Distinguishing between bread wheat and spelt grains using molecular

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Distinguishing between bread wheat and spelt grains using molecular markers and spectroscopy. Arie Curzon, Chandrasekhar Kottakota, Kamal Nashef, Shahal Abbo, David Jacobus Bonfil, Ram Reifen, Shimrit Bar-El Dadon, Asaf Avneri, and Roi Ben-David J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b00131 • Publication Date (Web): 26 Feb 2019 Downloaded from http://pubs.acs.org on February 28, 2019

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Journal of Agricultural and Food Chemistry

Distinguishing between bread wheat and spelt grains using molecular markers and spectroscopy.

A.Y. Curzona,b, K. Chandrasekhara, Y. K. Nashefa, S. Abbob, D.J. Bonfilc, R. Reifend, S. Bareld, A. Avnerib and R. Ben-Davida a Department

of Vegetable and Field Crops, Institute of Plant Sciences, Agricultural Research Organization (ARO)-Volcani Center, Rishon LeZion 7528809, Israel. b

The Levi Eshkol School of Agriculture, The Hebrew University of Jerusalem, Rehovot 7610001, Israel. c Department

of Vegetableand Field Crops, Institute of Plant Sciences, Agricultural Research Organization (ARO)-Gilat Research Center, 8531100, Israel. d The

School of Nutritional Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel.

Corresponding author: Dr. Roi Ben-David Department of Vegetable and Field Crops, Institute of Plant Sciences, Agricultural Research Organization (ARO)-Volcani Center, Bet Dagan 5025000, Israel. Tel: +972-3-9683681 Fax: +972-3-9669642 Email: [email protected]

Keywords: spelt, wheat, NIRS, Q gene, adulteration

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Abstract

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The increasing demand for spelt products requires the baking industry to develop

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accurate and efficient tools to differentiate between spelt and bread wheat grains. We

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subjected a 272-sample spelt-bread wheat set to several potential diagnostic methods.

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DNA markers for γ-gliadin-D (GAG56D), γ-gliadin-B (GAG56B) and the Q-gene were

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used, alongside phenotypic assessment of ease-of-threshing, and Near-infrared

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spectroscopy (NIRS). The GAG56B and GAG56D markers demonstrated low diagnostic

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power in comparison to the Q-gene genotyping, which showed full accordance with the

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threshing phenotype, providing a highly accurate distinction between bread wheat and

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spelt kernels. A highly reliable Q classification was based on a three-waveband NIR model

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[Kappa (0.97), R-square (0.93)], which suggested that this gene influences grain

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characteristics. Our data ruled out a protein concentration bias of the NIRS-based

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diagnosis. These findings highlight the Q gene and NIRS as important, valuable, but

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simple tools for distinguishing between bread wheat and spelt.

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Introduction

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Spelt wheat (Triticum aestivum ssp. spelta) is a hulled grain that belongs to the species of bread

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wheat (Triticum aestivum ssp. aestivum), but is considered to have a separate gene pool1.

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During the 20th century, spelt and other wheat landraces were replaced by high-yielding modern

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wheats. Today, traditional landraces survive as rare relics in low-input farming systems in

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remote world regions. However, recent demand for traditional wheats has created new

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marketing opportunities2. Spelt popularity among consumers is attributed to its being

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considered a 'healthy alternative' to bread wheat3.

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Spelt and bread wheat differ in spike morphology, with bread wheat presenting a dense

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compact spike, and spelt a less compact, lax 'speltoid' spike. Bread wheat is free-threshing,

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while spelt lines are hulled and requires further processing to release the grains from the chaff4.

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These attributes are mainly controlled by the Q gene (5A), which has a pleiotropic influence

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on many important traits including: glume toughness, rachis fragility, spike shape, heading date

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and plant height5,6. Spelt cultivars carry a recessive q allele, while bread wheats possess a semi-

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dominant Q allele conferring the free-threshing character.

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In light of the increasing demand for grains from traditional wheat cultivars, and the need to

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maintain high quality control and prevent adulteration or contamination, the baking industry

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must distinguish between the flours from different kernel types7. Breeding programs which

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involve spelt and wheat may also benefit from an efficient kernel discrimination method8.

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Different methods have been proposed for distinguishing between bread wheat and spelt.

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Discriminating markers have been developed8,9 based on the differences between the spelt and

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wheat γ-gliadin D gene on chromosome 1D (GAG56D), which has a 9-bp deletion/duplication

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(9-bp addition in bread wheat), and a single nucleotide polymorphism in spelt10. Similarly,

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spelt and wheat bear different alleles at the pseudogene γ-gliadin B locus on chromosome 1B

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(GAG56B)11. A cleaved amplified polymorphic sequence (CAPS) marker differentiating



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between the two alleles was developed, but was only validated for a small number of cultivars,

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and was not suggested as a routine discrimination method12. Other infrared-based methods have

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been tested for distinguishing bread wheat from spelt: Fourier transform infrared–attenuated

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total reflection spectroscopy (FTIR-ATR), and near-infrared spectroscopy (NIRS) both

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successfully differentiated between grains from the two gene pools7,12. This discriminative

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ability could be ascribed to the generally higher protein concentration in spelt wheat as

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compared to bread wheat1; a higher protein concentration was found in spelt wheat even when

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receiving a 35% lower N fertilizer rate in the field2. However, as protein concentration is

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affected by many factors, such as environment and fertilizer application rate13,14, basing a

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model on protein concentration could lead to future misclassifications.

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This study aimed at evaluating the potential of candidate markers to differentiate between spelt

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and bread wheat. We also tested the ability of NIRS to distinguish between grains from the two

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gene pools by using different classification models, and by eliminating the possibility that

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discriminative power is based on protein levels. We hypothesized that integrating high-

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throughput tools, such as molecular markers and NIRS, would enable a reliable and accurate

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model for differentiation between wheat and spelt kernels.

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Material and methods

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Plant material

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Germplasm lines (classified by the institute/company providing the samples) consisted of 77

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spelt lines (all grown in Bet-Dagan, Israel in 2016/17) and 10 wheat lines (grown in Esdraelon

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Valley, Eden Farm, Gat and Beeri, Israel in 2016/17). Some of the lines were represented by

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several seed samples from repeats that were grown in different plots/environments. In total,

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227 grain samples were used, including 132 spelt (18 lines had four samples, one line had two

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samples and 59 had one sample) and 95 wheat samples (7-10 seed samples for each line). As

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each sample was repeated technically a 454 grain sample set was used for later NIRS analysis.

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Germplasm classification

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The plant material panel was classified in four different ways: (1) Initial classification, as per

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the definition of the gene-bank/company providers; (2) Ear morphology - a relatively compact

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ear was classified as 'bread wheat', while a speltoid spike was classified as 'spelt'; (3) Free-

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threshing/hulled classification - all spikes were threshed mechanically (LD 350, Wintersteiger,

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Ried im Innkreis, Austria), and samples yielding free grain after threshing were classified as

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'bread wheat'. Grain samples that remained hulled after threshing were peeled with a spelt

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peeling machine (VILI 11, Santec, Vydrany, Slovakia) and consequently classified as 'spelt';

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(4) Molecular marker classification - based on allelic composition at the genes: (a) GAG56B,

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(b) GAG56D, and (c) Q.

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Assays for GAG56D, GAG56B and Q genes

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Genomic DNA from the spelt and wheat germplasm lines (total 87 lines, see plant material)

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was isolated from leaves of two-week-old seedlings, using the CTAB method15. Leaves were

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grinded (Geno/grinder 2010, SPEX Sample Prep, Metuchen, NJ, USA) and CTAB buffer was



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added to each tube and incubated at 60°C for 60 minutes. After one chloroform extraction, the

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DNA was precipitated with isopropanol, washed with 75% ethanol and air-dried. The DNA

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pellet was dissolved in molecular-grade water and DNA concentration was measured using A

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Nano Drop Spectrophotometer (ND100, NanoDrop Technologies, Wilmington, DE, USA).

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Later, polymerase chain reactions (PCRs) were performed using a Thermal cycler (300A, Hy-

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labs, Rehovot, Israel). The PCR reactions were carried out in a 20 μl reaction volume

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comprising 100ng DNA template, 10 μl Taq DNA polymerase (2x Master Mix RED,

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Ampliqon, Odense, Denmark), 0.5 µM of each primer (Table 1) and molecular-grade water

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(Bio-labs, Jerusalem, Israel). PCR conditions for each primer pair, and the different products

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for spelt and wheat are summarized in Table 1. Fragments were separated on a 1.5% agarose

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gel stained with ethidium bromide, and visualized under UV light. For the two CAPS markers,

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GAG56B and Q, an enzymatic cleavage was performed with PvuII and MspI, respectively: 5

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μl of the PCR product was directly digested with PvuII/MspI (ThermoFisher Scientific,

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Waltham, Massachusetts) at 370 for 3-h. The different products for spelt and wheat are

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summarized in Table 1.

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NIRS and statistical analyses

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A total of 227 seed samples (see plant material) were used for NIRS spectrometry analysis: A

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NIR System (6500, Foss, Hilleroed, Denmark), which measures reflectance in the 400–2498

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nm wavelength range at 2 nm intervals, was used to determine grain protein concentration,

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after calibration against protein concentration of bread wheat and spelt samples (N% x 5.7)

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using the micro-Kjeldhal method17. Each 20-gram free-grain (not hulled) sample was scanned

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twice (a 454-sample reading set was obtained). Spectra were resampled to 10 nm bands for

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classification analysis, which included logistic regression models to differentiate between spelt

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and bread wheat. The JMP statistical package (Pro 13, SAS Institute, Cary, NC) was used for


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building logistic regression models, using stepwise modeling based on minimum Akaike

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information criterion (AICc). For each model, Cohen’s Kappa conflict was calculated18.

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Cohen’s Kappa is a unitless value ranging from 1, for perfect agreement, to –1, for complete

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disagreement:

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𝐾=

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where po is the relative observed agreement among raters (classification and prediction in this

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case), and pe is the hypothetical probability of chance agreement.

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Results

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Spelt phenotypic classification

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Differing results for the classification of spelt and wheat lines were obtained in all the

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classification methods. (Table 2 & 3). Ear morphology classification in the most part paralleled

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the germplasm provider classification (98%, Table 3). However, when classifying the lines

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according to grain appearance following threshing, more lines were classified as 'bread wheat'

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(Table 2). Consequently, the agreement between the threshing character classification and the

135

germplasm provider classification was 85% (Table 3). Interestingly, all hulled germplasm lines

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had a speltoid spike, yet a number of free-threshing lines (n=11) also exhibited this feature.

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Therefore, classifying spelt by threshing character is more accurate than classification based

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on ear morphology.

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Association between phenotypic and DNA markers

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Scanning the different alleles for the specific genes tested allowed us to examine the association

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between the different markers and the phenotype, as PCR products were obtained from all

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DNA markers, as previously reported (Table 1). Examples are presented in Figs. S1, S2 & S3

143

(Supplementary).

𝑃𝑜 ― 𝑃𝑒 1 ― 𝑃𝑒 ,



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The Q gene allele Q/q genotype fully correlated with the observed threshing character, and is

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therefore referred to with the same classification hereafter. The GAG56D alleles partly

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correlated with the phenotypic classifications, showing 88% agreement with threshing

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character and ear morphology (Table 3). When classifying the lines according to the GAG56D

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allele, all but one (CGN08306) of the bread wheat cultivars with a complete bread wheat

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phenotype (i.e., free-threshing and compact spike) carried a bread wheat allele (Table S1,

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Supplementary). Except for seven, all bread wheat lines defined as bread wheat by free-

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threshing character, carried a bread wheat allele at this locus. Nine lines with a speltoid spike

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(12% of total spelt lines) also carried a bread wheat allele. Five of these nine were defined as

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bread wheat by their free-threshing character, leaving only four possible genuine spelt lines

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(according to spike and hulled-grain phenotype), which, carrying the bread wheat GAG56D

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allele.

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Classification based on alleles of the GAG56B marker did not correlate with the phenotypic or

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the germplasm provider classifications (neither threshing nor spike phenotypes, Table 3). This

158

is due to the high abundance of bread wheat alleles among spelt lines. For example, out of 75

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spelt lines characterized by ear morphology (Table 2), 50 lines (67%, Table S1 supplementary)

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were found to carry the bread wheat allele at the GAG56B locus. Lines with a complete bread

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wheat phenotype (i.e., threshing and ear morphology) showed a good association with the

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respective allelic bread wheat variant of this gene. Nevertheless, a spelt allele was identified in

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one of the free-threshing lines (PI367199).

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NIRS models

165

Models for distinguishing bread wheat from spelt were based on different classification

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methods. The first model was based on classifying the lines into three groups: (a) Genuine

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bread wheat lines that conform with all characteristics; (b) Genuine spelt genotypes that


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conform with all characteristics; (c) A mix of spelt and wheat (WS) (non-reproducible

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classifications by spike morphology, threshing character or GAG56D marker). For this three-

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group model, we did not manage to achieve complete explanation of the variance (R2=1) with

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stepwise modeling, and the models which were obtained showed poor discrimination of the

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samples in comparison to alternative models, even when using 10 wavebands (Table 4).

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Alternative models were based on classifying the germplasm lines into two groups, spelt vs.

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bread wheat, by: (1) ear morphology, (2) threshing character (Q gene classification), and (3)

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the GAG56D allele (Table 4). The best model was achieved using threshing character for

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sample classification. Fewer wavebands were needed for complete explanation of the variance

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(R2=1), and lower AICc and Bayesian information criterion (BIC) values were achieved (Table

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4). Use of only three wavebands (2130, 2430, 2490 nm) yielded a good quality model with a

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0.93 R2 value (Table 4), and with only six misclassifications (three of bread wheat, and three

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of spelt). Half of these misclassifications were WS lines (strengthening the model), despite the

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fact that they were a smaller proportion of the total sample pool. Protein content distribution

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for bread wheat and spelt overlapped for all classification methods, as displayed for the Q gene

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classification (Fig.1). Most importantly, for all the classification methods used for the bread

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wheat and spelt samples, the validation and calibration sets (described below, 3.4) exhibited an

185

overlapping similar distribution (Table 5); in the calibration set, bread wheat samples had a

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generally lower protein content in comparison to spelt, while an opposite result was exhibited

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in the validation set. These results demonstrate that the lines cannot be divided into two distinct

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groups by protein content, confirming a non-protein bias model.

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Prediction power

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In order to assess the predictive potential of a model based on the Q gene classification results,

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the samples were divided into calibration and validation sets. The calibration data set included



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eleven spelt lines and nine bread wheat lines that conformed with the Q gene grouping. The

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remaining germplasm lines were used for validation. Using the three wavebands mentioned

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above (2130, 2430, 2490 nm), a 0.93 R2 for both the calibration and validation, and Kappa

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≥0.95 were obtained (Table 4). This included eight misclassifications, three of which were WS,

196

leaving only five actual misclassifications (1% of observations). Adding the 680 nm waveband

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improved the accuracy to 0.94-0.95 R2, and Kappa to 0.96-0.98 for the calibration and

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validation data sets (Table 4). This resulted in seven misclassifications, out of which four were

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WS samples.

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The predictive potential of models based on ear morphology or on GAG56D classification was

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high, but generally had lower predictive power for validation results than the Q gene (Table 4).

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Moreover, these models required more wavebands to reach reasonable accuracy in comparison

203

with the Q gene classification results.

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Discussion

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GAG56B as a diagnostic marker for bread wheat vs. spelt

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Distinct allelic variations in the GAG56B locus have been reported to differentiate between

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spelt and bread wheat11. Interestingly, our data did not agree with this although all complete

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bread wheat lines by phenotype carried the allele previously reported as the wheat allele11,

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within phenotypic spelt lines, polymorphism was found and many spelt lines had the putative

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GAG56B 'bread wheat allele'. This resulted in low correspondence between phenotypic

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classification and GAG56B locus classification (Table 3 and Table S1, supplementary). For

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this reason, we do not consider the GAG56B locus a useful discriminating tool for bread wheat

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and spelt.

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GAG56D as a diagnostic marker for bread wheat vs. spelt


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As reported by other authors8,9, GAG56D clearly distinguishes spelt from bread wheat. Our

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results showed that, with only one exception, all genuine bread wheat lines carried a GAG56D

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bread wheat allele. Likewise, most of the genuine spelt lines carried a GAG56D spelt allele.

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Despite this fact, this marker should be used with caution for diagnostic purposes since, as

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mentioned above (3.1), some genuine spelt lines (hulled, speltoid ear) carry the GAG56D bread

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wheat allele and some free-threshing lines with a speltoid ear (supposedly bread wheat) carry

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a GAG56D spelt allele. Thus far, the GAG56D gene has not been associated with any important

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quality trait (bread making quality, nutrition or other) that distinguishes the two gene

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pools.Consequently, we do not consider this marker an important diagnostic tool for spelt and

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bread wheat.

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Spelt classification based on the Q gene and ease of threshing

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Although ease of threshing character is thought to be associated with ear morphology6, this

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was not always exhibited in our data set: all the hulled lines had a speltoid spike, but so did

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some free-threshing lines. As spelt is considered to be both hulled and t having a speltoid spike,

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both attributes should be present for genuine spelt classification. Currently, there is no clear

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distinction between spelt and bread wheat that is based on grain quality traits, rendering ease

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of threshing the only clear criterion for classifying bread wheat and spelt. As it is accepted that

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spelt carries a 'q' allele and bread wheat carries a 'Q' allele5, and in light of our data which

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showed a correlation between the Q gene genotype and ease of threshing, it seems that the Q

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gene is a good diagnostic marker for classification purposes.

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NIRS modelling

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The most successful NIRS models were based on Q gene classification (Table 4), suggesting

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that this gene influences naked grain characteristics. As the Q gene is known to have pleiotropic

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effects6, this seems a reasonable interpretation. If the Q gene indeed controls important grain



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quality features together with ease of threshing, breeding free-threshing spelt might lead to loss

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of certain desired traits associated with consumer demand for spelt. Although other NIRS

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models were successfully built for other classification techniques (Table 4), the superiority of

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the Q gene models suggests that this might be due to their resemblance to the Q gene

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classification (Table 3).

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As shown before, NIRS is a good method for distinguishing spelt from bread wheat7,12.

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However, both studies7,12 fail to report on protein distribution, and, as protein concentration is

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thought to be higher in spelt1, the models may be protein-biased. In the present study, the

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protein concentration of the samples was tested by NIRS to prevent such bias. The wide protein

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concentration distribution in our bread wheat and spelt line samples (Table 5, Fig. 1), and the

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similar protein concentration distribution for bread wheat and spelt line samples in the

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calibration and validation sets (Table 5), ruled out the possibility of a protein-biased model.

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Unfortunately, only small amount of grains were available for this study, and therefore a non-

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destructive assay was applied, enabling the grains to be sown in the future. However, another

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challenge for the industry (bakers) is to identify wheat in milled products (flour), and it should

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be tested it in further research. Nevertheless, our results suggest that NIRS is a robust, non-

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destructive tool, capable of distinguishing between the bread wheat and spelt gene pools.

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Acknowledgments

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This research was supported by #2010-0500 grant from the Chief Scientist of the Israeli

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Ministry of Agriculture and Rural Development, and by a grant from the Israeli Gene Bank,

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ARO, Volcani Center, Israel.

260 261

Conflict of interest statement

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The authors declare no conflict of interest.

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Supporting Information

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Table S1, Figs S1,S2 & S3

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https://doi.org/10.1266/ggs.84.233.

320

(17)

AACC, Approved Methods of the American Association of Cereal Chemists. Method

321

38-12,10th edition, American Association of Cereal Chemists St. Paul, Minnesota,

322

USA: 2000.

323 324

(18)

Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 1960, 20 (1), 37–46. https://doi.org/10.1177/001316446002000104.

325



Number of samples

Fig. 1

CV (%) 16.08

125 100 75 50 25 8

10

12

14

16

18

Protein concentration (%)


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Table 1 Primer details and product sizes for DNA markers for the genes GAG56D, GAG56B and Q Gene primers Sequence (5-3) Alleles Expected Annealing Size (bp) temp (C)

Size after cleaving (bp) No cleaving

GAG56D

GAG31

GCAGCAAGAACAACAAGAACAA

W (Wheat)

236

60

GAG56B

GAG28 GAG29

CGGCGACTACGCTGGA AATCCTTGTGATGGCAGTAA

S (Spelt) P (Wheat)

Null 956

60

496+460

Q

GAG30 L1

CACCAATTCCGGTGACT CCTCCTCCATGACTATAGTTATTAC

A (Spelt) Q (Wheat)

323

55

956 323

R1

CCACGCCACACCGTCTCA

Q (Spelt)


137+186

Source von Büren et al. 200010 von Büren et al. 200111 Asakura et al. 200916


Table 2 Number of wheat, spelt, non-classified lines and grain samples, as determined by the different classification methods. Classification class Spelt Wheat Non-classified Lines Samples Lines Samples Source 77 264 10 190 (institute\company) Ear morphology 75 248 12 206 Threshing character 64 194 23 260 Q gene 64 194 23 260 GAG56D 67 212 20 242 GAG56B 24 90 62 356 1 (8 samples)


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Table 3 The percent of samples with identical classification for each two classification methods Classification class Source Ear Threshing Q GAG56D (institute\company) morphology character gene Source (institute\company) Ear morphology Threshing character Q gene GAG56D GAG56B

GAG56B

# 98 85 85 88 39

# 87 87 88 41

# 100 76 52

# 76 52


# 46

#


Table 4 Model details of nominal logistic regression built from NIRS readings based on different classification methods Classification Data set & Spelt Bread model’s Kappa R-square AICc4 BIC5 model and no. of sample no. wheat accuracy wavebands used % correct Three groups1, 10 All (454) 174 198 84 0.74 0.44 554.5 603.2 Threshing All (454) 194 260 100 1 1 14.2 42.8 Character (Q/q), 6 Threshing All (454) 99 0.97 0.93 48.5 64.9 Character (Q/q), 3 Ear morphology, All (454) 248 206 100 1 1 28.9 85.7 13 Ear morphology, All (454) 97 0.95 0.89 86.7 127.4 10 GAG56D marker, All (454) 212 242 100 1 1 50.8 146.9 23 GAG56D marker, All (454) 94 0.88 0.81 138.9 183.6 10 Threshing 186 98 0.95 0.92 Val2 (292) 106 Character (Q/q), 3 Threshing Cal3 (162) 88 74 99 0.97 0.92 25.1 37.2 Character (Q/q), 3 Threshing Val (292) 106 186 98 0.96 0.95 Character (Q/q), 4 Threshing Cal (162) 88 74 99 0.98 0.94 24.1 39.2 Character (Q/q), 4 Ear morphology, Val (292) 118 174 95 0.89 0.35 13 Ear morphology, Cal (162) 130 32 100 1 1 30.8 71.2 13 GAG56D marker, Val (292) 108 184 97 0.93 0.73 23 GAG56D marker, Cal (162) 104 58 100 1 1 56.7 122.1 23 1 - Spelt, bread wheat, and a mix of spelt and wheat (WS). 2 - Validation 3 - calibration 4 - Akaike information criterion 5 - Bayesian information criterion


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Table 5 Protein concentration details for calibration and validation sets used in NIRS models Classification Data Sample No. of Mean Std Minimum Median method type group samples Dev3 Ear spelt 130 11.9 1.8 8.0 12.0 Cal1 morphology wheat 32 9.5 0.9 8.0 9.5 Val2 spelt 118 11.7 1.9 8.2 11.5 wheat 174 13.1 1.6 10.3 12.5 GAGD Cal spelt 104 11.8 1.7 8.1 11.9 wheat 58 10.8 2.0 8.0 10.3 spelt 108 11.9 1.8 9.1 11.6 GAGD Val wheat 184 13.0 1.8 8.2 12.5 spelt 88 12.6 1.4 10.2 12.7 Q gene Cal wheat 74 10.2 1.5 8.0 9.8 spelt 106 11.9 1.9 9.0 11.6 Q gene Val wheat 186 13.0 1.8 8.2 12.4 1 - Calibration 2 - Validation 3 - Standard deviation

TOC Graphic


Maximum 15.6 12.1 18.7 17.2 14.9 15.6 18.7 17.2 15.6 13.6 18.7 17.2


NIRS

Integration

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Marker Classification

Data


Improved Model

Distinguishing between bread wheat and spelt grains using molecular

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