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Finding a balance between the agroecological benefits of in-field crop diversification and the associated management demands, while maintaining expected production levels, is essential for making crop diversity work for farmers. The aim of this study was to find a workable resolution of diversity within the context of an on-station organic arable cropping systems experiment in the Netherlands. The experiment tested a gradient of crop diversity treatments from sole-crop references to strip cropping (3 m x 54 m strips sown in adjacent crop pairs of varying complexity) and pixel cropping (0.25 m2 plots each sown with one out of six total crops and arranged in 7.5 m x 12 m grids). In these treatments we assessed the performance of multiple agroecosystem service (AES) indicators (soil fertility, crop yield and quality, weed cover and diversity, and natural enemy activity density) for three focal crops (cabbage, wheat, and potato) using three years of field data and a three-part analysis. First, we used linear mixed models to assess the effects of each diversification treatment on the AES indicators. We found no clear indication that one treatment performed better than the rest across AES indicators. Second, we developed a novel method for quantifying the temporal, spatial, and genetic structural diversity of the tested treatments into compound diversity scores, and used these scores to analyze response relationships between increasing in-field diversity and AES delivery. Here we found that increasing compound diversity had a positive effect on the indicators weed species diversity and natural enemy activity density. For production indicators, we observed an inflection point between the most diverse strip cropping treatment and the pixel cropping treatment, with pixel cropping performing notably poorly. Third, we used a multivariate analysis approach to assess the contribution of temporal, spatial, and genetic diversity to AES delivery, but found no clear effects of indiv

Statistical modeling is an inherent part of any metabolomics study. Statistical models mainly assess the association between metabolites and the trait(s) of interest. Complicating factors are that not all metabolites are connected to the trait of interest, and modeling is hampered by the large numbers of metabolites especially in relation to the number of samples. To remedy this situation, several variable selection strategies that operate at different levels are discussed. Low-level variable selection is focused on removing noninformative or redundant metabolites. Medium-level variable selection involves methods that explicitly select a subset of most predictive metabolites. Lastly, high-level variable selection entails statistical techniques that select metabolites as part of their inner workings, or their importance is indicated using an auxiliary criterion. By selecting metabolites at these different levels, the complexity of the problem is reduced. This helps in statistical modeling and with the subsequent interpretation of the results. It helps researchers to focus on the most important metabolites that have a clear association with the trait under investigation.

An autoclave treatment at 121 °C on periodate-oxidized plant polysaccharides and mixes thereof was investigated for the release of oligosaccharides to obtain a generic polysaccharide depolymerization method for polysaccharides fingerprinting. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) analysis of the oligosaccharides released showed that each polysaccharide had a unique oligosaccharides profile, even the same type of polysaccharide derived from different sources and/or having different fine structures (e.g. class of (arabino)xylans, galactomannans, glucans, or pectic materials). Various polysaccharide classes present in a polysaccharide mix could be identified based on significantly different (p < 0.05) marker m/z values present in the mass spectrum. Principal component analysis and hierarchical cluster analysis of the obtained MALDI-TOF MS data highlighted the structural heterogeneity of the polysaccharides studied, and clustered polysaccharide samples with resembling oligosaccharide profiles. Our approach represents a step further towards a generic and accessible identification of plant polysaccharides individually or in a mixture.

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Isothiocyanates (ITCs) derived from Brassicaceae are potential food preservatives. Their antimicrobial ac-tivity is strongly influenced by their subclass and side chain. This is the first quantitative structure-activity relationships (QSAR) study of ITCs as antibacterials. Twenty-six ITCs covering 9 subclasses were tested against Escherichia coli and Bacillus cereus. Minimum inhibitory concentration (MIC) and growth inhibitory response (GIR) were determined and used to develop QSAR models. MIC of the most active ITCs was 6.3–9.4 μg/mL. The QSAR models were validated with leave-one-out cross validation. The proposed models had a good fit (R2adj 0.86–0.93) and high internal predictive power (Q2adj 0.80–0.89). Partial charge, polarity, reactivity, and shape of ITCs were key physicochemical properties underlying antibacterial activity of ITCs. Furthermore, ITC compositions and antibacterial activity of Sinapis alba, Brassica napus, B. juncea, B. oleracea, and Camelina sativa extracts were determined, after myrosinase treatment. B. oleracea ITC-rich extract showed promising activity (MIC 750–1000 μg/mL) against both bacteria. C. sativa ITC-rich extract showed promising activity (MIC 188 μg/mL) against B. cereus. The QSAR models successfully predicted activity of the extracts based on ITC compositions. The models are useful to predict antibacterial activity of new ITCs and ITC-rich mixtures. Brassicaceae ITC-rich extracts are promising natural food preservatives.

This study aimed at determining how the degradation of cereal non‐starch polysaccharides (NSP) by dietary enzymes during feed digestion can influence nutrient digestibility and NSP fermentability in broilers. Ninety‐six one‐day‐old male broilers were assigned to 4 different treatments: control and enzyme‐supplemented wheat‐based (WC, WE) or maize‐based (MC, ME) treatments. Enzyme supplementation with endo‐xylanase and endo‐glucanase occurred from day 20 onwards. On day 28, digesta samples were collected. Nutrient digestibility, NSP recovery, oligosaccharide profile, and short‐chain fatty acids (SCFA) content were determined. Enzyme supplementation in WE resulted in a higher starch (3%; p = 0.004) and protein (5%; p = 0.002) digestion in the ileum compared to WC. Xylanase activity in WE led to in situ formations of arabinoxylan‐oligosaccharides consisting of 5 to 26 pentose units in the ileum. This coincided with decreased arabinose (p = 0.059) and xylose (p = 0.036) amounts in the ceca and higher acetate (p = 0.014) and butyrate (p = 0.044) formation in WE compared to WC. Conversely, complete total tract recovery of arabinoxylan in MC and ME suggested poor maize NSP fermentability. Overall, enzyme action improved nutrient digestibility and arabinoxylan fermentability in the wheat‐based diet. The lower response of the maize‐based diet to enzyme treatment may be related to the recalcitrance of maize arabinoxylan as well as to the high nutritive value of maize.

In plants, secondary metabolites 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 feature detection, grouping, and annotation 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. Together with this comprehensive data set, we also provide a detailed tutorial for the processing steps in R.

Assessment of the impact of variation in chloroplast and mitochondrial DNA (collectively termed the plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from nuclear-derived variation (the nucleotype). Haploid-inducer lines can be used as efficient plasmotype donors to generate new plasmotype–nucleotype combinations (cybrids)1. We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1,859 phenotypes under both stable and fluctuating conditions. We show that natural variation in the plasmotype results in both additive and epistatic effects across all phenotypic categories. Plasmotypes that induce more additive phenotypic changes also cause more epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average, epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of multi-level nucleotype–plasmotype–environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation that is predominantly exposed under certain conditions. The production of cybrids using haploid inducers is a rapid and precise method for assessment of the phenotypic effects of natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype–plasmotype combinations to both improve our understanding of natural variation in these combinations and identify favourable combinations to enhance plant performance.

Crop yield stability requires an attenuation of the reduction of yield losses caused by environmental stresses such as drought. Using a combination of metabolomics and high-throughput colorimetric assays, we analysed central metabolism and oxidative stress status in the flag leaf of 292 indica rice (Oryza sativa) accessions. Plants were grown in the field and were, at the reproductive stage, exposed to either well-watered or drought conditions to identify the metabolic processes associated with drought-induced grain yield loss. Photorespiration, protein degradation, and nitrogen recycling were the main processes involved in the drought-induced leaf metabolic reprogramming. Molecular markers of drought tolerance and sensitivity in terms of grain yield were identified using a multivariate model based on the values of the metabolites and enzyme activities across the population. The model highlights the central role of the ascorbate-glutathione cycle, particularly dehydroascorbate reductase, in minimizing drought-induced grain yield loss. In contrast, malondialdehyde was an accurate biomarker for grain yield loss, suggesting that drought-induced lipid peroxidation is the major constraint under these conditions. These findings highlight new breeding targets for improved rice grain yield stability under drought.

In a Chinese Hamster Ovary (CHO) cell fed-batch process, arrest of cell proliferation and an almost threefold increase in cell size occurred, which is associated with an increase in cell-specific productivity. In this study, transcriptome analysis is performed to identify the molecular mechanisms associated with this. Cell cycle analysis reveals that the cells are arrested mainly in the G0/G1 phase. The cell cycle arrest is associated with significant up-regulation of cyclin-dependent kinases inhibitors (CDKNs) and down-regulation of cyclin-dependent kinases (CDKs) and cyclins. During the cell size increase phase, the gene expression of the upstream pathways of mechanistic target of rapamycin (mTOR), which is related to the extracellular growth factor, cytokine, and amino acid conditions, shows a strongly synchronized pattern to promote the mTOR activity. The downstream genes of mTOR also show a synchronized pattern to stimulate protein translation and lipid synthesis. The results demonstrate that cell cycle inhibition and stimulated mTOR activity at the transcriptome level are related to CHO cell size increase. The cell size increase is related to the extracellular nutrient conditions through a number of cascade pathways, indicating that by rational design of media and feeds, CHO cell size can be manipulated during culture processes, which may further improve cell growth and specific productivity.

To better understand the variability of the type and level of serum proteins in human milk, the milk serum proteome of Chinese mothers during lactation was investigated using proteomic techniques and compared to the milk serum proteome of Dutch mothers. This showed that total milk serum protein concentrations in Chinese human milk decreased over a 20-week lactation period, although with variation between mothers in the rate of decrease. Variation was also found in the composition of serum proteins in both colostrum and mature milk, although immune-active proteins, enzymes, and transport proteins were the most abundant for all mothers. These three protein groups account for many of the 15 most abundant proteins, with these 15 proteins covering more than 95% of the total protein concentrations, in both the Chinese and Dutch milk serum proteome. The Dutch and Chinese milk serum proteome were also compared based on 166 common milk serum proteins, which showed that 22% of the 166 serum proteins differed in level. These differences were observed mainly in colostrum and concern several highly abundant proteins. This study also showed that protease inhibitors, which are highly correlated to immune-active proteins, are present in variable amounts in human milk and could be relevant during digestion.

Assessing the impact of variation in chloroplast and mitochondrial DNA (collectively termed the plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from nuclear derived variation (the nucleotype). Haploid inducer lines can be used as efficient plasmotype donors to generate new plasmotype-nucleotype combinations (cybrids). We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1859 phenotypes under stable and fluctuating conditions. We show that natural variation in the plasmotype results in additive as well as epistatic effects across all phenotypic categories. Plasmotypes which induce more additive phenotypic changes also cause more significant epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of multilevel Nucleotype x Plasmotype x Environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation which is predominantly exposed under certain conditions. The production of cybrids using haploid inducers is a quick and precise method for assessing the phenotypic effects of natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype-plasmotype combinations to both improve our understanding of natural variation in nucleotype-plasmotype interactions and identify favourable combinations to improve plant performance.

Assessing the impact of variation in chloroplast and mitochondrial DNA (collectively termed the plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from nuclear derived variation (the nucleotype). Haploid inducer lines can be used as efficient plasmotype donors to generate new plasmotype-nucleotype combinations (cybrids). We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1859 phenotypes under stable and fluctuating conditions. We show that natural variation in the plasmotype results in additive as well as epistatic effects across all phenotypic categories. Plasmotypes which induce more additive phenotypic changes also cause more significant epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of multilevel Nucleotype x Plasmotype x Environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation which is predominantly exposed under certain conditions. The production of cybrids using haploid inducers is a quick and precise method for assessing the phenotypic effects of natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype-plasmotype combinations to both improve our understanding of natural variation in nucleotype-plasmotype interactions and identify favourable combinations to improve plant performance.

Assessing the impact of variation in chloroplast and mitochondrial DNA (collectively termed the plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from nuclear derived variation (the nucleotype). Haploid inducer lines can be used as efficient plasmotype donors to generate new plasmotype-nucleotype combinations (cybrids). We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1859 phenotypes under stable and fluctuating conditions. We show that natural variation in the plasmotype results in additive as well as epistatic effects across all phenotypic categories. Plasmotypes which induce more additive phenotypic changes also cause more significant epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of multilevel Nucleotype x Plasmotype x Environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation which is predominantly exposed under certain conditions. The production of cybrids using haploid inducers is a quick and precise method for assessing the phenotypic effects of natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype-plasmotype combinations to both improve our understanding of natural variation in nucleotype-plasmotype interactions and identify favourable combinations to improve plant performance.

Synthetic derivatives of 1,4-benzoxazin-3-ones have been shown to possess promising antimicrobial activity, whereas their natural counterparts were found lacking in this respect. In this work, quantitative structure-activity relationships (QSAR) of natural and synthetic 1,4-benzoxazin-3-ones as antimicrobials were established. Data published in literature were curated into an extensive dataset of 111 compounds. Descriptor selection was performed by a genetic algorithm. QSAR models revealed differences in requirements for activity against fungi, gram-positive and gram-negative bacteria. Shape, VolSurf, and H-bonding property descriptors were frequently picked in all models. The models obtained for gram-positive and gram-negative bacteria showed good predictive power (Q2 Ext 0.88 and 0.85, respectively). Based on the models generated, an additional set of 1,4-benzoxazin-3-ones, for which no antimicrobial activity had been determined in literature, were evaluated in silico. Additionally, newly designed lead compounds with a 1,4-benzoxazin-3-one scaffold were generated in silico by varying the positions and combinations of substituents. Two of these were predicted to be up to 5 times more active than any of the compounds in the current dataset. The 1,4-benzoxazin-3-one scaffold was concluded to possess potential for the design of new antimicrobial compounds with potent antibacterial activity, a multitarget mode of action, and possibly reduced susceptibility to gram negatives’ efflux pumps.

Due to the unpredictable natural droughts that occur, causing tea farmers significant losses in tea estates, a two-day method for distinguishing between drought tolerant (DT) and drought susceptible (DS) Camellia sinensis cultivars was developed. This work was based on known cultivars developed at the Tea Research Institute in Kenya and the Tea Research Foundation for Central Africa in Malawi. This paper contains an in-depth description of the application of the SWAPDT method on four 60-year old, C. sinensis seedling fields in Kenya. The in-filling history of the four fields due to drought-related deaths was obtained from historical records. The SWAPDT method scores correlated very well with the historical records. It has been indicated, from the results obtained in this study, that a sample size of 20 tea trees is sufficient to accurately determine the drought susceptibility of a large tea field of approximately 5-20 hectares, containing 50 000-200 000 tea trees, were the difference between their mean values, as measured by the SWAPDT method, is approximately 10%.

In this study a mapping population (F8) of ca 200 progeny from a cross between the commercial rice varieties Apo and IR64 has been both genotyped and phenotyped. A genotyping-by-sequencing approach was first used to identify 2,681 polymorphic SNP markers which gave dense coverage of the genome with a good distribution across all 12 chromosomes. The coefficient of parentage was also low, at 0.13, confirming that the parents are genetically distant from each other. The progeny, together with both parents, were grown under irrigated and water restricted conditions in a randomised block design. All grain was harvested to determine variation in yield across the population. The grains were then polished following standard procedures prior to performing the phenotyping analyses. A Gas Chromatography-Mass Spectrometry approach was used to determine the volatile biochemical profiles of each line and after data curation and processing, discriminatory metabolites were putatively identified based on in-house and commercial spectral libraries. These data were used to predict the potential role of these metabolites in determining differences in aroma between genotypes. A number of QTLs for yield and for individual metabolites have been identified. Following these combined multi-disciplinary analyses, it proved possible to identify a number of lines which appeared to combine the favourable aroma attributes of IR64 with the favourable (higher) yield potential of Apo. As such, these lines are excellent candidates to assess further as potential genotypes to work up into a new variety of rice which has both good yield and good quality, thus meeting the needs of both farmer and consumer alike.

Introduction: Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. Objectives: This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects. Methods: Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana. Results: The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered. Conclusion: The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, bu

Here we provide data from shot-gun proteomics, using filtered-aided sample preparation (FASP), dimethyl labeling and LC-MS/MS, to quantify the changes in the repertoire of human milk proteins over lactation. Milk serum proteins were analyzed at week 1, 2, 3 4, 8, 16, and 24 in milk from four individual mothers. A total of 247 proteins were identified, of which 200 proteins were quantified. The data supplied in this article supports the accompanying publication (Zhang et al., 2006) [1]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (Vizcaíno et al., 2016) [2] via the PRIDE partner repository with the dataset identifier PXD003465.