Disease Identification

Inflammatory Bowel Disease (IBD) is an umbrella term for a group of inflammatory diseases of the gastrointestinal tract, including Crohn's Disease and ulcerative colitis. Changes to the intestinal microbiome, the community of micro-organisms that resides in the human gut, have been shown to contribute to the pathogenesis of IBD. IBD diagnosis is often delayed due to its non-specific symptoms and because an invasive colonoscopy is required for confirmation, which leads to poor growth in children and worse treatment outcomes. Feature selection algorithms are often applied to microbial communities to identify bacterial groups that drive disease. It has been shown that aggregating Ensemble Feature Selection (EFS) can improve the robustness of feature selection algorithms, which is defined as the variation of feature selector output caused by small changes to the dataset. In this work, we apply a two-step filter and an EFS process to generate robust feature subsets that can non-invasively predict IBD subtypes from high-resolution microbiome data. The predictive power of the robust feature subsets is the highest reported in literature to date. Furthermore, we identify five biologically plausible bacterial species that have not previously been implicated in IBD aetiology.

NMR metabolomics reveals effects of Cryptosporidium infections on host cell metabolome

Background

Cryptosporidium is an important gut microbe whose contributions towards infant and immunocompromise patient mortality rates are steadily increasing. Over the last decade, we have seen the development of various tools and methods for studying Cryptosporidium infection and its interactions with their hosts. One area that is sorely overlooked is the effect infection has on host metabolic processes.

Results

Using a ¹H nuclear magnetic resonance approach to metabolomics, we have explored the nature of the mouse gut metabolome as well as providing the first insight into the metabolome of an infected cell line. Statistical analysis and predictive modelling demonstrated new understandings of the effects of a Cryptosporidium infection, while verifying the presence of known metabolic changes. Of note is the potential contribution of host derived taurine to the diarrhoeal aspects of the disease previously attributed to a solely parasite-based alteration of the gut environment, in addition to other metabolites involved with host cell catabolism.

Conclusion

This approach will spearhead our understanding of the Cryptosporidium-host metabolic exchange and provide novel targets for tackling this deadly parasite.

Serum Metabolomics Identifies Altered Bioenergetics, Signaling Cascades in Parallel with Exposome Markers in Crohn's Disease

: Inflammatory bowel disease (IBD) has stimulated much interest due to its surging incidences and health impacts in the U.S. and worldwide. However, the exact cause of IBD remains incompletely understood, and biomarker is lacking towards early diagnostics and effective therapy assessment. To tackle these, the emerging high-resolution mass spectrometry (HRMS)-based metabolomics shows promise. Here, we conducted a pilot untargeted LC/MS metabolomic profiling in Crohn's disease, for which serum samples of both active and inactive cases were collected, extracted, and profiled by a state-of-the-art compound identification workflow. Results show a distinct metabolic profile of Crohn's from control, with most metabolites downregulated. The identified compounds are structurally diverse, pointing to important pathway perturbations ranging from energy metabolism (e.g., β-oxidation of fatty acids) to signaling cascades of lipids (e.g., DHA) and amino acid (e.g., L-tryptophan). Importantly, an integral role of gut microbiota in the pathogenesis of Crohn's disease is highlighted. Xenobiotics and their biotransformants were widely detected, calling for massive exposomic profiling for future cohort studies as such. This study endorses the analytical capacity of untargeted metabolomics for biomarker development, cohort stratification, and mechanistic interpretation; the findings might be valuable for advancing biomarker research and etiologic inquiry in IBD.

A Metabolomics Approach to Pharmacotherapy Personalization

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

Rat Fecal Metabolomics-Based Analysis

Fecal metabolomics-based analysis indisputably constitutes a very useful tool for elucidating the biochemistry of digestion and absorption of the gastrointestinal system. Fecal samples represent the most suitable, non-invasive, specimen for the study of the symbiotic relationship between the host and the intestinal microbiota.It is well established that the balance of the intestinal microbiota changes in response to some stimuli, physiological such as gender, age, diet, exercise and pathological such as gastrointestinal and hepatic disease. Fecal samples have been analyzed using the most widespread analytical techniques, namely, NMR spectroscopy, GC-MS, and LC-MS/MS. Rat fecal sample is a frequently used and particularly useful substrate for metabolomics-based studies in related fields. The complexity and diversity of the nature of fecal samples require careful and skillful handling for the effective quantitative extraction of the metabolites while avoiding their deterioration. Parameters such as the fecal sample weight to extraction solvent volume, the nature and the pH value of the extraction solvent, and the homogenization process are some important factors for the optimal extraction of samples, in order to obtain high-quality metabolic fingerprints, using either untargeted or targeted metabolomics.

Sample preparation optimization in fecal metabolic profiling

Metabolomic analysis of feces can provide useful insight on the metabolic status, the health/disease state of the human/animal and the symbiosis with the gut microbiome. As a result, recently there is increased interest on the application of holistic analysis of feces for biomarker discovery. For metabolomics applications, the sample preparation process used prior to the analysis of fecal samples is of high importance, as it greatly affects the obtained metabolic profile, especially since feces, as matrix are diversifying in their physicochemical characteristics and molecular content. However there is still little information in the literature and lack of a universal approach on sample treatment for fecal metabolic profiling. The scope of the present work was to study the conditions for sample preparation of rat feces with the ultimate goal of the acquisition of comprehensive metabolic profiles either untargeted by NMR spectroscopy and GC-MS or targeted by HILIC-MS/MS. A fecal sample pooled from male and female Wistar rats was extracted under various conditions by modifying the pH value, the nature of the organic solvent and the sample weight to solvent volume ratio. It was found that the 1/2 (w_f/v_s) ratio provided the highest number of metabolites under neutral and basic conditions in both untargeted profiling techniques. Concerning LC-MS profiles, neutral acetonitrile and propanol provided higher signals and wide metabolite coverage, though extraction efficiency is metabolite dependent.

Noninvasive metabolic profiling for painless diagnosis of human diseases and disorders

Metabolic profiling provides a powerful diagnostic tool complementary to genomics and proteomics. The pain, discomfort and probable iatrogenic injury associated with invasive or minimally invasive diagnostic methods, render them unsuitable in terms of patient compliance and participation. Metabolic profiling of biomatrices like urine, breath, saliva, sweat and feces, which can be collected in a painless manner, could be used for noninvasive diagnosis. This review article covers the noninvasive metabolic profiling studies that have exhibited diagnostic potential for diseases and disorders. Their potential applications are evident in different forms of cancer, metabolic disorders, infectious diseases, neurodegenerative disorders, rheumatic diseases and pulmonary diseases. Large scale clinical validation of such diagnostic methods is necessary in future.

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.

Challenges of metabolomics in human gut microbiota research

The review highlights the role of metabolomics in studying human gut microbial metabolism. Microbial communities in our gut exert a multitude of functions with huge impact on human health and disease. Within the meta-omics discipline, gut microbiome is studied by (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. The goal of metabolomics research applied to fecal samples is to perform their metabolic profiling, to quantify compounds and classes of interest, to characterize small molecules produced by gut microbes. Nuclear magnetic resonance spectroscopy and mass spectrometry are main technologies that are applied in fecal metabolomics. Metabolomics studies have been increasingly used in gut microbiota related research regarding health and disease with main focus on understanding inflammatory bowel diseases. The elucidated metabolites in this field are summarized in this review. We also addressed the main challenges of metabolomics in current and future gut microbiota research. The first challenge reflects the need of adequate analytical tools and pipelines, including sample handling, selection of appropriate equipment, and statistical evaluation to enable meaningful biological interpretation. The second challenge is related to the choice of the right animal model for studies on gut microbiota. We exemplified this using NMR spectroscopy for the investigation of cross-species comparison of fecal metabolite profiles. Finally, we present the problem of variability of human gut microbiota and metabolome that has important consequences on the concepts of personalized nutrition and medicine.

Metabonomics of human fecal extracts characterize ulcerative colitis, Crohn's disease and healthy individuals

This study employs spectroscopy-based metabolic profiling of fecal extracts from healthy subjects and patients with active or inactive ulcerative colitis (UC) and Crohn's disease (CD) to substantiate the potential use of spectroscopy as a non-invasive diagnostic tool and to characterize the fecal metabolome in inflammatory bowel disease (IBD). Stool samples from 113 individuals (UC 48, CD 44, controls 21) were analyzed by ¹H nuclear magnetic resonance (NMR) spectroscopy (Bruker 600 MHz, Bruker BioSpin, Rheinstetten, Germany). Data were analyzed with principal component analysis and orthogonal-projection to latent structure-discriminant analysis using SIMCA-P + 12 and MATLAB. Significant differences were found in the metabolic profiles making it possible to differentiate between active IBD and controls and between UC and CD. The metabolites holding differential power primarily belonged to a range of amino acids, microbiota-related short chain fatty acids, and lactate suggestive of an inflammation-driven malabsorption and dysbiosis of the normal bacterial ecology. However, removal of patients with intestinal surgery and anti-TNF-α antibody treatment eliminated the discriminative power regarding UC versus CD. This study consequently demonstrates that ¹H NMR spectroscopy of fecal extracts is a potential non-invasive diagnostic tool and able to characterize the inflammation-driven changes in the metabolic profiles related to malabsorption and dysbiosis. Intestinal surgery and medication are to be accounted for in future studies, as it seems to be factors of importance in the discriminative process.

Urinary metabolite profiling combined with computational analysis predicts interstitial cystitis-associated candidate biomarkers

Interstitial cystitis/painful bladder syndrome (IC) is a chronic syndrome of unknown etiology that presents with bladder pain, urinary frequency, and urgency. The lack of specific biomarkers and a poor understanding of underlying molecular mechanisms present challenges for disease diagnosis and therapy. The goals of this study were to identify noninvasive biomarker candidates for IC from urine specimens and to potentially gain new insight into disease mechanisms using a nuclear magnetic resonance (NMR)-based global metabolomics analysis of urine from female IC patients and controls. Principal component analysis (PCA) suggested that the urinary metabolome of IC and controls was clearly different, with 140 NMR peaks significantly altered in IC patients (FDR < 0.05) compared to that in controls. On the basis of strong correlation scores, fifteen metabolite peaks were nominated as the strongest signature of IC. Among those signals that were higher in the IC group, three peaks were annotated as tyramine, the pain-related neuromodulator. Two peaks were annotated as 2-oxoglutarate. Levels of tyramine and 2-oxoglutarate were significantly elevated in urine specimens of IC subjects. An independent analysis using mass spectrometry also showed significantly increased levels of tyramine and 2-oxoglutarate in IC patients compared to controls. Functional studies showed that 2-oxoglutarate, but not tyramine, retarded growth of normal bladder epithelial cells. These preliminary findings suggest that analysis of urine metabolites has promise in biomarker development in the context of IC.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Fecal metabolomics: assay performance and association with colorectal cancer

Metabolomic analysis of feces may provide insights on colorectal cancer (CRC) if assay performance is satisfactory. In lyophilized feces from 48 CRC cases, 102 matched controls, and 48 masked quality control specimens, 1043 small molecules were detected with a commercial platform. Assay reproducibility was good for 527 metabolites [technical intraclass correlation coefficient (ICC) >0.7 in quality control specimens], but reproducibility in 6-month paired specimens was lower for the majority of metabolites (within-subject ICC ≤0.5). In the CRC cases and controls, significant differences (false discovery rate ≤0.10) were found for 41 of 1043 fecal metabolites. Direct cancer association was found with three fecal heme-related molecules [covariate-adjusted 90th versus 10th percentile odds ratio (OR) = 17-345], 18 peptides/amino acids (OR = 3-14), palmitoyl-sphingomyelin (OR = 14), mandelate (OR = 3) and p-hydroxy-benzaldehyde (OR = 4). Conversely, cancer association was inverse with acetaminophen metabolites (OR <0.1), tocopherols (OR = 0.3), sitostanol (OR = 0.2), 3-dehydrocarnitine (OR = 0.4), pterin (OR = 0.3), conjugated-linoleate-18-2N7 (OR = 0.2), N-2-furoyl-glycine (OR = 0.3) and p-aminobenzoate (PABA, OR = 0.2). Correlations suggested an independent role for palmitoyl-sphingomyelin and a central role for PABA (which was stable over 6 months, within-subject ICC 0.67) modulated by p-hydroxy-benzaldehyde. Power calculations based on ICCs indicate that only 45% of metabolites with a true relative risk 5.0 would be found in prospectively collected, prediagnostic specimens from 500 cases and 500 controls. Thus, because fecal metabolites vary over time, very large studies will be needed to reliably detect associations of many metabolites that potentially contribute to CRC.

1H NMR based metabolic profiling in Crohn's disease by random forest methodology

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy ((1)H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R(2)) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

Evaluation of yellow pea fibre supplementation on weight loss and the gut microbiota: a randomized controlled trial

BACKGROUND

Fibre intake among North Americans is currently less than half the recommended amount. Consumers are interested in food products that could promote weight loss and improve health. Consequently, evaluation of unique fibre sources with potential gut-mediated benefits for metabolic health warrants investigation. Our objective is to assess the effects of yellow pea fibre supplementation on weight loss and gut microbiota in an overweight and obese adult population.

METHODS/DESIGN

In a double blind, placebo controlled, parallel group study, overweight and obese (BMI = 25-38) adults will be randomized to either a 15 g/d yellow pea fibre supplemented group or isocaloric placebo group for 12 weeks (n = 30/group). The primary outcome measure is a change in body fat from baseline to 12 weeks. Secondary outcomes include glucose tolerance, appetite regulation, serum lipids and inflammatory markers. Anthropometric data (height, weight, BMI, and waist circumference) and food intake (by 3-day weighed food records) will be measured at baseline and every 4 weeks thereafter. Subjective ratings of appetite will be recorded by participants at home on a weekly basis using validated visual analogue scales. At week 0 and at the end of the study (week 12), an ad libitum lunch buffet protocol for objective food intake measures and dual-energy X-ray absorptiometry (DXA) scan for body composition will be completed. Participants will be instructed not to change their exercise habits during the 12 week study. Glucose and insulin will be measured during an oral glucose tolerance test at weeks 0 and 12. Levels of lipids and CRP will be measured and inflammatory markers (adiponectin, leptin, TNF-α, IL-6 and IL-8) in the serum will be quantified using Milliplex kits. Mechanisms related to changes in gut microbiota, serum and fecal water metabolomics will be assessed.

DISCUSSION

Globally the development of functional foods and functional food ingredients are critically needed to curb the rise in metabolic disease. This project will assess the potential of yellow pea fibre to improve weight control via gut-mediated changes in metabolic health in overweight and obese adults.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT01719900) Registered October 23, 2012.

NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements.

Global gas chromatography/time-of-flight mass spectrometry (GC/TOFMS)-based metabonomic profiling of lyophilized human feces

Gas chromatography mass spectrometry (GC/MS)-based fecal metabonomics represents a powerful systems biology approach for elucidating metabolic biomarkers of lower gastrointestinal tract (GIT) diseases. Unlike metabolic profiling of fecal water, the profiling of complete fecal material remains under-explored. Here, a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) method was developed and validated for the global metabonomic profiling of human feces. Fecal and fecal water metabotypes were also profiled and compared. Additionally, the unclear influence of blood in stool on the fecal metabotype was investigated unprecedentedly. Eighty milligram of lyophilized feces was ultrasonicated with 1mL of methanol:water (8:2) for 30min, followed by centrifugation, drying of supernatant, oximation and trimethylsilylation for 45min. Lyophilized feces demonstrated a more comprehensive metabolic coverage than fecal water, based on the number of chromatographic peaks. Principal component analysis (PCA) indicated occult blood (1mgHb/g feces) exerted a negligible effect on the fecal metabotype. Conversely, a unique metabotype related to feces spiked with gross blood (100mgHb/g feces) was revealed (PCA, R(2)X=0.837, Q(2)=0.794), confirming the potential confounding effect of gross GIT bleeding on the fecal metabotype. This pertinent finding highlights the importance of prudent interpretation of fecal metabonomic data, particularly in GIT diseases where bleeding is prevalent.

Metabolic profiling predicts response to anti-tumor necrosis factor α therapy in patients with rheumatoid arthritis

Objective

Anti–tumor necrosis factor (anti-TNF) therapies are highly effective in rheumatoid arthritis (RA) and psoriatic arthritis (PsA), but a significant number of patients exhibit only a partial or no therapeutic response. Inflammation alters local and systemic metabolism, and TNF plays a role in this. We undertook this study to determine if the patient's metabolic fingerprint prior to therapy could predict responses to anti-TNF agents.

Methods

Urine was collected from 16 RA patients and 20 PsA patients before and during therapy with infliximab or etanercept. Urine metabolic profiles were assessed using nuclear magnetic resonance spectroscopy. Discriminating metabolites were identified, and the relationship between metabolic profiles and clinical outcomes was assessed.

Results

Baseline urine metabolic profiles discriminated between RA patients who did or did not have a good response to anti-TNF therapy according to European League Against Rheumatism criteria, with a sensitivity of 88.9% and a specificity of 85.7%, with several metabolites contributing (in particular histamine, glutamine, xanthurenic acid, and ethanolamine). There was a correlation between baseline metabolic profiles and the magnitude of change in the Disease Activity Score in 28 joints from baseline to 12 months in RA patients (P = 0.04). In both RA and PsA, urinary metabolic profiles changed between baseline and 12 weeks of anti-TNF therapy. Within the responders, urinary metabolite changes distinguished between etanercept and infliximab treatment.

Conclusion

The clear relationship between urine metabolic profiles of RA patients at baseline and their response to anti-TNF therapy may allow development of novel approaches to the optimization of therapy. Differences in metabolic profiles during treatment with infliximab and etanercept in RA and PsA may reflect distinct mechanisms of action.

Nutrigenomics and nutrigenetics in inflammatory bowel diseases

Inflammatory bowel diseases (IBD) including ulcerative colitis and Crohn's disease are chronically relapsing, immune-mediated disorders of the gastrointestinal tract. A major challenge in the treatment of IBD is the heterogenous nature of these pathologies. Both, ulcerative colitis and Crohn's disease are of multifactorial etiology and feature a complex interaction of host genetic susceptibility and environmental factors such as diet and gut microbiota. Genome-wide association studies identified disease-relevant single-nucleotide polymorphisms in approximately 100 genes, but at the same time twin studies also clearly indicated a strong environmental impact in disease development. However, attempts to link dietary factors to the risk of developing IBD, based on epidemiological observations showed controversial outcomes. Yet, emerging high-throughput technologies implying complete biological systems might allow taking nutrient-gene interactions into account for a better classification of patient subsets in the future. In this context, 2 new scientific fields, "nutrigenetics" and "nutrigenomics" have been established. "Nutrigenetics," studying the effect of genetic variations on nutrient-gene interactions and "Nutrigenomics," describing the impact of nutrition on physiology and health status on the level of gene transcription, protein expression, and metabolism. It is hoped that the integration of both research areas will promote the understanding of the complex gene-environment interaction in IBD etiology and in the long-term will lead to personalized nutrition for disease prevention and treatment. This review briefly summarizes data on the impact of nutrients on intestinal inflammation, highlights nutrient-gene interactions, and addresses the potential of applying "omic" technologies in the context of IBD.

Development of an untargeted metabolomics method for the analysis of human faecal samples using Cryptosporidium-infected samples

Faecal metabolite profiling, though in its infancy, allows for investigation of complex metabolic interactions between gastrointestinal infections or diseases and host health. In the present study, we describe a faecal metabolite extraction method for untargeted gas chromatography-mass spectrometry (GC-MS) analysis using Cryptosporidium positive and negative human faecal samples. The extraction method takes into account the varying faecal consistencies and quantities received for clinical diagnosis. Optimisation was carried out using different extraction solvents and on three different faecal quantities to determine the minimum amount of faecal sample required. The method was validated by untargeted GC-MS analysis on 8 Cryptosporidium positive and 8 Cryptosporidium negative human faecal samples, extracted using the optimised conditions. The method showed good extraction reproducibility with a relative standard deviation of 9.14%. Multivariate analysis of the GC-MS generated dataset showed distinct differences between profiles of Cryptosporidium positive and Cryptosporidium negative samples. The most notable differences included changes in amino acid, nitrogen and energy metabolism, demonstrating the association of infection with Cryptosporidium and altered permeability of the small intestine.

Quantitative metabolomic profiling of serum, plasma, and urine by (1)H NMR spectroscopy discriminates between patients with inflammatory bowel disease and healthy individuals

Serologic biomarkers for inflammatory bowel disease (IBD) have yielded variable differentiating ability. Quantitative analysis of a large number of metabolites is a promising method to detect IBD biomarkers. Human subjects with active Crohn’s disease (CD) and active ulcerative colitis (UC) were identified, and serum, plasma, and urine specimens were obtained. We characterized 44 serum, 37 plasma, and 71 urine metabolites by use of ¹H NMR spectroscopy and “targeted analysis” to differentiate between diseased and non-diseased individuals, as well as between the CD and UC cohorts. We used multiblock principal component analysis and hierarchical OPLS-DA for comparing several blocks derived from the same “objects” (e.g., subject) to examine differences in metabolites. In serum and plasma of IBD patients, methanol, mannose, formate, 3-methyl-2-oxovalerate, and amino acids such as isoleucine were the metabolites most prominently increased, whereas in urine, maximal increases were observed for mannitol, allantoin, xylose, and carnitine. Both serum and plasma of UC and CD patients showed significant decreases in urea and citrate, whereas in urine, decreases were observed, among others, for betaine and hippurate. Quantitative metabolomic profiling of serum, plasma, and urine discriminates between healthy and IBD subjects. However, our results show that the metabolic differences between the CD and UC cohorts are less pronounced.

Biofluid metabonomics using (1)H NMR spectroscopy: the road to biomarker discovery in gastroenterology and hepatology

Metabolic profiling or 'metabonomics' is an investigatory method that allows metabolic changes associated with the presence of an underlying pathological process to be investigated. Various biofluids can be utilized in the process but urine, serum and fecal extract are most pertinent to the investigation of gastrointestinal and hepatological disease. Nuclear magnetic resonance spectroscopy-based metabonomic research has the potential to generate novel noninvasive diagnostic tests, based on biomarkers of disease, which are simple and cost effective yet retain high sensitivity and specificity characteristics. The process involves a number of key steps, including sample collection, data acquisition, chemometric techniques and, finally, validation. This technique-driven review aims to demystify the metabonomics pathway, while also illustrating the potential of this technique with recent examples of its application in hepato-gastroenterological disease.

Metabolic phenotyping of the Crohn's disease-like IBD etiopathology in the TNF(ΔARE/WT) mouse model

The underlying biochemical consequences of inflammatory bowel disease (IBD) on the systemic and gastrointestinal metabolism have not yet been fully elucidated but could help to better understand the disease pathogenesis and to identify tissue-specific markers associated with the different disease stages. Here, we applied a metabonomic approach to monitor metabolic events associated with the gradual development of Crohn's disease (CD)-like ileitis in the TNF(ΔARE/WT) mouse model. Metabolic profiles of different intestinal compartments from the age of 4 up to 24 weeks were generated by combining proton nuclear magnetic resonance ((1)H NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). From 8 weeks onward, mice developed CD similar to the immune and tissue-related phenotype of human CD with ileal involvement, including ileal histological abnormalities, reduced fat mass and body weight, as well as hallmarks of malabsorption with higher energy wasting. The metabonomic approach highlighted shifts in the intestinal lipid metabolism concomitant to the histological onset of inflammation. Moreover, the advanced disease status was characterized by a significantly altered metabolism of cholesterol, triglycerides, phospholipids, plasmalogens, and sphingomyelins in the inflamed tissue (ileum) and the adjacent intestinal parts (proximal colon). These results describe different biological processes associated with the disease onset, including modifications of the general cell membrane composition, alteration of energy homeostasis, and finally the generation of inflammatory lipid mediators. Taken together, this provides novel insights into IBD-related alterations of specific lipid-dependant processes during inflammatory states.

Functional metagenomic investigations of the human intestinal microbiota

The human intestinal microbiota encode multiple critical functions impacting human health, including metabolism of dietary substrate, prevention of pathogen invasion, immune system modulation, and provision of a reservoir of antibiotic resistance genes accessible to pathogens. The complexity of this microbial community, its recalcitrance to standard cultivation, and the immense diversity of its encoded genes has necessitated the development of novel molecular, microbiological, and genomic tools. Functional metagenomics is one such culture-independent technique, used for decades to study environmental microorganisms, but relatively recently applied to the study of the human commensal microbiota. Metagenomic functional screens characterize the functional capacity of a microbial community, independent of identity to known genes, by subjecting the metagenome to functional assays in a genetically tractable host. Here we highlight recent work applying this technique to study the functional diversity of the intestinal microbiota, and discuss how an approach combining high-throughput sequencing, cultivation, and metagenomic functional screens can improve our understanding of interactions between this complex community and its human host.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

Identification of urinary biomarkers of colon inflammation in IL10-/- mice using Short-Column LCMS metabolomics

The interleukin-10-deficient (IL10^−/−) mouse develops colon inflammation in response to normal intestinal microflora and has been used as a model of Crohn's disease. Short-Column LCMS metabolite profiling of urine from IL10^−/− and wild-type (WT) mice was used, in two independent experiments, to identify mass spectral ions differing in intensity between these two genotypes. Three differential metabolites were identified as xanthurenic acid and as the glucuronides of xanthurenic acid and of α-CEHC (2,5,7,8-tetramethyl-2-(2′-carboxyethyl)-6-hydroxychroman). The significance of several differential metabolites as potential biomarkers of colon inflammation was evaluated in an experiment which compared metabolite concentrations in IL10^−/− and WT mice housed, either under conventional conditions and dosed with intestinal microflora, or maintained under specific pathogen-free (SPF) conditions. Concentrations of xanthurenic acid, α-CEHC glucuronide, and an unidentified metabolite m/z 495⁻/497⁺ were associated with the degree of inflammation in IL10^−/− mice and may prove useful as biomarkers of colon inflammation.

Quantitative metabolomic profiling of serum and urine in DSS-induced ulcerative colitis of mice by (1)H NMR spectroscopy

Quantitative profiling of a large number of metabolic compounds is a promising method to detect biomarkers in inflammatory bowel diseases (IBD), such as ulcerative colitis (UC). We induced an experimental form of UC in mice by treatment with dextran sulfate sodium (DSS) and characterized 53 serum and 69 urine metabolites by use of (1)H NMR spectroscopy and quantitative ("targeted") analysis to distinguish between diseased and healthy animals. Hierarchical multivariate orthogonal partial least-squares (OPLS) models were developed to detect and predict separation of control and DSS-treated mice. DSS treatment resulted in weight loss, colonic inflammation, and increase in myeloperoxidase activity. Metabolomic patterns generated from the OPLS data clearly separated DSS-treated from control mice with a slightly higher predictive power (Q(2)) for serum (0.73) than urine (0.71). During DSS colitis, creatine, carnitine, and methylamines increased in urine while in serum, maximal increases were observed for ketone bodies, hypoxanthine, and tryptophan. Antioxidant metabolites decreased in urine whereas in serum, glucose and Krebs cycle intermediates decreased strongly. Quantitative metabolic profiling of serum and urine thus discriminates between healthy and DSS-treated mice. Analysis of serum or urine seems to be equally powerful for detecting experimental colitis, and a combined analysis offers only a minor improvement.