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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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1
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
8
4
9
NR4 7UA, United Kingdom
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Quadram Institute Bioscience, Institute of Food Research, Norwich Research Park,
*Author for correspondence: peter.shewry@rothamsted.ac.uk
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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
21
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
27
ionization-tandem mass spectrometry (ESI-MS/MS). This showed clear differences
28
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
40
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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105
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
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(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
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ammonium
+
ions by a
Journal of Agricultural and Food Chemistry
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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
184
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
223
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
236
classes of NLs (DAG, TAG and FFA) and PL (PC, LPC and PE). The notation for
237
FFA species reflects the number of carbons and double bonds (for example, oleic
238
acid is C18:1) while the notation for species of neutral lipids and PL reflects the total
239
numbers of double carbons and double bonds for the acyl chains.
15
was used to identify and quantify the major
240
241
Relative abundance of lipids. An ESI-MS/MS approach was used to compare the
242
amount of each lipid molecular species in the different grain samples. The lipid
243
molecular species were identified by precursor or neutral loss scanning, and the
244
lipids in each head group class were quantified in comparison with internal standards
245
of that class. Acquisition of MS data requires long periods of sample infusion.
246
Therefore, a quality-controlled approach was employed to remove any instrument or
247
analytical variation from the data acquisition process. For example, the intensity of
248
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
251
example, the position of the PC 184 m/z head group scan was varied between the
252
first and last positions within the analytical run. Lipids account for about 1.4-2.0% of
253
the dry weight of white wheat flour20 with FFA, TAG, DAG, PC, LPC and PE
254
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.
256
The lipid data are presented as a mol% of the total lipids, obtained by normalizing
257
the peak area of each lipid to the internal standard which was further normalized to
258
the weight of the initial flour sample.
259
Of the lipids extracted from white flour in this study, FFA were generally the most
260
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%
262
and 11% of the total lipids, respectively (Figure 1). The observed percentages agree
263
with Chung et al
264
used samples grown at about 200 kgN/Ha), although in the present study TAGs
265
were more abundant than FFA or LPC for Cadenza and Crusoe grown at 350N.
266
Nevertheless, other authors have reported similar results, with TAGs being more
267
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,
270
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
278
with total PL (LPC, PC, PE) and total NL (FFA, TAG and DAG) were analysed by
279
Analysis of Variance (ANOVA) (Table 1).
280
No main effects of cultivar and nitrogen, or of cultivar x nitrogen interactions, were
281
observed for total LPC, total DAG or total PL. However, cultivar strongly affected the
282
levels of PC, PE, TAG and total NL, while nitrogen treatment had the greatest effects
283
on total NL, FFA, TAG and PE. Although interactions between cultivar and nitrogen
284
were less significant than the main effects of cultivar and nitrogen alone, they did
285
significantly (p < 0.05, F-test) affect the levels of PE, FFA and total NL (Table 1).
286
Although cultivar had the greatest effects on PC and PE, there was no effect on LPC
287
and hence on total PL. By contrast, total NL was significantly (p < 0.05, F-test)
288
affected by both factors and the interactions between them. The effects on NL
289
probably resulted mainly from effects on TAG species, which were greatly affected
290
by both cultivar and nitrogen (see Table 1). However, there was no effect of cultivar x
291
nitrogen on total TAG. Total FFAs were not affected by cultivar but were significantly
292
(p < 0.05, F-test) affected by nitrogen, and by cultivar x nitrogen.
293
294
Effects of Genotype and Nitrogen on Lipid Species. Differences in PL
295
composition between cultivars were observed with Cadenza having lower
296
proportions of total PC and PE species than the other cultivars (Figure 2). The
297
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
299
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
301
all three PE species were significantly (p < 0.05, F-test) affected by cultivar, with four
302
PC species and all three PE species also being affected by cultivar x nitrogen. Only
303
one out of five LPC species (LPC 18:0) was affected by cultivar and cultivar x
304
nitrogen.
305
The compositions of NLs differed to a limited extent between cultivars, but greater
306
differences were observed depending on nitrogen supply, particularly for Crusoe
307
grown at N100 and N200 (Figure 2). Statistical analysis of the NL species showed
308
that only five of the twelve TAG species (TAG 50:1, 52:2, 52:5, 54:3 and 56:5) were
309
significantly (p < 0.05, F-test) affected by cultivar and three of these (TAG 50:1, 52:5,
310
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
312
only DAG 32:0 being affected by the interaction. Although ten species of FFA were
313
determined, only four of these showed significant (p < 0.05, F-test) effects of
314
nitrogen, with none being affected by cultivar or cultivar x nitrogen.
315
In broad terms, these analyses show that PLs were generally more affected by
316
cultivar and NLs by nitrogen.
317
318
Multivariate Statistical Analysis. Canonical Variates Analysis (CVA) was used to
319
obtain a linear discrimination between the cultivar by nitrogen combinations and
320
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
327
effects of cultivar, nitrogen and cultivar x nitrogen on the lipid composition could be
328
identified.
329
The first three CVs accounted for 66.17% of the total variance and the possible
330
discrimination in the dataset. DAG 36:4, DAG 36:3, FFA 16:1 and PC 34:1 were the
331
most important lipids for separation in the first CV dimension, TAG 52:5, FFA 18:2,
332
TAG 54:7, TAG 54:3, FFA 24:0, TAG 50:2, LPC 16:0, DAG 36:4 and DAG 36:5 for
333
CV2 and LPC 16:0, LPC 18:2, DAG 36:3, PC 34:1, TAG 52:5, DAG 36:5, FFA 18:2,
334
FFA 24:0, DAG 32:0, PC 36:2 and FFA 18:1 for CV3. The full data regarding the
335
loading values for the dataset, which determines the “important” lipids, are given in
336
Supporting Information, Table S3.
337
CV1 clearly separates Istabraq at all three nitrogen levels and Avalon grown at 100
338
and 200kg/N/Ha from the other cultivars and nitrogen treatments (Figure 4).
339
Cadenza is separated from the other cultivars in the CV2 dimension, particularly the
340
samples grown at 200 and 350kg/N/Ha. In broad terms, the separations in CV1 and
341
CV2 are determined more by differences between cultivars than between nitrogen
342
treatments. Istabraq is particularly well separated from the other cultivars by CV3
343
with good separation also of Hereward grown at 350N and Crusoe grown at 200N.
344
345
GENERAL DISCUSSION
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Comparison of six cultivars grown at three levels of applied nitrogen showed that
347
cultivar and nitrogen had effects on the lipid composition of the white wheat flour,
348
with stronger effects on PC, PE and TAG species than on other lipid species.
349
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
352
from partial hydrolysis of PC by phospholipase A2 during storage of the flour,
353
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
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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
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while a strong inverse
34
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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: peter.shewry@rothamsted.ac.uk
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
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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).
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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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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