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.

Postprandial amino acid response after the ingestion of pea protein, milk protein, casein and a casein-pea blend, in healthy older adults.

To identify the potential anabolic properties of a dairy-plant protein blend as compared to single plant-based and single dairy protein, the postprandial amino acid (AA) response of pea protein, milk protein, micellar casein, and a casein-pea protein blend was investigated in healthy older adults (age 72.3 ± 3.4 years, BMI 25.3 ± 2.9 kg/m2). Plasma AA levels were measured, before and up to 5 h after ingestion of each 20 g protein. Blending casein-pea in a 60/40 mixture resulted in improved plasma AA availability, i.e. area under the curve (AUC) and peak height, of total (essential) AA and of key AAs methionine and leucine compared to pea only, while preserving the higher availability of arginine. The casein/pea blend clearly showed an AA response that was in between that of its single constituents, indicating that blending could be a solution to improve a lower quality (plant) protein, which could be of relevance for older adults.

Identification of environment types and adaptation zones with self-organizing maps; applications to sunflower multi-environment data in Europe.

KEY MESSAGE: We evaluate self-organizing maps (SOM) to identify adaptation zones and visualize multi-environment genotypic responses. We apply SOM to multiple traits and crop growth model output of large-scale European sunflower data. Genotype-by-environment interactions (G × E) complicate the selection of well-adapted varieties. A possible solution is to group trial locations into adaptation zones with G × E occurring mainly between zones. By selecting for good performance inside those zones, response to selection is increased. In this paper, we present a two-step procedure to identify adaptation zones that starts from a self-organizing map (SOM). In the SOM, trials across locations and years are assigned to groups, called units, that are organized on a two-dimensional grid. Units that are further apart contain more distinct trials. In an iterative process of reweighting trial contributions to units, the grid configuration is learnt simultaneously with the trial assignment to units. An aggregation of the units in the SOM by hierarchical clustering then produces environment types, i.e. trials with similar growing conditions. Adaptation zones can subsequently be identified by grouping trial locations with similar distributions of environment types across years. For the construction of SOMs, multiple data types can be combined. We compared environment types and adaptation zones obtained for European sunflower from quantitative traits like yield, oil content, phenology and disease scores with those obtained from environmental indices calculated with the crop growth model Sunflo. We also show how results are affected by input data organization and user-defined weights for genotypes and traits. Adaptation zones for European sunflower as identified by our SOM-based strategy captured substantial genotype-by-location interaction and pointed to trials in Spain, Turkey and South Bulgaria as inducing different genotypic responses.

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.

The Effect of Calcium Buffering and Calcium Sensor Type on the Sensitivity of an Array-Based Bitter Receptor Screening Assay.

The genetically encoded calcium sensor protein Cameleon YC3.6 has previously been applied for functional G protein-coupled receptor screening using receptor cell arrays. However, different types of sensors are available, with a wide range in [Ca2+] sensitivity, Hill coefficients, calcium binding domains, and fluorophores, which could potentially improve the performance of the assay. Here, we compared the responses of 3 structurally different calcium sensor proteins (Cameleon YC3.6, Nano140, and Twitch2B) simultaneously, on a single chip, at different cytosolic expression levels and in combination with 2 different bitter receptors, TAS2R8 and TAS2R14. Sensor concentrations were modified by varying the amount of calcium sensor DNA that was printed on the DNA arrays prior to reverse transfection. We found that ~2-fold lower concentrations of calcium sensor protein, by transfecting 4 times less sensor-coding DNA, resulted in more sensitive bitter responses. The best results were obtained with Twitch2B, where, relative to YC3.6 at the default DNA concentration, a 4-fold lower DNA concentration increased sensitivity 60-fold and signal strength 5- to 10-fold. Next, we compared the performance of YC3.6 and Twitch2B against an array with 11 different bitter taste receptors. We observed a 2- to 8-fold increase in sensitivity using Twitch2B compared with YC3.6. The bitter receptor arrays contained 300 spots and could be exposed to a series of 18 injections within 1 h resulting in 5400 measurements. These optimized sensor conditions provide a basis for enhancing receptomics calcium assays for receptors with poor Ca2+ signaling and will benefit future high-throughput receptomics experiments.

Metabolite variation in the lettuce gene pool: towards healthier crop varieties and food.

INTRODUCTION: Lettuce (Lactuca sativa L.) is generally not specifically acknowledged for its taste and nutritional value, while its cultivation suffers from limited resistance against several pests and diseases. Such key traits are known to be largely dependent on the ability of varieties to produce specific phytochemicals. OBJECTIVES: We aimed to identify promising genetic resources for the improvement of phytochemical composition of lettuce varieties. METHODS: Phytochemical variation was investigated using 150 Lactuca genebank accessions, comprising a core set of the lettuce gene pool, and resulting data were related to available phenotypic information. RESULTS: A hierarchical cluster analysis of the variation in relative abundance of 2026 phytochemicals, revealed by untargeted metabolic profiling, strongly resembled the known lettuce gene pool structure, indicating that the observed variation was to a large extent genetically determined. Many phytochemicals appeared species-specific, of which several are generally related to traits that are associated with plant health or nutritional value. For a large number of phytochemicals the relative abundance was either positively or negatively correlated with available phenotypic data on resistances against pests and diseases, indicating their potential role in plant resistance. Particularly the more primitive lettuces and the closely related wild relatives showed high levels of (poly)phenols and vitamin C, thus representing potential genetic resources for improving nutritional traits in modern crop types. CONCLUSION: Our large-scale analysis of phytochemical variation is unprecedented in lettuce and demonstrated the ample availability of suitable genetic resources for the development of improved lettuce varieties with higher nutritional quality and more sustainable production.

Calcium Imaging of GPCR Activation Using Arrays of Reverse Transfected HEK293 Cells in a Microfluidic System.

Reverse-transfected cell arrays in microfluidic systems have great potential to perform large-scale parallel screening of G protein-coupled receptor (GPCR) activation. Here, we report the preparation of a novel platform using reverse transfection of HEK293 cells, imaging by stereo-fluorescence microscopy in a flowcell format, real-time monitoring of cytosolic calcium ion fluctuations using the fluorescent protein Cameleon and analysis of GPCR responses to sequential sample exposures. To determine the relationship between DNA concentration and gene expression, we analyzed cell arrays made with variable concentrations of plasmid DNA encoding fluorescent proteins and the Neurokinin 1 (NK1) receptor. We observed pronounced effects on gene expression of both the specific and total DNA concentration. Reverse transfected spots with NK1 plasmid DNA at 1% of total DNA still resulted in detectable NK1 activation when exposed to its ligand. By varying the GPCR DNA concentration in reverse transfection, the sensitivity and robustness of the receptor response for sequential sample exposures was optimized. An injection series is shown for an array containing the NK1 receptor, bitter receptor TAS2R8 and controls. Both receptors were exposed 14 times to alternating samples of two ligands. Specific responses remained reproducible. This platform introduces new opportunities for high throughput screening of GPCR libraries.

Fast parametric time warping of peak lists.

UNLABELLED: Alignment of peaks across samples is a difficult but unavoidable step in the data analysis for all analytical techniques containing a separation step like chromatography. Important application examples are the fields of metabolomics and proteomics. Parametric time warping (PTW) has already shown to be very useful in these fields because of the highly restricted form of the warping functions, avoiding overfitting. Here, we describe a new formulation of PTW, working on peak-picked features rather than on complete profiles. Not only does this allow for a much more smooth integration in existing pipelines, it also speeds up the (already among the fastest) algorithm by orders of magnitude. Using two publicly available datasets we show the potential of the new approach. The first set is a LC-DAD dataset of grape samples, and the second an LC-MS dataset of apple extracts. AVAILABILITY AND IMPLEMENTATION: Parametric time warping of peak lists is implemented in the ptw package, version 1.9.1 and onwards, available from Github (https://github.com/rwehrens/ptw) and CRAN (http://cran.r-project.org). The package also contains a vignette, providing more theoretical details and scripts to reproduce the results below. CONTACT: ron.wehrens@wur.nl.

Self-organizing maps: a versatile tool for the automatic analysis of untargeted imaging datasets.

MS-based imaging approaches allow for location-specific identification of chemical components in biological samples, opening up possibilities of much more detailed understanding of biological processes and mechanisms. Data analysis, however, is challenging, mainly because of the sheer size of such datasets. This article presents a novel approach based on self-organizing maps, extending previous work in order to be able to handle the large number of variables present in high-resolution mass spectra. The key idea is to generate prototype images, representing spatial distributions of ions, rather than prototypical mass spectra. This allows for a two-stage approach, first generating typical spatial distributions and associated m/z bins, and later analyzing the interesting bins in more detail using accurate masses. The possibilities and advantages of the new approach are illustrated on an in-house dataset of apple slices.

Transcriptome analysis during berry development provides insights into co-regulated and altered gene expression between a seeded wine grape variety and its seedless somatic variant.

BACKGROUND: Seedless grapes are greatly appreciated for fresh and dry fruit consumption. Parthenocarpy and stenospermocarpy have been described as the main phenomena responsible for seedlessness in Vitis vinifera. However, the key genes underpinning molecular and cellular processes that play a significant role in seed development are not well characterized. To identify important regulators and mechanisms that may be altered in the seedless phenotype, we performed a comprehensive transcriptional analysis to compare the transcriptomes of a popular seeded wine cultivar (wild-type) and its seedless somatic variant (mutant) at three key developmental stages. RESULTS: The transcriptomes revealed by Illumina mRNA-Seq technology had approximately 98% of grapevine annotated transcripts and about 80% of them were commonly expressed in the two lines. Differential gene expression analysis revealed a total of 1075 differentially expressed genes (DE) in the pairwise comparison of developmental stages, which included DE genes specific to the wild-type background, DE genes specific to the mutant background and DE genes commonly shared in both backgrounds. The analysis of differential expression patterns and functional category enrichment of wild-type and mutant DE genes highlighted significant coordination and enrichment of pollen and ovule developmental pathways. The expression of some selected DE genes was further confirmed by real-time RT-PCR analysis. CONCLUSIONS: This study represents the most comprehensive attempt to characterize the genetic bases of seed formation in grapevine. With a high throughput method, we have shown that a seeded wine grape and its seedless somatic variant are similar in several biological processes. Nevertheless, we could identify an inventory of genes with altered expression in the mutant compared to the wild-type, which may be responsible for the seedless phenotype. The genes located within known genomic regions regulating seed content may be used for the development of molecular tools to assist table grape breeding. Therefore the data reported here have provided a rich genomic resource for practical use and functional characterization of the genes that potentially underpin seedlessness in grapevine.

Constructing a mass measurement error surface to improve automatic annotations in liquid chromatography/mass spectrometry based metabolomics.

RATIONALE: Estimation of mass measurement accuracy is an elementary step in the application of mass spectroscopy (MS) data towards metabolite annotations and has been addressed several times in the past. However, the reproducibility of mass measurements over a diverse set of analytes and in variable operating conditions, which are common in high-throughput metabolomics studies, has, to the best of our knowledge, not been addressed so far. METHODS: A method to automatically extract mass measurement errors from a large data set of measurements made on a quadrupole time-of-flight (QTOF) MS instrument has been developed. The size of the data processed in this study has enabled us to use a statistical data driven approach to build a model which reliably predicts the confidence interval of the absolute mass measurement error based on individual ion peak conditions in a fast, high-throughput manner. RESULTS: We show that our model predictions are reproducible in external datasets generated in similar, but not identical conditions, and have demonstrated the advantage of our approach over the common practice of fixed mass measurement error limits. CONCLUSIONS: Outlined is an approach which can promote a more rational use of MS technology by automatically evaluating the absolute mass measurement error based on the individual peak conditions. The immediate application of our method is integration in high-throughput peak annotation pipelines for database searches.

High-throughput carotenoid profiling using multivariate curve resolution.

We present automated data analysis of high-throughput high-performance liquid chromatography with diode array detection (HPLC-DAD) data using multivariate curve resolution. This technique provides spectra and elution profiles of all UV-Vis active compounds present in the mixture. The specifics of using this method in noninteractive fashion are discussed. A case study on the stability of isoprenoids in grape extracts under two different experimental regimes serves to illustrate the potential of the method: quantitative results clearly show that the addition of triethylamine is beneficial in that carotenoid, chlorophyll, and tocopherol compounds are much more stable and in this way can be kept up to at least 30 days without any sign of degradation.

Evaluating Brewers' Spent Grain Protein Isolate Postprandial Amino Acid Uptake Kinetics: A Randomized, Cross-Over, Double-Blind Controlled Study.

Valorization and utilization of brewers' spent grain (BSG) are of great interest in terms of reducing food waste and promoting more sustainable food systems. In this study, we aimed to evaluate the nutritional value of upcycled barley/rice proteins (BRP) extracted from BSG and compare this with pea proteins (PP). A randomized, cross-over, double-blind controlled trial was conducted with twelve participants (age: 24 ± 2.8 years, BMI: 23.3 ± 3.0 kg/m2). During three separate visits with a one-week washout period between visits, participants received 20 g BRP, PP, or the benchmark protein whey (WP). Blood-free amino acids (AA) were measured to determine postprandial AA uptake kinetics. The estimated total AA (TAA) uptake of BRP was 69% when compared to WP and 87% when compared to PP. The time to reach the maximum values was similar between the three protein sources. When comparing individual essential AA responses between BRP and PP, we observed higher responses in methionine and tryptophane and lower responses in lysine, histidine, and isoleucine for BRP compared to PP. This study demonstrates that BRP exhibits comparable postprandial TAA uptake profiles to PP. The findings highlight the complementarity of BRP and PP, which may offer the potential for blending approaches to optimize protein quality for overall health.

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.

A comprehensive full factorial LC-MS/MS proteomics benchmark data set.

An important prerequisite for the development and benchmarking of novel analysis methods is a well-designed comprehensive LC-MS/MS data set. Here, we present our data set consisting of 59 LC-MS/MS analyses of 50 protein samples extracted individually from Escherichia coli K12 and spiked with different concentrations of bovine carbonic anhydrase II and/or chicken ovalbumin, according to a 2 × 3 full factorial design. Using the well-annotated and commonly used E. coli proteome as the sample background ensures that the complexity of the data is on a par with most current proteomic analyses. Data were acquired over a 2-month period using multiple reversed-phase columns and instrument calibrations to include real-life challenges faced when analyzing large proteomics data sets. Moreover, so-called "ground truth" data, comprised by LC-MS/MS measurements of the pure spikes are included in the data set. The current manuscript elaborates this comprehensive benchmark data set for future development and evaluation of analysis methods and software.

D-optimal design of an untargeted HS-SPME-GC-TOF metabolite profiling method.

In recent times we have seen the development of many "-omics" technologies. One of the youngest is undoubtedly metabolomics, which aims to define the whole chemical fingerprint unique to each specific organism. The development and optimisation of an untargeted high-throughput method capable of investigating the volatile fraction of a biological system represents a crucial step for the success of such holistic approaches, and specific optimisation criteria must be developed in connection with suitable experimental designs. In this paper experimental designs (D-optimal) were applied for the first time as an automatic optimisation tool to an untargeted HS-SPME-GC-TOF method. In this case, optimal conditions correspond to a maximal number of detected features, in order to provide a fingerprint that is as complete as possible. The system under study is the grape berry. Four variables were considered: the type of fibre, extraction time, equilibration time and temperature. The results show that the D-optimal design methodology provides an easily interpretable assessment of experimental settings. This and other specific properties of the D-optimal design, such as the possibility to explicitly exclude certain experimental conditions, make it an extremely suitable strategy for method optimisation in untargeted metabolomics.

Pinpointing biomarkers in proteomic LC/MS data by moving-window discriminant analysis.

The identification of differential patterns in data originating from combined measurement techniques such as LC/MS is pivotal to proteomics. Although "shotgun proteomics" has been employed successfully to this end, this method also has severe drawbacks, because of its dependence on largely untargeted MS/MS sequencing and databases for statistical analyses. Alternatively, several MS-signal-based (MS/MS-independent) methods have been published that are mainly based on (univariate) Student's t-tests. Here, we present a more robust multivariate alternative employing linear discriminant analysis. Like the t-test-based methods, it is applied directly to LC/MS data, instead of using MS/MS measurements. We demonstrate the method on a number of simulated data sets, as well as on a spike-in LC/MS data set, and show its superior performance over t-tests.

Traceability along the production chain of Italian tomato products on the basis of stable isotopes and mineral composition.

The paper shows the variability of stable isotope ratios and mineral composition in tomato and derivatives along the production chain (juice, passata and paste) in order to evaluate the possibility of tracing their geographical origin. The ratios (13)C/(12)C, (15)N/(14)N, (18)O/(16)O, D/H, (34)S/(32)S and the content of Li, Be, B, Na, Mg, Al, P, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Y, Mo, Ag, Cd, Sn, Sb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Tm, Yb, Ir, Tl, Pb, U and of nitrates, chlorides, sulphates and phosphates were measured by Isotope Ratio Mass Spectrometry, Inductively Coupled Plasma Mass Spectrometry and Ion Chromatography, respectively. The tomato products were from three Italian regions - Piedmont, Emilia Romagna, and Apulia. By applying linear discriminant analysis on 17 of these parameters (Gd, La, Tl, Eu, Cs, Ni, Cr, Co, δ(34)S, δ(15)N, Cd, K, Mg, δ(13)C, Mo, Rb and U) excellent discrimination among products from the three regions was achieved. Irrespective of the processing technology, over 95% of the samples were correctly reclassified in cross-validation into the production site. The use of these parameters will allow the development of analytical control procedures that can be used to check the geographical provenance of Italian tomatoes and products derived from them.

LC-MS based plant metabolic profiles of thirteen grassland species grown in diverse neighbourhoods.

In plants, secondary metabolite profiles provide a unique opportunity to explore seasonal variation and responses to the environment. These include both abiotic and biotic factors. In field experiments, such stress factors occur in combination. This variation alters the plant metabolic profiles in yet uninvestigated ways. This data set contains trait and mass spectrometry data of thirteen grassland species collected at four time points in the growing season in 2017. We collected above-ground vegetative material of seven grass and six herb species that were grown in plant communities with different levels of diversity in the Jena Experiment. For each sample, we recorded visible traits and acquired shoot metabolic profiles on a UPLC-ESI-Qq-TOF-MS. We performed the raw data pre-processing in Galaxy-W4M and prepared the data for statistical analysis in R by applying missing data imputation, batch correction, and validity checks on the features. This comprehensive data set provides the opportunity to investigate environmental dynamics across diverse neighbourhoods that are reflected in the metabolomic profile.

Ayurvedic Herbal Preparation Supplementation Does Not Improve Metabolic Health in Impaired Glucose Tolerance Subjects; Observations from a Randomised Placebo Controlled Trial.

The increased usage of alternative Ayurvedic treatments as potential health-beneficial therapies emphasizes the importance of studying its efficacy in sound placebo-controlled intervention trials. An example of such a traditional Ayurvedic herbal preparation is Mohana Choorna, a mixture composed of 20 different herbs and used to prevent and treat type 2-diabetes (T2D). We studied the efficacy of "Mohana Choorna" on T2D-related parameters in subjects with impaired glucose tolerance. In a double blind, placebo-controlled cross-over trial, 19 overweight (BMI > 27 kg/m2) subjects aged 50-70 years with an impaired glucose tolerance received two four-week interventions, i.e., herbal or placebo with a four-week wash-out between interventions. HbA1c, glucose, insulin, triglycerides, cholesterol, blood pressure and augmentation index were measured before and after both interventions at fasting and during a glucose tolerance test. After both interventions, urine was collected to measure treatment exposure using LCMS-based metabolomics and whole genome gene-expression in adipose tissue of 13 subjects. The herbal intervention did not affect plasma glucose triglycerides, cholesterol, blood pressure or the augmentation index but showed a trend towards an increased insulin, HOMA-IR and postprandial insulin levels (p = 0.054, p = 0.056 and p = 0.095 respectively). An increase in expression of inflammation-related gene sets in adipose tissue was observed after the herbal intervention compared to placebo. Urine metabolomic analysis did not reveal a correlation of the presence of specific plant metabolites with "health markers". Our findings suggest that there is no substantiating evidence to claim that four weeks' use of the Ayurvedic herbal supplement Mohana Choorna beneficially affects glucose homeostasis.