One Hundred Years of Grain Omics: Identifying the Glutens That

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One hundred years of grain omics: identifying the glutens that feed the world Miguel Ribeiro, Júlio D. Nunes-Miranda, Gérard Branlard, Jose Maria Carrillo, Marta Rodriguez-Quijano, and Gilberto Igrejas J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr400663t • Publication Date (Web): 16 Sep 2013 Downloaded from http://pubs.acs.org on September 19, 2013

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Journal of Proteome Research

One hundred years of grain omics: identifying the glutens that feed the world Miguel Ribeiroa,b,‡, Júlio D. Nunes-Mirandaa,b,‡, Gérard Branlardc, Jose Maria Carrillod,, Marta Rodriguez-Quijanod and Gilberto Igrejasa,b,*

a

Department of Genetics and Biotechnology, University of Trás-os-Montes and Alto

Douro, 5001-801 Vila Real, Portugal; b

Institute for Biotechnology and Bioengineering, Centre

of Genomics and

Biotechnology, University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal; c

Institut National de la Recherche Agronomique GDEC/UBP, UMR 1095, Clermont-

Ferrand, France; d

Unidad de Genética y Mejora de plantas Departamento de Biotecnología, E.T.S.

Ingenieros Agrónomos Universidad Politécnica de Madrid, España. ‡These authors contributed equally. *Author to whom correspondence should be addressed: Prof. Dr. Gilberto Igrejas, Institute

for

Biotechnology

and

Bioengineering,

Centre

of

Genomics

and

Biotechnology, University of Trás-os-Montes and Alto Douro, School of Life & Environment Sciences, Department of Genetics and Biotechnology, 5001-801 Vila Real, Portugal; E-mail [email protected]; Tel. +351 259 350 530; Fax +351 259 350 480.

1 ACS Paragon Plus Environment


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Keywords: Wheat; Genomics; Proteomics; Storage proteins; MALDI-TOF-MS; Bioinformatics.

Abstract Glutens, the storage proteins in wheat grains, are a major source of protein in human nutrition. The protein composition of wheat has therefore been an important focus of cereal research. Proteomic tools have been used to describe the genetic diversity of wheat germplasms from different origins at the level of polymorphisms in alleles encoding glutenin and gliadin, the two main proteins of gluten. More recently proteomics has been used to understand the impact of specific gluten proteins on wheat quality. Here we review the impact of proteomics on the study of gluten proteins as it has evolved from fractionation and electrophoretic techniques to advanced mass spectrometry. In the post-genome era, proteomics is proving essential in the effort to identify and understand the interactions between different gluten proteins. This is helping to fill in gaps in our knowledge of how the technological quality of wheat is determined by the interaction between genotype and environment. We also collate information on the various storage protein alleles identified and their prevalence, which makes it possible to infer the effects of wheat selection on grain protein content. We conclude by reviewing the more recent use of transgenesis aimed at improving the quality of gluten.


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1. Introduction

Wheat (Triticum aestivum L.) is one of world's major food crops and wheat glutens, the storage proteins in grain, are the single greatest source of protein in the human diet. If the global population continues to grow at its current pace, the production of this cereal has to increase at least 2% per year to meet human needs in about 40 years without greatly expanding the area of production 1. To achieve this it is necessary to improve worldwide wheat yield by developing new varieties that can be managed in a way that is also conducive to preserving local environments and natural resources. However, an increase in grain yield is normally associated with a decrease in grain protein content, a strong determinant of wheat quality for different end-uses. So researchers need to consider whether potential gains in yield could adversely affects grain quality 2. The processing industries now rely on different wheat varieties to supply different quality attributes that meet the requirements of specific products as leavened and unleavened breads, noodles, cookies, cakes, pastries, while increasing the diversity of supply. To meet these challenges, the integration of wheat genomics, transcriptomics and proteomics with rapidly evolving bioinformatics tools and interactive databases is required (Figure 1). The genome of common wheat (Triticum aestivum L.) is large and complex. Estimated to be 17-gigabase-pair, the wheat genome has been shaped by recent polyploidy events between 8,000 and 10,000 years ago

3-5

. Essentially, in wheat cells

the genomes of three different primitive species coexist which may explain its great capacity to adapt to various ecological conditions. At present, studies of ancestral genomes of wheat, namely A

6

and D

7

genomes, have led to the discovery of



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agronomically important gene families. Moreover, the natural polyploidy of wheat facilitates the incorporation of genes from closely related species. Other recent study revealed that the wheat genome is highly dynamic, with significant loss of gene family members on polyploidization and domestication. In contrast to the overall loss of gene family members, several classes of gene families with predicted roles in defence, nutritional content, energy metabolism and growth have increased sizes in the Triticeae lineage, possibly as a result of selection during domestication 5. Wheat genome sequencing is enabling a more effective and focused approach to breeding, which will hopefully lead to improved human nutrition and health. However, knowledge of the genome alone does not indicate how a variety interacts with the environment and an open reading frame in a genome does not necessarily designate a functional gene. Proteomics is a major area of research in the post-genome era

8

and in wheat it is

proving powerful in elucidating the expression of certain proteins and their contribution to the final technological value of grain. Significant progress has also been made on the characterization of storage proteins through comparative proteomics using mass spectrometry and bioinformatics (Figure 2). For example, gluten proteomics is providing an important link to genomic data and functional biology, gathering information about post-transcriptional modifications and changes in protein expression. Bridging the gap between DNA and proteins, the genome and the proteome, will provide new solutions for future wheat breeding. Desirable attributes to be incorporated in promising new genotypes are ease of adaption to new environments, increased yield and quality of the commercial product, and the inclusion of genes of economic importance. The constitutive species of the hexaploid wheat genome differ considerably in terms of grain composition and food end-use quality. The glutenins and gliadins are the two main protein components that determine in a complementary way the


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technological characteristics of wheat flour. The balance between glutenin and gliadin is crucial to the quality of the gluten network formed in the baking process, and this balance is seen as a major end-use determinant 9. The purpose of this paper is to review how proteomics techniques have been and continue to be used to characterize wheat gluten proteins to identify varieties, assess germplasm genetic diversity, and infer how some gluten subunits may have been selected.

2. Wheat storage proteins

The scientific study of proteins from cereal grains dates back to the mid eighteenth century when Beccari 10 described the isolation of wheat gluten. Later, in 1907, Osborne 11

separated wheat proteins into four fractions on the basis of their solubility: the water-

soluble albumins, the salt-soluble globulins, the prolamins that were soluble in 70% aqueous ethanol, and the glutelins that remained in the flour residue. Of these fractions, the glutelins (glutenins) and prolamins (gliadins) (Figure 3) are the most widely studied proteins due to their contribution to the rheological characteristics of dough made from wheat flour. The Osborne fraction of glutelins can be further separated into two sub-fractions. All the proteins in the residue are first dissolved in 50% aqueous 1-propanol at 60ºC with dithiol butanols (e.g. dithioerythritol and dithiothreitol) or 2-mercaptoethanol to reduce the disulfide bonds. On increasing the propanol concentration to 60%, the high molecular weight glutenin subunits (HMW-GS) between 80,000 and 120,000 Da precipitate, while the low molecular weight glutenin subunits (LMW-GS) from 30,000 to 50,000 Da remain in solution 12, 13.



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HMW-GS are encoded by the complex Glu-1 loci composed of two genes on each of the group 1 hexaploid wheat homologous chromosomes which respectively encode the x-type (large) and y-type (small) subunits of HMW-GS 14, 15. Over the last few decades, the variability among HMW-GS alleles has been repeatedly shown to influence breadmaking quality. In general, HMW-GS are the major genotypic determinants of dough strength that by conferring viscoelasticity to the dough determine whether a particular wheat variety is suitable for bread-making 16. The glutenin nomenclature, specifically for HMW-GS, is based on the apparent molecular weight of a sub-unit after reduction of disulfide bonds as revealed by polyacrylamide gel electrophoresis in the presence of sodium dodecyl sulfate (SDSPAGE). The distinction between HMW-GS and LMW-GS glutenin subunits is relatively straightforward because of the obvious difference in molecular weight. The location of their genes in the wheat genome is the next way of distinguishing between glutenin subunits

9, 17

. Individual subunits are therefore referred to using a combination

of their locus and whether they are x- or y-type subunits. For example, the subunits 5 and 10 encoded by the Glu-D1d allele 15, are identified individually as 1Dx5 and 1Dy10 subunits, respectively. LMW-GS are encoded by genes at Glu-A3, Glu-B3 and Glu-D3 loci on the short arms of group 1 chromosomes

18-20

. In addition to HMW-GS, LMW-GS also affects

dough strength signiﬁcantly in common wheat 21, 22. The nomenclature used for LMWGS is very similar to the nomenclature used for HMW-GS. In this case, the nomenclature is based on the lower molecular weight estimated in SDS-PAGE and the alleles may be assigned according to their loci on small arms of group 1 chromosomes. However, mainly due to its complexity when compared with HMW-GS, no system of numbering individual subunits has been adopted

9, 17

. However some of these subunits,


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namely B and C, were defined according to their mobility in SDS-PAGE

20

. Genetic

linkage between Gli-1 loci that encode some gliadins and Glu-3 loci (LMW-GS) was described in Australian cultivars

23

and, somewhat later, a new system of nomenclature

combining the classification systems of alleles of Gli-1

24

and Glu-3

20

loci in bread

wheat was proposed 25. The Osborne fraction of prolamins, the gliadins, can be divided into four groups, namely α-, β-, γ- and ω-gliadins in decreasing of mobility in acid-PAGE (A-PAGE). Most gliadins are encoded at six main loci, Gli-A1, Gli-B1, Gli-D1, Gli-A2, Gli-B2, and Gli-D2, on the short arms of homeologous chromosome groups 1 and 6 26. The amounts of certain gliadin subgroups and total gliadins in flour are associated with different rheological properties. For example, γ-gliadin fraction 45 is associated with strong gluten/protein characteristics producing good quality pasta in contrast to the γ-gliadin type 42, which is not suitable for pasta making 27, 28. A key finding was the discovery that the group 1 chromosome genes that encode gliadins are tightly linked to LMW-GS genes 17, 29 known to influence the properties of wheat dough

30-32

. Interest in gliadin alleles grew suddenly because of these quality

relationships, but before that, the heterogeneity of gliadins had already led to the application of new methods and techniques for the fractionation and separation of these proteins 33. The nomenclature for naming gliadins proposed by Metakovsky 24 has been most widely used although other nomenclatures are used particularly when focusing on one group of proteins, for example, the ω-gliadin group 34. The high degree of polymorphism of gliadin and glutenin subunits was revealed in one-dimensional electrophoresis such as A-PAGE electrophoresis

36-38

24, 35

, SDS-PAGE

15

, capillary

and high-performance capillary electrophoresis (HPCE)

39

. It is

expected that advanced technologies in proteomics will facilitate the identification of



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novel or unusual alleles from the large wheat gene pool and provide leads to further improve dough properties and gluten quality through breeding 40, 41.

3. Proteomics and the study of gluten proteins

The first electrophoretic methods used to analyze gluten proteins include moving boundary

42

and starch gel electrophoresis

43

. After SDS-PAGE was introduced, Bietz

and Wall soon adopted the technique for the separation of wheat grain proteins, and to date this approach has been the most straightforward to use 44. In recent decades, there have been significant efforts to develop more powerful methods to rapidly differentiate between good and poor quality wheat proteins for germplasm screening and improvement

45, 46

. As well as SDS-PAGE, A-PAGE and reversed-phase high-

performance liquid chromatography (RP-HPLC) are all routinely used in several breeding programs to characterize gluten proteins and select specific subunits associated with superior quality

47

. Even though these techniques provided some information on

the extent of polymorphisms and primary structures of these proteins, they have not always been successful in distinguishing between some subunits that possess similar physico-chemical properties, like similar molecular weights or identical hydrophobicity 48

. Despite the low abundance of HMW-GS, among gluten proteins they have received

more attention, likely due to the complexity, heterogeneity and overlap among the LMW-GS and gliadin fractions 9. The simplicity and ease with which SDS-PAGE was performed in most laboratories, led to it becoming a method to screen for desirable subunits and predict gluten quality. However, the molecular weights of HMW-GS are generally overestimated when determined by their relative mobility in SDS-PAGE. This


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inconsistency may be explained by the highly repetitive structures of glutenins and/or the attachment of SDS molecules to the protein subunits 45. Thus, the somewhat reduced resolution and reproducibility of SDS-PAGE generally confounds the identification of some subunits, such as 1By8 and 1By8*

44

. The quantitation of different subunits is

particularly difficult 45, 49, 50. Another

broadly

used

electrophoretic

technique

is

two-dimensional

gel

electrophoresis (2DE), which combines two different protein separations, first on the basis of the isoelectric point by isoelectric focusing (IEF) and then on the basis of molecular weight by SDS-PAGE. Since it was developed in 1975 by O’Farrell 51, 2DE has been considered to be a powerful tool for identifying the polymorphism of proteins in wheat flours, due to its extremely high resolution 52. In the 1980s, RP-HPLC was developed and used for gluten protein identification 53. Compared with the SDS-PAGE, RP-HPLC had several advantages for the separation and characterization of HMW-GS such as high resolving power, rapidity, repeatability and automation. RP-HPLC separates proteins according to surface hydrophobicity, such that proteins with higher hydrophobicities are eluted first. HMW-GS have higher surface hydrophobicities than the LMW-GS

54

. Sometimes several subunits, such as

Glu-1 subunits, have a similar elution times which means that RP-HPLC cannot be relied on as the sole method to determine gluten protein composition. However this approach remains a useful complement to SDS-PAGE, particularly as protein subunits can be accurately quantified

44

. Recently, RP-HPLC was combined with other MS

techniques, such as LC-MS/MS, and used for characterization of HMW glutenin subunits in bread and tetraploid wheats 54. New technologies to separate and characterize HMW-GS that are more effective than the traditional methods were also developed, such as HPCE, ultra performance liquid



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chromatography (UPLC) and various mass spectrometry (MS) methods. For example, UPLC proved to be a faster technique and achieving higher resolution and efficiency compared to traditional HPLC for the study of LMW-GS in wheat grain

55

and water-soluble proteins

56

. Concerning MS methods, matrix assisted laser desorption/ionization

time-of-flight mass spectrometry (MALDI-TOF-MS) has developed particularly rapidly and has recently became the tool of choice to study storage proteins 41, 45, 57-59, especially HMW glutenins. The identification of HMW-GS masses by a single MALDI mass spectrum was welcomed since the abnormal migration of these subunits in SDS–PAGE due to conformational effects on mobility, resulted in molecular mass estimates higher than those calculated from gene sequences

60

. It has been suggested that the MALDI mass

spectrum of a crude extract with the complete HMW-GS pattern profile, in which the intact molecular mass of the protein is measured, could be used routinely in breeding programs to rapidly identify varieties, especially those that contain subunits related to quality

47, 61

. This approach, intact protein profiling (IPP), as shown in Table 1, is

commonly used to identify HMW-GS; the difference between the measured and the predicted molecular masses is in most of the cases lower than 1%, which evidences the accuracy of the technique. HMW-GS contain large repetitive domains which hinders the application of conventional sequencing methods, such as Edman degradation. Thus, MALDI-TOF mass spectrometry has been used in structural characterization studies in order to verify the gene-derived sequence of several HMW-GS 61, 66, 68-70. For instance, Cozzolino et al. 71

confirm the DNA-deduced sequence by analyzing tryptic peptides without prior

HPLC separation with three different matrices, obtaining protein sequence coverages above 99%. Additionally, Cunsolo et al.

68, 69

coupled RP-HPLC/ESI-MS/MS data with


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MALDI spectra, being able to achieve a protein sequence coverage over 95%, which allowed the confirmation of the gene-derived sequences. Besides the verification of the gene-derived sequences, the aforementioned studies revealed the absence of glycosylation or other post-translational modifications (PTMs) in the subunits studied, giving new evidences into this controversial topic. As pointed out by Cunsolo et al. 61, the current knowledge of the primary structure of various HMW subunits may lead to an upgrade of the nomenclature system, based on amino acid sequence similarity instead of electrophoretic mobility. A recent study performed by Gao et al.

45

set out to compare and evaluate SDS-PAGE,

RP-HPLC, HPCE, and MALDI-TOF-MS methods in terms of resolution, sensitivity, accuracy and throughput by analyzing the HMW-GS compositions of 60 genotypes. The results showed that some subunits that could not be discriminated by the first three methods were clearly differentiated in the MALDI-TOF mass spectra. In general, the resulting mass spectrum for each sample could be obtained in less than 5 min, much faster than the other three methods. Furthermore, in most cases, the molecular mass of a particular subunit from different genotypes determined by MALDI-TOF-MS was consistent with that predicted from its gene sequence. The differences between the translated coding sequences and the MS spectra masses, which were generally less than 1%, could be related with PTMs, indicating that these errors are a reflection of the protein variation rather than a lack of accuracy in MALDI-TOF measurements, which is in accordance with other studies (Table 1).

Moreover, this work revealed some

technical advantages of MALDI-TOF-MS in analyzing HMW-GS, including the high resolution and sensitivity. As the method can be used for high-throughput identification and screening for desirable HMW-GS, it is ideal for wheat breeding programs. Still, the clear drawback of MALDI-TOF-MS methods is that the equipment costs much more



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than that needed for SDS-PAGE, RP-HPLC, or HPCE, which might deter some breeding programs from taking advantage of this technology

45

. Nonetheless, MALDI-

TOF-MS is a powerful tool to rapidly and accurately analyze the glutenin content for breeding purposes, so much so that sequences and PTMs of several HMW-GS from bread wheat have been verified by direct MALDI-TOF mass spectrometry of crude extracts 72. LMW-GS and gliadins present a more complex pattern when compared with HMWGS, which is consistent with the extensive diversity of the subunits

47, 73

. Table 2 lists

several allelic variants of LMW-GS and gliadins identified through mass spectrometry. Although mass spectra can potentially differentiate between wheat varieties, the unequivocal identification of LMW and gliadin subunits in unfractionated extracts through direct MALDI-TOF-MS analysis as a stand-alone technique, may be precluded by the pattern complexity

47

. To reduce the complexity of the sample, an alternative

approach would be to use MALDI-TOF MS after chromatographic isolation or 2DE separation of the LMW and gliadin subunits. Liu et al. tested and compared four different methods, SDS-PAGE, 2DE, PCR and MALDI-TOF-MS, to characterize the LMW-GS composition of grain from 103 cultivars from 12 countries

73

. From the 25

Glu-3 alleles identified, only 10 alleles were detected by all four methods. Therefore, a combination of techniques was recommended to identify certain alleles and especially to characterize new alleles. Additionally, they reported significant overestimation of the molecular masses (42-51 kDa) determined by SDS-PAGE when confronted with the corresponding molecular masses (25-43 kDa) exhibited in MALDI-TOF-MS

73

. This

shows a real limitation of the electrophoretic approach, as already mentioned for HMWGS.


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The use of peptide sequence data from tandem mass spectrometry (MS/MS) measurements, known as peptide fragment fingerprint (PFF), can be used to interrogate search engines to look for parent proteins

75

. However, the identification of gluten

proteins by this approach is currently hindered by the limited information contained in gene and protein databases. The scarcity of arginine and lysine residues in gluten protein sequences makes it difficult to produce enough suitable tryptic fragments for subsequent analysis. Moreover, the many very similar proteins with repetitive sequences within each gluten protein fraction make it challenging to distinguish one group member from another 76. Recently, Vensel et al.

76

demonstrated that combining

three enzymes, two search algorithms and a specialized database, increases the number of peptides generated and the number of identified proteins. Proteomics has been employed successfully in the study of other issues related to the quality and yield of wheat grain. For example, the replacement of chromosome 1BS of wheat by the chromosome 1RS of rye (the so-called 1BL.1RS translocation) is commonly associated with decreases in bread-making quality and poor rheological characteristics

77

. Gobaa et al.

78

reported that the 1BL.1RS translocation produced

drastic changes in the endosperm proteome of wheat, including a quantitative and qualitative impact on prolamin production (mainly of LMW-GS and γ-gliadins). Proteomics was again used to elucidate why the quality of translocated genotypes varies so considerably with the finding that some cultivars show an unexpectedly high quality with respect to dough rheology and bread-making 79. Other proteomic studies looked at the impact of heat stress during wheat grain development. High temperatures during grain filling have been reported to be one of the factors that affect the dough properties and quality characteristics of wheat. Majoul et al.

80, 81

identified and characterized several heat-responsive wheat endosperm proteins.



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Short heat shocks were applied during grain development then 2DE, MALDI-TOF and MS/MS were used to identify other heat stress responsive proteins 82. The effect of different levels of nitrogen and sulphur on the gluten proteins was also investigated through proteomics, since the amount and timing of application of fertilizer are factors that affect wheat quality 83, 84. Also related to wheat breeding, the impact of aneuploidy in wheat

85

and the effects of salicylic acid on growth and salt tolerance

because exogenous salicylic acid pretreatment enhances the growth and tolerance to subsequent drought stress 86, 87, have been studied using proteomics.

4. Genetic diversity and changes in the prevalence of glutenin proteins worldwide

Nowadays, it is known that most of the wheat genome, about 80%, is composed of repetitive sequences and transposable elements. Both cytogenetics and more recent analysis of genome sequences have revealed that genes are probably arranged in blocks. Recombination appears to be an infrequent event that does not take place homogeneously along all wheat chromosomes. Knowledge of the physical organization of the genome is still limited, but in the last two decades significant progress has been made in the molecular cytogenetics and cytogenomics of wheat. Molecular maps are already available for some types of wheat, marking gene-rich regions and recombination points 4-7, 88-90. Wheat proteomics is fundamental research in its own right and complementary to genomics. Protein expression reveals how a genotype interacts with the environment, so proteomics can be used to understand which parts of the genome are responsible for grain protein composition, which enzymatic activities are involved, and which genes are specifically expressed in different growth conditions. Analysis of storage protein alleles


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is well known to be a powerful method of genotyping genetic resources. Allelic identification of glutenins and gliadins also contributes to better data on their frequencies in wheat germplasm and consequently to better indices of quality. In 1985, Branlard and Dardevet 91 proposed a coefficient of quality based on the composition of HMW-GS. For each quality parameter, the fraction of phenotypic variation explained by the composition of high molecular weight glutenin varies but can reach 35% Later, Payne et al.

93

92

.

assigned quality scores for the different HMW-GS alleles

according to their effects on baking properties, with an overall score for a variety being calculated by the simple addition of scores for the individual subunits present. Many favourable alleles encoded at Glu-1, Glu-3 and Gli-1 loci are worth analyzing before crossing selected parents and cumulating the alleles by pyramidal breeding 94, 95. The contribution of individual gluten proteins to quality is well established. The x-type HMW-GS appears to be responsible for forming high molecular protein aggregates through its ability to form linear polymers via its cysteine residues. Conversely, the ytype HMW-GS does not have a particularly positive effect on the consistency of the dough. LMW-GS contributes positively to the strength of dough and gluten, but about twice the amount of these proteins is required to produce the same effect as x-type HMW-GS 96. Furthermore, recent studies have shown a role of some LMW-GS certain allele combinations of these proteins

98

97

and

in determining wheat quality.

Monomeric gliadins are regarded as a “solvent” or “lubricant” for aggregated glutenins being responsible for the viscosity of wheat dough. The contribution of gliadins to rheological properties is not correlated with absolute amounts but with the ratio of gliadin to glutenin

96

. Each locus has a specific allelic variation, which in turn is

responsible for the differences in quality between the wheat varieties for bread-making



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purposes. The currently somewhat incomplete data points to the relative importance of different alleles for gluten quality as being Glu-1 > Glu-3 or Gli-1 > Gli-2. Since the late 1970s until the late 20th century, HMW-GS were studied in wheat varieties from many different countries like England 12, France 99, Australia 100, Finland 101

, India 102, Nepal 103, Spain 104-106, former Yugoslavia 107, 108, Italy 109, Canada

Germany

112

, United States

113-115

, Norway

116

, former USSR

117

110, 111

, The Netherlands

,

118

,

South Africa 119, China 120, Sweden 121, Pakistan 122, Slovakia 123, Japan 124 and Portugal 125-127

. Morgunov et al.

128

reported that the distribution of alleles of Glu-1 loci in

worldwide collections is not associated with global ecogeographic parameters. This was best demonstrated by calculating genetic distances in a collection of 1380 varieties from 21 countries. As described, European varieties of common wheat generally show that for HMW-GS, the “null” allele c is the most frequent Glu-A1 locus allele. The higher frequency of this allele contributes to a lower quality score. The frequency of this allele in other countries like the United States is close to zero. For the Glu-B1 locus, subunits 7+8 and 7+9 are the most frequent, as is found in most of the collections referenced above. For the Glu-D1 locus, we found subunits 5+10 were the most common, although the frequency of subunits 2+12 should not be overlooked. This might be because 5+10 93

subunits are well correlated with bread-making quality of wheat

and hence an effect

of selection. This overall similarity in storage proteins allelic constitution may be explained by common plant breeding objectives. Approximately 20 years later, some scenarios remain virtually unchanged. Figure 4 shows allele frequencies in several countries for each of the three loci encoding the HMW-GS from the most recently published studies

94, 95, 129-136

. For example, for the

Glu-A1 locus the United States continues to have a low frequency of the c allele while France has a very high frequency (Figure 4). Glu-A1 allele frequencies can be separated


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into two different groups. One group consists of Algeria, Iran and France in which c is the most frequent allele, ranging from 50% to 69.5% frequency. The second group consists of all the other countries in which alleles a (subunit 1) and b (subunit 2*) are clearly the most representative. Furthermore, in some countries such as the U.S. and Argentina the frequency of allele a plus b is nearly 100%, an allele known to contribute more to bread-making quality than allele c, for example 93. This is also the case for GluB1 locus alleles known to have a favourable effect on dough properties, Glu-B1b (subunits 7+8) and Glu-B1c (subunits 17+18), the only ones that showed a non-zero frequency in all the countries studied (Figure 4). Wheat from Spain has a large frequency of unusual subunits which may reflect the fact that the study was performed with landraces. Comparing Glu-D1 locus alleles known to have a positive impact on quality, we can group U.S., Argentina and Ukraine because they showed a very high percentage (80.6 to 94.1%) of allele d (subunits 5+10) compared to the other countries (Figure 4). Allele d is known to have a major positive impact on bread-making quality 93

and dough strength

137

. The other countries also showed considerable frequencies of

this allele, with the exception of Spain, and for allele a (subunits 2+12). In general, we can say that the increased frequency of a certain allele is essentially due to its association with bread-making quality because of the dough rheological properties it confers. It is this that has been selected in breeding programs. If as the protein variants associated with good quality were gradually identified, they were then widely bred into parents of similar genetic background, this may have reduced wheat genetic variability making crops less plastic in response to climatic changes, diseases, or agricultural practices

138, 139

. This scenario emphasizes the usefulness of allelic

identification of storage proteins for genotyping genetic resources. An integrated genetic resources program will be a necessity to prevent genetic erosion and to conserve



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genetic diversity so new alleles with favourable indices can be identified. For example, wheat from Argentina, Ukraine and U.S. (data not shown) showed an average genetic diversity index for the Glu-1 loci that was much lower than for the other countries. In an attempt to reverse the situation, several landraces (local varieties) have been studied as sources of new diversity that have a significant adaptive plasticity to several soil and climatic conditions, being better adapted to unfavourable conditions 140.

5. Genetic manipulation of gluten proteins

The manipulation of gluten proteins, whether to improve the quality and technological properties of gluten, to better understand the structure and interrelationships between different proteins or to diminish their toxicity for celiac patients, can be envisaged when breeding strategies are augmented with available proteomic tools. The close linkage of HMW-GS genes makes it difficult to manipulate them by traditional breeding methods

141

. This barrier to the improvement of gluten quality can

be overcome by the introduction and expression of additional or novel HMW-GS genes by genetic transformation resulting in qualitative and quantitative changes in HMW-GS 142

. Blechl and Anderson 143, and Altpeter et al. 142 changed the amount and composition

of HMW-GS in the ‘Bobwhite’ variety. Through different strategies, they reported that the additional subunit accumulated to levels comparable to those of the native HMWGS and transgene expression was stable for at least three seed generations in the majority of lines, demonstrating the feasibility of constructing wheat plants with novel seed protein compositions. Further work showed that the genetic transformation of wheat by introducing and expressing or over-expressing high molecular weight subunits


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can alter the functional and rheological properties of gluten, resulting in stronger dough than usual

144-147

. From here it was established that genetic engineering, beyond

traditional breeding practices, can improve the functional properties of wheat. After the initial work of manipulating and transforming HMW-GS genes, proteomic tools were used to check the success of transformation by quantifying expression levels of each group of storage proteins of the wheat endosperm. SDS-PAGE is generally used by all authors to confirm the expression of transgenic subunits

142-150

. Other techniques

are also successfully used such as size exclusion-HPLC to determine glutenin, gliadin, and albumin/globulin content 144, 147 and RP-HPLC to determine the relative amounts of HMW-GS

146

. Dumur et al.

148

study the cumulative and interactive effects on wheat

gluten strength and mixing properties of dough associated with the duplication of the Glu-D1 locus through homeologous translocation. The duplication of the Glu-D1 allele was associated with a significant effect on dough strength and mixing resistance, and on the Zeleny sedimentation volume, although baking parameters were not significantly modified between the translocated and normal lines. The agronomic variables of 1000kernel weight and yield were also lower in translocated lines. LMW-GS composition has also been altered by genetic transformation. Some studies demonstrate that the over-expression of an LMW-GS transgene influences the amount and distribution of glutenin polymers 151, 152. Modifying the gliadin content of grain stands out as one of the strategies adopted to decrease the allergenicity of these proteins for celiac patients. Gliadins may be strong food allergens as these proteins can aggravate celiac disease (CD), a geneticallydetermined gluten-dependent disease in which gluten proteins damage intestinal membranes, causing serious disorders of the intestine in susceptible individuals 153.



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The inactivation of genes by knock-out or by blocking the translation of their transcripts is an extremely powerful strategy for assigning function to genes, to map the interrelationship of various components of intracellular regulatory pathways and to down-regulate the expression of specific genes. RNA interference (RNAi) is an important pathway that is used in many different organisms to regulate gene expression and has already been proved to be an extremely versatile tool in biotechnology. Concerning celiac disease, RNAi was successfully used to down-regulate gliadins in wheat lines that were after reported as having very low levels of toxicity for CD patients 154

. Recent investigations into the effects on dough quality of down-regulating γ-

gliadins in different genetic backgrounds of bread wheat showed that reducing the amounts of γ-gliadins seems not to have a direct effect on mixing and bread-making properties of flour 155.

6. Conclusions and future perspectives

Centuries of wheat breeding have gradually increased the frequency of specific alleles coding for storage proteins that have a positive effect on gluten quality, resulting in a reduction in wheat genetic variability. Identifying novel or unusual alleles from the large wheat gene pool is thought to be a way of reversing this loss in variability. Advanced technologies like proteomics are needed to identify such rare alleles. Then if these gluten variants are to be exploited they will need to be manipulated either by genetic transformation or using classic molecular markers to incorporate them into germplasm suited to different environments. Targeting wheat gluten in this way is a more efficient and focused strategy to improve the rheological properties of dough. On the other hand, improved knowledge of the biodiversity of the wheat and the


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identification of new alleles will provide better conservation of genetic resources, helping to stop genetic erosion. Just as the biochemical and genetic study of wheat glutens has progressed over the years, so have separation techniques (Figure 2). Cereal research has adopted techniques such as SDS-PAGE and more recently the combination of RP-HPLC with MS and MS/MS techniques as they have developed. Now proteomics techniques are successfully used to characterize gluten proteins. In particular, high-throughput MALDI-TOF-MS is faster and much more accurate than traditional separation methods and is gradually being adopted and adapted for the study of gluten proteins in varietal identification. The major issues in characterizing HMW-GS through mass spectrometry refers to the close sequence similarity between subunits in this group and to the relatively low frequencies of arginine and lysine residues in these proteins. For instance, the reduced number of cleavage sites for trypsin, a proteolytic enzyme with high substrate specificity, produces large peptides hindering an efficient peptide fragmentation by MS/MS (PFF). However, good results have been obtained concerning the intact protein profiling (IPP) method. The predicted masses of intact subunits, based on published DNA sequences, are normally in good agreement with the observed m/z values obtained by MALDI-TOF-MS. LMW-GS and gliadins present a more complex challenge than HMW-GS. The use of chromatographic isolation or 2DE separation before MALDI-TOF-MS to reduce the complexity of the sample appears to be the most effective strategy to identify and characterize these proteins by mass spectrometry approach. On the other hand, UPLC have been successfully used for the characterization of LMW-GS and water-soluble proteins.



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Apart from the identification and monitoring of gluten proteins, several proteomic studies of the wheat kernel have focused on other issues related to the quality and yield of wheat grain, like the impact of 1BL.1RS translocation, heat stress, nitrogen and sulphur fertilization, aneuploidy, salicylic acid and salt tolerance. Proteomics data is filling in gaps in our knowledge of how genotype and environment interact and the outcome in terms of technological quality, climate adaptation or resistance to pests. Knowledge of the complete wheat genome will be an important asset to breeders, so specific traits can be linked with sequence and polymorphisms, and new quality markers can be identified. However, it is unlikely that the manipulation of genes, individually, is sufficient to study wheat gluten. The full complement of genomics and proteomics appears to be essential, i.e. understanding genes and their products, their interactions and rheological properties. The discovery of marker proteins associated with genotype-environment interactions is one of the main goals that proteomics is likely to achieve. Other objectives will undoubtedly emerge as we learn more about the functionality of gluten in relation to the glutenin polymer size resulting from disulfide bonds

156

, particularly in interaction with

oxidative stress 157, the secondary and tertiary structure. In the post-genome era, proteomics is a powerful tool to elucidate the expression, diversity and interactions of gluten proteins, major determinants of the technological quality of wheat. The resulting knowledge will contribute to the strategic conservation of wheat genetic resources, and improve and accelerate wheat breeding to meet the challenges of the 21st century.


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Figure legends Figure 1. Integration of omics technologies to elucidate the interactions of the wheat genome and proteome with the environment. Figure 2. Chronology of the main theoretical and technological advances relevant to wheat omics. Figure 3. Examples of electrophoretic profiles of wheat proteins. (A) Reduced and alkylated glutenin (lanes 2-9) and gliadin (lanes 10-18) subunits from selected bread wheat accessions separated by 12% SDS–PAGE. Lane 1, sizes (in Daltons) of protein molecular weight markers are shown on the left, lane 2, ‘Centauro’; lane 3, ‘Tejo’; lane 4, ‘Amazonas’; lane 5, ‘Sideral’; lane 6, ‘Sarina’; lane 7, ‘Fitti’; lane 8, ‘Almansor’; lane 9, ‘Mercero’; lane 10, ‘Alter’; lane 11, ‘Arruda’; lane 12, ‘Bacum’; lane 13, ‘Clercal’; lane 14, ‘Tritano’; lane 15, ‘Trimaran; lane 16, ‘Chinese Spring’; lane 17, ‘Beagle’; lane 18, ‘Crato’. (B) Two-dimensional electrophoresis pattern (IEF followed by SDS-PAGE) of the HMW-GS of wheat line ‘Barbela’. The arrowhead points to the protein encoded by the unusual Glu-A1 allele 1.1 41. The one-dimensional SDS-PAGE separation of the same proteins is shown on the left. Figure 4. HMW-GS allele frequencies in different countries since the year 2000. Data were taken from reports on allele frequencies at the Glu-A1 (A), Glu-B1 (B) and Glu-D1 (C) loci in Portugal 95, Spain 136, France 94, Ukraine

130

, U.S.

129

, Argentina

131

,

China 132, Algeria 133, Iran 134, and Pakistan 135.

Table legends



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Table 1. Molecular weights of HMW-GS from common wheat (Triticum aestivum L.) identified and measured by MALDI-TOF mass spectrometry. Table 2. Allelic variants of LMW-GS and gliadins in common wheat (Triticum aestivum L.) identified through mass spectrometry.

Associated content Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org.

Author information Corresponding author *Prof. Dr. Gilberto Igrejas, Institute for Biotechnology and Bioengineering, Centre of Genomics and Biotechnology, University of Trás-os-Montes and Alto Douro, School of Life & Environment Sciences, Department of Genetics and Biotechnology, 5001-801 Vila Real, Portugal; E-Mail: [email protected]; Tel.: +351 259 350 530; Fax: +351 259 350 480.

Author contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Funding sources Miguel Ribeiro and Júlio D. Nunes Miranda have PhD fellowships granted by FCTFundação para a Ciência e a Tecnologia and ESF-European Social Fund (SFRH/BD/82334/2011 and SFRH/BD/80496/2011, respectively).


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bread-making quality of the hexaploid wheat cv. Courtot. Journal of Cereal Science 2010, 51, (2), 175-181. 149. León, E.; Piston, F.; Aouni, R.; Shewry, P. R.; Rosell, C. M.; Martin, A.; Barro, F., Pasting properties of transgenic lines of a commercial bread wheat expressing combinations of HMW glutenin subunit genes. Journal of Cereal Science 2010, 51, (3), 344-349. 150. León, E.; Aouni, R.; Piston, F.; Rodríguez-Quijano, M.; Shewry, P. R.; Martín, A.; Barro, F., Stacking HMW-GS transgenes in bread wheat: Combining subunit 1Dy10 gives improved mixing properties and dough functionality. Journal of Cereal Science 2010, 51, (1), 13-20. 151. Masci, S.; Ovidio, R.; Scossa, F.; Patacchini, C.; Lafiandra, D.; Anderson, O. D.; Blechl, A. E., Production and characterization of a transgenic bread wheat line overexpressing a low-molecular-weight glutenin subunit gene. Molecular Breeding 2003, 12, (3), 209-222. 152. Tosi, P.; Masci, S.; Giovangrossi, A.; D’Ovidio, R.; Bekes, F.; Larroque, O.; Napier, J.; Shewry, P., Modification of the low molecular weight (LMW) glutenin composition of transgenic durum wheat: effects on glutenin polymer size and gluten functionality. Molecular Breeding 2005, 16, (2), 113-126. 153. Wieser, H., Relation between gliadin structure and coeliac toxicity. Acta Paediatrica 1996, 85, (s412), 3-9. 154. Gil-Humanes, J.; Piston, F.; Tollefsen, S.; Sollid, L. M.; Barro, F., Effective shutdown in the expression of celiac disease-related wheat gliadin T-cell epitopes by RNA interference. Proc Natl Acad Sci U S A 2010, 107, (39), 17023-8. 155. Gil-Humanes, J.; Piston, F.; Gimenez, M. J.; Martin, A.; Barro, F., The introgression of RNAi silencing of gamma-gliadins into commercial lines of bread wheat changes the mixing and technological properties of the dough. PLoS One 2012, 7, (9), e45937. 156. Lesage, V. S.; Bouchet, B.; Rhazi, L.; Elmorjani, K.; Branlard, G.; Marion, D., New insight into puroindoline function inferred from their subcellular localization in developing hard and soft near-isogenic endosperm and their relationship with polymer size of storage proteins. Journal of Cereal Science 2011, 53, (2), 231-238. 157. Lesage, V. S.; Merlino, M.; Chambon, C.; Bouchet, B.; Marion, D.; Branlard, G., Proteomes of hard and soft near-isogenic wheat lines reveal that kernel hardness is related to the amplification of a stress response during endosperm development. Journal of Experimental Botany 2012, 63, (2), 1001-1011. 158. Tiselius, A., The Moving Boundary Method of Studying the Electrophoresis of Proteins. Almquist & Wiksells Boktryckeri AB, Uppsala 1930. 159. Beadle, G. W.; Tatum, E. L., Genetic Control of Biochemical Reactions in Neurospora. Proc Natl Acad Sci U S A 1941, 27, (11), 499-506. 160. Edman, P., A method for the determination of amino acid sequence in peptides. Arch Biochem 1949, 22, (3), 475. 161. Kendrew, J. C.; Bodo, G.; Dintzis, H. M.; Parrish, R. G.; Wyckoff, H.; Phillips, D. C., A Three-Dimensional Model of the Myoglobin Molecule Obtained by X-Ray Analysis. Nature 1958, 181, (4610), 662-666. 162. Dayhoff, M. O.; Eck, R. V.; Chang, M. A.; Sochard, M. R., Atlas of Protein Sequence and Structure. National Biomedical Research Foundation: Silver Spring, Md., 1965. 163. Hogeweg, P., The roots of bioinformatics in theoretical biology. PLoS Comput Biol 2011, 7, (3), e1002021.



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164. Laemmli, U. K., Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 1970, 227, (5259), 680-5. 165. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E., The Protein Data Bank. Nucleic Acids Research 2000, 28, (1), 235-242. 166. O'Farrell, P. H., High resolution two-dimensional electrophoresis of proteins. J Biol Chem 1975, 250, (10), 4007-21. 167. Jorgenson, J. W.; Lukacs, K. D., Free-zone electrophoresis in glass capillaries. Clin Chem 1981, 27, (9), 1551-3. 168. Bjellqvist, B.; Ek, K.; Righetti, P. G.; Gianazza, E.; Gorg, A.; Westermeier, R.; Postel, W., Isoelectric focusing in immobilized pH gradients: principle, methodology and some applications. J Biochem Biophys Methods 1982, 6, (4), 317-39. 169. Barker, W. C.; Garavelli, J. S.; Huang, H.; McGarvey, P. B.; Orcutt, B. C.; Srinivasarao, G. Y.; Xiao, C.; Yeh, L.-S. L.; Ledley, R. S.; Janda, J. F.; Pfeiffer, F.; Mewes, H.-W.; Tsugita, A.; Wu, C., The Protein Information Resource (PIR). Nucleic Acids Research 2000, 28, (1), 41-44. 170. Bairoch, A.; Boeckmann, B.; Ferro, S.; Gasteiger, E., Swiss-Prot: Juggling between evolution and stability. Briefings in Bioinformatics 2004, 5, (1), 39-55. 171. Karas, M.; Bachmann, D.; Bahr, U.; Hillenkamp, F., Matrix-assisted ultraviolet laser desorption of non-volatile compounds. International Journal of Mass Spectrometry and Ion Processes 1987, 78, (0), 53-68. 172. Tanaka, K.; Waki, H.; Ido, Y.; Akita, S.; Yoshida, Y.; Yoshida, T.; Matsuo, T., Protein and polymer analyses up to m/z 100 000 by laser ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry 1988, 2, (8), 151-153. 173. Fenn, J. B.; Mann, M.; Meng, C. K.; Wong, S. F.; Whitehouse, C. M., Electrospray ionization for mass spectrometry of large biomolecules. Science 1989, 246, (4926), 64-71. 174. Henzel, W. J.; Billeci, T. M.; Stults, J. T.; Wong, S. C.; Grimley, C.; Watanabe, C., Identifying proteins from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases. Proc Natl Acad Sci U S A 1993, 90, (11), 5011-5. 175. Wasinger, V. C.; Cordwell, S. J.; Cerpa-Poljak, A.; Yan, J. X.; Gooley, A. A.; Wilkins, M. R.; Duncan, M. W.; Harris, R.; Williams, K. L.; Humphery-Smith, I., Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 1995, 16, (7), 1090-4. 176. Unlu, M.; Morgan, M. E.; Minden, J. S., Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis 1997, 18, (11), 2071-7. 177. Link, A. J.; Eng, J.; Schieltz, D. M.; Carmack, E.; Mize, G. J.; Morris, D. R.; Garvik, B. M.; Yates, J. R., 3rd, Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol 1999, 17, (7), 676-82. 178. MacBeath, G.; Schreiber, S. L., Printing proteins as microarrays for highthroughput function determination. Science 2000, 289, (5485), 1760-3. 179. Consortium, T. U., Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Research 2013, 41, (D1), D43-D47. 180. Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 2002, 1, (5), 376-86.


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181. Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D. J., Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004, 3, (12), 1154-69. 182. Jaffe, J. D.; Berg, H. C.; Church, G. M., Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics 2004, 4, (1), 59-77. 183. Sherman, J.; McKay, M. J.; Ashman, K.; Molloy, M. P., Unique ion signature mass spectrometry, a deterministic method to assign peptide identity. Mol Cell Proteomics 2009, 8, (9), 2051-62.



Figure captions

Figure 1. Integration of omics technologies to elucidate the interactions of the wheat

Environment

Climate, nutrients, soil composition

genome and proteome with the environment.

Proteomics ►1-DE and 2-DE electrophoresis ►Fluorescent staining of proteins / DIGE ►Western blotting ►MALDI-TOF-MS ►Sequencing

Transcriptomics

+

►TILLING ►RNAi ►Epigenetics ►QTLs ►Real-time PCR ►Microarrays

Genome

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 46

Genomics ►Molecular markers (RFLPs, SSRs, AFLPs, SNPs, DArT) ►Construction of molecular genetic and physical maps ►ESTs and their use for developing functional markers ►Genome sequencing

Bioinformatics Database interrogation


Page 39 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


Figure 2. Chronology of the main theoretical and technological advances relevant to wheat omics a.

a

See from reference no.158 to no.183 for more details concerning the events cited.

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 46

Figure 3. Examples of electrophoretic profiles of wheat proteins. (A) Reduced and alkylated glutenin (lanes 2-9) and gliadin (lanes 10-18) subunits from selected bread wheat accessions separated by 12% SDS–PAGE. Lane 1, sizes (in Daltons) of protein molecular weight markers are shown on the left, lane 2, ‘Centauro’; lane 3, ‘Tejo’; lane 4, ‘Amazonas’; lane 5, ‘Sideral’; lane 6, ‘Sarina’; lane 7, ‘Fitti’; lane 8, ‘Almansor’; lane 9, ‘Mercero’; lane 10, ‘Alter’; lane 11, ‘Arruda’; lane 12, ‘Bacum’; lane 13, ‘Clercal’; lane 14, ‘Tritano’; lane 15, ‘Trimaran; lane 16, ‘Chinese Spring’; lane 17, ‘Beagle’; lane 18, ‘Crato’. (B) Two-dimensional electrophoresis pattern, i.e., IEF followed by SDS-PAGE, of the HMW-GS of wheat line ‘Barbela’. The arrowhead points to the protein encoded by the unusual Glu-A1 allele 1.1 41. The one-dimensional SDS-PAGE separation of the same proteins is shown on the left.

A 1 5 7 8 10

97 400

66 200

2 7 9

2* 17 18

6 8

12

g

ω-gliadins

d4

a g

45 000

HMW-GS -

a

c

d3' d6' d8'

d5 d9*

d11 d12

d5'

d5'

d5 d6

d11'

e

i

LMW-GS α-, β-, γgliadins

a

c

a

d3

i

d j d

b'

d2 d8 d9 d10 d11 d12

a

c

31 000 1

2

3

4

5

6

7

8

9

10

B

ACS Paragon Plus Environment

11

12

13

14

15

16

17

18

Page 41 of 46

HMW-GS

1.1 2 13 16 12

3 Acid

pH

Basic 10

1.1 2

-

13 16 12

SDS-PAGE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


d4

LMW-GS

+



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 46

Figure 4. HMW-GS allele frequencies in different countries since the year 2000. Data were taken from reports on allele frequencies at the Glu-A1 (A), Glu-B1 (B) and Glu-D1 (C) loci in Portugal

95

,

Spain 136, France 94, Ukraine 130, U.S. 129, Argentina 131, China 132, Algeria 133, Iran 134, and Pakistan 135. A 100%

80%

60% Others N

40%

2* 1

20%

0%

B 100%

Others

80%

17+18 60%

14+15 13+16

40%

20 6+8

20%

7+9 7+8

0%

7

C 42 ACS Paragon Plus Environment

Page 43 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


100%

80%

60%

Others 5+10

40%

4+12 3+12

20%

2+12

0%



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 46

Table 1. Molecular weights of HMW-GS from common wheat (Triticum aestivum L.) identified and measured by MALDI-TOF mass spectrometry. Locus

Gene

Glu-A1

1Ax

1Bx

Glu-B1

1By

1Dx Glu-D1

Subunit

Molecular mass (Da) deduced from coding gene a (% RSD b)

Molecular mass (Da) measured by MALDI-TOF-MS a (% RSD b)

Error %

Ref.

1.1

91508 (0.00)

91858 (0)

0.38

41

1

87678 (0.00)

87882 (0.16)

0.23

45, 62-65

2*

86320 (0.01)

86715 (0.52)

0.46

45, 47, 63-67

6

86414 (0.00)

86520 (0.02)

0.12

45, 65, 67

7

82609 (0.15)

82743 (0.37)

0.16

62-64, 66-68

7*

Unknown (-)

82279 (0.00)

-

47, 65

7b*

Unknown (-)

82600 (0.00)

-

64, 67

7OE

83134 (0.00)

82900 (0.00)

-0.28

64, 65, 67

13

83249 (0.00)

83132 (0.16)

-0.14

48, 64, 65, 67

14

84012 (0.00)

83139 (0.81)

-1.05

45, 48, 64, 65, 67

17

78607 (0.00)

78422 (0.28)

-0.24

45, 48, 64, 67

20

83913 (0.00)

83435 (0.53)

-0.57

45, 64, 67, 68

8

75156 (0.00)

75312 (0.27)

0.21

45, 64, 65, 67

8a*

Unknown (-)

74800 (0.00)

-

67

8b*

Unknown (-)

75000 (0.00)

-

67

9

73516 (0.00)

73426 (0.29)

-0.12

45, 47, 62, 64, 65, 67

15

75733 (0.00)

75175 (0.18)

-0.74

45, 48, 64, 65

16

77282 (0.00)

77050 (0.19)

-0.30

48, 65, 67

18

Unknown (-)

75229 (0.15)

-

45, 48, 64, 67

19

Unknown (-)

75565 (0.00)

-

45

20

75733 (0.00)

76883 (3.11)

1.50

45, 64, 67

1.5

86807 (0.00)

86649 (0.07)

-0.18

45

2

87022 (0.00)

87218 (0.27)

0.22

45, 48, 62, 64, 65, 67

2.2

107000 (0.00)

86620 (0.00)

-23.53

45

2.2*

101000 (0.00)

86340 (0.00)

-16.98

45

3

86664 (0.00)

86400 (0.00)

-0.31

67

4

87655 (0.00)

86460 (1.23)

-1.38

45, 67

5

87940 (0.34)

88148 (0.18)

0.24

45, 48, 63-65

10

67478 (0.01)

67750 (0.28)

0.40

45, 47, 48, 62-65, 67, 69

12

68652 (0.00)

68795 (0.35)

0.21

45, 48, 62, 64, 65, 67, 69

1Dy

a

Average HMW-GS molecular mass calculated from the different values cited in the references;

b

%RSD, relative standard deviation.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2. Allelic variants of LMW-GS and gliadins in common wheat (Triticum aestivum L.) identified through mass spectrometry.

Locus

Allele

Identification approach

Reference

a

IPP 1

65, 73

b

IPP

65, 73

c

IPP

65, 73

d

IPP

65, 73

e

IPP

65, 73

f

IPP / PFF 2

65, 73, 74

a

IPP

65, 73

b

IPP

65, 73

c

IPP

73

d

IPP

73

f

IPP

65, 73

g

IPP

65, 73

h

IPP / PFF

65, 73, 74

j

IPP

65, 73

a

IPP / PFF

65, 73, 74

b

IPP

65, 73

c

IPP

65, 73

d

IPP

65

m

IPP

73

Gli-A3

f

PFF

74

Gli-B3

h

PFF

74

Gli-D3

a

PFF

74

LMW-GS

Glu-A3

Glu-B3

Glu-D3

Gliadins

1

IPP, Intact protein profiling;

2

PFF, Peptide fragment fingerprinting.



Graphical Abstract

Climate, nutrients, soil composition

Environment

Figure 1. Integration of omics technologies to elucidate the interactions of the wheat genome and proteome with the environment

Proteomics ►1-DE and 2-DE electrophoresis ►Fluorescent staining of proteins / DIGE ►Western blotting ►MALDI-TOF-MS ►Sequencing

Transcriptomics

+

►TILLING

Genome

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 46 of 46

►RNAi ►Epigenetics ►QTLs ►Real-time PCR ►Microarrays

Genomics ►Molecular markers (RFLPs, SSRs, AFLPs, SNPs, DArT) ►Construction of molecular genetic and physical maps ►ESTs and their use for developing functional markers ►Genome sequencing

Bioinformatics Database interrogation


One Hundred Years of Grain Omics: Identifying the Glutens That

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