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.

One approach to controlling type 2 diabetes (T2D) is to lower postprandialglucose spikesby slowing down the digestion of carbohydrates and the absorption of glucose in the small intestine. The consumption of walnuts is associated with a reduced risk of chronic diseases such as T2D, suggested to be partly due to the high content of (poly)phenols. This study evaluated, for the first time, the inhibitory effect of a (poly)phenol-rich walnut extract on human carbohydrate digesting enzymes (salivary and pancreatic α-amylases, brush border sucrase-isomaltase) and on glucose transport across fully differentiated human intestinal Caco-2/TC7 monolayers. The walnut extract was rich in multiple (poly)phenols (70 % w/w) as analysed by Folin-Ciocalteau and by LCMS. It exhibited potent inhibition of both human salivary (IC₅₀: 32.2 ± 2.5 µg walnut (poly)phenols (WP)/mL) and pancreatic (IC₅₀: 56.7 ± 1.7 µg WP/mL) α-amylases, with weaker effects on human sucrase (IC₅₀: 990 ± 20 µg WP/mL), maltase (IC₅₀: 1300 ± 80 µg WP/mL), and isomaltase (IC₂₅: 830 ± 60 µg WP/mL) activities. Selected individual walnut (poly)phenols inhibited human salivary α-amylase in the order: 1,3,4,6-tetragalloylglucose > ellagic acid pentoside > 1,2,6-tri-O-galloyl-β-D-glucopyranose, with no inhibition by ellagic acid, gallic acid and 4-O-methylgallic acid. The (poly)phenol-rich walnut extract also attenuated (up to 59 %) the transfer of 2-deoxy-D-glucose across differentiated Caco-2/TC7 cell monolayers. This is the first report on the effect of (poly)phenol-rich extracts from any commonly-consumed nut kernel on any human starch-digesting enzyme, and suggests a mechanism through which walnut consumption may lower postprandial glucose spikes and contribute to their proposed health benefits.

Recent years have seen an increase in research focusing on the amelioration of apple pomace waste for use in the food and nutraceutical industries. Much of this work has concentrated on the characterisation of the polyphenol composition of apple pomace materials to determine their role in conferring nutritional and health benefits. Although apples contain substantial quantities of polymeric procyanidins (condensed tannins), this class of compounds has received limited attention in apple research. This study quantified the polymeric procyanidins in apple pomace extracts using a rapid, methyl-cellulose precipitation (MCP) approach for the first time. In addition, a non-targeted metabolomics approach was applied to determine the most abundant phenolic classes present. Polymeric procyanidins were found to be the most abundant type of polyphenol in apple pomace extracts and were generally oligomeric in nature. Multivariate statistical analysis revealed that the ferric-reducing antioxidant power (FRAP) was most strongly correlated with the polymeric procyanidin concentration. Noting that polymeric procyanidins may not cross the cell layer to exert antioxidant activity in vivo, their presence in apple pomace extracts may therefore overestimate the FRAP. This work highlights the importance of polymeric procyanidins in the phenolic diversity of apple pomaces, and it is proposed that in future studies, rapid MCP assays may be used for their quantification.

The aim of this preliminary study was to identify potential colour components, volatile and sensory attributes that could discriminate Pinot noir wines from five Australian winegrowing regions (Adelaide Hills, Yarra Valley, Mornington Peninsula, Northern and Southern Tasmania). The sensory analysis consisted of the Pivot© Profile method that was performed by wine professionals. A headspace solid-phase microextraction-gas chromatography-mass spectrometry method was used to quantify multiple volatile compounds, while the Modified Somers method was used for colour characterisation. Analysis of data suggested ethyl decanoate, ethyl 2-methylpropanoate, ethyl 2-methylbutanoate, in addition to decanoic acid as important contributors to the discrimination between regions. Similarly, wine hue, chemical age indices, total anthocyanin, and (%) non-bleachable pigment also discriminated wines between regions. The sensory analysis showed that wines from Mornington Peninsula were associated with the ‘red fruits’ aroma, ‘acidic’, and ‘astringency’ palate descriptors, while those from Adelaide Hills were associated with the ‘brown’ colour attribute. This study indicates regionality is a strong driver of aroma typicity of wine.

Background: Effects on insulin sensitivity and postprandial glycemia through enzyme inhibition and regulation of glucose transport have been extensively researched; however, the role of sugarcane (poly)phenols remain underexplored. Methods: In a randomized, placebo-controlled, single-blinded crossover study, 12 healthy participants consumed a bread-based meal containing 50 g of carbohydrates, supplemented with either 0.5% or 5% liquid PRSE or sugar-balanced controls. Glucose and plasma insulin levels were assessed over 180 min. The extract was evaluated for its inhibitory effect on human α-amylases (salivary and pancreatic) and α-glucosidases (sucrase, maltase, and isomaltase) utilizing solid PRSE. Results: The postprandial glucose and insulin responses to bread sandwiches in healthy volunteers remained unchanged by both PRSE dosages. High-dose treatment reduced the Matsuda index by 9.8%, perhaps due to a subtle alteration in whole-body insulin sensitivity. Low-dose intervention postponed the insulin peak by 30 min without altering HOMA-IR. In vitro, PRSE diminished sucrase activity by 67% (IC₅₀ = 425.8 ± 18.7 µg/mL) and lowered maltase and isomaltase activity by 40% (IC₂₅ = 876.3 ± 131 and 960.6 ± 95.2 µg/mL, respectively). It enhanced the activity of human salivary and pancreatic α-amylases. Conclusion: In healthy people, acute PRSE supplementation had a minor impact on postprandial glucose and insulin levels. Low-dose PRSE postponed the insulin peak, whereas high-dose PRSE reduced Matsuda index potentially via α-amylase activation, suggesting a modest alteration in whole-body insulin sensitivity without significantly changing the glucose or insulin response. In vitro, PRSE exhibited modest inhibition of human α-glucosidases.

Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.