A Simultaneous Metabolic Profiling and Quantitative Multimetabolite Metabolomic Method for Human Plasma Using Gas-Chromatography Tandem Mass Spectrometry.

For the first time it is possible to simultaneously collect targeted and nontargeted metabolomics data from plasma based on GC with high scan speed tandem mass spectrometry (GC-MS/MS). To address the challenge of getting broad metabolome coverage while quantifying known biomarker compounds in high-throughput GC-MS metabolomics, we developed a novel GC-MS/MS metabolomics method using a high scan speed (20 000 Da/second) GC-MS/MS that enables simultaneous data acquisition of both nontargeted full scan and targeted quantitative tandem mass spectrometry data. The combination of these two approaches has hitherto not been demonstrated in metabolomics. This method allows reproducible quantification of at least 37 metabolites using multiple reaction monitoring (MRM) and full mass spectral scan-based detection of 601 reproducible metabolic features from human plasma. The method showed good linearity over normal concentrations in plasma (0.06-343 to 0.86-4800 µM depending on the metabolite) and good intra- and interbatch precision (0.9-16.6 and 2.6-29.6% relative standard deviation). Based on the parameters determined for this method, targeted quantification using MRM can be expanded to cover at least 508 metabolites while still collecting full scan data. The new simultaneous targeted and nontargeted metabolomics method enables more sensitive and accurate detection of predetermined metabolites and biomarkers of interest, while still allowing detection and identification of unknown metabolites. This is the first validated GC-MS/MS metabolomics method with simultaneous full scan and MRM data collection, and clearly demonstrates the utility of GC-MS/MS with high scanning rates for complex analyses.

A high-throughput method for liquid chromatography-tandem mass spectrometry determination of plasma alkylresorcinols, biomarkers of whole grain wheat and rye intake.

Plasma alkylresorcinols are increasingly analyzed in cohort studies to improve estimates of whole grain intake and their relationship with disease incidence. Current methods require large volumes of solvent (>10 ml/sample) and have relatively low daily sample throughput. We tested five different supported extraction methods for extracting alkylresorcinols from plasma and improved a normal-phase liquid chromatography coupled to a tandem mass spectrometer method to reduce sample analysis time. The method was validated and compared with gas chromatography-mass spectrometry analysis. Sample preparation with HybridSPE supported extraction was most effective for alkylresorcinol extraction, with recoveries of 77-82% from 100 µl of plasma. The use of 96-well plates allowed extraction of 160 samples per day. Using a 5-cm NH2 column and heptane reduced run times to 3 min. The new method had a limit of detection and limit of quantification equivalent to 1.1-1.8 nmol/L and 3.5-6.1 nmol/L plasma, respectively, for the different alkylresorcinol homologues. Accuracy was 93-105%, and intra- and inter-batch precision values were 4-18% across different plasma concentrations. This method makes it possible to quantify plasma alkylresorcinols in 100 µl of plasma at a rate of at least 160 samples per day without the need for large volumes of organic solvents.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples.

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

High-throughput metabolomics for the design and validation of a diauxic shift model.

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

Identification and quantification of even and odd chained 5-n alkylresorcinols, branched chain-alkylresorcinols and methylalkylresorcinols in Quinoa (Chenopodium quinoa).

Quinoa is a pseudocereal grown in the Andean region of South America that is of increasing interest worldwide as an alternative staple food. We have detected a complex mixture of both odd- and even-alkyl chain alkylresorcinols (AR), branched-chain alkylresorcinols (bcAR) and methylalkylresorcinols (mAR) in ethyl acetate extracts of quinoa. We quantified the content of AR in 17 commercial samples of quinoa, and found that the mean±SD content of AR was 58±16µg/g, bcAR was 182±52µg/g, and mAR was 136±40µg/g. AR from quinoa could also be detected in plasma after eating quinoa, indicating that some of these unique AR could be used as biomarkers of quinoa intake in humans. Further work is required to understand the role of these ARs in the quinoa plant and whether any of the novel ARs may be of particular interest in human nutrition.

Splenic Immune Response Is Down-Regulated in C57BL/6J Mice Fed Eicosapentaenoic Acid and Docosahexaenoic Acid Enriched High Fat Diet.

Dietary n-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are associated with reduction of inflammation, although the mechanisms are poorly understood, especially how the spleen, as a secondary lymphoid organ, is involved. To investigate the effects of EPA and DHA on spleen gene expression, male C57BL/6J mice were fed high fat diets (HFD) differing in fatty acid composition, either based on corn oil (HFD-CO), or CO enriched with 2 g/100 g EPA and DHA (HFD-ED), for eight weeks. Spleen tissue was analyzed using transcriptomics and for fatty acids profiling. Biological processes (BPs) related to the immune response, including T-cell receptor signaling pathway, T-cell differentiation and co-stimulation, myeloid dendritic cell differentiation, antigen presentation and processing, and the toll like receptor pathway were downregulated by HFD-ED compared with control and HFD-CO. These findings were supported by the down-regulation of NF-κB in HFD-ED compared with HFD-CO fed mice. Lower phospholipid arachidonic acid levels in HFD-ED compared with HFD-CO, and control mice suggest attenuation of pathways via prostaglandins and leukotrienes. The HFD-ED also upregulated BPs related to erythropoiesis and hematopoiesis compared with control and HFD-CO fed mice. Our findings suggest that EPA and DHA down-regulate the splenic immune response induced by HFD-CO, supporting earlier work that the spleen is a target organ for the anti-inflammatory effects of these n-3 fatty acids.

Herring and chicken/pork meals lead to differences in plasma levels of TCA intermediates and arginine metabolites in overweight and obese men and women.

SCOPE: What effect does replacing chicken or pork with herring as the main dietary source of protein have on the human plasma metabolome? METHOD AND RESULTS: A randomised crossover trial with 15 healthy obese men and women (age 24-70 years). Subjects were randomly assigned to four weeks of herring diet or a reference diet of chicken and lean pork, five meals per week, followed by a washout and the other intervention arm. Fasting blood serum metabolites were analysed at 0, 2 and 4 weeks for eleven subjects with available samples, using GC-MS based metabolomics. The herring diet decreased plasma citrate, fumarate, isocitrate, glycolate, oxalate, agmatine and methyhistidine and increased asparagine, ornithine, glutamine and the hexosamine glucosamine. Modelling found that the tricarboxylic acid cycle, glyoxylate, and arginine metabolism were affected by the intervention. The effect on arginine metabolism was supported by an increase in blood nitric oxide in males on the herring diet. CONCLUSION: The results suggest that eating herring instead of chicken and lean pork leads to important metabolic effects, particularly on energy and amino acid metabolism. Our findings support the hypothesis that there are metabolic effects of herring intake unrelated to the long chain n-3 polyunsaturated fatty acid content.

Eicosapentaenoic and Docosahexaenoic Acid-Enriched High Fat Diet Delays Skeletal Muscle Degradation in Mice.

Low-grade chronic inflammatory conditions such as ageing, obesity and related metabolic disorders are associated with deterioration of skeletal muscle (SkM). Human studies have shown that marine fatty acids influence SkM function, though the underlying mechanisms of action are unknown. As a model of diet-induced obesity, we fed C57BL/6J mice either a high fat diet (HFD) with purified marine fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (HFD-ED), a HFD with corn oil, or normal mouse chow for 8 weeks; and used transcriptomics to identify the molecular effects of EPA and DHA on SkM. Consumption of ED-enriched HFD modulated SkM metabolism through increased gene expression of mitochondrial ß-oxidation and slow-fiber type genes compared with HFD-corn oil fed mice. Furthermore, HFD-ED intake increased nuclear localization of nuclear factor of activated T-cells (Nfatc4) protein, which controls fiber-type composition. This data suggests a role for EPA and DHA in mitigating some of the molecular responses due to a HFD in SkM. Overall, the results suggest that increased consumption of the marine fatty acids EPA and DHA may aid in the prevention of molecular processes that lead to muscle deterioration commonly associated with obesity-induced low-grade inflammation.

The role of oxygen in the liquid fermentation of wheat bran.

The extensive use of wheat bran as a food ingredient is limited due to its bitter taste and hard texture. To overcome these, some preprocessing methods, such as fermentation with yeast and lactic acid bacteria or enzymatic treatments have been proposed. The current work studied microbial communities, acidification, ethanol formation and metabolite profile of wheat bran fermented in either aerated or anaerobic conditions. In aerated conditions, yeasts grew better and the production of organic acids was smaller, and hence pH was higher. In anaerobic conditions, lactic acid bacteria and endogenous heterotrophic bacteria grew better. Aeration had a large effect on the sourdough metabolite profile, as analyzed by UPLC-qTOF-MS. Anaerobic conditions induced degradation of ferulic and caffeic acids, whereas the amount of sinapic acid increased. Aeration caused degradation of amino acids and hydroxycinnamic acid derivatives of polyamines. The results suggest that the control of oxygen could be used for tailoring the properties of bran sourdough.