Effects of Organic and Conventional Crop Nutrition on Profiles of Polar

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Agricultural and Environmental Chemistry

Effects of Organic and Conventional Crop Nutrition on Profiles of Polar Metabolites in Grain of Wheat Peter R. Shewry, Marianna Rakszegi, Alison Lovegrove, Dominic Amos, Delia-Irina Corol, Ahmed Tawfike, Peter Miko, and Jane L. Ward J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b01593 • Publication Date (Web): 10 May 2018 Downloaded from http://pubs.acs.org on May 11, 2018

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Journal of Agricultural and Food Chemistry

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Effects of Organic and Conventional Crop Nutrition on Profiles of Polar Metabolites in Grain

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of Wheat

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Peter Shewry*a,b, Marianna Rakszegic, Alison Lovegrovea, Dominic Amosd, Delia-Irina Corole,

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Ahmed Tawfikee, Péter Mikóc and Jane L. Warde

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a

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Harpenden, Hertfordshire AL5 2JQ, UK

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b

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Campus, Early gate RG6 6AR, Reading UK

Department of Plant and Biology and Crop Science, Rothamsted Research, West Common,

School of Agriculture, Policy and Development, University of Reading, Whiteknights

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c

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Brunszvik u. 2., Martonvásár, 2462, Hungary

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d

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UK.

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e

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Common, Harpenden, Hertfordshire AL5 2JQ, UK

Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences,

Organic Research Centre, Elm Farm, Hamstead Marshall, Newbury, Berkshire, RG20 0HR,

Department of Computational and Analytical Sciences, Rothamsted Research, West

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*Corresponding author. Tel: (44) 1582 763133. FAX: (44) 1582 763010. E-mail:

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[email protected]

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ABSTRACT

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The profiles of polar metabolites were determined in wholemeal flours of grain from the

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Broadbalk wheat experiment and from plants grown under organic and low input systems to

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study the effects of nutrition on composition. The Broadbalk samples showed increased

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amino acids, acetate and choline, and decreased fructose and succinate, with increasing

ACS Paragon Plus Environment


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nitrogen fertilisation. Samples receiving farm yard manure had similar grain nitrogen to those

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receiving 96kgN/Ha, but had higher contents of amino acids, sugars and organic acids.

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Comparison of the profiles of grain from organic and low input systems showed only partial

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separation, with clear effects of climate and agronomy. However, supervised multivariate

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analysis showed that the low input samples had higher contents of many amino acids,

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raffinose, glucose, organic acids and choline and lower sucrose, fructose and glycine.

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Consequently, although difference between organic and conventional grain occur these

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cannot be used to confirm sample identity.

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KEY WORDS: wheat, wholemeal, metabolomics, organic agriculture, low input

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agriculture

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INTRODUCTION

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Although organic cereals are grown over a small area compared to conventional cereal

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production (accounting for less than 2% of the total area in the UK), they command a

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premium and hence form an important sector of the market. This is because they are

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considered by some as being healthier (although there is little evidence for this1-4) and to

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have environmental benefits5 compared to conventionally-produced crops. Consequently,

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establishing the authenticity and traceability of organic cereals is important for those trading

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in, processing, and marketing organic foods and products. This is a significant challenge,

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because grain composition is known to be affected by genotype, climatic factors and

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genotype x environment interactions, as well as by agronomy, with commercial samples of

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the same genotypes varying widely in composition6. Because of this, it is unlikely that a

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simple discrimination between organic and conventionally grown cereals can be made based

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on single grain components and determining multiple components using conventional

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analytical approaches is too time-consuming and expensive to be used on a routine basis.

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For example, Stracke et al.7 used conventional HPLC-based systems to show that climatic

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factors had a much greater impact on the profiles of phenolic acids and carotenoids than the

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production system.


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However, modern metabolomic analyses provide an opportunity to carry out more

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sophisticated comparisons, by identifying multiple metabolites in a single analytical system

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and providing datasets that can be compared by multivariate statistical analysis. For

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example, Zörb et al.8 and Bonte et al.9 used GC-MS to determine polar metabolites in

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extracts of wholemeal flour made with 80% methanol. Both studies showed that the effects

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of genotype were greater than those of farming system (organic or conventional), although

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the latter study did identify five metabolites, and 11 unidentified peaks in the chromatograms,

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which differed significantly between the farming systems.

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We have developed high throughput 1H-NMR of un-purified extracts made directly into

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deuterated aqueous methanol as a routine screening tool in plant metabolomics10,11

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including grain composition6,12. We have therefore used this approach to explore the

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relationship between wheat nutrition and grain metabolite composition. Firstly, using

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samples of grain grown with varying amounts of nitrogen fertiliser, including farm yard

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manure, from the Broadbalk long term continuous wheat experiment at Rothamsted, and

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secondly using grain samples grown in Austria, Switzerland and the UK using organic and

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low input conventional systems13-15.

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MATERIALS AND METHODS

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Broadbalk wheat samples

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Samples of mature wheat grain were obtained from the 2016 harvest from the Broadbalk

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experiment at Rothamsted Research. This is a long term (since 1843) field experiment in

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which a single cultivar (currently the UK winter wheat Crusoe) is grown with various nutrient

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regimes16. Four sections of the experiment were sampled: three continuous wheat sections

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(0, 1, 9), one of which had straw incorporation since 1986 (section 0), and one first wheat in

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an oats-maize-wheat-wheat-wheat rotation (section 4). Within these sections plots were

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sampled with zero fertilisation, the application of farmyard manure (35 tonnes/Ha) and the

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application of nitrogen (as ammonium nitrate) at 48, 96, 144, 192 and 288 kg/Ha. Details of



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the treatments and grain nitrogen contents are provided in Table S1. Soil inorganic nitrogen

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was not measured, but a previous study showed 16 and 25 kg N /Ha between 0-23 cm and

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19 and 20 kg/Ha between 23-50 cm in plots receiving 144 and 288 kg N /Ha, respectively,

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and 45 kg N /Ha in a plot receiving FYM. These measurements were made in March with a

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standing crop of winter wheat, but before the application of inorganic fertiliser17.

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samples were ground in a ball mill (Glen Creston, Stanmore, UK) to give wholemeal flour.

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Low input and organic samples

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Wholemeal samples were obtained from an international research project in which a range

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of cultivars were grown under “low input” (as defined in Europe in which high input systems

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are the norm) and organic systems in several countries in 2012-201313-15. The material

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available for this study comprised 33 cultivars and 3 breeding lines grown under

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conventionally-fertilised low input conditions in Austria and organic conditions in Switzerland,

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and three cultivars also grown under similar conventionally-fertilised low input and organic

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conditions in the UK (Wakelyns site). In these trials, the low input plots received 100 to 120

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kgN/Ha, while the nutrients in the organic plots were derived from the previous crops, which

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were mainly legumes15. Samples of 17 wheat cultivars and 2 breeding lines were also

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obtained from an organic wheat trial carried out at the University of Reading experimental

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farm (Sonning on Thames) in 2015-2016. Hence, the sets of material varied in geographical

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origin and year of harvest as well as farming system, providing a good example of the

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variation that could be expected in commercial samples. Available nitrogen was determined

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as ammonium nitrate.

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Details of the genotypes are given in Table S2 and of the sites and treatments in Table S3.

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Sample preparation for nuclear magnetic resonance (1H-NMR) spectroscopy was carried out

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according to the procedures described previously6,12. Wholemeal samples (30 mg) were

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extracted in triplicate using 80:20 D2O:CD3OD containing 0.05% d4– trimethylsilylpropionate

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(TSP) (1ml) as internal standard. 1H-NMR spectra were acquired under automation at 300

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°K using an Avance Spectrometer (Bruker Biospin, Coventry, UK) operating at 600.0528

Grain

H-NMR spectroscopy


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MHz and equipped with a 5mm selective inverse probe. Spectra were collected using a

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water suppression pulse sequence with a 90° pulse and a relaxation delay of 5 s. Each

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spectrum was acquired using 128 scans of 64,000 data points with a spectral width of

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7309.99 Hz. Spectra were automatically Fourier-transformed using an exponential window

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with a line broadening value of 0.5 Hz. Phasing and baseline correction were carried out

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within the instrument software. 1H chemical shifts were referenced to d4-TSP at δ0.00.

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BrukerBiospin), to ASCII files containing integrated regions or ‘buckets’ of equal width

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(0.001ppm) . Spectral intensities were scaled to the d4-TSP region (δ0.05 to –0.05). The

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ASCII file was imported into Microsoft Excel for the addition of sampling/treatment details.

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Known metabolites identified via comparison to a library of standard spectra collected under

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identical conditions. Individual metabolites were quantified against the known concentration

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of internal standard present in each sample and data was expressed as mg/g.

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Statistical analysis

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Multivariate statistical analysis (Principal Component Analysis (PCA) and orthogonal partial

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least squares discriminant analysis (OPLS-DA)) was conducted on quantified data using

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SIMCA-P software (version 13, MKS Umetrics). Statistical models were constructed using

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data scaled to unit variance.

H-NMR spectra were automatically reduced, using Amix (Analysis of MIXtures software,

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RESULTS

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Comparison of Broadbalk samples

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Figure S1 shows a typical 1H-NMR spectrum of a polar extract of wholemeal wheat flour.

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The central part of the spectrum, between about δ3 and δ4.3, comprises overlapping peaks

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corresponding to the most abundant carbohydrates (sucrose, maltose, raffinose, glucose,

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fructose). These peaks are flanked by aliphatic and aromatic regions, corresponding to

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anomeric protons of sugars, organic acids, amino acids and other polar low molecular mass

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components, including choline and glycine betaine. The assignments are described in detail

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by Baker et al.6 and Shewry et al.12. In order to compare the compositions of grain samples



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grown under different nitrogen regimes, the major metabolites in the spectra were quantified

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and compared by multivariate statistical analysis. Figure 1 shows a Principal Component

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Analysis (PCA) of the metabolite profiles of polar extracts from the Broadbalk samples, in

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which Principal Components (PCs) 1 and 2 account for 40% and 8% of the variation in the

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dataset, respectively. The different N applications are colour coded while the different

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symbols represent the four sections (see Materials and Methods). The nitrogen treatments

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range from 0 to 288 kg/Ha of N as ammonium nitrate, with the “organic” plots receiving 35

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tonnes of farm yard manure (FYM)/Ha. The latter contains about 240 kgN/Ha, but only half

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or less of this appears to be available to the crop17. Hence the nitrogen content of grain from

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the FYM treatment was most similar to that of the conventionally fertilised grain which

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received 96 kgN/Ha (Table S1).

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The PCA analysis in Figure 1A shows clear separation of the conventionally fertilised

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samples in PC2, from the lowest application at the top of the PC and the highest at the

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bottom, with the FYM samples overlapping with those that received 96 and 144 kgN/ha. The

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samples within each treatment show some separation in PC1, but this is not related to the

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individual sections. However, the most striking separation in PC1 was between the

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conventionally fertilised and FYM samples, with the latter being towards the left hand side of

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the plot.

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In order to identify the compounds responsible for the separations, loadings plots were

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prepared. Figure 1B therefore shows the compounds which are increased and decreased

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with high amounts of conventional fertilisation (corresponding essentially to PC2). It is not

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surprising that the major components which are elevated under high nitrogen are amino

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acids, (notably aspartate, asparagine and tryptophan), together with acetate and choline. By

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contrast, fructose and succinate are substantially reduced at high nitrogen. A similar loading

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plot comparing the FYM and conventionally-fertilized samples is shown in Figure 1C. In this

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case the major differences are increases in a range of amino acids (notably glutamate),

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sugars (notably sucrose) and organic acids in the FYM plots. However, it is notable that

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tryptophan, which responds significantly to nitrogen fertilisation in the conventionally


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fertilized samples (Figure 1B), differs little between the conventionally-fertilized and FYM

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samples (Figure 1C).

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In order to focus on the differences between the conventionally-fertilised and FYM samples,

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a supervised multivariate analysis, orthogonal partial least squares discriminant analysis

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(OPLS-DA), was used to compare only differences relating to the FYM and 96 kgH/Ha

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treatments (Figure 2). This clearly separated the two sets of samples in the X-axis, with both

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sets varying in the Y-axis. The contribution plot (Figure 2B) essentially confirmed the

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analysis in Figure 1C, although in this case maltose, tryptophan and glycine betaine are

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reduced in the conventionally-fertilized samples.

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Comparison of organic and low-input conventionally grown samples

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Although the Broadbalk experiment provides well characterised material from plots receiving

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conventional and organic fertiliser, it is not typical of commercial organic farming in that the

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other farming inputs are not organic, and the amount of nitrogen applied as FYM is very high

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compared with commercial systems in which leys, legumes and green manures are more

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widely used to provide sources of nitrogen. We therefore compared samples from organic

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and conventionally-fertilised low input field experiments, using organic wheat grown in

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Switzerland (36 genotypes), conventionally-fertilised wheat grown in Austria (36 genotypes),

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organic and conventionally-fertilised wheat grown at Wakelyn in the UK (3 cultivars), and

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organic wheat grown on a second UK site at Reading (19 genotypes). This material

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therefore represents a wide range of environments as well as cultivars and farming systems.

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Although the mean metabolite concentrations determined for the sample sets (Table S4)

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showed relatively small differences between treatments, PCA of the NMR profiles (Figure

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3A) (with PC1 and PC2 accounting for 35% and 26% of the variation, respectively) showed a

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clear separation into two groups, with the Reading organic samples being separated from

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the other sample sets. However, although the Swiss, Austrian and UK organic and low input

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samples form a single cluster, some separation is observed in both PCs, and this separation

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is shown more clearly in Figure 3B in which only these sample sets are analysed. This

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analysis accounts for 85% of the variation in the dataset, with PC1 accounting for 73% and



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PC2 12%. The UK samples were grown in the same location and clear separation between

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those grown under low input conventional and organic conditions. However, although the

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Swiss organic samples and the Austrian conventionally-fertilised low input samples are

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concentrated in the same areas of the PCA separation as the organic and conventionally-

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fertilised low input samples from the UK, respectively, they were widely spread with the two

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sets of samples overlapping.

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Nevertheless, despite the failure of PCA to clearly separate the organic and conventionally-

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fertilised samples, it is still possible to focus on the differences associated with nutrition

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using supervised multivariate OPLS-DA analysis (Figure 3C). This gives a good separation

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of the organic and low input samples, on the left and right hand sides of the separation,

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respectively, and also partial separation of the Reading organic samples from the other

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organic samples.

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The contribution plot (Figure 3D) for the OPLS-DA separation (Figure 3C) shows that, in

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contrast to the Broadbalk samples shown in Figure 2, the low input samples all have higher

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contents of metabolites than the organic samples, including a range of amino acids

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(including glutamine, glutamate, leucine, tryptophan and valine), raffinose (a trisaccharide),

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glucose, organic acids and choline. However, the organic samples were higher in some

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metabolites, notably sucrose, fructose and glycine.

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These analyses therefore suggest that the precise farming system (which differed between

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the Reading organic experiment and other samples) as well as the cultivar, year, type of

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nutrition and location affect grain composition. Although the present study was not intended

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to elucidate these effects, it should be noted that metrological data for the sites show wide

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variation in temperature and precipitation, including in final 100 days of growth (which

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includes grain development and desiccation) (Table S3), and that a previous study showed

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significant positive and negative correlations between grain components and these factors18 .

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DISCUSSION


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The Broadbalk FYM plots are not fully organic, and the amount of nitrogen applied is greatly

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above the amounts that are available to the crop in most organic systems. For example,

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although no nitrogen was applied to the organic treatments, the available soil nitrogen in the

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Reading plots was measured as 19.6 kg/Ha and in the Swiss plots as 43.1 kg/Ha, compared

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with 250 kg/Ha applied nitrogen (of which over half appears to be available) in the Broadbalk

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treatment. This contrasts with about 120 kg/Ha nitrogen applied in the Austrian and UK low

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input treatments. Hence, the lower proportions of metabolites, notably amino acids, in the

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organic samples may be related to the lower amounts of available nitrogen.

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Differences relating to total nitrogen application could be avoided when comparing the

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Broadbalk samples by selecting grain of similar nitrogen content to the FYM samples from a

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range of application rates. In this case, although the grain protein contents were similar, the

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grain from the FYM plots contained higher concentrations of a range of polar metabolites,

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including amino acids. Hence, it is to be concluded that the contents of polar metabolites,

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including many amino acids, are related to the farming system (presumable the form in

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which the nitrogen is available to the plant) as well as to the total nitrogen availability.

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Allowing for these effects, it is also of interest to determine whether nutrition affects the

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pattern of metabolites which are accumulated as well as their amounts and, in particular

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whether it is possible to identify a pattern which is diagnostic for organically grown grain.

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Comparing Figures 1C, 2B and 3D, they show that some components, such as glutamate

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and glutamine, appear to be elevated in response to total nitrogen. However, two

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compounds differ from this trend, being only marginally increased in the Broadbalk FYM

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samples, and clearly lower in the organic samples. These are tryptophan and choline.

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Several other studies have also used metabolite profiling to compare grain from

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conventionally grown and organic wheat and generally showed little difference8,19. Bonte et

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al.9 showed that 5 metabolites differed consistently in concentration between 11 cultivars

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grown under conventional and organic conditions, including tryptophan which was

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consistently lower in the organic samples. However, they also showed lower concentrations

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of alanine and GABA in the organically-grown grain, whereas these were only slightly lower



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and slightly higher, respectively, in the organic samples shown in Figure 3, and both were

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increased in the Broadbalk FYM samples. The study did not report the concentration of

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choline, so the only consistent effect observed in the two studies is the decreased

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concentration of tryptophan.

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Hence, it is concluded that although conventionally-grown and organic wheat grain do exhibit

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differences in metabolite profiles related to the farming system, these are not consistent

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between sites and hence metabolite composition cannot be used as a diagnostic criterion to

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identify organic samples.

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ACKNOWLEDGEMENTS

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Swiss organic samples were kindly provided by Jürg Hiltbrunner (Agroscope, Zürich,

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Switzerland)) and Austrian low input samples kindly provided by Franziska Löschenberger

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(Saatzucht Donau GmbH & Co KG, Probstdorf, Austria). UK (Waklyns) samples were

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developed in the frame of the FP7 program of the European Community under Grant

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Agreement No. 245058-SOLIBAM. UK (Reading) samples were developed as part of the

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FP7 program of the European Community Wheatalbi under Grant Agreement No FP7-

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613556 and were provided by Caroline Hadley (Crop Research Unit, University of Reading,

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UK). We thank Andy Macdonald and colleagues at Rothamsted Research for providing the

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Broadbalk samples and Margaret Glendining for providing data in Table S1 from the

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Rothamsted Electronic Archive (e-RA) (http://www.era.rothamsted.ac.uk/).

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FUNDING SOURCES

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Rothamsted Research receives grant-aided support from the Biotechnology and Biological

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Sciences Research Council (BBSRC) of the UK and the work forms part of the Designing

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Future Wheat strategic programme (BBS/E/C/000I0250 ). The Broadbalk experiment is part

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of the Rothamsted Long-term Experiments National Capability (LTE-NCG) supported by the

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BBSRC (BBS/E/C/000J0300) and the Lawes Agricultural Trust.


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Supporting Information

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Table S1, Details of grain samples harvested in 2016 from the Rothamsted Broadbalk

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experiment.; Table S2, Details of genotypes, sites and farming systems of grain samples;

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Table S3, Growing conditions and management practices at organic and low input fields of

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the three countries (2012/2013); Table S4, Mean concentrations of polar metabolites (mg/g)

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in the sets of low input and organically grown wheat samples; Figure S1, Typical 600 MHz

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1

H NMR spectrum of wholemeal flour extracted with 20:80 CD3OD:D2O.

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345

HEALTHGRAIN Diversity Screen. J. Agric. Food Chem. 2010, 58, 9372-9383.

346

(19) Belaggia, R.; Platani, C.; Nigro, F.; De Vita, P.; Cattivelli, L. Effect of genotype,

347

environment and genotype-by-environment interaction on metabolite profiling in

348

durum wheat (Triticum durum Desf.) grain. J. Cereal Sci. 2013, 57, 183-192.

349



A

PC2, 8%

3

0 Kg/ha FYM 48 Kg/ha 96 Kg/ha 144 Kg/ha 192 Kg/ha 288 Kg/ha Section 0 Section 1 Section 4 Section 9

0

2

48

1

96

0 -1

144 192

-2

288

-3 -4 -10 -8 -6 -4 -2 0

2

4

6

8

Reduced in High N

-2

Acetate Glycine Betaine Choline

Fumarate

Valine

Succinate Glutamine Glycine Alanine Aspartate Asparagine GABA Isoleucine Leucine Tryptophan Valine Malate Succinate Fumarate Acetate Glycine Betaine Choline

Fructose Sucrose

1

Maltose

2

Raffinose

3

βGalactose α-Galactose β-Glucose α-Glucose Fructose Galactinol

Elevated in FYM

C

Glutamate

-4

Malate

-1

GABA

Glycine

0

-3

Score Contribution

Leucine

1

Raffinose

Score Contribution

2

Aspartate Asparagine

Elevated in High N

3

Alanine

B

Tryptophan

PC1, 40%

0 -1 -2

350 351

Figure 1. Principal Component Analysis (PCA) of polar metabolite profiles from the

352

Rothamsted Broadbalk samples.

353

A: PCA Scores plot, coloured according to fertilisation regime; B: Contribution plot

354

comparing High and Low N; C: Contribution plot comparing FYM samples and N fertilised

355

samples


Page 14 of 18

Page 15 of 18


356

A

6 1.52611 * to[1]

4

FYM 96 Kg/ha

2 0

Section 0 Section 1 Section 4 Section 9

-2 -4 -6 -8 -2 0 2 1.0008 * t[1]

4

6

1 0.5

Valine Malate Succinate

2 1.5

βGalactose α-Galactose β-Glucose α-Glucose Fructose Galactinol Glutamate Glutamine Glycine Alanine Aspartate Asparagine GABA Isoleucine Leucine

2.5

Raffinose Sucrose

Elevated in FYM

Choline

-4

Fumarate

B

-6

Acetate

-8

-2

357

Glycine Betaine

Maltose

-1 -1.5

Tryptophan

0 -0.5

358

Figure 2. Orthogonal partial least squares discriminant analysis (OPLS-DA) of polar

359

metabolite profiles of grain samples from the Rothamsted Broadbalk experiment (FYM and

360

96 Kg N/Ha only).

361

A: Scores plot, coloured according to fertilisation regime; B: Contribution plot comparing

362

FYM and 96 Kg N/Ha



A

Reading Organic Swiss Organic UK Organic Austrian Low Input UK Low Input

8

R-O

6

PC1, 26%

4 2

A-LI

0

Page 16 of 18

-2

S-O

-4 -6 -8 -10

-8

-6

-4

-2

0

2

4

6

t[1]

PC1, 35%

B 8

Swiss Organic UK Organic Austrian Low Input UK Low Input

6 PC2, 12%

4 2 0 -2 -4 -6 -0.4

-0.2

0

2

4

6

PC1, 73%

C

Organic Low Input

Reading Organic Swiss Organic UK Organic Austrian Low Input UK Low Input

6 4 2 0 -2 -4 -6 -8 -6

D

-2

0 1.00031 * t[1]

2

4

Elevated in Low Input samples


Choline

Glutamate Glutamine

-1.5

β-Glucose α-Glucose

-0.5

Alanine Aspartate

0.5

Leucine Tryptophan Valin e Malate Succinate Fumarate Acetate

GABA Isoleucine

Glycine

Fructose

Sucrose

1.5

Raffinose

Score Contribution

Elevated in Organic samples

-2.5

363

-4

Page 17 of 18


364

Figure 3. Multivariate analysis of grain from organic and conventionally fertilised field trials in

365

the UK (Wakelyns organic and low inputs; Reading organic), Austria and Switzerland.

366

A: PCA Scores plot, coloured according to fertilisation regime and growth location; B: PCA

367

scores plot of grain grown in the UK (Wakelyns only), Austria and Switzerland, coloured by

368

fertilisation regime and location; C: OPLS-DA analysis of grain grown in the UK, Austria and

369

Switzerland, coloured according to fertilisation regime and growth location; D: Contribution

370

plot comparing organic samples and conventional low input samples

371 372 373 374 375 376 377 378 379 380 381 382 383 384 385



386

TOC graphic

Choline

GABA

Elevated in low input samples

Alanine Tryptophan Valine Malate Succinate Fumarate Acetate

-2.5

Glutamate

-1.5

β-Glucose α-Glucose

-0.5

Alanine Aspartate

0.5

Raffinose

Score Contribution

1.5

Elevated in organic samples sa=sasamplessamples Glycine

Sucrose

388

Fructose

387


Page 18 of 18

Effects of Organic and Conventional Crop Nutrition on Profiles of Polar

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