Skip to content

Advertisement

  • Research article
  • Open Access

Association mapping for total polyphenol content, total flavonoid content and antioxidant activity in barley

  • Zhigang Han1,
  • Jingjie Zhang1,
  • Shengguan Cai1,
  • Xiaohui Chen1,
  • Xiaoyan Quan2 and
  • Guoping Zhang1Email author
BMC Genomics201819:81

https://doi.org/10.1186/s12864-018-4483-6

Received: 15 June 2017

Accepted: 16 January 2018

Published: 25 January 2018

Abstract

Background

The interest has been increasing on the phenolic compounds in plants because of their nutritive function as food and the roles regulating plant growth. However, their underlying genetic mechanism in barley is still not clear.

Results

A genome-wide association study (GWAS) was conducted for total phenolic content (TPC), total flavonoid content (FLC) and antioxidant activity (AOA) in 67 cultivated and 156 Tibetan wild barley genotypes. Most markers associated with phenolic content were different in cultivated and wild barleys. The markers bPb-0572 and bPb-4531 were identified as the major QTLs controlling phenolic compounds in Tibetan wild barley. Moreover, the marker bPb-4531 was co-located with the UDP- glycosyltransferase gene (HvUGT), which is a homolog to Arabidopsis UGTs and involved in biosynthesis of flavonoid glycosides .

Conclusions

GWAS is an efficient tool for exploring the genetic architecture of phenolic compounds in the cultivated and Tibetan wild barleys. The DArT markers applied in this study can be used in barley breeding for developing new barley cultivars with higher phenolics content. The candidate gene (HvUGT) provides a potential route for deep understanding of the molecular mechanism of flavonoid synthesis.

Keywords

Antioxidant activityGenome-wide association study (GWAS)UDP- glycosyltransferasesPhenolic compoundsTibetan wild barley (Hordeum spontaneum L.)

Background

Barley (Hordeum vulgare L.) is mainly used as animal feed and raw material for malt and beer production. In recent decades, its use in the production of functional or healthy food has been increasingly focused because of its rich phytochemicals [1, 2]. Some phytochemicals in cereal grains, including phenolic acids, flavonoids and anthocyanins, can reduce risk of human chronic inflammation, cardiovascular diseases, certain concers, and diabetes, and they are also involved in cell wall formation by forming bridges between polysaccharide and lignin [3, 4]. These phytochemicals in barley grains could be extracted as natural antioxidants. In fact, antioxidants have distinct effects on malting and brewing processes, including foam stability and beer bitterness, and flavonoids may affect beer taste, color and haze formation [58].

Identification of genes or even QTLs responsible for phenolic metabolism is necessary for the genetic improvement of the trait. Although multiple studies have identified QTLs associated with phenolic compounds in rice and sorghum, there were few studies on total phenolic content (TPC), total flavonoid content (FLC) and antioxidant activity (AOA) in barley [9, 10]. Glycosylation is one of the key steps in flavonoid biosynthesis, as it promotes the solubility, stability and bioactivity of flavonoids [11]. UDP-glycosyltransferases (UGTs) are often characterized by a conserved plant secondary product glycosyltransferase (PSPG) box of 44 amino acids binding UDP-conjugates as their activated sugar donor substrates. In plants, UGTs are involved in the transferring of glycosyl moieties into a wide range of acceptors including flavonoids in the process of glycosylation [12]. A few UGT protein coding genes are associated with biosynthesis of flavonoid glycosides in Arabidopsis thaliana and soybean (Glycine max) [13, 14]. However, little is known about the relevant genes or QTLs controlling UGTs in barley. Moreover, the genetic diversity of cultivated barley became bottlenecked due to its domestication, posing a limitation for success of barley breeding. Tibetan wild barley, as one of the progenitors of cultivated barley, shows a wider genetic diversity in agronomic traits and abiotic stress tolerance [1517]. Phenolics might have important functions in developing tolerance to salinity, high radiation and low temperature [1820]. Wild barley may accumulate high polyphenol content in order to adapt to harsh environments with strong ultraviolet (UV) radiation and other abiotic stresses, including extremely variable temperature [2023]. Therefore it is meaningful to identify the genetic factors associated with polyphenol synthesis and accumulation in wild barley.

In this study, we measured TPC, FLC and AOA contents in grains of 156 Tibetan wild barley accessions and 67 cultivated barley genotypes, and performed association mappings for these traits with aims at (1) determining the genotypic variation of TPC, FLC and AOA contents in barley grains; (2) identifying the QTLs associated with phenolics and antioxidant activity in cultivated and wild barley grains. (3) evolution analysis of marker-associated gene (HvUGT) with UGTs in Arabidopsis thaliana.

Methods

Plant materials

In total 223 barley genotypes (Additional file 1: Table S1), including 156 wild types(planted in two seasons of 2013 and 2014)and 67 cultivars (they were widely planted in southern east China since 1960s, planted in 2014) were planted at Zijingang campus of Zhejiang University (Hangzhou, China, 30°22′N, 119°26′E). Each genotype was sown in a plot consisting of three rows (each row was 2 m length and row distance is 0.25 m). All plots were supplied with 150 kg/ha of N, including 40 kg/ha of N as compound fertilizer applied before seeding and 110 kg/ha of N as urea supplied at two-leaf stage and booting stage with equal amount, respectively. In addition, 180 kg/ha of potassium chloride was applied prior to seeding. The experiment was arranged in a completely randomized block with three replicates. All other field managements, including weed and disease control, were the same as those applied locally. At maturity, barley grains were harvested, dried and then stored in a cool room (4 °C) for further analysis. The grain samples were milled using grinder to pass through a 0.5 mm screen.

Extraction of phenolic compounds

All samples were defatted by blending hexane and extracted according to Shao et al. [24] with minor modifications. Briefly, the mixture was shaken at 250 rpm for 1 h at room temperature and centrifuged at 5000 g for 15 min. After the residue was dried at 25 °C in a fume hood for 12 h, the samples were mixed with 4 ml of 80% methanol. Then the mixture was centrifuged at 5000 g for 20 min. The supernatant was collected for measurement of total phenolic content (TPC), total flavonoid content (FLC) and antioxidant activity (AOA).

TPC determination

Total phenolic content in barley extract was determined according to Zhao et al. [24] with minor modifications. Briefly, barley extract of 0.25 mL was mixed with 1.25 mL of 0.2 mol/L Folin-Ciocalteu’s phenol reagent. After 5 min, 2 mL of 7.5% Na2CO3 solution (w/v) and 5 mL of deionized water were added. The mixture was incubated at room temperature under dark condition for 1 h on a shaking table. Then the absorbance was measured at 760 nm and a standard curve of gallic acid solution was made. TPC was expressed as micrograms of gallic acid equivalents (GAE) per gram of barley flour (μg GAE/g).

FLC determination

Total flavonoid content was assayed according to the modified method of Shao et al. [25]. Briefly, 0.5 ml of barley extract was mixed with 3 ml ddH2O. Thereafter 150 μL of 5% NaNO2 was added and then incubated for 5 min. After adding 150 μL of 10% AlCl3·6H2O and incubating for another 5 min, 1 ml NaOH (1 M) was added and thoroughly mixed, and reacted for 15 min, and then the absorbance of solution was measured at 510 nm. FLC was expressed as micrograms of catechin equivalent (CAE) per g of barley flour (μg CAE/g).

AOA determination

Antioxidant activity of barley extract was evaluated according to the procedure described by Saint-Cricq de Gaulejac et al. [26] with some modifications. Briefly, 0.1 ml of barley extract was mixed with 2.9 mL of 6 × 10− 5 M methanolic solution of 1, 1-Diphenyl-2-picrylhydrazyl Radical (DPPH). After 60 min under dark condition on a shaking table, the absorbance of solution was measured at 517 nm. Inhibition of free radical DPPH in percent (I %) was calculated by using the following equation: I % = [(A0- Ae)/A0] × 100, where A0 is the absorbance of the blank sample and Ae is the absorbance of the tested sample.

Population structure, kinship and linkage disequilibrium (LD) analysis

The DArT markers used were derived from Diversity Arrays Technology Pty Ltd. in Australia (http://www.triticarte.com.au/content/barley_diversity_analysis.html) [27, 28]. Totally, 780 barley DArT markers (MAF > 0.03) (Additional file 2: Table S2), were used for population structure analysis using STRUCTURE software (v2.3.4) [29], setting the number of clusters (k) from 1 to 10 with 100,000 MCMC (Markov Chain Monte Carlo) and eight independent iterations were performed in an admixture model. The largest value of k was used as the indicator of the most probable number of clusters (k) [30]. Kinship (K) was estimated using SPAGeDi (version 1.3d) software [30]. We calculated genetic distance and developed an UGPDA tree with NTSYSpc (version 2.10e). Tassel 3.0 was used to calculate linkage disequilibrium (LD) according to parameter r2, which is a measurement of the correlation between a pair of variables [31]. The genetic distance was derived from LD decay distance in the whole genome, when r2 = 0.1 using the fitted equation.

GWAS for TPC, FLC and AOA

Association analysis of TPC, FLC, AOA was performed by TASSEL version 3.0 (http://www.maizegenetics.net) using 780 DArT markers. In order to acquire positive results in GWAS, four different models were performed [32]. Q model was applied to reduce the confounding caused by the sub-population membership. Q matrix using STRUCTURE software, and this model was expressed as y = Xβ + Qν + e, where X is the DArT marker matrix, Q is the sub-population membership matrix, e is the random error vector, and β and ν are coefficient vectors for the DArT marker and sub-population membership, respectively [29]. K model includes the kinship matrix which contributes to reducing the confounding associated with false positive results. The K model was expressed as y = Xβ + Zμ + e, where Z is the kinship matrix and μ is a vector of random genetic effects [μ N (0, 2 K)] [33]. The third approach was the Q + K model, including both sub-population membership and kinship, y = Xβ + Qν + Zμ + e [34]. Different models were evaluated for the fitness and efficiency by the quantile-quantile (Q-Q) plot using TASSEL v3.0. In the significance test −log10(p) > 2.0 was used as the lowest threshold.

Putative functions of HvUGT (HORVU1Hr1G020560) for phenolic compounds

The nucleotide and amino acid sequence of HvUGT was obtained from IPK barley BLAST Server (http://webblast.ipk-gatersleben.de/barley_ibsc/) [35]. SMART (http://smart.embl-heidelberg.de/) was used to predicate functional domain of HvUGT. The alignment protein sequences of this gene with Arabidopsis thaliana were collected by the BLAST result of EnsemblePlants (http://plants.ensembl.org) and then performed by CLUSTALW with default options [36]. FastTree version 2.1.10 was used Maximus Likelihood (ML) method with 1000 replications [37].

Statistical analysis

Data analysis for the distribution frequency of TPC, FLC and AOA were performed with the SPSS 20.0 (SPSS, Inc., Chicago, IL). Manhattan plot and Boxplot were made with the R version 3.4.0 (http://www.r-project.org/) and Sigmaplot version 12.5 (http://www.sigmaplot.com/) [38].

Results

Genotypic variations of TPC, FLC and AOA

There were large variations in the concentrations of TPC, FLC and AOA among the 223 barley genotypes in 2014, including 67 cultivated and 156 wild barley genotypes (Fig. 1). The mean value and variation of each parameter in wild barley had little difference in the two experimental years (2013 and 2014) (Fig. 2). There was significantly positive correlation between the 2 years’ data for each trait (r = 0.81**, r = 0.73** and r = 0.70** for TPC, FLC and AOA, respectively), indicating that these traits might be mainly controlled by genetic factors (Additional file 3: Figure S1). In the year of 2014, compared with cultivated barley, wild barley had the much higher TPC and AOA concentrations, while the difference in FLC between wild and cultivated barleys was relatively small (Fig. 2).
Figure 1
Fig. 1

Distribution of TPC, FLC and AOA in 2014. The X-axis shows the TPC, FLC and AOA in 2014, the Y-axis shows the number of barley individuals

Figure 2
Fig. 2

Boxplots of TPC, FLC and AOA in Tibetan wild barley and cultivated barley. a mean of TPC; b mean of TFC; c mean of AOA. wb2013: wild barley grown in 2013. wb2014: wild barley grown in 2014. cb: cultivated barley. For each marker, significant difference (p-value< 0.05) was marked by different letters

Population structure

Linkage disequilibrium (LD) decay distance (r2) was applied to determine the possibility if GWAS could be used in association analysis of phenolic compounds and genetic markers. LD decay of genetic distance in the genome of all the 223 barley genotypes was 4.18 cM (r2 = 0.1) (Additional file 4: Figure S2). Therefore, 801 DArT markers used in the present study were distributed over the whole genomic region with the average genetic distance of 0.70 cM and these makers were sufficient for genome wide association analysis.

The population structure was utilized in the model development in order to reduce nonfunctional and spurious associations caused by population stratification and an unequal distribution of alleles within these groups [39, 40]. The largest value of statistic index Δk was 64 when likelihood for sub-population (k values) calculated with STRUCTURE software were k = 7 (Additional file 5: Figure S3), indicating seven sub-populations (k = 7) were the most evident level of differentiation. These seven sub-populations consisted of 23, 31, 24, 8, 71, 21, 45 genotypes, respectively (Fig. 3; Additional file 6: Table S3). Interestingly, k7Q7 (the seventh population with k value = 7) consisted of most cultivated barleys and majority of wild barley was within other sub-populations, revealing the existence of considerable genetic diversity between Tibetan wild and cultivated barley. The result was consistent with the data from the cluster analysis (Additional file 7: Figure S4). The population structure of 223 barley genotypes was shown in Additional file 6: Table S3.
Figure 3
Fig. 3

Population structure of 223 barley genotypes. Population structure of all genotypes was divided based on genetic diversity detected by 801 DArT markers with k = 7. Seven subpopulations were represented by different colors

Association mapping of TPC, FLC and AOA

The contents of phenolic compounds in the wild and cultivated barleys were used to perform association mapping using 780 DArT markers. Three models, including Q model, K model and Q + K model, were tested to find the best fit model in association analysis, which was evaluated by Q-Q plot of P value distribution (Additional file 8: Figure S5). When Q or K model was applied, the observed P value was deviated from that expected, indicating the presence of abundant false positive results. Instead, the application of Q + K models reduced the number of false positive results. Thus, Q + K model was applied in this study. GWAS identified 13 unique loci for TPC, FLC and AOA in 223 barley genotypes in 2014 with the threshold -log10(p) > 3, and they were located on 6 of 7 chromosomes (except for 2H). Most of the loci were distributed on 3H, 5H and 6H, and each of them accounted for 4.0%- 7.0% of phenotypic variation (Fig. 4; Additional file 9: Table S4).
Figure 4
Fig. 4

GWAS of phenolic compounds within and across 67 cultivated and 156 Tibetan wild subgroups. Three grain parameters were applied to indicate content of phenolic compounds: TPC (), FLC (+) and TPC (×). GWA analysis was firstly conducted using three different methods: Q, K and Q + K methods. Then, Q + K method was selected to perform GWA analysis in cultivated and Tibetan wild subgroups individually. Dashed lines presented different p value of the four markers (bPb-4531, bPb-1068, bPb-0572 and bPb-3227) associated with these three parameters in wild and cultivated barley genotypes. Significant associations were identified using criterion of -log10(P) > 2 or 3

Association analysis was also performed in subgroups. When the threshold of significant association was set at p < 0.001, 20 loci were detected in the 2 years’ wild groups (Fig. 5; Additional file 9: Table S4). In the wild group, bPb-0572 (6H, 17.9 cM) was considered as the major locus controlling TPC (−log10(p) = 5.61 and 3.56 in 2 years), and contributed to 13.7 and 7.2% of phenotypic variation, respectively. BPb-4531 on Chr. 1H explained 5.8% in 2013 and 10.7% in 2014 for the variation of TPC. Notably, bPb-4531 was closely associated with all three parameters in the wild genotypes in both years except for FLC in 2013. Furthermore, the locus was significantly associated with AOA (−log10(p) = 4.72 and 3.15 in 2 years, respectively), accounting for 9.8 and 7.1% of phenotypic variation. In the cultivated barley, 7 loci were associated with these three parameters with -log10(p) > 2.5. With the threshold of -log10(p) > 3, only one marker bPb-1068 on Chr.3H was detected for TPC with -log10(p) = 3.61 and explaining 25.7% of phenotypic variation. BPb-3227 was detected using 223 genotypes with the three models and 156 Tibetan wild accessions with Q + K model in the year of 2014. It may be assumed that this locus is powerful in genetic effect for wild barley but not for cultivated barley, suggesting that Tibetan wild barley have wider genetic diversity than cultivated barley.
Figure 5
Fig. 5

Location of 21 QTLs (P < 0.001) associated with TPC, TFC, AOA and candidate genes. Green triangle represented QTLs (p < 0.001) in Tibetan wild barley. Black triangle represented QTLs (p < 0.001) in cultivated barley. The location of C4H, HvUGTs (HORVU1Hr1G020560), HvFB (HORVU7Hr1G120390), was predicted by BLAST in the website of the barley genome database (http://webblast.ipk-gatersleben.de/barley/)

Haplotype analysis of bPb-0572 and bPb-4531 for TPC in cultivated and wild barleys

Haplotype analysis was performed in cultivated and Tibetan barleys for the markers bPb-0572 and bPb-4531, which showed the closest association with the examined traits (Figs. 5 and 6). For bPb-0572, the marker associated with TPC in wild barley, haplotypes ‘2013-0’ and ‘2014-0’ showed higher TPC than haplotypes ‘2013-1’ and ‘2014-1’, respectively. On the contrary, there was no distinct difference in TPC between haplotypes ‘c-0’ and ‘c-1’ in the cultivated barley. For another marker associated with TPC, bPb-4531, haplotypes ‘2013-0’, ‘2014-0’ and ‘c-0’ showed higher TPC than haplotypes ‘2013-1’, ‘2014-1’ and ‘c-1’, respectively. However, in the cultivated groups, there was no significant difference.
Figure 6
Fig. 6

Distribution of TPC based on marker polymorphism in wild and cultivated barley. 2013: wild barley grown in 2013. 2014: wild barley grown in 2014. c: cultivated barley. 0 and 1 are DArT marker polymorphism. The markers and phenolic compounds include: bPb-0572 and bPb-4531 were associated with TPC. For each marker, significant difference (p-value< 0.05) was marked by different letters

Candidate genes for phenolic compounds

By BLAST analysis of the sequences of DArT markers using IPK barley Blast Server (http://webblast.ipk-gatersleben.de/barley), we identified some candidate genes as shown in Fig. 5. Marker bPb-4531 was aligned to the location (Chromosome chr1H: 81,294,699-81,295,151) containing a gene (HORVU1Hr1G020560) encoding the UDP-Glycosyltransferase (UGTs) superfamily protein. Other genes were shown in Additional file 10: Table S5. Marker bPb-5403 was associated with the location (Chromosome chr7H: 651,255,911-651,256,221) containing a gene (HORVU7Hr1G120390) encoding F-box protein. In addition, the FLC-associated marker bPb-8978 (133.5 cM, 3H) was co-located with the gene encoding C4H (133.3 cM, 3H), which is the first Cyt-dependent mono-oxygenase of phenylpropanoid pathway for flavonoid synthesis [41].

High similarity of HvUGT with UGT91s family in Arabidopsis thaliana

HvUGT protein sequence based on bPb-4531 was derived from IPK database (http://webblast.ipk-gatersleben.de/barley/) (Additional file 11: Figure S6). Then the amino acid sequence of HvUGT was analyzed by BLAST on genome of Arabidopsis thaliana (http://www.arabidopsis.org/) and 113 UGTs proteins (E-value< 1 × 10− 15) were found (Additional file 12: Table S6). All these protein sequences were listed in Additional file 10: Figure S6. The phylogenetic analysis with Arabidopsis thaliana, showed that HvUGT had the most similarity with UGT91s family (UGT91A1, UGT91B1 and UGT91C1), which might include functional domain catalyzing glycosyl transfer to flavonoid glycosides [42] (Fig. 7). The function domain of HvUGT was also predicted by SMART (http://smart.embl-heidelberg.de/), and the result showed that this gene consists of a similar UDPGT domain with UGT91s, indicating it is one of UDP-glucosyltransferase family members (Additional file 13: Figure S7).
Figure 7
Fig. 7

Phylogenetic tree of HvUGT and other UGT proteins in Arabidopsis thaliana. The UGT proteins of Arabidopsis thaliana was selected according to the E-value (E-value< 1 × 10− 15). Phylogenetic tree was constructed by FastTree with Maximus Likelihood (ML) method (Bootstrap:1000)

Discussion

In this study, we determined TPC, FLC and AOA in grains of 156 Tibetan wild barley accessions and 68 cultivated barley genotypes. The results showed Tibetan wild barley has a wider variation in all three traits than cultivated barley. A previous study found that Tibetan wild barley had a significantly higher ferulic acid concentration than cultivated barley [43]. Total phenolics mainly consists of phenolic acids, flavonoids and anthocyanins. The higher TPC concentration in Tibetan wild barley may be partially attributed to more ferulic acid, which is one of the major phenolic acids in barley grain. Flavonoids seem to have little contribution to the higher TPC in wild barley, as the difference in FLC between Tibetan wild and cultivated barleys was quite small. Currently, whether higher TPC in wild barley is also contributed by other phenolic compound is not clear. In this study, we found the high correlation between AOA and TPC (r = 0.729**) and in fact AOA,was an important component of phenolic compounds (Additional file 14: Table S7). In addition, Tibetan wild barley contained higher AOA than cultivated barley. Tibetan wild barley has the great potential for use in improving phenolic compounds of cultivated barley.

Currently, we identified 20 unique QTLs (p < 0.001) associated with TPC, FLC and AOA in Tibetan wild barley (Fig. 5). Most of the identified QTLs exist differently in the wild and cultivated barleys, suggesting the distinct difference in genetic diversity between the two barley groups. Some QTLs associated significantly with TPC, such as bPb-0572 and bPb-4531 were identified in Tibetan wild barleys, but not in the cultivated barley suggesting some genes or alleles controlling phenolics remain in the Tibetan wild barley and have lost in the cultivated barley [44]. The Tibetan Plateau, so called “the roof of the world” because of its very high altitude, is well known for its extreme environment, such as low and variable temperature and strong UV irradiation [15, 16, 45]. According to previous studies, wild barley might develop unique mechanisms for adapting to such harsh environments [22, 46]. Under these abiotic stresses, excessive reactive oxygen species (ROS) could be formed in plant tissues, causing damage to plant cells [47]. On the other hand, the plants exposed to severe abiotic stress preferentially accumulate phenolic compounds in order to scavenge these ROS, and alleviate peroxidation of lipids [48, 49]. Thus Tibetan wild barley has developed its own mechanism of abiotic stress tolerance by producing more phenolics. In fact, phenolic compounds also play important roles in adjusting other plant growth and metabolisms, such as differentiation, pigmentation formation [5052].

Although phenolic acids are components of phenolics and may partially contribute to TPC and AOA, only one common locus (bPb-0836) was found in this study (Fig. 5). A possible explanation would be that the individual phenolic acids only account for a small amount of total phenolics. Mohammadi et al. [53] reported three QTLs for total phenolics in barley genotypes collected from eight US breeding programs. One of them (SNP_11502, 3H, 67.86 cM) was located near the locus (bPb-1608, 3H, 66.16 cM), which was found in this study to be associated with TPC in the cultivated barley.

The release of barley genome sequence will facilitate the prediction of possible candidate genes based on the identified markers [35]. The UGT proteins are partly involved in the synthesis of anthocyanins and flavonoids [54]. Moreover, UGTs in plants participate in the formation of 3-O-glucoside and 4’-O-diglucoside, thus promoting flavonoid glycosylation [55]. In the current study, marker bPb-4531 was located on the Chr. 1H, which contains a gene encoding the UDP-glycosyltransferase (UGT) superfamily protein (HvUGT). According to the phylogenetic tree, HvUGT is closely related to gene family UGT91s in Arabidopsis thaliana. UGT91A1, one of the UGT91 family members in Arabidopsis thaliana might be involved in the synthesis of flavonol glycosides [42, 56]. Without UGT91A1 activity, there would be no galactose transferring into kaempferitrin, indicating that this protein specifically accepts and transfers galactose [57]. In fact this protein was found to have the similar specificity to flavonoid as UGT73C6 and UGT78D1 did [58]. Thus, HvUGT may be involved in pathway of flavonoids. Markers bPb-5403 was associated with contig_41360 containing a gene (HORVU7Hr1G120390) encoding F-box protein. Kelch domain-containing F-box proteins (KFBs) negatively regulated naringenin chalcone accumulation, thus reducing the production of polyphenols [59, 60]. Kelch domain-containing F-box proteins (KFBs) have also been found to regulate the biosynthesis of phenylpropanoids, such as anthocyanins, flavonoids, phenolic ester and lignin [61]. In addition, the FLC-associated marker bPb-8978 (133.5 cM, 3H) was co-located with the gene encoding C4H (133.3 cM, 3H), which is the first Cyt-dependent mono-oxygenase of phenylpropanoid pathway for flavonoid synthesis, which have significant effect on lignin development [41]. In short, these identified genes are closely associated with phenolic metabolism, although their functions are still unclear in barley. Furthermore, the detected markers in this study should be helpful for better understanding the genetic control of TPC, FLC and AOA in barley.

Conclusions

The current results showed the wide variation among barley genotypes and obvious difference between Tibetan wild and cultivated barleys in grain TPC, FLC and AOA. Tibetan wild barley had higher concentration and wider genetic diversity of phenolic compounds than cultivated barley. Most QTLs were identified in the Tibetan wild barley and only one was detected in cultivated barley with p < 0.001, indicating Tibetan wild barley is potentially useful in barley breeding for improving phenolic compounds. The marker (bPb-4531) was co-located with HvUGT, which is a homolog to Arabidopsis UGT and may be responsible for flavonoid synthesis. This finding may serve as the foundation for further in-depth studies on molecular mechanism of natural variation in phenolic compounds.

Abbreviations

AOA: 

Antioxidant activity

C4H: 

Cinnamic acid 4-hydroxylase

CAE: 

Catechin equivalent

F3H: 

Flavanone 3-hydroxylase

FLC: 

Total flavonoid content

GAE: 

Gallic acid equivalent

GWAS: 

Genome-Wide Association Study

KFBs: 

Kelch domain-containing F-box proteins

TPC: 

Total phenolic content

UGT: 

UDP-Glycosyltransferase

Declarations

Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (31620103912, 31471480, and 31401369), the China Agriculture Research System (CARS-05), the Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP), and the Fundamental Research Funds for the Central Universities. The funding bodies had no role in the design of the study and collection, analysis and interpretation of data and in writing the manuscript.

Availability of data and materials

The data supporting the results of this article are included within the article.

Author’s contributions

GZ and ZH designed the experiments; ZH, JZ, XC and XQ did the experiments; ZH and SC analyzed data; ZH, SC and GZ wrote the paper. All authors read and approved the final manuscript.

Ethics approval

Tibetan wild barley used in this study were introduced from Huazhong Agricultural University (HZAU) and cultivated barley were collected from southern east China. All these samples were permitted to do scientific research. The experimental research on barley was complied with China’s legislation and field studies was in accordance with legislation of Zhejiang University. All the experiments were not involved in any endangered or protected species.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Agronomy, Zhejiang Key Laboratory of Crop Germplasm, Zhejiang University, Hangzhou, China
(2)
School of Biological Science and Technology, University of Jinan, Jinan, China

References

  1. Madhujith T, Izydorczyk M, Shahidi F. Antioxidant properties of pearled barley fractions. J Agr Food Chem. 2006;54(9):3283–9.View ArticleGoogle Scholar
  2. Han Z, Cai S, Zhang X, Qian Q, Huang Y, Dai F, Zhang G. Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy. Food Chem. 2017;227:342–8.View ArticlePubMedGoogle Scholar
  3. Adom KK, Liu RH. Antioxidant activity of grains. J Agr Food Chem. 2002;50(21):6182–7.View ArticleGoogle Scholar
  4. McMurrough I, Baert T. Identification of proanthocyanidins in beer and their direct measurement with a dual electrode electrochemical detector. J I Brewing. 1994;100(6):409–16.View ArticleGoogle Scholar
  5. Bizjak J, Weber N, Mikulic-Petkovsek M, Alam Z, Thill J, Stich K, et al. Polyphenol gene expression and changes in anthocyanins and polyphenols in the skin of ‘Braeburn’apples after the autumn application of prohexadione-calcium. Plant Growth Regul. 2013;71(3):225–33.View ArticleGoogle Scholar
  6. Dvořáková M, Guido LF, Dostálek P, Skulilová Z, Moreira MM, Barros AA. Antioxidant properties of free, soluble Ester and Insoluble-bound Phenolic compounds in different barley varieties and corresponding malts. J I Brewing. 2008;114(1):27–33.View ArticleGoogle Scholar
  7. Gupta M, Abu-Ghannam N, Gallaghar E. Barley for brewing: characteristic changes during malting, brewing and applications of its by-products. Compr Rev Food Sci F. 2010;9(3):318–28.View ArticleGoogle Scholar
  8. Cai SG, Han ZG, Huang YQ, Chen ZH, Zhang GP, Dai F. Genetic diversity of individual phenolic acids in barley and their correlation with barley malt quality. J Agr Food Chem. 2015;63(31):7051–7.View ArticleGoogle Scholar
  9. Shao YF, Jin L, Zhang G, Lu Y, Shen Y, Bao JS. Association mapping of grain color, phenolic content, flavonoid content and antioxidant capacity in dehulled rice. Theor Appl Genet. 2011;122(5):1005–16.View ArticlePubMedGoogle Scholar
  10. Rhodes DH, HoffmannJr L, Rooney WL, Ramu P, Morris GP, Kresovic T. Genome-wide association study of grain polyphenol concentrations in global sorghum [Sorghum bicolor (L.) Moench] germplasm. J Agr Food Chem. 2014;62(45):10916–27.View ArticleGoogle Scholar
  11. Vogt T, Jones P. Glycosyltransferases in plant natural product synthesis: characterization of a supergene family. Trends Plant Sci. 2000;5(9):380–6.View ArticlePubMedGoogle Scholar
  12. McIntosh CA, Owens DK. Advances in flavonoid glycosyltransferase research: integrating recent findings with long-term citrus studies. Phytochem Rev. 2016;15(6):1075–91.View ArticleGoogle Scholar
  13. Yonekura-Sakakibara K, Hanada K. An evolutionary view of functional diversity in family 1 glycosyltransferase. Plant J. 2011;66(1):182–93.View ArticlePubMedGoogle Scholar
  14. Yin Q, Shen G, Di S, Fan C, Chang Z, Pang Y. Genome-wide identification and functional characterization of UDP-Glucosyltransferase genes involved in Flavonoid biosynthesis in Glycine max. Plant Cell Physiol. 2017;58(9):1558–72.View ArticlePubMedGoogle Scholar
  15. Dai F, Nevo E, Wu DZ, Comadran J, Zhou MX, Qiu L, et al. Tibet is one of the centers of domestication of cultivated barley. Proc Natl Acad Sci U S A. 2012;109(42):16969–73.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Dai F, Chen ZH, Wang XL, Li ZF, Jin GL, Wu DZ, et al. Transcriptome profiling reveals mosaic genomic origins of modern cultivated barley. Proc Natl Acad Sci U S A. 2014;111(37):13403–8.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Cai SG, Yu G, Chen XH, Huang YC, Jiang XG, Zhang GP, et al. Grain protein content variation and its association analysis in barley. BMC Plant Biol. 2013;13(1):35.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Koehler G, Wilson RC, Goodpaster JV, Sonsteby A, Lai XY, Witzmann FA, et al. Proteomic study of low-temperature responses in strawberry cultivars (Fragaria× ananassa) that differ in cold tolerance. Plant Physiol. 2012;159(4):1787–805.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Diaz C, Saliba-Colombani V, Loudet O, Belluomo P, Moreau L, Daniel-Vedele F, et al. Leaf yellowing and anthocyanin accumulation are two genetically independent strategies in response to nitrogen limitation in Arabidopsis thaliana. Plant Cell Physiol. 2006;47(1):74–83.View ArticlePubMedGoogle Scholar
  20. Singh B, Kumar A, Malik AK. Flavonoids biosynthesis in plants and its further analysis by capillary electrophoresis. Electrophoresis. 2017;38(6):820–832.Google Scholar
  21. Fabón G, Martínez-Abaigar J, Tomás R, Núñez-Olivera E. Effects of enhanced UV-B radiation on hydroxycinnamic acid derivatives extracted from different cell compartments in the aquatic liverwort Jungermannia exsertifolia subsp. cordifolia. Physiol Plantarum. 2010;140(3):269–79.Google Scholar
  22. Nevo E. Evolution in action across phylogeny caused by microclimatic stresses at “evolution canyon”. Theor Popul Biol. 1997;52(3):231–43.View ArticlePubMedGoogle Scholar
  23. Bedada G, Westerbergh A, Müller T, Galkin E, Bdolach E, Moshelion M, et al. Transcriptome sequencing of two wild barley (Hordeum Spontaneum L.) ecotypes differentially adapted to drought stress reveals ecotype-specific transcripts. BMC Genomics. 2014;15(1):995.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Zhao HF, Dong JJ, Lu J, Chen J, Li Y, Shan LJ, et al. Effects of extraction solvent mixtures on antioxidant activity evaluation and their extraction capacity and selectivity for free phenolic compounds in barley (Hordeum vulgare L.). J Agr Food Chem. 2006;54(19):7277–86.View ArticleGoogle Scholar
  25. Shao YF, Tang FF, Huang Y, Xu FF, Chen YL, Tong C, et al. Analysis of genotype× environment interactions for polyphenols and antioxidant capacity of rice by association mapping. J Agr Food Chem. 2014;62(23):5361–8.View ArticleGoogle Scholar
  26. Saint-Cricq de Gaulejac N, Provost C, Vivas N. Comparative study of polyphenol scavenging activities assessed by different methods. J Agr Food Chem. 1999;47(2):425–31.View ArticleGoogle Scholar
  27. Wenzl P, Li H, Carling J, Zhou M, Raman H, Paul E, et al. A high-density consensus map of barley linking DArT markers to SSR, RFLP and STS loci and agricultural traits. BMC Genomics. 2006;7(1):206.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, et al. Diversity arrays technology (DArT) for whole-genome profiling of barley. Proc Natl Acad Sci U S A. 2004;101(26):9915–20.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.PubMedPubMed CentralGoogle Scholar
  30. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.View ArticlePubMedGoogle Scholar
  31. Hardy OJ, Vekemans X. SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol. 2002;2(4):618–20.View ArticleGoogle Scholar
  32. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5.View ArticlePubMedGoogle Scholar
  33. Ritland K. Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res. 1996;67(02):175–85.View ArticleGoogle Scholar
  34. Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38(2):203–8.View ArticlePubMedGoogle Scholar
  35. Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544(7651):427–33.View ArticlePubMedGoogle Scholar
  36. Yu J, Hu F, Dossa K, Wang Z, Ke T. Genome-wide analysis of UDP-glycosyltransferase super family in Brassica Rapa and Brassica Oleracea reveals its evolutionary history and functional characterization. BMC Genomics. 2017;18(1):474.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Hiersche M, Rühle F, Stoll M. Postgwas: advanced GWAS interpretation in R. PLoS One. 2013;8(8):e71775.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Knowler WC, Williams RC, Pettitt DJ, Steinberg AG. Gm3; 5, 13, 14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture. Am J Hum Genet. 1988;43(4):520.PubMedPubMed CentralGoogle Scholar
  40. Sharbel TF, Haubold B, Mitchell-Olds T. Genetic isolation by distance in Arabidopsis Thaliana: biogeography and postglacial colonization of Europe. Mol Ecol. 2000;9(12):2109–18.View ArticlePubMedGoogle Scholar
  41. Peukert M, Weise S, Röder MS, Matthies IE. Development of SNP markers for genes of the phenylpropanoid pathway and their association to kernel and malting traits in barley. BMC Genet. 2013;14(1):97.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Yonekura-Sakakibara K, Fukushima A, Nakabayashima R, Hanada K, Matsuda F, Sugawara S, et al. Two glycosyltransferases involved in anthocyanin modification delineated by transcriptome independent component analysis in Arabidopsis Thaliana. Plant J. 2012;69(1):154–67.View ArticlePubMedGoogle Scholar
  43. Cai SG, Han ZG, Huang YQ, Hu HL, Dai F, Zhang GP. Identification of quantitative trait loci for the phenolic acid contents and their association with agronomic traits in Tibetan wild barley. J Agr Food Chem. 2016;64(4):980–7.View ArticleGoogle Scholar
  44. Purugganan MD, Fuller DQ. The nature of selection during plant domestication. Nature. 2009;457(7231):843–8.View ArticlePubMedGoogle Scholar
  45. Cai S, Wu D, Jabeen Z, Huang Y, Zhang G. Genome-wide association analysis of aluminum tolerance in cultivated and Tibetan wild barley. PLoS One. 2013;8(7):e69776.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Wu D, Qiu L, Xu L, Ye L, Chen M, Sun D, et al. Genetic variation of HvCBF genes and their association with salinity tolerance in Tibetan annual wild barley. PLoS One. 2011;6(7):e22938.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Mittler R, Vanderauwera S, Gollery M, et al. Reactive oxygen gene network of plants. Trends Plant Sci. 2004;9(10):490–8.View ArticlePubMedGoogle Scholar
  48. Agati G, Azzarello E, Pollastri S, Tattini M. Flavonoids as antioxidants in plants: location and functional significance. Plant Sci. 2012;196:67–76.View ArticlePubMedGoogle Scholar
  49. Nakabayashi R, Yonekura-Sakakibara K, Urano K, Suzuki M, Yamada Y, Nishizawa T, et al. Enhancement of oxidative and drought tolerance in Arabidopsis by overaccumulation of antioxidant flavonoids. Plant J. 2014;77(3):367–79.View ArticlePubMedGoogle Scholar
  50. Tohge T, de Souza LP, Fernie AR. Current understanding of the pathways of flavonoid biosynthesis in model and crop plants. J Exp Bot. 2017;68(15):4013–28.View ArticlePubMedGoogle Scholar
  51. Shao Y, Bao J. Polyphenols in whole rice grain: genetic diversity and health benefits. Food Chem. 2015;180:86–97.View ArticlePubMedGoogle Scholar
  52. Cheynier V, Comte G, Davies KM, Lattanzio V, Martens S. Plant phenolics: recent advances on their biosynthesis, genetics, and ecophysiology. Plant Physiol Biochem. 2013;72:1–20.View ArticlePubMedGoogle Scholar
  53. Mohammadi M, Endelman B, Nair S, Chao S, Jones SS, Muehlbauer GJ, et al. Association mapping of grain hardness, polyphenol oxidase, total phenolics, amylose content, and β-glucan in US barley breeding germplasm. Mol Breed. 2014;34(3):1229–43.View ArticleGoogle Scholar
  54. Bowles D, Isayenkova J, Lim EK, Poppenberger B. Glycosyltransferases: managers of small molecules. Curr Opin Plant Biol. 2005;8(3):254–63.View ArticlePubMedGoogle Scholar
  55. Yin R, Messner B, Faus-Kessler T, Hoffmann T, Schwab W, Hajirezaei MR. Feedback inhibition of the general phenylpropanoid and flavonol biosynthetic pathways upon a compromised flavonol-3-O-glycosylation. J Exp Bot. 2012;63(7):2465–78.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Stracke R, Ishihara H, Huep G, Barsch A, Mehrtens F, Niehaus K, Weisshaar B. Differential regulation of closely related R2R3-MYB transcription factors controls flavonol accumulation in different parts of the Arabidopsis thaliana seedling. Plant J. 2007;50(4):660–77.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Mönchgesang S, Strehmel N, Schmidt S, Westphal L, Taruttis F, Müller E, et al. Natural variation of root exudates in Arabidopsis thaliana-linking metabolomic and genomic data. Sci Rep. 2016;6:29033.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Jones P, Messner B, Nakajima JI, Schäffner AR, Saito K. UGT73C6 and UGT78D1, glycosyltransferases involved in flavonol glycoside biosynthesis in Arabidopsis Thaliana. J Biol Chem. 2003;278(45):43910–8.View ArticlePubMedGoogle Scholar
  59. Zhang XB, Gou MY, Liu CJ. Arabidopsis Kelch repeat F-box proteins regulate phenylpropanoid biosynthesis via controlling the turnover of phenylalanine ammonia-lyase. Plant Cell. 2013;25(12):4994–5010.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Feder A, Burger J, Gao S, Lewinsohn E, Katzir N, Schaffer AA, et al. A Kelch domain-containing F-box coding gene negatively regulates flavonoid accumulation in Cucumis melo L. Plant Physiol. 2015; https://doi.org/10.1104/pp.15.01008.
  61. ul Hassan MN, Zainal Z, Ismail I. Plant kelch containing F-box proteins: structure, evolution and functions. RSC Adv. 2015;5(53):42808–14.View ArticleGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement