Introduction

Improvement of N-use efficiency (NUE) is a current critical issue for plant breeding in order to maximize crop production with less harmful environmental consequences. Recent review papers have dealt with the genetic basis of this complex trait (Cormier et al. 2016; Islam et al. 2021; Liu et al. 2022; Teng et al. 2022) whose relatively low genetic progress in the past decades may reflect an indirect effect of breeding programmes oriented to increase grain yield at standard high input cropping practices (Cormier et al. 2016). Active selection for efficient N uptake or utilization seems then an urgent breeding strategy to create novel crop cultivars that can respond to the increasing global food demand in more sustainable agroecosystems. Furthermore, in the case of wheat, NUE is not only relevant for grain yield but also for grain quality and flour or semolina industrial processing properties (Laidig et al. 2022; OrtizMonasterio et al. 1997).

Glutamine synthetase (GS) catalyses the synthesis of the amino acid glutamine, the first step by which plants incorporate inorganic N into an organic molecule, thus having a key role in the assimilation and mobilization of N to the target plant organs (e. g., Liu et al. 2022; Masclaux-Daubresse et al. 2010; Miflin and Habash 2002). Several GS isoforms have been chemically and functionally described, the most widely characterized being the cytosolic GS1 and the plastidic GS2 (Thomsen et al. 2014; Tobin et al. 1985; Unno et al. 2006; Wang et al. 2015). In common wheat, GS1 is encoded by three genes (hereafter referred to as GS1A, GS1B and GS1D) located on the homoeologous group-6 chromosomes, while the plastidic enzyme is controlled by three genes (hereafter referred to as GS2A, GS2B and GS2D) located on the homoeologous group-2 chromosomes (Habash et al. 2007). Two additional cytoplasmic isoforms, GSr and GSe, are each encoded by two homoeogenes located on chromosomes 4A and 4B (Gadaleta et al. 2014; Habash et al. 2007).

Many studies have aimed to relate the level of activity of GS enzymes with grain characteristics that are involved in yield performance or end-use quality of wheat cultivars, with a special focus on thousand kernel weight (TKW) and grain protein content (GPC) (e.g., Fontaine et al. 2009; Habash et al. 2007; Nigro et al. 2016). The role of GS activity in plant response to deprived fertilizer regimes has been widely examined, and the relationship between GS activity and crop performance under drought, heat or salinity stress has also been explored (e.g., Jallouli et al. 2019; Kichey et al. 2006; Nagy et al. 2013). In a few instances, the enzymatic activity of specific isoforms has been determined, making it possible to ascribe the effects eventually detected to the expression of a particular GS gene family at the protein level (Habash et al. 2001; Thomsen et al. 2014). After cloning of GS1 and GS2 wheat homoeogenes by Bernard et al. (2008), transcriptomic analyses have allowed to further characterize the expression at the RNA level of separate GS gene families and, in some instances, of specific GS1 or GS2 genes (e.g., Gayatri et al. 2021; Wei et al. 2020, 2021; Yousfi et al. 2016; Zhang et al. 2017a, b).

Changes in the expression of GS1 and GS2 genes between plant organs, and during plant development, have been reported either at the protein or RNA level (Bernard et al. 2008; Habash et al. 2001; Kichey et al. 2006; Zhang et al. 2017b, c). Overall, it has been showed that the expression of GS2 declines from leaf vegetative development to leaf senescence and grain filling stage, when GS1 expression seems relatively more relevant. This has led to suggest that GS2 activity could be mainly associated with traits eventually determined at the pre-anthesis stages (i.e., number of spikes per plant or kernels per spike), GS1 activity having a greater influence on traits which depend on N remobilization during grain development (Bernard et al. 2008; Habash et al. 2001; Masclaux-Daubresse et al. 2010).

In addition to the influence of external conditions and developmental stage of wheat plants on GS activity, genotype effects are also widely demonstrated (Fatholahi et al. 2020; Fontaine et al. 2009; Nigro et al. 2016). However, the studies that have attempted to link specific genotype variation at GS loci with plant traits assumed to be affected by GS activity are relatively scarce. The earliest among them conducted quantitative genetic analyses to determine the mapping position of QTLs influencing GS activity and, subsequently, their eventual colocalization with QTLs associated to agronomic traits (Fontaine et al. 2009; Habash et al. 2007). This indirect approach to identify allelic variants of GS genes with enhanced effect on NUE-related traits has been questioned not only because enzymatic activities, usually measured under controlled conditions, are highly dependent on the environment but also because genotype-by-environment effects are common on physiological traits (Fontaine et al. 2009). Nevertheless, several of those indirect genetic mapping approaches have reported QTLs for NUE-related traits that colocalize with structural genes coding for GS1 and GS2 in wheat (see Cormier et al. 2016 and references therein).

From a practical breeding point of view, routine screening of early segregant populations can hardly be based on measurement of protein activity or gene expression level. Some studies have then aimed to directly associate allelic variation at individual GS1 or GS2 homoeogenes, or genetically linked QTLs, with agronomic or quality traits of interest for NUE improvement (Cui et al. 2016; Guo et al. 2013; Li et al. 2019, 2011); but their results, when contrastable, have not always been consistent. So, Guo et al. (2013) reported the beneficial effect on TKW of one GS1 allele (erroneously mapped to chromosome 6D; see the Discussion section) in a RIL population. However, no significant effect of that allele was found in a wide collection of Chinese winter wheat cultivars where, by contrast, the allele previously associated to lower TKW was present in the entries with the highest TKW values (Li et al. 2019). Furthermore, based on the inconsistent effects of overexpressing GS1, the success of improving NUE by means of simply increasing GS1 activity has been questioned (Thomsen et al. 2014). Regarding the wheat homoeogenes coding for the plastidic GS isoform, Li et al. (2011) reported a GS2A allele (originally designated TaGS2-A1b and later renamed to TaGS2-2Ab) associated with higher TKW and GPC, whose transgenic overexpression increased yield and several yield components (Hu et al. 2018). Li et al. (2011) described also a GS2D allele, designated TaGS2-D1a, associated with higher grain weight. A positive effect of that allele on TKW has been pointed out in a later study (Cui et al. 2016). However, no clue can be found in the literature cited by these authors (Cui et al. 2014a, b) on the actual relationship between the putatively functional marker used in the study, namely IN10, and the wheat GS locus on chromosome 2D. In durum wheat, the relationship between genotype variation at GS2 homoeogenes and GPC has been analysed (Gadaleta et al. 2011; Nigro et al. 2017). Nigro et al. (2020) have actually reported a functional marker for GS2B related with high GPC. To our knowledge, no other earlier studies than those already mentioned have reported association between NUE-related traits and specific alleles of GS1 and GS2 genes or functional markers in wheat.

[On this point, it can be noted that gene symbols for wheat glutamine synthetase genes are not yet officially catalogued (https://wheat.pw.usda.gov/GG3/wgc). Renaming of some wheat GS genes and alleles in later reports and the use of almost identical nomenclature for genes controlling grain size (for example, TaGS1a, TaGS2-A1 or TaGS-D1 in Khalid et al. 2019), makes it troublesome to contrast published results on the effects of glutamine synthetase genes from literature reports.]

NUE is a highly complex trait as dependent on varied plant physiological processes. Nevertheless, the relatively few enzymatic activities involved in N assimilation supports that pyramiding of a small number of favourable alleles may have a significant impact on this component of NUE enhancement (Cormier et al. 2016; Islam et al. 2021). The application for wheat improvement of the cumulated knowledge on the effects of GS activity on NUE-related traits requires identification of GS genes alleles responsible of favourable N-use phenotypes. But molecular markers that may assist their selection in early breeding progenies and facilitate prebreeding germplasm screening are also needed. With that in mind, the final objective of this study was to develop functional markers for glutamine synthetase GS1 and GS2 wheat homoeogenes. A primary goal was to determine their sequence polymorphisms in a panel of 15 bread wheat genotypes whose phenotypic variation for agronomic and quality traits was also characterized by multi-environment field trials. The subsequent goal was to study whether molecular markers based on these polymorphisms were associated with yield components and grain quality traits in a wide, diverse, germplasm collection of bread wheat landraces.

Materials and methods

Plant material

The present study has been conducted on two distinct sets of bread wheat genotypes: a sequencing panel and a diversity panel. The sequencing panel was composed of fifteen varieties of bread wheat (Triticum aestivum L.) widely cultivated in Spain, either in the past or the present (Table 1). This group of genotypes included six landraces or pre-Green Revolution varieties (collected or released before 1960), four old commercial cultivars (released between 1970 and 1985) and five modern cultivars (released after 1990). The diversity panel was formed by 187 Spanish bread wheat landraces that constituted the primary set from which the Spanish bread wheat core collection was developed (Pascual et al. 2020a). The genetic structure and genomic variability of this collection was reported in Pascual et al. (2020b) while its phenotypic characterization for some agronomic and quality traits was further described in López-Fernández et al. (2021).

Table 1 Varieties forming the sequencing panel. Their haplotypes for GS1 and GS2 homoeogenes are indicated. In all cases, haplotype 1 corresponds to the gene sequence in the reference cultivar Chinese Spring
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DNA extraction and sequencing

All the accessions were germinated and DNA extraction was performed from 50 mg of coleoptile according to Doyle and Doyle (1990). The DNA was quantified in 1% agarose gels and stored at − 20 °C until used.

The sequence of the three homoeogenes coding for the cytosolic glutamine synthetase GS1 (GS1A, GS1B and GS1D), cloned by Bernard et al. (2008), and of the three homoeogenes coding for the plastidic glutamine synthetase (GS2A, GS2B and GS2D), cloned by Li et al. (2011), were blasted against the bread wheat reference genome (https://plants.ensembl.org/Triticum_aestivum/Tools/Blast). The obtained alignments were used to manually annotate the GS genes, determine the position of the UTRs and obtain the complete gene sequences including a 500 pb upstream and downstream window. From these sequences, primers were designed to cover the complete genomic sequence of each of the genes with three overlapping amplicons. The details of primers combinations and melting temperatures are given as supplementary material (Online Resource 1). PCR reactions were carried out with the DNA AmpliTools Complex Master Mix (Biotools, Madrid) following the manufacturer’s protocol.

The GS1 and GS2 amplicons were obtained for all the varieties included in the sequencing set. PCR amplified fragments were purified with sepharose columns and sequenced by capillary electrophoresis at Macrogen (Macrogen Europe, Amsterdam, The Netherlands). Sequences were analyzed with Sequencher® version 5.0 sequence analysis software (Gene Codes Corporation, Ann Arbor, MI, USA (http://www.genecodes.com) and aligned against the Chinese Spring sequence (IWGSC RefSeq v1.0) (International Wheat Genome Sequencing Consortium 2018). The obtained information was used to reconstruct the complete genomic sequence of all GS1 and GS2 homoeogenes and to define alleles (i.e., haplotypes). Nucleotide sequences for each identified allele were translated with ExPASy translation tool (http://web.expasy.org/translate/).

Development of molecular markers and genotyping

Molecular markers were developed from the polymorphisms detected after sequencing the GS genes and employed to genotype the 187 varieties in the diversity panel. PCR markers were developed when the underlining polymorphism was an INDEL (GS2A and GS2D), and CAPS marker (Cleaved amplified polymorphic sequence) when the underling polymorphism was a SNP located within a restriction site (GS1A). For the CAPS marker, GS1A 2nd amplicon was amplified as described in Online Resource 1 and PCR amplified fragments were digested with the restriction enzyme EcoRI (New England BioLabs Inc., MA, USA) following manufacturer’s protocol. Restriction fragments were then resolved on a 1% agarose gel. Regarding PCR markers, GS2A INDEL region was amplified with the pair of primers TTGATTGACTTCCATGAGAGCACA-Fw and TTAGCATAAAGCACGTCCAGATGA-Rev. For GS2D INDEL region, the primers employed to amplify the 2nd amplicon of this gene were used (see Online Resource 1). The obtained amplicons were resolved on 1–1.5% agarose gels.

Field trails and phenotype evaluation

The sequencing set of bread wheat varieties was evaluated in a total of six environments (i.e., site × year combinations). The trial design at La Canaleja (Alcalá de Henares; LC trials in 2014–2015, 2015–2016 and 2016–2017) consisted of three replicate plots (1.2 × 10 m in 2014–2015 and 2016–2017; 1.2 × 3 m in 2015–2016). In Limagrain’ station at Elorz (Navarra; LG trials in 2015–2016 and 2017–2018), the plots (1.5 × 8 m) were sown by duplicate. In both locations, completely randomized block designs were used. Finally, small plots (1 × 1 m) without replicate were established in the experimental fields of Universidad Politécnica de Madrid (Madrid; UPM trial in 2016–2017), the completely randomized design including two repeated control varieties. Fertiliser treatment in LC and UPM trials was 68 N units ha−1 in a single application. In LG trials, 70 N units ha−1 were split in two applications. Fungicides were not used in any of the trials. A post-emergency herbicide treatment was applied in LC and LG trials, metsulfuron-methyl being the active compound. No herbicide treatment was used in the UPM trial where weeds were controlled by manual weeding.

Summarized information on the experimental conditions and traits analysed in each trial is provided as supplementary information (Online Resource 2). Grain yield (GY, k ha−1 at 12% humidity) was estimated from grain weight at harvest in trials LC15, LC17, LG16 and LG18, while no GY estimation was attempted in trials with small plot sizes (LC16 and UPM17). After harvest, test weight (TW, kg hL−1) and thousand-kernel weight (TKW, g at 12% humidity) were measured for all accessions. Spike number (SN, m−2) was determined by extrapolating to 1 square meter the counting of the spikes in all plants contained in one-meter-long row. The number of kernels per spike (KS) was estimated as the mean number of kernels counted on 10 main spikes. Grain protein content (GPC, % on a dry matter basis) and gluten quality were evaluated on wholemeal flour samples. GPC was measured by near-infrared reflectance analysis using a PerCon Inframatic 8600 (Perten Instruments AB, Sweden). Gluten strength was estimated on 1 g of flour samples by the sodium dodecyl sulfate sedimentation volume (SVol, mm) test following the procedure of Dick and Quick (1983) with some modifications described in Chacon et al. (2020). All traits were analysed individually in each replicated plot, except SVol in LG trials where one flour sample per cultivar, obtained from a balanced mix of grains from the two replicates, was analysed. For GPC and SVol, technical duplicates were always prepared.

Statistical analyses

Analyses of variance (ANOVA) were used to determine any significant effect of the replications in LC and LG trials. Once no effect was found, mean values of a given cultivar in each trial were used for further data analyses. The set of data for the agronomic and quality variables analysed in the diversity panel was taken from López-Fernández et al. (2021; provided there as supplementary information). Normality and homoscedasticity of variables was tested by the Shapiro–Wilk and Levene tests, respectively. For a better fit to normality, a logarithmic transformation was performed for KS before further analysis.

The influence of haplotypes or marker variants for GS1 and GS2 homoeogenes on the evaluated traits was studied by an analysis of variance using the general linear model (GLM) procedure, the significance threshold being set at P < 0.01. The GS genes, the environment (E) and GS gene × E interactions were used as sources of variation. In the sequencing panel, only haplotypes present in at least three cultivars were considered for analysis. In the diversity panel, interaction effects between GS genes were also estimated. Comparison of least squares means between alleles of a given GS gene, and between non-allelic variants combinations, was performed using Fisher’s protected least significant difference (LSD). Contingency chi-tests were used to check two-by-two independence between allelic variants at different GS loci in the diversity panel. All statistical analyses were completed with the InfoStat statistical package (Di Rienzo et al. 2020), which was also used for graphic representation of mean comparison analyses.

Results

Haplotyping of GS1 and GS2 homoeogenes

The sequences of GS1 and GS2 homoeogenes were determined and comparatively analysed in a panel of 15 wheat cultivars including local landraces, old cultivars and modern varieties (Table 1). Cultivar Chinese Spring was always used as reference for haplotype characterization.

For the GS1A gene, 12 SNPs and 3 INDELs were identified among the accessions analysed. The sequence variants allowed to define two new haplotypes (GS1A-hap2 and GS1A-hap3) with respect to Chinese Spring (GS1A-hap1; see Online Resource 3). Two of the SNPs were located at the UTRs while the rest of polymorphisms affected at intronic regions. The most frequent allele (GS1A-hap2) was present in 9 cultivars including all the landraces. The other two haplotypes differed only in one SNP located in an intronic region, each of them being found in 3 varieties. At the gene GS1B we identified 13 SNPs and 3 INDELs, all except one (UTR) being located at intronic regions (see Online Resource 4). These polymorphisms were combined in a total of 4 alleles (GS1B-hap1 to 4). GS1B-hap4, only present in the local landrace Rojo Caravaca, was the most distant from the reference presenting a total of 15 polymorphisms. The other haplotypes differed only in one SNPs between them and were present in six (GS1B-hap1), six (GS1B-hap2) and two (GS1B-hap3) cultivars. No polymorphism was detected for gene GS1D, for which all 15 varieties presented the Chinese Spring reference sequence.

Sequence polymorphisms were detected for the three GS2 homoeoloci (see Online Resources 5 to 7). For GS2A, a new allele (GS2A-hap2) presenting 8 SNPs and 5 INDELs with respect to Chinese Spring was identified. One of the polymorphisms was located in an UTR, another in an exon (synonymous) and the rest of them at intronic regions. GS2A-hap2 was present in 12 cultivar including all the landraces, whereas the Chinese Spring allele (GS2A-hap1) was present in the remaining 3 cultivars. For GS2B we only obtained high quality sequence from 1200 pb downstream from the start codon to the end of the gene. In the sequenced region, which fully covered the 2nd and 3rd amplicons, we were able to detect a total of 9 SNPs and 1 INDEL. Two of the SNPs were located at exonic regions and one of them, located 1390 bp from the starting codon, produced a change in the coded protein (H163Q). This non-synonymous SNP was only present in cultivar Marius (GS2B-hap6 allele). In total, we were able to identify 8 different alleles, the most frequent being the allelic variant present in Chinese Spring (GS2B-hap1, in 4 cultivars) and GS2B-hap2 (4 cultivars). Finally, when we sequenced GS2D more than 50 polymorphisms were detected compared to the Chinese Spring reference. Thus, we decided to include in the alignment the GS2D sequence of cultivar Xiaoyan-54, reported by Li et al. (2011) (genebank accession no. GQ169689), and greatly differing from the Chinese Spring sequence either at the coding and 5’ flanking regions. Then, our cultivars were found to present either the Chinese Spring allele (GS2D-hap1) or the Xiaoyan-54 allele (here named GS2D-hap2), the only exceptions being two new SNPs. One of these changes was identified in cultivar Califa Sur (designated GS2D-hap1bis), that presented A instead of G at 72 bp from the start codon while no other polymorphism compared to Chinese Spring was detected for the rest of the gene. The other new SNP was detected in cultivar Nogal (designated GS2D-hap2bis), whose GS2D sequence was identical to GS2D-hap2 except by a change A2190C (see Online Resource 7).

The haplotypes of the six GS genes under study in the varieties forming the sequencing panel are shown in Table 1. It can be highlighted that the five landraces in the set presented the same haplotypes for the GS1 and GS2 homoeogenes in the A and D genome, even though these haplotypes were also found in commercial varieties. However, some sequence variants of GS1B and GS2B were only found in the landraces (GS1B-hap3 and 4; GS2B-hap8).

Field evaluation of the sequencing panel of bread wheat varieties

The data obtained for the seven agronomic and quality traits evaluated by field experiments on the sequencing panel of varieties are provided as supplementary information (Online Resource 8). The results of the analyses of variance conducted to determine the influence of environment and allelic variation at GS1 and GS2 homoeogenes are summarized in Online Resource 9. These analyses revealed that the environment was a highly significant source of variation for all parameters, except SVol. Figure 1 shows the mean phenotypic value associated to individual haplotypes for the GS loci with significant effect on the traits examined. As noted above, for each locus, only variants present in 3 or more accessions were considered for analysis.

Fig. 1
figure 1

Mean phenotypic value associated to individual haplotypes for the GS loci with significant effect (P < 0.01) on the traits evaluated in the sequencing panel of wheat varieties. At each locus, the black bars indicate the Chinese Spring haplotype. Means with a common letter are not statistically different (P > 0.05). TKW: thousand-kernel weight; GPC: grain protein content; SVol: SDS-sedimentation volume

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None of the 5 GS genes under analysis had a significant influence on GY, TW, SN or KS (see Online Resource 9). Among the traits related to yield potential, only TKW was influenced by variation at a GS locus, the haplotype GS2B-hap1 being associated to heavier grains in the sequencing panel (Fig. 1). Grain protein content was found to be significantly affected by genotype variation at GS2A, GS2A-hap2 being the haplotype with a positive effect on GPC. Finally, SVol, the unique trait for which environment effects were not detected, was found to be significantly influenced by genotype variation at GS1A, GS1B and GS2A.

Molecular markers to discriminate GS1 and GS2 haplotypes

In order to develop GS gene-based molecular markers that could be useful for MAS in breeding programs, we focused on GS1A, GS2A and GS2D. For these three GS genes, the sequenced varieties either presented only two alleles or the additional alleles showed only one SNP compared to one of two clearly contrasting haplotypes. The latter occurred for GS1A-hap3, with regard to the Chinese Spring haplotype (GS1A-hap1), and for GS2D-hap1bis and 2bis with regard to the Chinese Spring and Xiaoyan-54 haplotypes, GS2D-hap1 and GS2D-hap2, respectively (Table 1; Online Resources 3, 5 and 7).

For GS1A, the CAPS marker described above discriminate allelic variants based on the SNP [C/T] present at 1777 from the starting codon. Hence, the restriction enzyme will not cut the amplicon in varieties presenting the GS1A-hap1 and GS1A-hap3 (namely GS1A-LM marker allele) while the amplicon will be excised in varieties with the GS1A-hap2 (GS1A-SM marker allele). For GS2A, the pair of primers designed is able to detect by PCR the presence/absence of the 239 bp deletion. Thus, a band of 935 bp is amplified in varieties with the GS2A-hap1 sequence (namely GS2A-Del marker allele) whereas a band of 1174 bp is obtained for GS2A-hap2 (GS2A-NoDel marker allele). Finally, the 1st amplicon of the GS2D gene, containing several INDELs (not shown) was used to discriminate varieties carrying the GS2D-hap1 against those with the GS2D-hap2, a smaller amplicon being obtained in the former (1391pb; namely GS2D-M1 marker allele) than in the latter (1433pb; GS2D-M2 marker allele). Figure 2 illustrates the easy resolution of these biallelic markers on agarose gels. The correspondence between molecular marker alleles and haplotypes expected according to their in silico design was further confirmed by marker profiling of the sequenced varieties.

Fig. 2
figure 2

Resolution of alleles of the sequence-based molecular markers developed for GS1A, GS2A and GS2D on agarose gel (1.5%). GS1A lanes: R corresponds to the marker allele LM (marking the haplotypes GS1A-hap1 and -3) and A to the marker allele SM (marking GS1A-hap2). GS2A lanes: R corresponds to the marker allele Del (GS2A-hap1) and A to the marker allele NoDel (GS2A-hap2). GS2D lanes: R corresponds to the marker allele M1 (GS2D-hap1 and -hap1bis) and A to the marker allele M2 (GS2D-hap2 and -hap2bis). The first lane corresponds to NZYDNA ladder VII (NZYTech, Lisboa)

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GS marker-trait relationships in the diversity panel

The gene sequence-based markers developed for GS1A, GS2A and GS2D allowed allelic profiling of 99.5, 96.8 and 93.6%, respectively, of the Spanish bread wheat landraces forming the diversity panel (Table 2). Online Resource 10 provides information on the specific marker alleles present in each of the 187 accessions and on their population assignment after the genetic structure analysis conducted on this germplasm collection by Pascual et al. (2020b). GS1A-SM was present in almost 85% of varieties. GS2A-NoDel was the most frequent allelic alternative at GS2A (66% of varieties) while GS2D-M1 was present in 71% of the genotyped varieties. It can be noted that for GS1A and GS2D the predominant alleles were the noted above in all the four populations but, contrary to the overall trend, the GS2A-Del allele was the most frequent in Pop 4 (Table 2). Two-by-two contingency tests demonstrated independent distribution of the marker alleles for GS2D and either GS1A and GS2A in the diversity panel of varieties. However, some statistical association between GS1A and GS2A was found, the non-allelic combinations GS1A-SM:GS2A-Del and GS1A-LM:GS2A-NoDel being slightly more frequent than expected (Χ2 = 4.44, d. f. = 1; P = 0.035).

Table 2 Frequency of marker variants for genes GS1A, GS2A and GS2D in the 187 bread wheat landraces forming the diversity panel. Their distribution in the 4 populations determined in this germplasm collection in Pascual et al. (2020b) is also indicated
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The results of the statistical analyses conducted to determine the influence of environmental and GS genotype factors, as well as GS × E, on the traits analysed in the diversity panel by López-Fernández et al. (2021) are summarized in Online Resource 11. The effects of GS genes considered in the analysis included not only allelic variation at GS1A, GS2A and GS2D, but also their two-by-two interactions. The environment had always a great effect on the evaluated traits while none of the GS gene × E interaction was significant.

TKW was very significantly affected by allelic variation at GS2 homoeogenes, GS2A-Del and GS2D-M2 having a positive effect on this yield component trait (Fig. 3). GS1A did not show to affect TKW when analysed individually, but its interaction with either of those two GS2 genes was significant (see Online Resource 11). The mean comparison analyses revealed that the favourable alleles for GS2A and GS2D were both epistatic over GS1A; but in their absence (i.e., in genotypes GS2A-NoDel or GS2D-M1), the GS1A-SM allele produced higher TKW than its alternative allele GS1A-LM (Fig. 3).

Fig. 3
figure 3

Mean phenotypic value associated to allelic variants and non-allelic combinations for the GS1A, GS2A and GS2D functional markers developed in the study. Only the genes and gene-by-gene interactions with significant effect (P < 0.01) on the traits are represented. Among allelic variants, the black bars indicate the Chinese Spring marker allele. Means with a common letter are not statistically different (P > 0.05). TKW: thousand-kernel weight; KS: kernels per spike; GPC: grain protein content

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The number of kernels per spike was significantly influenced by GS1A, the favourable allele being GS1A-LM. The significant interaction between GS1A and GS2D and mean comparison of the non-allelic combinations for these loci further showed differences for KS between GS2D alleles M1 and M2 in genotypes carrying the favourable LM allele at the GS1A locus (Fig. 3; Online Resource 11).

Grain protein content was highly affected by GS1A and GS2A and also, but to a lower extent, by GS2D. The favourable alleles were GS1A-SM (as noted above, also beneficial for TKW in certain GS genotype backgrounds), GS2A-NoDel (with negative effect on TKW) and GS2D-M1 (with negative effect on TKW as well as on KS in GS1A-LM accessions).

Allelic variation at none of the loci examined showed to influence SVol, a wheat quality parameter tightly associated to gluten strength. No significant interaction among loci was either detected (see Online Resource 11).

Discussion

Allelic diversity at GS1 and GS2 homoeogenes

Our haplotype characterization in the sequencing panel has revealed a wider allelic variability at the B genome GS1 and GS2 genes compared to the A and D genome counterparts (Table 1). Li et al. (2011) characterized the allelic diversity for the GS2 homoeogenes in the Mini core collection of Chinese wheat, composed of around 150 landraces and 100 commercial varieties, and reported quite similar results, with 2, 6 and 2 haplotypes for GS2A, GS2B and GS2D, respectively. Regarding GS1 homoeogenes, Guo et al. (2013) found two distinct haplotypes of a gene designated TaGS1a (Bernard et al. 2008) in a set of 60 Chinese winter wheat varieties. Guo and coworkers reported mapping of that gene to chromosome 6D, which led them and others to assume that corresponded to the GS1D homoeogene (see also Li et al. 2019). However, our alignment of the sequence cloned by Bernard et al. (2008) to the wheat reference genome showed that it corresponds to the GS1 gene located on chromosome 6A. This has been furtherly confirmed by the successful amplification of the GS1A marker reported here (actually based on the same polymorphism used in Guo et al. 2013), in a wide collection of durum wheat landraces (Pascual, unpublished results). To our knowledge, no wide survey of GS1B and GS1D haplotype variations has yet been documented in common wheat. Nigro et al. (2019) have conducted association mapping of GPC by analysing genotype variation at several NUE-related candidate genes in 240 accessions of tetraploid wheat. However, although GS1 and GS2 homoeogenes on the A and B genome were included in the study, data on the allelic diversity found in that germplasm collection are not reported.

Most of the haplotype polymorphisms found in the present study do not provoke protein sequence differences between alleles, the exceptions being a single non-synonymous substitution in GS2B and two amino acid substitutions that differentiate the deduced protein sequence of GS2D in Chinese Spring and Xiaoyan-54 (Li et al. (2011); Online Resources 3 to 7). This could lead to question that allelic variation at GS1A, GS1B and GS2A may actually be responsible for any of the phenotypic effects reported here. However, non-coding regions (promoters, introns, UTRs) can affect the function of a gene by influencing its RNA processing and/or stability. Actually, a recent study has described differential expression of a glutamine synthetase GSr gene, related to differences in TKW, in two wheat varieties which have identical coding regions (Yang et al. 2022).

For the three molecular markers that were genotyped in the diversity panel, we have found that one of the alleles was predominant (Table 2). It makes less unexpected that the Spanish landraces in the sequencing panel shared the same haplotypes for all GS genes but those located on the B genome (Table 1). Li et al. (2011) reported also a quite uneven distribution of allelic alternatives at GS2 genes, especially at GS2D, in the 153 landraces present in the Mini core collection of Chinese wheat varieties. Even at the much more polymorphic GS2B locus, these authors found that one out of the 6 existing haplotypes was present in around 70% of the landraces. Their results can be further compared with those reported here since the gene sequences of Chinese Spring have been used as reference in the two studies. Notably, the Chinese Spring GS2A allele (namely GS2A-Del in the present work) is present in a quite similar proportion in the Chinese and Spanish sets of landraces (33% and 41%, respectively). Regarding GS2D, the Chinese Spring allele is the most frequent in both collections but much more abundant in the Chinese local germplasm (95% versus 66% in the present study), its allelic alternative (i.e., the Xiaoyan-54 allele, represented here by GS2D-M2) being clearly minority in the two cases.

Khalid et al. (2019) determined the allele frequencies of 87 functional markers in a panel of 213 wheat breeding advanced lines and found a higher frequency of the favourable alleles associated to grain size and weight. In our panel of Spanish landraces, the more frequent alleles of the three genes under analysis were those for which a positive effect on GPC has been detected (Table 2; Fig. 3). This finding could relate with the selection by local farmers of materials with good bread-making properties in a country like Spain, where it is documented that, until the 1930s of the XXth century, most of the wheat production was used for human consumption (Rivero 2013).

Phenotypic effects of GS1 and GS2 genes

Several discrepancies are evidenced between the sequencing and the diversity panel regarding the effects of GS1A, GS2A and GS2D genes on the traits that were evaluated in both germplasm sets: TKW, KS, GPC and SVol. Such discrepancies refer mostly to the finding of a significant effect of a specific GS gene in the diversity panel, but not in the sequencing panel. This holds for GS1A on KS and GPC, for GS2A on TKW, and for GS2D on TKW and GPC (Figs. 1 and 3; see also Online Resources 9 and 11). The opposite situation occurs for SVol, where the positive influence of some specific alleles has only been detected in the sequencing panel. Gluten strength of bread wheat varieties is well known to be mainly dependent on two factors: the quantity of protein in grain and the specific high-molecular-weight glutenins encoded by the Glu-1 homoeogenes Glu-A1, Glu-B1 and Glu-D1 (e.g., Branlard and Dardevet 1985; Laidig et al. 2022; López-Fernández et al. 2021; Payne et al. 1987). Actually, SVol was included among the parameters under study to determine whether an eventual effect of a given GS gene on GPC could indirectly affect functional quality of wheat flour, which had never been tested before. Our results, however, clearly indicate no relation between both traits associated to variation at GS loci. The significant effects of most GS genes on SVol detected in the sequencing panel must not be really reflecting any functional gene-trait relationship, being likely attributable to the casual combination of specific alleles at GS and Glu-1 loci in the set of 15 varieties analysed.

The results obtained in the sequencing panel, evaluated by field assays conducted in six environments, can be taken as reliable for that particular group of wheat genotypes, but our study shows that they are not generalizable. To some extent, this might explain the inconsistency among results derived from earlier studies on the influence of GS activity on NUE-related traits in response to external traits — necessarily based on a single or a few genotypes — especially since between-genotype differences for the level and pattern of GS expression have been reported (Bernard et al. 2008; Gayatri et al. 2021; Thomsen et al. 2014). All the discussed below is then based on our results from the diversity panel mostly contrasted with studies conducted on large number of genotypes, either wide germplasm panels or biparental populations.

To facilitate further discussion, a summary of the main findings of the marker-trait association study conducted on the diversity panel is presented in Table 3. It must be noted that the findings reported in López-Fernández et al. (2021) on the effect of the genetic structure of the Spanish collection of bread wheat landraces on TKW, KS, GP and SVol support that no relationship exists between the differences among populations at GS2A noted above (Table 2) and the phenotypic variation in this germplasm panel.

Table 3 Summary of alleles showing beneficial effects on thousand-kernel weight, number of kernels per spike and grain protein content. The marker alleles corresponding to the Chinese Spring GS1A, GS2A and GS2D gene sequences are, respectively, LM, Del and M1. The GS2D-M2 allele corresponds to the Xiaoyan-54 haplotype. Varieties presenting the GS1A and GS2A sequences marked by the SM and NoDel alleles (GS1A-hap2 and GS2A-hap2, respectively) can be found in Table 1
Full size table

The spatial–temporal changes in the relative activity of GS1 and GS2 isozymes during plant development have led to suggest that the cytosolic GS1 isoform is mainly involved in grain traits determined at post-anthesis (Bernard et al. 2008; Zhang et al. 2017c). However, its central role in the remobilization of N to the developing spike, when the number of fertile florets is being stablished, has also been suggested (Thomsen et al. 2014). Confirming this view, we have demonstrated that genotype variation at the GS1A locus may influence KS, a critical component of yield potential determined at the pre-anthesis stage. To our knowledge, no earlier report has documented the positive effect of a wheat GS1 allele on this NUE-related trait. Our study has further found that opposite GS1A allelic variants have a positive influence on the pre- and post-anthesis traits analysed; i.e., GS1A-LM on KS and GS1A-SM on GPC (Fig. 3). This finding may provide one piece on the puzzling genetic network underlying the general observation that the number of grains per ear is negatively related to size and protein content of grains (Habash et al. 2007; reviewed in Teng et al. 2022).

By conducting marker to trait association analyses, Guo et al. (2013) and Li et al. (2019) have reported some discrepant results on the effect of the Chinese Spring GS1A allele on TKW. So, while the first authors detected a positive influence of this allele in a RIL population, Li and coworkers have not found any significant difference between allelic variants at GS1A in a wide collection of winter wheat lines. Our results are partly in agreement with the latter study since no significant influence of GS1A on TKW was detected when its individual effect was tested in our set of landraces (Online Resource 11). However, our gene-by-gene interaction analysis has shown that this locus behaves as hypostatic of either GS2A-Del and GS2D-M2 marker alleles (Fig. 3). But contrarily to the reported by Guo and coworkers, the Chinese Spring allele (GS1A-LM) was the associated to lower TKW when some effect of this locus was detected in our study. The discovery of genetic factors which simultaneously increase TKW and GPC is a matter of great breeding interest in order to select genotypes where the usual inverse relation between both traits can be broken (Cormier et al. 2016). According to the results reported here, the GS1A-SM allele could be a good candidate for that aim.

It can be added that Habash et al. (2007) colocalized large-effect QTLs for GPC and TKW with the GS1 locus on 6B (namely, GS1B) as well as a major QTL for KS on 6DL, where GS1D is located. However, none of these GS1 homoeogenes could be included in our marker-trait study for having been found to be too polymorphic (GS1B) or monomorphic (GS1D) among the accessions in the sequencing panel.

Regarding the wheat homoeogenes coding for the plastidic GS isoform, our results show significant effects of both GS2A and GS2D on TKW and GPC in the diversity panel of landraces (Fig. 3; Table 3). By studying a RIL population, Li et al. (2011) detected that GS2A influenced TKW and GPC when genotypes were cultivated under low-N conditions, the increasing effect being attributed to the Xiaoyan-54 GS2A allele. Overexpression of this allele by Hu et al. (2018) demonstrated later its beneficial influence on GPC at any N regime; but its improving effect on TKW was not consistent in all transgenic lines and treatments. The GS2A-hap2 allele found in 12 out of 15 varieties of the sequencing panel is almost identical to the Xiaoyan-54 allele, presenting a unique additional SNP at position 1722 from the start codon when both are compared with the Chinese Spring GS2A sequence (see Online Resource 5 for comparison between GS2A-hap2 and the Chinese Spring allele). It is then likely that they are functionally similar, which closely agrees with the finding of higher GPC in landraces presenting the GS2A-NoDel marker allele at GS2A locus. For TKW our study indicates some increasing effect of the Chinese Spring allele (GS2A-Del marker allele). This is not coincident with the reported in Li et al. (2011) but it is less unexpected according to the inconsistent results on TKW obtained after the transgenic overexpression of the Xiaoyan-54 GS2A sequence (Hu et al. 2018). The QTL mapping approach Habash et al. (2007) detected higher GS2 enzymatic activity in genotypes bearing this allele, but no impact of GS2A genotype on the traits evaluated (which included KS, TKW and GPC) was evidenced. These authors neither found colocalization between any QTL for GS activity and some large-effect QTLs for TKW and GPC that could be associated to GS2B. All that reinforces the doubtful reliability of using GS activity to predict improved NUE phenotypes as has been suggested in some instances (Cormier et al. 2016; Zhang et al. 2017c).

Li et al. (2011) studied the effect of GS2D in a doubled haploid population that segregated for the Chinese Spring and Xiaoyan-54 alleles, the same allelic variants contrasted in our study (namely, GS2D-M1 and GS2D-M2). These authors found that the Chinese Spring allele was associated with higher TKW, and also higher N uptake before flowering. On the contrary, our results have indicated that genotypes bearing the Xiaoyan-54 allele have higher TKW, but mean comparison analysis of non-allelic combinations at GS1A and GS2D further reveals that the improving effect of GS2D-M2 is not significant in landraces bearing the GS1A-SM allele (Fig. 3). Regarding GPC, we have detected a positive effect of the Chinese Spring GS2D-M1, which also disagrees the lack of any influence of GS2D on grain N content (equivalent to GPC) reported by (Li et al. 2011). Nevertheless, and although the interaction between GS2A and GS2D was not significant at the threshold of 0.01 (P = 0.0187; see Online Resource 11), mean comparison between non-allelic combinations at these loci suggests that such effect on GPC could be masked in genotypes bearing the beneficial GS2A-NoDel allele at GS2A locus.

GS2D has also showed to affect KS but only in genotypes where the beneficial GS1A-LM allele is present. Considering all cases of genetic interactions reported in the present study, this is the unique instance where pyramiding of favourable alleles would be clearly successful to improve a NUE-related trait (Table 3; Fig. 3). It can be noted that, as occurred for GS1A, the two GS2D allelic variants seem to exert opposite effects on the improvement of KS and GPC.

Overview

In spite that physiological connections must exist between the enzymatic activities of cytosolic and plastidic glutamine synthetase, no earlier report has documented the influence of combined allelic variation at GS1 and GS2 homoeogenes on NUE-related traits in wheat. Nigro et al. (2019) included the A and B homoeogenes coding for both glutamine synthetase families in their association study of GPC in durum wheat. However, their effects were analysed on a gene-by-gene basis, providing no clue on genetic interactions that could eventually explain part of the trait variation characterized in the panel of genotypes under study. The same holds for studies that have been focused on the effect of individual homoeogenes of a given GS family (i.e., Li et al. 2011).

Overall, the present study has revealed that epistatic interactions can mask the effect of individual loci or alleles. Between-gene interactions are acknowledged as a component of phenotypic variation of complex polygenic traits in text-books and theoretical papers. However, they are commonly ignored in marker-trait association studies attempting to identify alleles that can predict a favourable phenotype for NUE-related traits. In agreement with the reported here, Li et al. (2019) have demonstrated that one allele of a given locus, earlier classified as having a positive effect on TKW and kernel associated traits, can be not so beneficial when combined with particular alleles at other loci. That study further showed that the Chinese Spring GS1A allele, for which a positive effect on TKW had been demonstrated (Guo et al. 2013) was not present in the allele combinations that presented the genotypes with the highest TKW values. It is remarkable that, as noted above, our results indicate not only that the GS1A locus behaves as hypostatic of GS2A and GS2D, but also that the Chinese Spring allele (GS1A-LM in the present study) is associated to lower TKW (Table 3).

The knowledge of the epistatic effects that may exist between target genes can be critical for the success of breeding strategies aiming to pyramid favourable alleles. Additionally, genetic interaction effects may provoke inconsistencies and discrepancies between gene/QTL mapping studies of complex polygenic traits, like NUE components, especially if conducted on materials with narrow genetic segregation (i.e., DH, RILs and, especially, NILs populations). Its allopolyploid nature makes the handling of epistatic effects a much more troublesome challenge in wheat than in other major crops. Because of their practical implications, validation of the marker-trait relationships detected here on other wheat germplasm collections would be of great interest.