Analysing postprandial amino acid responses in crossover studies with the aaresponse package for R.

Nowadays, a healthier and more sustainable lifestyle is the subject of much research. One example is the use of crossover trials to investigate the uptake of proteins, usually from alternatives to animal-based sources, by healthy volunteers. The data analysis is complex and requires many decisions on the part of the scientists involved. Such a process can be streamlined and made more objective and reproducible through bespoke software. This paper describes such a software package, aaresponse , for the R environment, available as open source. It features ample visualization functions, supports consistent curation strategies, and compares amino acid uptake of different protein meals (interventions) through the use of mixed models analysing parameters of interest like the area under the curve (AUC). The defining feature is the use of parametric curves to fit the amino acid levels over time, increasing the robustness of the approach and allowing for more strict quality control strategies.

Unravelling the seasonal dynamics of the metabolome of white asparagus spears using untargeted metabolomics.

INTRODUCTION: The white asparagus season lasts 4 months while the harvest period per field is 8 weeks. Different varieties are better suited for harvesting early or late in the season. Little is known of the dynamics of secondary metabolites of white asparagus during the production season. OBJECTIVE: Characterization of the metabolome of white asparagus spears covering volatile and non-volatile composition in relation to quality aspects. METHODS: Eight varieties, harvested repeatedly during two consecutive seasons were analysed following an untargeted metabolomics workflow using SPME GC-MS and LC-MS. Linear regression, cluster and network analyses were used to explore the profile dynamics, unravel patterns and study the influence of genotype and environment. RESULTS: The metabolite profiles were influenced by the harvest moment and genetic background. Metabolites that significantly changed over time were distributed across seven clusters based on their temporal patterns. Two clusters including monoterpenes, benzenoids and saponins showed the most prominent seasonal changes. The changes depicted by the other five clusters were mainly ≤ 2-fold relative to the harvest start. Known asparagus aroma compounds were found to be relatively stable across the season/varieties. Heat-enhanced cultivation appeared to yield spears early in season with a similar metabolome to those harvested later. CONCLUSION: The dynamics of the white asparagus metabolome is influenced by a complex relationship between the onset of spear development, the moment of harvest and the genetic background. The typical perceived asparagus flavour profile is unlikely to be significantly affected by these dynamics.

Comparative safety assessment of genetically modified crops: focus on equivalence with reference varieties could contribute to more efficient and effective field trials.

The initial compositional analysis of plants plays an important role within the internationally harmonized comparative safety assessment approach for genetically modified plants. Current EFSA guidance prescribes two types of comparison, namely difference tests with regard to a conventional comparator or control, and equivalence tests with regard to a collection of commercial reference varieties. The experience gained so far shows that most of the statistically significant differences between the test and control can be discounted based on the fact that they are still within equivalence limits of reference varieties with a presumed history of safe use. Inclusion of a test variety and reference varieties into field trial design, and of the statistical equivalence test would already suffice for the purpose of finding relevant parameters that warrant further assessment, hence both the inclusion of a conventional counterpart and the performance of difference testing can be omitted. This would also allow for the inclusion of safety testing regimes into plant variety testing VCU (value for cultivation and use) or other, independent variety trials.

A controlled human intervention trial to study protein quality by amino acid uptake kinetics with the novel Lemna protein concentrate as case study.

A human intervention trial was conducted to study amino acid uptake of the novel Lemna protein concentrate (LPC) in comparison to whey (WPC). The study was a cross-over, double-blind, controlled trial in which 12 healthy participants received 20 grams of LPC and WPC in randomised order. The LPC consumption resulted in a significant lower postprandial increase in almost all individual amino acids, total amino acid (TAA) and total essential amino acids (TEAA) compared to WPC based on area under the curve (AUC) calculations. When the AUC after WPC consumption was set at 100%, LPC showed a relative AUC of 60.4% for TAA and 66.3% for the TEAA. Interindividual variation for LPC was high with an uptake of TEAA of LPC compared to WPC ranging from 18.2 to 94.2%. Human intervention trials can partly replace animal trials as they fully reflect the human situation and provide estimates on individual variations.

A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics.

INTRODUCTION: When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr-Purcell-Meiboom-Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES: Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS: We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares-Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS: We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation (< 30%) in replicate samples. CONCLUSION: Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.

Correction to: A comparison of high­throughput plasma NMR protocols for comparative untargeted metabolomics.

Following publication of the original article, the authors would like to correct a sentence in the paragraph "1H-NMR spectra were recorded at 298 K…" under the heading "NMR experiments".

Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients.

The implementation of whole-exome sequencing in clinical diagnostics has generated a need for functional evaluation of genetic variants. In the field of inborn errors of metabolism (IEM), a diverse spectrum of targeted biochemical assays is employed to analyze a limited amount of metabolites. We now present a single-platform, high-resolution liquid chromatography quadrupole time of flight (LC-QTOF) method that can be applied for holistic metabolic profiling in plasma of individual IEM-suspected patients. This method, which we termed "next-generation metabolic screening" (NGMS), can detect >10,000 features in each sample. In the NGMS workflow, features identified in patient and control samples are aligned using the "various forms of chromatography mass spectrometry (XCMS)" software package. Subsequently, all features are annotated using the Human Metabolome Database, and statistical testing is performed to identify significantly perturbed metabolite concentrations in a patient sample compared with controls. We propose three main modalities to analyze complex, untargeted metabolomics data. First, a targeted evaluation can be done based on identified genetic variants of uncertain significance in metabolic pathways. Second, we developed a panel of IEM-related metabolites to filter untargeted metabolomics data. Based on this IEM-panel approach, we provided the correct diagnosis for 42 of 46 IEMs. As a last modality, metabolomics data can be analyzed in an untargeted setting, which we term "open the metabolome" analysis. This approach identifies potential novel biomarkers in known IEMs and leads to identification of biomarkers for as yet unknown IEMs. We are convinced that NGMS is the way forward in laboratory diagnostics of IEMs.

Dissimilarity based Partial Least Squares (DPLS) for genomic prediction from SNPs.

BACKGROUND: Genomic prediction (GP) allows breeders to select plants and animals based on their breeding potential for desirable traits, without lengthy and expensive field trials or progeny testing. We have proposed to use Dissimilarity-based Partial Least Squares (DPLS) for GP. As a case study, we use the DPLS approach to predict Bacterial wilt (BW) in tomatoes using SNPs as predictors. The DPLS approach was compared with the Genomic Best-Linear Unbiased Prediction (GBLUP) and single-SNP regression with SNP as a fixed effect to assess the performance of DPLS. RESULTS: Eight genomic distance measures were used to quantify relationships between the tomato accessions from the SNPs. Subsequently, each of these distance measures was used to predict the BW using the DPLS prediction model. The DPLS model was found to be robust to the choice of distance measures; similar prediction performances were obtained for each distance measure. DPLS greatly outperformed the single-SNP regression approach, showing that BW is a comprehensive trait dependent on several loci. Next, the performance of the DPLS model was compared to that of GBLUP. Although GBLUP and DPLS are conceptually very different, the prediction quality (PQ) measured by DPLS models were similar to the prediction statistics obtained from GBLUP. A considerable advantage of DPLS is that the genotype-phenotype relationship can easily be visualized in a 2-D scatter plot. This so-called score-plot provides breeders an insight to select candidates for their future breeding program. CONCLUSIONS: DPLS is a highly appropriate method for GP. The model prediction performance was similar to the GBLUP and far better than the single-SNP approach. The proposed method can be used in combination with a wide range of genomic dissimilarity measures and genotype representations such as allele-count, haplotypes or allele-intensity values. Additionally, the data can be insightfully visualized by the DPLS model, allowing for selection of desirable candidates from the breeding experiments. In this study, we have assessed the DPLS performance on a single trait.

Application of a cocktail approach to screen cytochrome P450 BM3 libraries for metabolic activity and diversity.

In the present study, the validity of using a cocktail screening method in combination with a chemometrical data mining approach to evaluate metabolic activity and diversity of drug-metabolizing bacterial Cytochrome P450 (CYP) BM3 mutants was investigated. In addition, the concept of utilizing an in-house-developed library of CYP BM3 mutants as a unique biocatalytic synthetic tool to support medicinal chemistry was evaluated. Metabolic efficiency of the mutant library towards a selection of CYP model substrates, being amitriptyline (AMI), buspirone (BUS), coumarine (COU), dextromethorphan (DEX), diclofenac (DIC) and norethisterone (NET), was investigated. First, metabolic activity of a selection of CYP BM3 mutants was screened against AMI and BUS. Subsequently, for a single CYP BM3 mutant, the effect of co-administration of multiple drugs on the metabolic activity and diversity towards AMI and BUS was investigated. Finally, a cocktail of AMI, BUS, COU, DEX, DIC and NET was screened against the whole in-house CYP BM3 library. Different validated quantitative and qualitative (U)HPLC-MS/MS-based analytical methods were applied to screen for substrate depletion and targeted product formation, followed by a more in-depth screen for metabolic diversity. A chemometrical approach was used to mine all data to search for unique metabolic properties of the mutants and allow classification of the mutants. The latter would open the possibility of obtaining a more in-depth mechanistic understanding of the metabolites. The presented method is the first MS-based method to screen CYP BM3 mutant libraries for diversity in combination with a chemometrical approach to interpret results and visualize differences between the tested mutants.

Improved family-wise error rate control in multiple equivalence testing.

Equivalence testing is an important component of safety assessments, used for example by the European Food Safety Authority, to allow new food or feed products on the market. The aim of such tests is to demonstrate equivalence of characteristics of test and reference crops. Equivalence tests are typically univariate and applied to each measured analyte (characteristic) separately without multiplicity correction. This increases the probability of making false claims of equivalence (type I errors) when evaluating multiple analytes simultaneously. To solve this problem, familywise error rate (FWER) control using Hochberg's method has been proposed. This paper demonstrates that, in the context of equivalence testing, other FWER-controlling methods are more powerful than Hochberg's. Particularly, it is shown that Hommel's method is guaranteed to perform at least as well as Hochberg's and that an "adaptive" version of Bonferroni's method, which uses an estimator of the proportion of non-equivalent characteristics, often substantially outperforms Hommel's method. Adaptive Bonferroni takes better advantage of the particular context of food safety where a large proportion of true equivalences is expected, a situation where other methods are particularly conservative. The different methods are illustrated by their application to two compositional datasets and further assessed and compared using simulated data.

Evaluating and comparing tolerance, nutritional quality and bio-functional activity of bovine-plasma, corn and whey proteins, outcomes of a randomized double blind controlled trial.

Important considerations in the choice of future sustainable protein sources for human application are tolerance, nutritional quality, and potential health benefits. We evaluated, in a double-blind cross-over intervention trial, tolerance, nutritional quality, and potential health effects of two sustainable protein sources. Thirty-six apparently healthy older adults (age 62.3 ± 7.2yrs, BMI 25 ± 3 kg/m2) received 40 g/day bovine-plasma protein (BP), corn protein (CP) or, as a benchmark, whey protein (WP) for one week with a washout period of one week in-between. In 12 participants, we also determined postprandial amino acid (PAA) uptake kinetics upon consumption of 20 g BP, CP, or WP. Changes in self-reported gastrointestinal complaints and intestinal permeability assessed using a multi-sugar acetylsalicylic acid test did not differ between the interventions. Clear differences in PAA responses were observed after consumption of the different proteins, but clear essential amino acid responses were observed for all proteins. BP consumption resulted in a small but significant increase in blood pressure outcomes, and CP consumption resulted in a small but significant decrease in insulin levels when compared to the other interventions. In conclusion, alternative protein concentrates and isolates studied here can be consumed in relative high quantities without experiencing unwanted GI complaints or gut barrier dysfunction and they can be a good source of essential amino acids. The rise in blood pressure observed during the BP intervention, potentially linked to the elevated salt content of the BP, constitutes a potential health issue. Future studies with longer intervention periods might however be recommended.

Circadian rhythms and diurnal patterns in the feed intake behaviour of growing-finishing pigs.

The feeding behaviour of growing-finishing pigs is an important indicator of performance, health and welfare, but this use is limited by its large, poorly-understood variation. We explored the variation in basal feed intake of individual pigs by detecting circadian rhythms, extracting features of diurnal patterns and assessing consistency over time, from day-to-day and across age. Hourly feed intake data of individual pigs (n = 110) was obtained during one growing-finishing phase, using electronic feeding stations. We applied wavelet analysis to assess rhythms and a hurdle generalised additive model to extract features of diurnal patterns. We found that circadian rhythms could be detected during 58 ± 3% (mean ± standard error) of days in the growing-finishing phase (range 0-100%), predominantly at older ages. Although the group diurnal intake pattern was alternans (small morning peak, larger afternoon peak), individual pigs showed a range of diurnal patterns that changed with age, differing mostly in the extent of night fasting and day-to-day consistency. Our results suggest that the type, day-to-day consistency and age development of diurnal patterns in feed intake show general group patterns but also differ between pigs. Using this knowledge, promising features may be selected to compare against production, health and welfare parameters.

Multivariate equivalence testing for food safety assessment.

Products for food and feed derived from genetically modified (GM) crops are only allowed on the market when they are deemed to be safe for human health and the environment. The European Food Safety Authority (EFSA) performs safety assessment including a comparative approach: the compositional characteristics of a GM genotype are compared to those of reference genotypes that have a history of safe use. Statistical equivalence tests are used to carry out such a comparative assessment. These tests are univariate and therefore only consider one measured variable at a time. Phenotypic data, however, often comprise measurements on multiple variables that must be integrated to arrive at a single decision on acceptance in the regulatory process. The surge of modern molecular phenotyping platforms further challenges this integration, due to the large number of characteristics measured on the plants. This paper presents a new multivariate equivalence test that naturally extends a recently proposed univariate equivalence test and allows to assess equivalence across all variables simultaneously. The proposed test is illustrated on plant compositional data from a field study on maize grain and on untargeted metabolomic data of potato tubers, while its performance is assessed on simulated data.

Effect of Root Storage and Forcing on the Carbohydrate and Secondary Metabolite Composition of Belgian Endive (Cichorium intybus L. Var. foliosum).

Belgian endive is grown in a two-step cultivation process that involves growing of the plants in the field, cold storage of the taproots, and a second growth period in dark conditions called forcing to yield the witloof heads. In this study, the changes in the carbohydrate content and the secondary metabolite composition were studied in different tissues of Belgian endive during the cultivation process. Belgian endive heads contain between 336-388 mg/g DW of total soluble carbohydrates, predominantly fructose and glucose. The heads also contain phenolic compounds and terpenoids that give Belgian endive its characteristic bitter taste. The terpenoid and phenolic compound composition of the heads was found to be constant during the cultivation season, regardless of the root storage time. In roots, the main storage carbohydrate, inulin, was degraded during storage and forcing processes; however, more than 70% of total soluble carbohydrates remained unused after forcing. Additionally, high amounts of phenolics and terpenoids were found in the Belgian endive taproots, predominantly chlorogenic acid, isochlorogenic acid A, and sesquiterpene lactones. As shown in this study, Belgian endive taproots, which are currently discarded after forcing, are rich in carbohydrates, terpenes, and phenolic compounds and therefore have the potential for further valorization. This systematic study contributes to the understanding of the carbohydrate and secondary metabolite metabolism during the cultivation process of Belgian endive.

Pre- versus post-operative untargeted plasma nuclear magnetic resonance spectroscopy metabolomics of pheochromocytoma and paraganglioma.

PURPOSE: Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors. DESIGN: A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics. METHODS: Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods. RESULTS: Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected. CONCLUSIONS: Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Special Issue: Development and Application of Statistical Methods for Analyzing Metabolomics Data.

In the last decade, the field of metabolomics has developed tremendously: it is now possible to routinely measure a wide range of metabolites for many specimens at reduced costs, opening the door to many exciting experiments [...].

Equivalence tests for safety assessment of genetically modified crops using plant composition data.

The evaluation of compositional characteristics of plants harvested from field trials is an important step in the safety assessment of a genetically modified crop and its derived products for food and feed. The European Food Safety Authority (EFSA) evaluates safety by testing for equivalence between the GM genotype and other genotypes, typically with a history of safe use. Here, a new equivalence test is proposed, which addresses issues with the EFSA test. The method is motivated by a recently proposed equivalence test for analysis of data from animal feeding trials. In order to be suitable for practical safety assessment, the new method has a statistical power set to a desired value, e.g. 95%, by construction. In addition, we assess distributions rather than average values. This way, equivalence limits can also be established when there is limited genotypic variation. The original EFSA equivalence test breaks down in this case. The method is illustrated by its application to data from a field study on maize grain. Simulation studies indicate that the proposed test has appropriate performance characteristics and is competitive with respect to recently proposed alternatives, including the EFSA/EU equivalence test.

Metabolomics of Photosynthetically Active Tissues in White Grapes: Effects of Light Microclimate and Stress Mitigation Strategies.

The effects of climate change are becoming a real concern for the viticulture sector, with impacts on both grapevine physiology and the quality of the fresh berries and wine. Short-term mitigation strategies, like foliar kaolin application and smart irrigation regimes, have been implemented to overcome these problems. We previously showed that these strategies also influence the photosynthetic activity of the berries themselves, specifically in the exocarp and seed. In the present work, we assessed the modulating effects of both canopy-light microclimate, kaolin and irrigation treatments on the metabolic profiles of the exocarp and seed, as well as the potential role of berry photosynthesis herein. Berries from the white variety Alvarinho were collected at two contrasting light microclimate positions within the vine canopy (HL-high light and LL-low light) from both irrigated and kaolin-treated plants, and their respective controls, at three fruit developmental stages (green, véraison and mature). Untargeted liquid chromatography mass spectrometry (LCMS) profiling of semi-polar extracts followed by multivariate statistical analysis indicate that both the light microclimate and irrigation influenced the level of a series of phenolic compounds, depending on the ripening stage of the berries. Moreover, untargeted gas chromatography mass spectrometry (GCMS) profiling of polar extracts show that amino acid and sugar levels were influenced mainly by the interaction of irrigation and kaolin treatments. The results reveal that both photosynthetically active berry tissues had a distinct metabolic profile in response to the local light microclimate, which suggests a specific role of photosynthesis in these tissues. A higher light intensity within the canopy mainly increased the supply of carbon precursors to the phenylpropanoid/flavonoid pathway, resulting in increased levels of phenolic compounds in the exocarp, while in seeds, light mostly influenced compounds related to carbon storage and seed development. In addition, our work provides new insights into the influence of abiotic stress mitigation strategies on the composition of exocarps and seeds, which are both important tissues for the quality of grape-derived products.

Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage.

One-class modelling is a useful approach in metabolomics for the untargeted detection of abnormal metabolite profiles, when information from a set of reference observations is available to model "normal" or baseline metabolite profiles. Such outlying profiles are typically identified by comparing the distance between an observation and the reference class to a critical limit. Often, multivariate distance measures such as the Mahalanobis distance (MD) or principal component-based measures are used. These approaches, however, are either not applicable to untargeted metabolomics data, or their results are unreliable. In this paper, five distance measures for one-class modeling in untargeted metabolites are proposed. They are based on a combination of the MD and five so-called eigenvalue-shrinkage estimators of the covariance matrix of the reference class. A simple cross-validation procedure is proposed to set the critical limit for outlier detection. Simulation studies are used to identify which distance measure provides the best performance for one-class modeling, in terms of type I error and power to identify abnormal metabolite profiles. Empirical evidence demonstrates that this method has better type I error (false positive rate) and improved outlier detection power than the standard (principal component-based) one-class models. The method is illustrated by its application to liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic response spectroscopy (NMR) untargeted metabolomics data from two studies on food safety assessment and diagnosis of rare diseases, respectively.