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

An open software development-based ecosystem of R packages for metabolomics data analysis

A frequent problem with scientific research software is the lack of support, maintenance and further development. In particular, development by a single researcher can easily result in orphaned software packages, especially if combined with poor documentation or lack of adherence to open software development standards. The RforMassSpectrometry initiative aims to develop an efficient and stable infrastructure for mass spectrometry (MS) data analysis. As part of this initiative, a growing ecosystem of R software packages is being developed covering different aspects of metabolomics and proteomics data analysis. To avoid the aforementioned problems, community contributions are fostered, and open development, documentation and long-term support emphasized. At the heart of the package ecosystem is the Spectra package that provides the core infrastructure to handle and analyze MS data. Its design allows easy expansion to support additional file or data formats including data representations with minimal memory footprint or remote data access. The xcms package for LC-MS data preprocessing was updated to reuse this infrastructure, enabling now also the analysis of very large, or remote, data. This integration simplifies in addition complete analysis workflows which can include the MsFeatures package for compounding, and the MetaboAnnotation package for annotation of untargeted metabolomics experiments. Public annotation resources can be easily accessed through packages such as MsBackendMassbank, MsBackendMgf, MsBackendMsp or CompoundDb, the latter also allowing to create and manage lab-specific compound databases. Finally, the MsCoreUtils and MetaboCoreUtils packages provide efficient implementations of commonly used algorithms, designed to be re-used in other R packages. Ultimately, and in contrast to a monolithic software design, the package ecosystem enables to build customized, modular, and reproducible analysis workflows. Future development will focus on improved data structures and analysis methods for chromatographic data, and better interoperability with other open source softwares including a direct integration with Python MS libraries

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

The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification

Compound identification is the main hurdle in LC-HRMS-based metabolomics, given the high number of ‘unknown’ metabolites. In recent years, numerous in silico fragmentation simulators have been developed to simplify and improve mass spectral interpretation and compound annotation. Nevertheless, expert mass spectrometry users and chemists are still needed to select the correct entry from the numerous candidates proposed by automatic tools, especially in the plant kingdom due to the huge structural diversity of natural compounds occurring in plants. In this work, we propose the use of a supervised machine learning approach to predict molecular substructures from isotopic patterns, training the model on a large database of grape metabolites. This approach, called ‘Compounds Characteristics Comparison’ (CCC) emulates the experience of a plant chemist who ‘gains experience’ from a (proof-of-principle) dataset of grape compounds. The results show that the CCC approach is able to predict with good accuracy most of the sub-structures proposed. In addition, after querying MS/MS spectra in Metfrag 2.2 and applying CCC predictions as scoring terms with real data, the CCC approach helped to give a better ranking to the correct candidates, improving users’ confidence in candidate selection. Our results demonstrated that the proposed approach can complement current identification strategies based on fragmentation simulators and formula calculators, assisting compound identification. The CCC algorithm is freely available as R package (https://github.com/lucanard/CCC) which includes a seamless integration with Metfrag. The CCC package also permits uploading additional training data, which can be used to extend the proposed approach to other systems biological matrices

Targeted metabolomics approach for the characterization of wild Vitis genotypes

Wine is one of the most popular beverages in the world which is exclusively produced from Vitis vinifera varieties due to the superior quality of their grapes. However, today a large amount of fungicides and pesticides are used in viticulture in order to protect grapevine from their pathogens with a strong impact on environment and human health. For this reason, research has focused on the development of new interspecific hybrids using wild American genotypes in order to introgress their resistant traits to pests and diseases in V. vinifera cultivars. Despite this, little is known regarding the metabolic profile of wild genotypes. The aim of this work was to characterize the grape composition of two hybrids varieties (41B and K5BB) and five wild genotypes (V. andersonii, V. arizonica Texas, V. champinii, V. cinerea and V. californica) in six different vintages. Also V. vinifera cultivars (Pinot Noir and Cabernet Sauvignon) were taken into consideration as references. A targeted metabolomics strategy was used for the investigation of simple phenolic compounds, anthocyanins, proanthocyanidins and lipids. In particular, grape skins anthocyanins were analyzed using LC-DAD [1]. Three genotypes contained diglucosides for less than 5% of the total anthocyanins while four genotypes accounted for more than 40% of the total. LC-MS/MS methods were used for the study of phenolic compounds and lipids [2,3]. The results obtained showed that three wild genotypes contained higher average amount of total phenols and that the one out of seven non-V. vinifera genotypes contained a higher content of total lipids compared to V. vinifera cultivars. Analysis of proanthocyaninds by LC-MS showed that wild genotypes were mainly rich in oligomers and short-chain polymers [4]. Heatmap analysis was used to point out the differences in genotypes’ content for the different metabolites studied. This work demonstrates the existence of a significant genotypic diversity between the grape composition of V. vinifera and other species. The information gained could be very useful for the future grapevine breeding

Anthocyanin profiles of non-V. vinifera genotypes

Anthocyanins are the main compounds responsible for the color of red grapes and wine. They play a key role in determining the quality of grape berries. Today, wild Vitis genotypes represent an important source of genetic resistance to biotic and abiotic stresses. In fact, these genotypes are used in breeding programs with V. vinifera in order to improve V. vinifera cultivars resistance to phylloxera and powdery mildew diseases (Yang et al. 2014). The resulting inter-specific hybrids present diglucoside anthocyanins which are characteristic of wild Vitis genotypes. Since the acceptable limits of diglucoside contained in wine is 15 mg/L (J. A. Considine, E. Frankish 2014), the aim of this work was to study the anthocyanin profiles of 9 wild genotypes collected in four different vintages. Grape skin anthocyanins were analyzed by HPLC-DAD and twenty different anthocyanins were detected and quantified. Diglucoside derivates were not detected in all wild Vitis genotypes. Out of the nine genotypes analyzed one had no diglucoside anthocyanins. In three genotypes less than 5% of the total amount of anthocyanins detected were diglucosides (from 11,6 to 56,9 mg/kg). In the five remaining genotypes more than 50% of the total were found to be diglucosides (from 522,1 to 1829,2 mg/kg). Cluster analysis showed that each genotype had characteristic distributions of anthocyanins consistent between harvest years

The metabolomic profile of red non-V. vinifera genotypes

Wild American genotypes represent an important part of the Vitis germplasm in relation to grape improvement. Today, these genotypes are currently involved in breeding programmes in order to introgress traits resistant to pests and diseases in V. vinifera cultivars. Use of wild genotypes in breeding programmes in order to introgress traits resistant to pests and diseases in V. vinifera cultivars has been actively pursued. Nevertheless, the metabolic composition of their grapes has not been widely investigated. This study aimed to explore in detail the metabolomic profile in terms of simple phenolic, proanthocyanidin, anthocyanin and lipid compounds in two hybrids and five American genotypes. The results were compared with those of two V. vinifera cultivars. A multi-targeted metabolomics approach using a combination of LC-MS and LC-DAD methods was used to separate and identify and quantify 124 selected metabolites. The genotypes studied showed considerable variability in the metabolomic profile according to the grape composition of V. vinifera and other Vitis genotypes. As regards the composition of anthocyanins, not all wild genotypes contained both mono- and di-glucoside derivatives. Wild genotype 41B and V. vinifera cultivars contained only monoglucoside anthocyanins. The proanthocyanidins of non-V. vinifera genotypes were mainly rich in oligomers and short-chain polymers. The analysis of lipids in wild Vitis genotypes, here reported for the first time, showed the existence of a certain diversity in their lipid composition was observed in non-V. vinifera genotypes suggesting a strong influence of the environmental conditions on the general lipid pattern