The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Impact of wheat aleurone on biomarkers of cardiovascular disease, gut microbiota and metabolites in adults with high body mass index: a double‑blind, placebo‑controlled, randomized clinical trial

Purpose Aleurone is a cereal bran fraction containing a variety of beneficial nutrients including polyphenols, fibers, minerals and vitamins. Animal and human studies support the beneficial role of aleurone consumption in reducing cardiovascular disease (CVD) risk. Gut microbiota fiber fermentation, polyphenol metabolism and betaine/choline metabolism may in part contribute to the physiological effects of aleurone. As primary objective, this study evaluated whether wheat aleurone supplemented foods could modify plasma homocysteine. Secondary objectives included changes in CVD biomarkers, fecal microbiota composition and plasma/urine metabolite profiles. Methods A parallel double-blind, placebo-controlled and randomized trial was carried out in two groups of obese/overweight subjects, matched for age, BMI and gender, consuming foods supplemented with either aleurone (27 g/day) (AL, n = 34) or cellulose (placebo treatment, PL, n = 33) for 4 weeks. Results No significant changes in plasma homocysteine or other clinical markers were observed with either treatment. Dietary fiber intake increased after AL and PL, animal protein intake increased after PL treatment. We observed a significant increase in fecal Bifidobacterium spp with AL and Lactobacillus spp with both AL and PL, but overall fecal microbiota community structure changed little according to 16S rRNA metataxonomics. Metabolomics implicated microbial metabolism of aleurone polyphenols and revealed distinctive biomarkers of AL treatment, including alkylresorcinol, cinnamic, benzoic and ferulic acids, folic acid, fatty acids, benzoxazinoid and roasted aroma related metabolites. Correlation analysis highlighted bacterial genera potentially linked to urinary compounds derived from aleurone metabolism and clinical parameters. Conclusions Aleurone has potential to modulate the gut microbial metabolic output and increase fecal bifidobacterial abundance. However, in this study, aleurone did not impact on plasma homocysteine or other CVD biomarkers. Trial Registration The study was registered at ClinicalTrials.gov (NCT02067026) on the 17th February 2014

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental AE tiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount

Metabolic phenotyping of diet and dietary Intake

Nutrition provides the building blocks for growth, repair, and maintenance of the body and is key to maintaining health. Exposure to fast foods, mass production of dietary components, and wider importation of goods have challenged the balance between diet and health in recent decades, and both scientists and clinicians struggle to characterize the relationship between this changing dietary landscape and human metabolism with its consequent impact on health. Metabolic phenotyping of foods, using high-density data-generating technologies to profile the biochemical composition of foods, meals, and human samples (pre- and postfood intake), can be used to map the complex interaction between the diet and human metabolism and also to assess food quality and safety. Here, we outline some of the techniques currently used for metabolic phenotyping and describe key applications in the food sciences, ending with a broad outlook at some of the newer technologies in the field with a view to exploring their potential to address some of the critical challenges in nutritional science

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Metabolic transformation of apple polyphenols in human body

Rationale: Fruit and vegetables are claimed to have beneficial effect on human health mostly due to their high polyphenol (PP) content. (1) Regular consumption is protective against age related diseases and different forms of cancer (2). As PP are largely metabolized both by the human organism and gut microbiota, identification of the forms of metabolites and the kinetics of their appearance into the circulation is essential for understanding their possible bioactivity in humans. Methods: In order to evaluate absorption and transformation of apple polyphenols, a human single-dose crossover controlled blind experiment was designed. In 2 different sessions 12 subjects were supplemented with apple juice (1 g/l total PF) or polyphenol enriched apple juice (4 g/l total PF). Urine and plasma samples were collected at different time points and analyzed using an untargeted metabolomics approach. Results: Scarcely metabolized polyphenols were recognized as potential biomarkers. These compounds showed two different kinetic patterns. Epicatechin methyl sulfate, ferulic acid sulfate and phloretin glucuronide reached their maximal concentration 1 hour after apple juice supplementation. While, the methyl, sulfate and glucuronide conjugates of valerolactons had their peak concentration 5 hours after the supplementation. The concentration of the majority of the biomarkers showed an increase four times greater in high PP diet than in low. Conclusion: Untargeted metabolomics allowed identification of biomarkers of apple consumption and demonstrated that an increase in polyphenol intake corresponds to an increase of circulating metabolites within the limits of ‘normal’ consumption. Thus, if the beneficial effects of these compounds are confirmed, it might prove beneficial to increase their plasma concentration by increasing their intake or choosing polyphenols richer food