Effects of Cultivar and Nitrogen Nutrition on the Lipid Composition of

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Effects of Cultivar and Nitrogen Nutrition on the Lipid Composition of Wheat Flour Byoung Min, Irene Gonzalez-Thuillier, Stephen J. Powers, Peter Wilde, Peter R. Shewry, and Richard P. Haslam J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b01437 • Publication Date (Web): 14 Jun 2017 Downloaded from http://pubs.acs.org on June 20, 2017

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Journal of Agricultural and Food Chemistry

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Effects of Cultivar and Nitrogen Nutrition on the Lipid Composition of Wheat

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Flour

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Byoung Min1, Irene González-Thuillier1, Stephen J. Powers2, Peter Wilde3, Peter R

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Shewry1* and Richard P Haslam1

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Plant Science, Rothamsted Research, Harpenden, AL5 2JQ, United Kingdom

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Computational and Analytical Sciences, Rothamsted Research, Harpenden, AL5

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2JQ, United Kingdom

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NR4 7UA, United Kingdom

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Quadram Institute Bioscience, Institute of Food Research, Norwich Research Park,

*Author for correspondence: [email protected]

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1 ACS Paragon Plus Environment


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ABSTRACT: Despite being minor components of flour, wheat (Triticum aestivum L.)

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lipids contribute to grain processing. They are particularly important for bread making

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where they adsorb to the surface of gas bubbles formed during the proving stage of

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bread making, stabilizing the gas cells and improving gas retention within the dough.

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This contributes to the volume and texture of the loaf. However, little is understood

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about how their amount, composition and properties vary in response to genotype

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(G), environment (E) or G x E interactions. Six wheat lines were therefore grown at

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three levels of nitrogen supply at Rothamsted Research and 48 lipid species across

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six lipid classes were identified and quantified in white flour using electrospray

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ionization-tandem mass spectrometry (ESI-MS/MS). This showed clear differences

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in the contents and compositions of lipids between cultivar, as well as effects of

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nitrogen fertilization, which would be expected to have impacts on the processing

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properties of the samples.

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KEY WORDS: wheat grain, lipids, bread making, lipidomics, genotype, environment

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INTRODUCTION

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Wheat (Triticum aestivum L.) flour is widely used for food production, including

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breads, other baked products, pasta, noodles and as an ingredient in many

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processed goods such as sauces and processed meats. White flour is derived from

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the starchy endosperm storage tissue of the grain and comprises mainly starch

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(about 75-85%) and protein (about 10%). These two major components largely

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determine the processing properties, with the gluten proteins determining the visco-

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elastic properties of dough that underpin many of its uses, including bread making.

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However, wheat flour also contains a range of other components including about 2.0

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to 2.5% lipids.1-3

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Despite their low concentrations, lipids have an impact on the quality of wheat flour

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for bread making, affecting the volume and crumb structure of the loaf.4 These

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effects are not completely understood, but they are thought to include indirect effects

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by binding to and plasticising the gluten network, and direct effects by stabilizing the

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structure of gas cells which are formed during dough mixing.5-6 These gas cells

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entrap the carbon dioxide released by yeast during fermentation leading to the

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expansion of dough mass, increasing its volume and determining the crumb

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structure. The importance of lipids in bread making has also been shown by recent

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studies in which specific lipases have been used to optimise lipid composition and

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improve processing quality.7-8 Therefore, understanding the processes that define

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grain lipid content and composition is critical to the development of wheat varieties

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with optimized quality for bread making.

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Lipids are present throughout the wheat grain and include phospholipids (PL) which

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are structural components of membranes in all tissues, and triacylglycerols (TAG)



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which are stored in oil bodies in the aleurone layer and embryo. PLs and TAGs are

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acyl lipids in that they contain fatty acids esterified to a glycerol backbone, while fatty

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acids also occur in their free form (FFA). Wheat flour lipids are often divided into

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starch and non-starch lipids. Starch lipids are intrinsic components of starch granules

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and as such they can only be extracted when the starch granules are broken down

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or damaged. They do not play a significant role in determining the properties of

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dough, and therefore do not affect the final quality of the bread.9 By contrast, non-

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starch lipids have significant effects on dough properties and product quality.6,9,10,11

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Lipidomic analysis has previous been used to show that lipid remodelling takes place

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in vegetative tissues of resilient wheat cultivars when exposed to temperature

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extremes.12-13 However, although two studies have used detailed lipidomic

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approaches to determine the compositions of lipids in wheat flour,14-15 currently little

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is known about the extent to which lipid composition varies between genotypes, or is

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affected by environmental factors (notably temperature and water availability), or by

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crop nutrition, in particular by the application of nitrogen fertilizer which varies greatly

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between production systems. This information is crucial to enable grain processors

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to predict and exploit differences in lipid composition to improve the quality and

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consistency of products. We have therefore applied a lipidomic approach to

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determine the contents and compositions of the major lipid classes (PLs, TAGs,

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diacylglycerols (DAGs) and FFAs) in five UK hard bread making wheat cultivars and

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one soft feed wheat, comparing material grown at three levels of nitrogen

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fertilization: 100 kg/Ha (low input), 200 kg/Ha (typical of UK intensive production) and

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350 kg/Ha (luxury supply).

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MATERIALS AND METHODS

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Samples. Six UK wheat cultivars were grown on the experimental farm at

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Rothamsted Research, Harpenden, UK in 2013 as part of the Wheat Genetic

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Improvement Network (WGIN) nitrogen use efficiency trial.16 These were five modern

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hard bread making cultivars (Avalon, Cadenza, Crusoe, Hereward and Malacca) and

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one soft feed cultivar (Istabraq).

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Cultivars were grown in triplicate 9 x 3 m plots at three levels of nitrogen fertilization,

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100, 200 and 350 kg/Ha using ammonium nitrate. 200 kg/Ha (200N) is typical for

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intensive wheat production in the UK, while 100 kg/Ha (100N) is more typical of less

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intensive systems used in many other countries. The highest application level of 350

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kg/Ha (350N) was above those typically used in crop production but was included to

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explore the effects of unusually high nitrogen availability.

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Plots were randomized within main plots of the nitrogen treatments. Grain was

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pooled from the three plots of each combination to obtain representative samples. 50

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g samples of grain per combination were then milled using a Chopin CD1 mill, which

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complies with the NF EN ISO 27971 standard, after being conditioned to a 14%

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moisture content. Straight grade flour was obtained for each sample, after

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undergoing a break and reduction stage and sifting through a centrifugal sifter. Flour

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samples were stored at -20°C. Starch damage was measured using the Megazyme

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starch damage kit (Megazyme, Bray, Ireland).

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Lipid Extraction. Non-starch lipids were extracted from 5 replicate samples of each

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flour as described by Finnie et al14 with some modifications. 150 mg of flour in a

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glass tube was heated in boiling water for 12 mins to inactivate lipid-hydrolysing 5 ACS Paragon Plus Environment


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enzymes. Samples were then extracted sequentially with petroleum ether (PEt),

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water-saturated butan-1-ol (WSB), and propan-2-ol/water (90:10) (IW). The PEt and

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WSB extracts were washed by shaking with 0.88% KCl in a 1:1 ratio for 2 mins at

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18000 rpm. The supernatants from the PEt and WSB extractions were combined,

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evaporated under nitrogen at 40 °C, re-suspended in an equal volume of chloroform

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and washed with 0.88% KCl. The lower phase was retained, filtered through a

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0.45µm Millex-FH filter (Merck Millipore, Germany), dried under a stream of nitrogen

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gas and re-suspended in 2mL of chloroform. These were then flushed with nitrogen

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and stored at -20 °C.

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Quantitative Lipid Analysis. Electrospray ionisation tandem triple-quadrupole mass

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spectrometry (4000 QTRAP; SCIEX; ESI-MS/MS) was used to quantify the major

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types

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phosphatidylethaloamine (PE), lysophosphatidylcholine (LPC), and the neutral lipids

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(NLs) FFA, DAG and TAG. Monoacylglycerols (MAG) were not analysed due to their

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low levels in flour. Internal standards for polar lipids were obtained from Avanti

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(Alabaster, AL, USA) and were incorporated as 0.857 nmol of 13:0-LPC, 0.086 nmol

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of di18:0-PI, and 0.080 nmol of di14:0-PE, 0.800nmol of di18:0 PI, and 0.080 nmol of

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di14:0-PG, dissolved in chloroform. 25 µL of sample dissolved in chloroform was

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combined with the standard and chloroform: methanol:400mM ammonium acetate

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(300:665:3.5 v/v) to make a final volume of 1ml. The lipid extracts were infused at

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15uL/min with an autosampler (HTS-xt PAL, CTC-PAL Analytics AG, Switzerland).

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Data acquisition and acyl group identification were as described by Gonzalez-

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Thuillier et al15 with modifications

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FFAs were analysed by combining 25 µl of sample, 0.607 nmol of 15:0-FFA (Sigma-

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Aldrich,

of

St

lipid

Louis,

in

wheat

MO,

USA)

flour:

the

and

PLs

phosphatidylcholine

propan-2-ol/methanol/50mM 6

ACS Paragon Plus Environment

(PC),

ammonium

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acetate/dichloromethane (4:3:2:1; v/v) to a final volume of 1ml, quantified by the Q1

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ESI-MS negative mode. DAG and TAG contents were quantified using the ESI-

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MS/MS method described by Li et al

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sample and 0.857 nmol of tri15:0-TAG (Nu-Chek Prep, Elysian, MN, USA) was

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combined with chloroform/methanol/300 mM ammonium acetate (24:24:1.75: v/v),

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and for DAG, 25uL of sample and 0.857 nmol of 18:0-20:4-DAG (Sigma-Aldrich)

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were

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acetate:dichloromethane (4:3:2:1; v/v), to the final volume of 1mL for direct infusion

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to the mass spectrometer. TAG and DAG were detected as [M+NH4]

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series of different neutral loss scans, targeting the losses of fatty acids. The scans as

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well as the parameters used for the three NLs are shown in Table S1 of the

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Supporting Information. The data were processed using the Lipid View Software

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(Sciex, Framingham, MA, USA) where isotope corrections were applied. The peak

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area for each lipid was normalized to the internal standard and further normalized to

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the weight of the initial sample.

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The ionization efficiency varies among acyl glycerol species with different fatty acyl

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groups, and response factors for individual species were not determined in this

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study. Consequently, the values are not directly proportional to the TAG/DAG

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contents of each species. However, the approach does provide a valid comparison

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of TAG/DAG species between samples.

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Determination of Total Fatty Acid Methyl Esters (FAMEs). Methyl ester

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derivatives of total fatty acids extracted were analysed by GC-FID (flame ionization

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detection) using a gas chromatography-flame ionization detector (GC-FID, Agilent

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6890, Palo Alto, CA, USA) with an AT-225 capillary column of fused silica (30 m

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length, 0.25 mm id, 0.20 µm film thickness). The oven temperature cycle was set

combined

with

17

with some modifications. For TAG, 15 µl of

propan-2-ol:

methanol:50


mM

ammonium

+

ions by a


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with a start temperature of 50 °C which was held for 1 min to allow vaporized

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samples and solvent (hexane) to condense at the front of the column. The oven

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temperature was then raised rapidly to 190 °C at a rate of 40 °C/min followed by a

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slower increase to 220 °C, which was held for 1 min, giving a total run time of 25 min

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and 50 s per sample. Hydrogen was used as the carrier gas. FAMEs were identified

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by comparison with known standards (37 FAMEs, Sigma, St Louis MO) and

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confirmed by GC-mass spectrometry (Agilent 6890N, Palo Alto, CA, USA).

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Validation of Quantification. Phospholipids are more precisely determined by MS

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than FFA and TAG, which are overestimated and underestimated, respectively.

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Correction factors were therefore determined and applied to the data for FFA and

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TAG. Equal volumes of lipid extractions from 28 flour samples were taken and

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quantified using two systems: ESI-MS/MS as described above, and by thin layer

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chromatography – gas chromatography (TLC-GC). TLC-GC was carried out by

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adding 50 µg of 45:0 TAG/15:0 FFA standard to each sample, and separating out the

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lipid extracts on a silica gel TLC (plate thickness = 0.25 mm) using the solvent

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hexane / diethyl ether/acetic acid (150:50:2 by volume). The individual lipid classes

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were identified under UV light after spraying with primuline (0.05% w/v in

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acetone/water, 80:20 v/v), and the TAG and FFA were scraped from the plate and

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directly methylated for FAME analysis.

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The quantity of each fatty acid was calculated in comparison to the internal standard

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and normalized for sample weight (g of flour). The values (nmol/g flour) from the two

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procedures were compared in a scatter plot. The data for FFA were directly

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compared, whereas for TAG, the data for molecular species determined by ESI-

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MS/MS systems were compared to the sum of FAMEs determined by GC-FID. The

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sum of FAMEs provides a valid representation of the quantities of TAG comparable 8 ACS Paragon Plus Environment

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to that measured by ESI-MS/MS. (see Figure S1A and S1B of the Supporting

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Information)

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The GenStat statistics package (2016, 18th edition © VSN International Ltd, Hemel

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Hempstead, UK) was used to fit an asymptotic exponential model using the method

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of non-linear least squares to estimate two parameters (the asymptote and the

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exponential rate) with standard error for both GC-FID (FAMES) FFA and TAG in

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terms of the ESI-MS/MS (QTRAP) equivalents. The SigmaPlot package (13th edition,

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Systat Software Inc, San Jose, California, USA) was used to produce the picture of

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the fitted curve on the scatter plot (Figure S1A, B). The model allowed prediction of

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values of FFA and TAG for one method given values for the other.

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Statistical Analysis. The dataset included a small number of missing values.

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Estimates of these values were therefore initially made based on the lipid means

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within each cultivar by nitrogen combination using the values for units that had no

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missing values for any lipid. The estimates were then recalculated as the fitted

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values from the multiple regression of that lipid on all the other lipids and the lipid

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means recalculated. If any of the means differed from the previous mean by more

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than a proposed tolerance (the initial standard error divided by 1000) the process

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was repeated.18-19

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This allowed the calculation of the percentage data per lipid for analysis, using total

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quantity per sample lipid profile without the issue of missing values biasing the

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calculation of the percentages.

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Following this, Canonical Variates Analysis (CVA) was performed to give a linear

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discrimination between the cultivar by N combinations and to provide a low

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dimensional representation of the differences between them, so that (in two



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dimensional plots) biologically meaningful differences could be tentatively assigned

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using non-overlapping 95% confidence circles around the means of CV scores per

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treatment combination, assuming of a multivariate Normal distribution for the data,

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although the differences cannot be assigned statistical significance because the

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replicates were technical rather than biological. However, with five technical,

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replicates the total number of units, 90, minus the number of treatment combinations,

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18, is 72 which is greater than the number of variables (lipids), 48, required for a

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statistically valid CVA. The distributional assumption was acceptable having applied

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a natural log (to base e) transformation to the data.

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The magnitudes of the CV loadings on the variables (quantified lipids) indicated the

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relative importance of the original lipids in the discrimination observed (see

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Supplementary Data S6 for loadings). This allowed only the most relevant results of

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univariate analysis of variance (ANOVA) to be interpreted, testing the main effects

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and interactions between the factors of cultivar and nitrogen (F-tests) for each lipid

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on the log scale, but again with the caveat of the replication being technical rather

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than biological. The GenStat statistics package (18th Edition, © VSN International

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Ltd, Hemel Hempstead, UK) was used for this analysis.

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RESULTS AND DISCUSSION

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In order to determine the effects of genotype and nutrition on grain lipid composition,

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six UK wheat cultivars, comprising five hard bread making cultivars (Avalon,

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Cadenza, Crusoe, Hereward and Malacca) and the soft cultivar Istabraq (used for

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making biscuits and livestock feed), were analysed from field trials grown with three

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levels of nitrogen fertilization.

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Mature grain from the replicated field trial was milled using a Chopin CD1 mill to

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produce white flour. This mill gives low levels of starch damage (determined as

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ranging from 4 to 6% in the present study, Supporting Information Figure S2) which

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would minimise the extraction of intrinsic starch granule lipids which do not affect

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bread making quality.9 ESI-MS/MS

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classes of NLs (DAG, TAG and FFA) and PL (PC, LPC and PE). The notation for

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FFA species reflects the number of carbons and double bonds (for example, oleic

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acid is C18:1) while the notation for species of neutral lipids and PL reflects the total

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numbers of double carbons and double bonds for the acyl chains.

15

was used to identify and quantify the major

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Relative abundance of lipids. An ESI-MS/MS approach was used to compare the

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amount of each lipid molecular species in the different grain samples. The lipid

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molecular species were identified by precursor or neutral loss scanning, and the

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lipids in each head group class were quantified in comparison with internal standards

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of that class. Acquisition of MS data requires long periods of sample infusion.

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Therefore, a quality-controlled approach was employed to remove any instrument or

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analytical variation from the data acquisition process. For example, the intensity of

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lipid species in samples was normalized to internal standards run with the biological



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extractions and separately. Furthermore, each sample infusion was replicated and

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the order of survey scans (Supporting Information Table S1) was changed, for

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example, the position of the PC 184 m/z head group scan was varied between the

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first and last positions within the analytical run. Lipids account for about 1.4-2.0% of

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the dry weight of white wheat flour20 with FFA, TAG, DAG, PC, LPC and PE

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accounting for over 90% of the total (15 and authors’ unpublished results). Minor lipid

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components, comprising galactolipids and minor phospholipids, were not analysed.

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The lipid data are presented as a mol% of the total lipids, obtained by normalizing

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the peak area of each lipid to the internal standard which was further normalized to

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the weight of the initial flour sample.

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Of the lipids extracted from white flour in this study, FFA were generally the most

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abundant class, accounting for an average of 31% of the lipids analysed. These

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were followed by LPC, TAG and DAG, which accounted for averages of 27%, 23%

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and 11% of the total lipids, respectively (Figure 1). The observed percentages agree

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with Chung et al

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used samples grown at about 200 kgN/Ha), although in the present study TAGs

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were more abundant than FFA or LPC for Cadenza and Crusoe grown at 350N.

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Nevertheless, other authors have reported similar results, with TAGs being more

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abundant than FFAs in four wheat cultivars20.

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The proportions of individual species within lipid classes also varied. Among the ten

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FFA species, C16:0 and C18:2 comprised about 22% and 48% of the total FFA,

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respectively, while LPC species LPC 16:0 and LPC 18:2 represented 86% to 88% of

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total LPC. Among the ten species of PC, PC 34:2 and PC 36:4 comprised 37% and

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32%, of the total, respectively. TAGs comprised 13 species with TAGs 52:4, 54:5

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and 54:6 comprising about 20%, 18% and 24% of the total, respectively.

21

and the more recent study by Gonzalez-Thuillier et al


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(which

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Effects of Genotype and Nitrogen on Lipid Classes. In order to test the main

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effects and interactions between the six cultivars and three nitrogen conditions, firstly

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the total amounts of six lipid classes (LPC, PE, PC, FFA, TAG and DAG), together

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with total PL (LPC, PC, PE) and total NL (FFA, TAG and DAG) were analysed by

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Analysis of Variance (ANOVA) (Table 1).

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No main effects of cultivar and nitrogen, or of cultivar x nitrogen interactions, were

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observed for total LPC, total DAG or total PL. However, cultivar strongly affected the

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levels of PC, PE, TAG and total NL, while nitrogen treatment had the greatest effects

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on total NL, FFA, TAG and PE. Although interactions between cultivar and nitrogen

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were less significant than the main effects of cultivar and nitrogen alone, they did

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significantly (p < 0.05, F-test) affect the levels of PE, FFA and total NL (Table 1).

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Although cultivar had the greatest effects on PC and PE, there was no effect on LPC

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and hence on total PL. By contrast, total NL was significantly (p < 0.05, F-test)

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affected by both factors and the interactions between them. The effects on NL

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probably resulted mainly from effects on TAG species, which were greatly affected

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by both cultivar and nitrogen (see Table 1). However, there was no effect of cultivar x

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nitrogen on total TAG. Total FFAs were not affected by cultivar but were significantly

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(p < 0.05, F-test) affected by nitrogen, and by cultivar x nitrogen.

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Effects of Genotype and Nitrogen on Lipid Species. Differences in PL

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composition between cultivars were observed with Cadenza having lower

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proportions of total PC and PE species than the other cultivars (Figure 2). The

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proportions of individual PE species also varied between cultivars and nitrogen 13 ACS Paragon Plus Environment


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inputs (Supporting Information Figure S4B). Statistical analyses (p-values from

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ANOVA F-tests) of the effects of cultivar, nitrogen and cultivar x nitrogen on the

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individual PL species are shown in Supporting Information Table S2. All nine PC and

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all three PE species were significantly (p < 0.05, F-test) affected by cultivar, with four

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PC species and all three PE species also being affected by cultivar x nitrogen. Only

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one out of five LPC species (LPC 18:0) was affected by cultivar and cultivar x

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nitrogen.

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The compositions of NLs differed to a limited extent between cultivars, but greater

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differences were observed depending on nitrogen supply, particularly for Crusoe

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grown at N100 and N200 (Figure 2). Statistical analysis of the NL species showed

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that only five of the twelve TAG species (TAG 50:1, 52:2, 52:5, 54:3 and 56:5) were

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significantly (p < 0.05, F-test) affected by cultivar and three of these (TAG 50:1, 52:5,

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56:5) by nitrogen and cultivar x nitrogen (Supporting Information Table S4). Six of

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the nine DAG species were also affected by either one or both main effects, with

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only DAG 32:0 being affected by the interaction. Although ten species of FFA were

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determined, only four of these showed significant (p < 0.05, F-test) effects of

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nitrogen, with none being affected by cultivar or cultivar x nitrogen.

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In broad terms, these analyses show that PLs were generally more affected by

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cultivar and NLs by nitrogen.

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Multivariate Statistical Analysis. Canonical Variates Analysis (CVA) was used to

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obtain a linear discrimination between the cultivar by nitrogen combinations and

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allowed a low-dimensional representation of the differences to be made (in two-

321

dimensional plots). Bearing in mind the technical nature of the replication, this


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allowed tentative biological differences to be assigned by non-overlapping 95%

323

confidence circles around the means of the CV scores for each treatment

324

combination, assuming a multivariate Normal distribution for the dataset on the log-

325

scale. The magnitudes of the CV loadings on the variables (quantified lipids) indicate

326

the relative importance of the lipids in the discrimination observed. In this way, the

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effects of cultivar, nitrogen and cultivar x nitrogen on the lipid composition could be

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identified.

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The first three CVs accounted for 66.17% of the total variance and the possible

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discrimination in the dataset. DAG 36:4, DAG 36:3, FFA 16:1 and PC 34:1 were the

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most important lipids for separation in the first CV dimension, TAG 52:5, FFA 18:2,

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TAG 54:7, TAG 54:3, FFA 24:0, TAG 50:2, LPC 16:0, DAG 36:4 and DAG 36:5 for

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CV2 and LPC 16:0, LPC 18:2, DAG 36:3, PC 34:1, TAG 52:5, DAG 36:5, FFA 18:2,

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FFA 24:0, DAG 32:0, PC 36:2 and FFA 18:1 for CV3. The full data regarding the

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loading values for the dataset, which determines the “important” lipids, are given in

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Supporting Information, Table S3.

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CV1 clearly separates Istabraq at all three nitrogen levels and Avalon grown at 100

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and 200kg/N/Ha from the other cultivars and nitrogen treatments (Figure 4).

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Cadenza is separated from the other cultivars in the CV2 dimension, particularly the

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samples grown at 200 and 350kg/N/Ha. In broad terms, the separations in CV1 and

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CV2 are determined more by differences between cultivars than between nitrogen

342

treatments. Istabraq is particularly well separated from the other cultivars by CV3

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with good separation also of Hereward grown at 350N and Crusoe grown at 200N.

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GENERAL DISCUSSION



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Comparison of six cultivars grown at three levels of applied nitrogen showed that

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cultivar and nitrogen had effects on the lipid composition of the white wheat flour,

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with stronger effects on PC, PE and TAG species than on other lipid species.

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PLs were most affected by cultivar, in terms of total classes and individual PC and

350

PE species, but little effect on LPC was observed. The proportions of LPC

351

determined here were also higher than in other studies.22,14 This may have resulted

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from partial hydrolysis of PC by phospholipase A2 during storage of the flour,

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although we failed to detect increases in PC in white flour stored over a period of 3

354

months (authors’ unpublished results). We therefore consider that it is more likely to

355

have resulted from endogenous enzyme activity in the grain before milling, as LPC is

356

produced from PC during germination.23

357

PLs have been shown to have a positive effect on loaf volume,24 acting as

358

emulsifiers to improve the baking performance of wheat dough.25-26 They have also

359

been shown to interact with gluten proteins, which may contribute to increased loaf

360

volume and improved dough strength.27 The effects of cultivar and nitrogen on PC

361

and PE demonstrated here may therefore have implications for processing quality.

362

The amount of TAG was significantly (p < 0.05, F-test) affected by both cultivar and

363

nitrogen. TAG is hydrolysed to DAG and FFA during germination and this may also

364

occur during flour storage at room temperature. High levels of FFA are undesirable

365

for food production as oxidation may lead to rancidity while NLs have been reported

366

to destabilize gas cells and negatively affect loaf volume.4 Unsaturated FFA in

367

particular have been reported to reduce bread loaf volume.28-29 FFA do not usually

368

accumulate in healthy living tissues and it is therefore likely that the high levels of

369

FFA reported here result largely from TAG degradation, either in the grain or stored


Page 16 of 31

Page 17 of 31


370

flour. The degradation of TAG into FFA has been widely reported in various

371

systems30 while our own studies have shown increases in FFA content together with

372

decreases in TAG in wheat flour.31

373

Multivariate analysis showed tentative differences between cultivars, with Cadenza

374

and Istabraq being the most distinct, followed by Avalon. The other three cultivars,

375

Crusoe, Hereward and Malacca showed no differences, except for Crusoe and

376

Malacca at N200 and Hereward at N350. In broad terms, the variation in lipid

377

composition between the cultivars was greatest at a nitrogen input of 200kg/N/Ha,

378

which is the typical level of fertilization for intensive wheat production in the UK.

379

The separation of Istabraq is not surprising as it is the only soft wheat in this study.

380

Several studies have shown a correlation between endosperm texture and the

381

amount and/or type of lipid. For example, hard Australian wheat cultivars have higher

382

levels of hexane-extractable free lipids than soft cultivars

383

relationship was reported between hardness and total PL content in British

384

cultivars.33 Cadenza is a hard bread making wheat but differs from the other five

385

cultivars in being a spring type (though it is winter hardy and generally sown in

386

autumn in the UK). Hence, the differences in composition between Cadenza and the

387

other cultivars could relate to their pedigree, as spring and winter breeding programs

388

generally use different sets of germplasm. Other authors have also reported

389

differences between the lipid profiles of cultivars, with Hargin and Morrison

390

comparing four bread wheat varieties and Beleggia et al (2013) four durum wheat

391

cultivars.35

392

We have therefore demonstrated effects of genotype and nitrogen nutrition on the

393

content and composition of PL, TAG and FFA, which could have impacts on dough


32

while a strong inverse

34


Page 18 of 31

394

properties and bread making performance. However, although this is the most

395

detailed study so far reported, it should be noted that only a single harvest year was

396

studied.

397

environmental factors can be expected, including those demonstrated here related to

398

genotype and agronomy. Consequently, the exploitation of differences in processing

399

quality resulting from lipid composition by wheat breeders and wheat processors

400

poses a challenge.

Hence,

year-to-year

differences

between

grain

samples

due

to

401

402

AUTHOR INFORMATION

403

Corresponding author

404

*Email: [email protected]

405

Funding

406

This work was supported by the BBSRC Crop Improvement Club (BB/J019526/1

407

“The role of lipids in determining gas bubble retention in wheat dough”) Rothamsted

408

Research and the IFR recieve strategic funding from the Biotechnological and

409

Biological Sciences Research Council (BBSRC).

410

Notes

411

The authors declare no competing financial interest.

412

ABBREVIATIONS USED

413

AV, Avalon; CA, Cadenza; CR, Crusoe; CVA, Canonical Variates Analysis; DAG,

414

diacylglycerols; ESI-MS/MS, electrospray ionization tandem triple quadrupole mass

415

spectrometry; FAME, fatty acid methyl ester; FFA, free fatty acid; GxE, genetic x


Page 19 of 31


416

environmental; HE, Hereward; IS, Istabraq; KCl, potassium chloride; LPC,

417

lysophosphatidylcholine; MA, Malacca; N, nitrogen; NL, neutral lipid; PC;

418

phosphatidylcholine; PE, phosphatidylethanolamine; PEt, petroleum ether; PL,

419

phospholipids; TAG, triacylglycerol; TLC-GC-FID, thin layer chromatography-gas

420

chromatography; WGIN, Wheat Genetic Improvement Network; WSB, water

421

saturated butal-1-ol

422

423

ACKNOWLEDGEMENTS

424

The work reported here was supported by the Crop Improvement Research Club by

425

a PhD studentship to BM as part of the grant BB/J019526/1 “The role of lipids in

426

determining gas bubble retention in wheat dough”). Rothamsted Research and IFR

427

receive strategic funding from the Biotechnology and Biological Sciences Research

428

Council (BBSRC).

429

430

ASSOCIATED CONTENT

431

Supporting Information

432

Table S1 The ESI-MS/MS methods and parameters to identify the molecular species

433

of the neutral lipids FFA, DAG and TAG.

434

Table S2 The p-values for F-tests of cultivar, nitrogen and cultivar x nitrogen

435

interactions on the lipids analysed in this study.

436

Table S3 The latent (loading) vector values on the variables (quantified lipids).



437

Figure S1A Fitted asymptotic exponential curve for the FFA data comparing ESI-

438

MS/MS and GC-FID.

439

Figure S1B Fitted asymptotic exponential curve for the TAG data comparing ESI-

440

MS/MS and GC-FID.

441

Figure S2 Starch damage analysis of the six cultivars in three nitrogen conditions.

442

Figure S3 Relevant means on the log scale for the WGIN lipid totals data.

443

Figure S4A Lipid composition of selected minor neutral lipids (mol% of total lipids).

444

Figure S4B Comparison of LPC and PE (mol% of total lipids) between the six

445

cultivars.

446

The Supporting Information is available free of charge on the ACS Publications

447

website at DOI

448

449

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Page 25 of 31


Table 1 The p-values for F-tests of cultivar, N and cultivar by N interactions. Lipids

Cultivar

N

Cultivar x Nitrogen (GxE)

LPC Total

0.651

0.950

0.997

PC Total

Effects of Cultivar and Nitrogen Nutrition on the Lipid Composition of

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