The wild emmer wheat grain protein content 5B QTL introgressed into bread wheat is associated with tolerance to nitrogen deficiency .

doi:10.21203/rs.3.rs-3979153/v1

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The wild emmer wheat grain protein content 5B QTL introgressed into bread wheat is associated with tolerance to nitrogen deficiency .

https://doi.org/10.21203/rs.3.rs-3979153/v1

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published 16 Jul, 2024

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Nitrogen (N) is an essential macronutrient for wheat growth and development, its deficiency negatively affects grain yield and grain protein content (GPC). We describe here the dissection of the high grain protein content (GPC) QTL (QGpc.huj.uh-5B.2) derived from chromosome 5B of tetraploid wild emmer wheat, after its introgression into bread wheat (Ruta) by marker assisted selection. The introgression line (IL99) grown for trait validation under three environments, had 33% higher GPC (p<0.05) compared to Ruta, only under low (LN) at the seedling stage. The pleiotropic effect of the QTL on tolerance to N deficiency was confirmed using a semi-hydroponic system under severe LN (10% N) at the seedlings stage. We further dissected the QTL by fine mapping which enabled to delimit the QTL region from ~ 28.55 Mb to a chromosomal segment of only ~ 1.29 Mb that was shared among 12 LN tolerant near-isogenic lines, and which all had the WEW haplotype. This region included 13 potential candidate genes for LN tolerance, annotated as associated with N-stress response (15-cis-ZETA-CAROTENE ISOMERASE), N transport (UREIDE PERMEASE1 and IMPORTIN SUBUNIT BETA-1), and six involved in stress responses (e.g., ATXR6, HISTONE-LYSINE N-METHYLTRANSFERASE), while two genes were uncharacterized. These candidate genes may improve tolerance to nitrogen deficiency and by extension, high nitrogen use efficiency and GPC in N deficient environments. Our study demonstrates the importance of WEW as a source of novel variation for genes and QTLs useful for a sustainable improvement tolerance to N deficiency in wheat.

Fine mapping

grain protein content (GPC)

Nitrogen stress

Abiotic stress

Wild emmer wheat (WEW).

Dissection of the wild emmer wheat grain protein content QTL (QGpc.huj.uh-5B.2) introgressed into bread wheat reveals candidate genes associated with tolerance to nitrogen deficiency and improvement of nitrogen use efficiency.

Wheat (Triticum aestivum L.) is a major cereal crop, providing 23% of calories and protein in the human diet (Milner et al. 2022). Nitrogen (N) is a critical macronutrient that supports wheat growth and development. Factors that limit N in the soil lead to reduced grain yield (GY) and grain protein content (GPC), thereby negatively affecting food security (Tedone et al. 2018; Zuluaga et al. 2018; Fu et al. 2022; Kaur et al. 2022; de Castro et al. 2022). To meet the growing global demand for food, the use of N fertilizers has increased by 20% over the last 50 years (Zhang et al. 2017; Gu et al. 2023). However, although wheat cultivation accounts for 18.2% of all N fertilizer used for agronomy (Teng et al. 2022), only 30–40% of the applied N fertilizer is taken up by the plant (Swarbreck et al. 2019; Milner et al. 2022). Inefficient N use leads to water and air pollution, fiscal losses, and degradation of natural resources; therefore, optimizing and limiting N fertilizer application would improve agricultural and environmental sustainability (Araus et al. 2020; Duma et al. 2022; Bharati et al. 2022; Effah et al. 2023). Strategies for managing N include optimizing the amount and timing of N fertilization. A common method is split N doses, with the first application at time of pre-sowing or at the seeding stage, and the second applied as a top dressing before flowering. However, N applied prior to or at the seeding stage can leach from the soil and be lost as a gas, or immobilized before its uptake by the plant, thus affecting its efficient use (Subedi et al. 2007; Zörb et al. 2018; Ghimire et al. 2021). Efficient uptake of N fertilizers by plants would enable lower N input and underpin sustainability (Fatholahi et al. 2020; Wang et al. 2020; Ghimire et al. 2021; Gezahegn et al. 2022). Therefore, an understanding of the complexity of regulatory mechanisms controlling N use-efficiency (NUE), especially when N is limited in the environment, is of great value (Kant et al. 2011; Fan et al. 2019; Hawkesford and Riche 2020; Vishnukiran et al. 2020).

The relationship between carbon (C) and N metabolism in wheat determines yield, but it is complex and depends on many factors, including the cultivars genetic background, the amount and timing of N fertilizer application, and the environmental conditions needed for N uptake and utilization, such as soil structure and water availability (Fatholahi et al. 2020; Wang et al. 2020; Gezahegn et al. 2022). Factors such as anthesis date, grain-filling duration, and canopy senescence also play crucial roles in N assimilation and partitioning (Hawkesford 2017). In Mediterranean environments, wheat is mainly grown under rain-fed conditions, and crops often experience water stress. Under such conditions, even if N is available, it will not be taken up. As a result, water scarcity, N deficiency, or their combination are major causes of yield loss in bread wheat production (De Laporte et al. 2021; Fu et al. 2022; Kaur et al. 2022; Duma et al. 2022). Thus, breeding cultivars for improved water-use efficiency and NUE is a cost-effective and sustainable approach to increasing GY in wheat (Ullah et al. 2019).

NUE has a direct impact on GY, it is determined by the ratio of GY to applied N, representing the GY per N supply or the rate of N fertilizer per unit area. However, it is important to note that the effective utilization of N will also influence GPC (Teng et al. 2022; Ayadi et al. 2022). Another approach to assessing NUE in landraces or wild plants which are relatively low yielding is to compare plant performance at low and high N inputs (Fan et al. 2019). The idea behind this approach is that traits associated with NUE, particularly N capture, may only be expressed under low-N conditions (Hawkesford 2017). NUE is determined by two processes: N-uptake efficiency (NUpE) which is related to the amount of absorbed N relative to N available in the soil, and N-utilization efficiency (NUtE), measured as yield relative to plant N. Furthermore, efficient NUpE and assimilation are influenced by agronomic practices and optimal environmental conditions, contributing to improved NUtE and GPC. NUE is relatively low in cultivated wheat, it is a multifaceted trait influenced by numerous genes with complex environmental interplay. It includes genes associated with N uptake, assimilation, and partitioning, which vary among wheat cultivars, with some exhibiting better NUtE, leading to higher GPC (Tegeder 2014; Hawkesford 2017; Wang et al. 2018; Hawkesford and Riche 2020; Alfatih et al. 2020; Rawal et al. 2022; Peng et al. 2022). For example, there are genes involved in N assimilation (e.g., NITRATE REDUCTASE and GLUTAMINE SYNTHETASE [GS]), genes encoding proteins that transport N compounds, and transcription factors which coordinate N movement in plant tissues, including during grain filling (Tegeder 2014; Zayed et al. 2023). Some of these genes have been found to improve the reaction the effect of N deficiency in wheat by causing the accumulation of abscisic acid (ABA), especially in guard cells, which leads to stomatal closure (Wilkinson et al. 2007; Seo and Koshiba 2011). This phenomenon is common to plants adaptive responses to both N starvation and drought stress occurring at a young stage (Kumar et al. 2019).

A frequently used approach to identifying candidate genes for complex traits such as NUE or GPC is fine mapping of genomic regions identified by QTL mapping. Genes found in QTL regions involved in metabolic processes, cellular activities, and catalytic functions, and key regulators such as transcription factors and transporters can be regarded as candidate genes for many agronomic traits, including NUE or GPC (Singh et al. 2021). Example by (Nigro et al. 2020), identified key genes in the GS/glutamate synthase (GOGAT) cycle, critical for N uptake and GPC control, within the GPC QTL regions of durum wheat (Fortunato et al. 2023). The grain protein content gene GPC-B1 was identified and further cloned from a genomic region carrying the most significant QTL for GPC derived from wild emmer wheat (WEW) (Triticum turgidum ssp. dicoccoides, 2n = 4x = 28, AABB) the tetraploid progenitor of cultivated wheat (Distelfeld et al. 2004; Uauy et al. 2006). Cultivars introgressed with the Gpc-B1 allele have been successfully adopted worldwide (Tabbita et al. 2017), resulting in consistent positive effects on GPC, and Fe and Zn contents, with only minor negative impacts on yield. Since GPC-B1 is a NAC transcription factor known to accelerate leaf senescence, its use can be disadvantageous in high-yielding environments and additional genes are needed to improve GPC. WEW is regarded as a potential source of advantageous WEW alleles for improving resistance to biotic and abiotic stresses and for key agronomic traits, including elevating NUE, GPC, and grain protein deviation (GPD) (Chatzav et al. 2010; Millet et al. 2014; Gioia et al. 2015; Huang et al. 2016; Krugman et al. 2021; El Haddad et al. 2021; Nehe et al. 2022; Sandhu et al. 2023). We previously identified several QTLs with advantageous WEW alleles for improving GPC and GPD under different water availabilities. One of these QTLs, QGpc.huj.uh-5B.2, showed significant effects for GPC and GPD under water-limited and well-watered conditions (Peleg et al. 2008, 2009; Fatiukha et al. 2020).

We describe here the introgression of QGpc.huj.uh-5B.2 into the bread wheat cv. Ruta, and validation of improved GPC in the introgression line only in the N deficient field at the seedling stage, whereas no differences in GPC were found under good N management either water-limited or well-watered conditions, suggesting that the QTL confers tolerance to N deficiency. We further dissected the QTL by fine mapping which enabled to delimit the QTL region from ~ 28.55 Mb to a short chromosomal segment of ~ 1.29 Mb that was shared among 12 tolerant near-isogenic lines (NILs), and which all had the WEW haplotype. Annotation of the genes found in this short region were associated with N transport, N-stress response, and abiotic stress. Our study confirms that WEW is an important source of novel variation for genes and QTLs that can be used for improved N-stress response and GPC in wheat grown under unfavorable conditions.

Marker assisted selection (MAS) of QTL QGpc.huj.uh-5B.2 and development of fine mapping population

The QTL QGpc.huj.uh-5B.2 was identified by QTL mapping based on 150 recombinant inbred lines (RILs) derived from a cross between Triticum durum var. Langdon and WEW (genotype G18-16) (Peleg et al. 2009; Fatiukha et al. 2021). One of these lines, RIL12, carrying the WEW allele of QGpc.huj.uh-5B.2, was selected as a donor parent for MAS. RIL12 was crossed and backcrossed to the Israeli elite bread wheat cv. Ruta as a female parent for three generations using MAS, as described previously (Merchuk-Ovnat et al. 2016). Molecular validation of the introgression and the MAS procedure was based on SNPs obtained by genotyping Ruta, RIL12, and the parents of the RIL population (WEW G18-16 and T. durum var. Langdon) with the Illumina Infinium 15K Wheat array (TraitGenetics, Gatersleben, Germany). For MAS we selected two SNPs flanking the QTL, and one in the middle of the QTL, were converted to a set of three Kompetitive allele-specific polymerase chain reaction (KASP) markers, designed using PolyMarker (Ramirez-Gonzalez et al. 2015) (Table S1). Seeds of BC₃F₂ plants were found to be homozygous for the WEW allele in the three SNPs, were selected for increasing to BC₃F_4:5 field experiments.

For the development of a fine mapping population, we first genotyped three BC₃F₃ ILs (IL72, IL73, and IL99) (in four biological replicates) using the 25K Wheat array (TraitGenetics). This enabled us to select BC₃F₄ IL99 (further designated as IL99) as a parent for development of a large fine mapping population, carrying the shortest introgression size and lower level of heterozygosity at the whole-genome level. IL99 was backcrossed as the female parent with Ruta to produce the BC₄F₁ population which was increased to BC₄F₄ following genotyping in each generation, as previously described (Deblieck et al. 2022). For the fine-mapping procedure, we first used the KASP markers that had been used for MAS and then saturated the QTL region with additional KASP markers obtained by genotyping the parental lines. The additional KASP markers were developed with Illumina short-read exome capture sequence data generated by the Whealbi project (http://wheat-urgi.versailles.inra.fr/Projects/Whealbi) using a custom R script and PolyMarker (http://www.polymarker.info/) (Table S2).

Trait validation under three environments

IL99 and the recurrent parent Ruta were grown in Israel in 2019 (BC₃F₄) and 2020 (BC₃F₅) in dense plots (250 seeds/m²), under three environments, at the experimental fields of the Israeli Ministry of Agriculture, providing the agronomic management suitable for each region (fertilizers, pesticides, and herbicides): (i) in 2019, plants were grown in the Upper Galilee in northern Israel (33.170296, 35.581314); the experiment was conducted in plots (1.5 m²) with four repeats; N was not applied at pre-sowing, but was applied as a top-dressing fertilizer (4 N units; Uran) 60 days after sowing (DAS); the annual precipitation was 680 mm and was particularly high after sowing in January; (ii) in 2020, seeds were sown in the Western Galilee in Acre (32.930549, 35.106374), in plots of 1.5 m x 4 m in five replicates; N management included a split N dose: 10 N units pre-sowing, and 3.5 N units (applied as urea) at 60 DAS; annual precipitation was 607 mm; (iii) in 2020, seeds were sown in Reim, in the south of Israel, characterized as a semi-desert climate (31.377419, 34.477823), in large plots of 1.5 m x 6 m in four replicates; split N management was applied: pre-sowing of 8 N units, and 4 N units as urea at 40 DAS; the annual precipitation of 220 mm was supplemented with 60 mm irrigation water to ensure germination. All three experiments were conducted in a randomized complete block design. In each of the three experiments, grains were harvested by a small dedicated experimental combine to measure total GY (kg/ha). In addition, 15 random spikes were selected from individual plants in each plot to measure thousand kernel weight (TKW, g) and GPC (%). GPC was measured in 1.5 g of grain ground in a Laboratory Mill 3310 (Perten Instruments, PerkinElmer, Waltham, MA, USA). The flour was tested for GPC using a Perten Inframatic 9520 NIR Flour Analyzer.

N stress phenotyping using a semi-hydroponic system

N treatments included full N (FN) vs. low N (LN; 10% of the FN) using a semi-hydroponic system that were composed of two pots, one inserted into the other, with a connecting cotton wick transferring the nutrients by capillary movement (Semananda et al. 2018; Heidari et al. 2022) (Fig. 1).

The top pot (0.5 L) was filled with vermiculite (V2P, granule size: 0.75–2.5 mm) to physically support the plant, and the bottom pot (1.0 L) contained modified Hoaglands solution (Hoagland 1920) that was transferred to the top pot by capillary movement. The FN treatment contained: 0.2 mM KH₂PO₄, 1 mM MgSO_4·7H₂O, 1.5 mM CaCl₂, 1.5 mM KCl, 0.001 mM H₃BO₃, 0.00005 mM (NH₄)₆Mo₇O_24·4H₂O, 0.0005 mM CuSO_4·5H₂O, 0.001 mM ZnSO_4·7H₂O, 0.001 mM MnSO_4·H₂O, 0.1 mM FeEDTA, 1.0 mM (NH₄)₂SO₄, and 1 mM KNO₃; for the LN treatment, the total amount of N was reduced to 10% (0.1 mM (NH₄)₂SO₄ and 0.1 mM KNO₃). The solutions were maintained at pH 6.0 using 0.1 N H₂SO₄.

Seeds were surface sterilized with 70% (v/v) ethanol for 1 min, treated with 0.5% (w/v) sodium hypochlorite for 10 min, and rinsed six times (1 min each time) in sterile distilled water. The seeds were then placed on a wet 11-cm filter paper (VWR®) with clean distilled water and kept sealed in Petri dishes (ø = 90 mm) to germinate in a growth chamber (Conviron Adaptis CMP6010, Winnipeg, Manitoba, Canada) at 23°C in the dark. When a coleoptile (Zadoks growth stage 0.7–0.9) appeared, healthy and equally developed seedlings were transferred to the semi-hydroponic growing system.

Ten biological replicates in each of four independent experiments were evaluated for the parental lines IL99 and Ruta, and each replicate consisted of a pot with a single plant. Leaf morphological traits were measured at 14 DAS (Zadoks growth stage 13), including second leaf length (SLL, mm) and number of leaves (NL) as was described for evaluation of wheat response to abiotic stress at the seedling stage (Sharma et al. 2022). Plants were hand-harvested to measure fresh shoot weight (FSW, mg), and dry shoot weight (DSW, mg) after drying the shoots at 80°C to constant weight in a heating chamber (Binder ED23 Classic Gravity Convection Oven). To determine in situ N status in leaves, chlorophyll concentration was measured using a SPAD-502 chlorophyll meter (Konica Minolta, Tokyo, Japan) in the morning from 09⁰⁰ to 10⁰⁰ h. Each value was an average of four measurements recorded from the middle of the leaves. Following the identification of 26 double-homozygous recombinant NILs by KASP markers, we used semi-hydroponics as a fast-phenotyping system to characterize the response to N stress in five biological replicates.

DNA extraction and KASP genotyping

DNA was extracted in a 96-well plate from fresh leaf tissue of the studied lines following a standard cetyltrimethylammonium bromide (CTAB) protocol slightly modified from (Doyle 1991). DNA quality and purity were assessed by A₂₆₀/A₂₈₀ and A₂₆₀/A₂₃₀ ratios respectively using a NanoDrop Microvolume Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA samples were genotyped using the 25K SNP array developed by TraitGenetics, and KASP assays were performed in a 96-well plate format (MicroAmp; Applied Biosystems, Waltham, MA, USA) by qPCR (SepOne model, Applied Biosystems). The reaction mixture included 2.5 µL KASP-2x Master Mix (LGC), 2.2 µL genomic DNA (80–100 ng/µL), and a mix of three KASP primers: 0.08 µL each of primers A and B, and 0.2 µL of primer C per sample (100 ng/µL). qPCR program: hot start at 94°C for 15 min, followed by 10 landing cycles (94°C for 20 s; initial landing at 61°C and decreasing by 1°C per cycle for 60 s), followed by 30 annealing cycles (94°C for 60 s; 55°C for 60 s). Additional cycles were performed to increase the intensity of the fluorescent signals as needed. For each assay, we included a template control (water) and positive heterozygotes of the two parental alleles.

Candidate gene identification and microcollinearity analysis

Annotation of the genes residing in the QGpc.huj.uh-5B.2 interval from WEW was based on the T. dicoccoides (WEW) genome assembly WEW_v2.1 from https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_002162155.2 (Zhu et al. 2019). We used GeneTribe (https://chenym1.github.io/genetribe/) for homology inference among genetically similar genomes (T. aestivum var. Chinese Spring [CS], T. turgidum var. Svevo, and T. dicoccoides var. Zavitan) that incorporated gene collinearity in our QGpc.huj.uh-5B.2 region, and showed better performance than traditional sequence similarity-based methods in terms of accuracy and scalability (Chen et al. 2020).

Real-time (RT)-qPCR

Leaf tissue for RT-qPCR analysis of the parental lines (Ruta and IL99) and the two double-homozygous recombinant lines (NIL21 and NIL38) were sampled after 14 days of growth on FN or LN, kept in RNAlater (Thermo Fisher Scientific), and used for RNA extraction with the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany). Three biological replicates were selected for each N treatment and each line, and each biological replicate consisted of three technical replicates. We selected seven genes located within the QGpc.huj.uh-5B.2 locus. One gene was in the sub-QTL (1.29 Mb) (UREIDE PERMEASE 1 [UPS1]), and six genes were located outside the subinterval. The primers used for each target gene are listed in Table S3. We used Primer3plus for primer design (https://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi). The RT-qPCR results were analyzed by comparative 2^{− ΔΔCt} method as described previously (Livak and Schmittgen 2001). Changes in expression were determined relative to the housekeeping gene UBIQUITIN.

Statistical analysis

All results were analyzed by STATISTICA.V10 (StatSoft Inc. 2011, Tulsa, OK, USA), and R (version 3.4.1). Tukeys test (ANOVA) was used to determine the significance of the difference of each trait with different factors, i.e., genotype (G), environment (E), and the G x E interaction. Statistical significance was set at p ≤ 0.05. The correlation analysis assessed the relationships between variables collected during the experiment.

Genotyping of introgression lines

We genotyped three introgression lines (IL) that were generated using MAS procedure, for identifying the best female parent showing the shortest introgression size and lower heterozygosity, for the backcrossing (BC₄) with Ruta. Of the 25K SNPs on the wheat array we identified ~ 5100 SNPs for both the A and B wheat genomes, with an average density of 0.5 SNPs/Mb. Filtering the data and ordering the SNPs according to their physical positions (Table S4) validated the presence of the target QGpc.huj.uh-5B.2 in the three ILs, however, the shortest introgression (58.37 Mb) was found in plant IL99-2 (further designated as IL99) compared to introgression size of 514 Mb in IL72 and IL73. A low level of retained alleles from the original tetraploid parent (LDN) was found in the three ILs, mainly on the short arm of chromosome (Chr) 2A, albeit with differing lengths and levels of heterozygosity. IL99 displayed a low proportion of the retained LDN allele, whereas IL72 and IL73 retained alleles of LDN or heterozygous regions on Chr 3B, and 4B and 5A. Notably, the genotyping also confirmed that all the 3 BC3 lines did not carry the Gpc-B1 functional allele of WEW, that was previously identified as a major QTL in other mapping populations (Uauy et al. 2006; Distelfeld et al. 2007). Taken together, the genotyping results showed that IL99-2 (hereafter IL99) retained a minimal % of the LDN allele, and no heterozygotes regions were found on Chr 5B; furthermore, it had a low percentage of heterozygosity (0.5%) at the whole-genome level (Table S5). Therefore, IL99 was selected for validation of GPC and GY in the field and was further used as a female parent for backcrossing with Ruta.

Trait validation of GPC and GY under three environments

Our assessment of IL99 and Ruta grown in the three environments showed that GPC and TKW in both genotypes were significantly lower in the Upper Galilee field, in which N was not applied at pre-sowing, compared to the two other fields that were treated with split N doses. The extremely low GPC values found for both genotypes in the Upper Galilee suggest that the field presented intense N deficiency that may have resulted from the absence of the pre-sowing N treatment, together with possible leaching of N due to heavy rains during the plant-establishment period. Furthermore, in this low-N field, a higher GPC (33%) was found in IL99 compared to Ruta (10.84 vs. 8.15, p = 0.007), whereas no differences in GPC were found between the two genotypes in the two other fields, both under high water availability, i.e., in Acre (14.14 vs. 14.24, p > 0.05), and in the semi-desert field in Reim with 220 mm rain (13.91 vs. 13.57, p > 0.05). In Reim, we found a significant difference (p = 0.045) in GPD between the average normalized residues for Ruta (-0.178) and IL99 (0.0008), indicating the latters ability to accumulate higher GPC than predicted from GY alone. In addition, we detected a significant negative correlation (r = -0.74, p < 0.05) between GPC and TKW in the field at Acre (Table S6). In the Upper Galilee and in Reim, these relationships were not a significant factor for the increased GPC in IL99. No significant differences were found in TKW between Ruta and IL99 in any of the fields, indicating that the high GPC was not negatively correlated with TKW. High variability was found in GY, and plant height was measured in only two environments (Acre and Upper Galilee), showing no significant difference between genotypes under these conditions (Fig. 2).

Phenotypic response to LN and FN using a semi-hydroponic system

To evaluate LN tolerance in IL99 as compared with Ruta and to establish a sensitive, reproducible, and accurate method for phenotyping under controlled conditions, sever N stress was applied using a semi-hydroponic system. Reduced leaf elongation rates compared with the optimal N level was found as a reliable trait for assessing N starvation at the seedling stage (Gioia et al. 2015). Furthermore, our measurements of SLL at 14 days were highly correlated with FSW and DSW (r = 0.76 and r = 0.81, p < 0.05) both in Ruta and IL99. SLL, NL, FSW, and DSW did not differ in IL99, or showed only a slight but non-significant change, in response to LN. Therefore, SLL measured after 14 days of N deficiency was used here as an indicator to evaluate the response to N stress for fine phenotyping of recombinants. Our results confirmed that severe N stress has a negative impact on the recurrent parent Ruta. Significant changes were found in Ruta in all the measured traits under LN compared to FN, while IL99 showed only small differences for most studied traits (Fig. 3; Table S7).

Fine mapping of QGpc.huj.uh-5B.2

For the fine-mapping procedure we designed 13 KASP markers, 2 flanking the QTL and 11 saturating the QTL interval, based on SNPs found between the parental lines (LDN and G18-16, RIL12 and Ruta). Exome capture data that produced more than 620,000 SNP/indels at the whole-genome level enabled to us to identify 13 SNPs evenly distributed across the QTL region of 28.28 Mb (from 27.05 to 55.33 Mb) (Fig. 4a). We further converted SNPs to KASP markers based on the Chinese Spring RefSeq v2.0 genome (Zhu et al., 2021) (Table S2). IL99 (BC₃F₄) was backcrossed with Ruta to produce the BC₄F₁ population. To confirm heterozygosity, all BC₄F₁ individuals were genotyped using the KASP markers flanking the QTL (P1 WTa_060365, and P13 WTa_060962). To select single- and then double-homozygous recombinants for fine mapping, we first genotyped 1416 BC₄F₂ with an additional KASP marker positioned in the middle of the QTL (P6 WTa_060520). This resulted in two recombinants at BC₄F₃: type I (homozygous at P1, and heterozygous at P6), which were genotyped in the next generation (BC₄F₄) with KASP markers P2 to P6; and type II (heterozygous at P6, and homozygous at P13) which were genotyped in the next generation (BC₄F₄) with KASP markers P6 to P12. In total, we genotyped 2500 BC₄F₄ plants using 13 KASP markers along the QTL interval. This procedure enabled us to identify 26 NILs homozygous recombinants, each representing recombination events along the QTL interval (Fig. 4b). These 26 NILs were classified into 16 haplotypes. For example, NILs 15, 20, 21, and 31 each carried a QTL of similar size (Fig. 4b).

The segregation of phenotypic response to LN found in all 26 BC₄F₅ NILs was conducted using the semi-hydroponic system. A reduction of SLL between the two conditions was recorded as a response to LN. Our results showed that 12 NILs (of 7 haplotype groups) (15, 20, 21, 31, 25, 29, 27, 3, 32, 33, 34, and 39) had no significant reduction in SLL, and were therefore regarded as having an IL99 phenotype (G phenotype; Fig. 4c). On the other hand, SLL of 14 NILs (1, 2, 4, 8, 9, 16, 18, 19, 40, 35, 38, 36, 43, and 44) was significantly reduced (233.67 vs. 167.78 mm, p < 0.05), and these were regarded as having the Ruta phenotype (R) (Fig. 4c).

Comparison of the relative reduction of SLL between the two alleles R and G (Fig. 5) confirmed that NILs with the R allele exhibit a notable (p < 0.05) reduction in leaf growth, with its frequency decreased by approximately 65.8%, whereas NILs of the WEW allele G decreased by a smaller margin, around 11.5%. Altogether, the fine mapping which integrated the genotyping and phenotyping of 26 NILs (Fig. 4b) showed that the 12 NILs showing tolerance to N stress share a sub-QTL haplotype derived from the WEW region. We anchored the KASP markers used for the fine mapping and determined that the physical size of the target QTL region is approximately 1.29 Mb.

Candidate gene identification and microcollinearity analysis

The delimited region 0f 1.29 Mb (from 36.36 to 37.65) was estimated based on WEW Zavitan reference genome. This size differed from the T. durum var. Svevo – 1.7 Mb (from 37.03 to 38.73), and the CS – 1.92 Mb (from 36.6 to 38.52) assemblies. In WEW genome we identified 13 high-confidence genes (listed in Table 1): 2 (TRIDC5BG005570 and TRIDC5BG005760) of unknown function (Gene Ontology [GO]), and 11 classified using GO functions, or from information in the literature, into different categories. The largest group consisted of 6 genes associated with abiotic stress (TRIDC5BG005540, TRIDC5BG005560, TRIDC5BG005580, TRIDC5BG005630, TRIDC5BG005710, and TRIDC5BG005770), encoding pentatricopeptide repeat 336, cold-regulated protein, histone-lysine N-methyltransferase (HKMT) ATXR6, serine protease HtrA, syntaxin-132 (SYP132), and a metallohydrolase/oxidoreductase (MHO) superfamily protein, respectively. The second group consisted of 3 genes that were involved in N transport or N metabolism, and N stress (TRIDC5BG005530, TRIDC5BG005550, and TRIDC5BG005600), encoding 15-cis-zeta-carotene isomerase (Z-ISO), UPS1, and importin subunit β1 (KPNB1), respectively. In addition, 1 gene was involved in signaling pathways and autophosphorylation (TRIDC5BG005520), encoding p21-activated protein kinase-interacting protein 1; and 1 gene (TRIDC5BG005640) encoded a disease resistance protein.

Table 1

Homologs of putative WEW candidate genes identified in the 1.29 Mb sub-QTL, compared with *T. durum* var. Svevo and CS assemblies.
#	T. dicoccoides Zavitan	Start, bp	End, bp	T. turgidum Svevo	T. aestivum CS	Gene annotation	GO function
1	TRIDC5BG005520	36360061	36361911	TRITD5Bv1G013710	TraesCS5B02G033700	p21-ACTIVATED PROTEIN KINASE-INTERACTING PROTEIN 1	Signaling pathways
2	TRIDC5BG005530	36365122	36372548	TRITD5Bv1G013720	NA	15-cis-ZETA-CAROTENE ISOMERASE	Nitrogen stress
3	TRIDC5BG005540	36396013	36398271	TRITD5Bv1G013670	TraesCS5B02G033500	PENTATRICOPEPTIDE REPEAT 336	Stress
4	TRIDC5BG005550	36404321	36406308	TRITD5Bv1G013770	TraesCS5B02G033400	UREIDE PERMEASE1	Nitrogen stress
5	TRIDC5BG005560	36428596	36428820	TRITD5Bv1G013610	TraesCS5B02G033300	Cold-regulated protein	Cold stress
6	TRIDC5BG005570	36428604	36428765	NA	NA	Undescribed protein	NA
7	TRIDC5BG005580	36437689	36438391	TRITD5Bv1G013570	NA	HISTONE-LYSINE N-METHYLTRANSFERASE ATXR6	Development and stress
8	TRIDC5BG005600	36531860	36537534	TRITD5Bv1G013910	TraesCS5B02G034200	IMPORTIN SUBUNIT BETA-1	Nitrogen stress
9	TRIDC5BG005630	36820431	36822867	TRITD5Bv1G013990	TraesCS5B02G034400	SERINE PROTEASE HTRA	Stress
10	TRIDC5BG005640	36947849	36949190	TRITD5Bv1G014030	TraesCS5B02G034600	Disease resistance protein	Biotic stress
11	TRIDC5BG005710	37234570	37237309	NA	TraesCS5B02G035600	SYNTAXIN-132	Stress
12	TRIDC5BG005760	37446998	37447553	TRITD5Bv1G018160	TraesCS5B02G035500	Unknown function	NA
13	TRIDC5BG005770	37645630	37648660	TRITD5Bv1G014330	TraesCS5B02G035300	METALLOHYDROLASE/OXIDOREDUCTASE SUPERFAMILY PROTEIN	Stress

Homology indicates sequence similarity due to a common ancestry, whereas collinearity indicates homology due to the linear arrangement of genes along a chromosome (Chen et al. 2020). Microcollinearity analysis of the 1.29 Mb sub-QTL found in QGpc.huj.uh-5B.2 between Zavitan (WEW), CS (T. aestivum), and Svevo (T. turgidum) was conducted to better understand structural variations, such as rearrangements, duplications, and inversions (Fig. 6). Notably, the microcollinearity tool developed by (Chen et al. 2020) uses V1 of the Zavitan genome as a reference; nevertheless, since for all of our previous analyses we used Zavitan v2, we first made sure that the order of our target genes in the analyzed sub-QTL is similar in these two versions. The homology and microcollinearity of the 13 candidate genes showed some differences between the three species and included insertions, inversions, deletions, and translocations. Notably, homologous genes observed in our analysis may or may not exhibit collinearity, depending on the evolutionary events that occurred after their divergence. We found three genes with no microcollinearity or homology to WEW, Svevo or CS (Fig. 6): one gene, TRIDC5BG005570, was not annotated, and was specific to WEW; the second gene (TRIDC5BG005710) encodes SYP132, and had a homolog in T. aestivum, but in a different location (38.35 Mb outside the sub-QTL), and the third gene (TRIDC5BG005760), which is unannotated, was homologous to both species but found in different locations, outside of the sub-QTL in the two genotypes (38.51 Mb in CS, and 51.18 Mb in Svevo).

Gene expression under N deficiency tested by RT-qPCR

We selected seven genes for analysis by RT-qPCR. The first gene encodes UPS1, resides within the 1.29 Mb QTL. The remaining six genes encoding auxin response factor 6 (ARF6), NRT1/PTR FAMILY 2.11 nitrogen transporter (NRT), ent-kaurenoic acid oxidase 1 (EKAO1), SEC1 family transport protein SLY1 (SEC1), 9-cis-epoxycarotenoid dioxygenase (NCED), and WD40 repeat-like protein (WD40) reside in the full QTL interval. UPS1 was significantly activated in response to N stress in IL99, showing a 2.39-fold increase (p < 0.05) compared to Ruta, and a 1.85-fold increase (p < 0.05) in NIL21 which showed tolerance to N stress (IL99 type), compared to NIL38 (Ruta type) under N stress (Fig. 7a). ARF6 was upregulated by 49.5% in IL99 compared to Ruta, and by 45.7% in NIL21 vs. NIL38. NRT expression was 2.36-fold higher (p < 0.05) in Ruta compared to IL99, and no significant differences were found between the NILs. In the RT-qPCR analysis, the remaining genes EKAO1, SEC1, NCED, and WD40, that are distributed across the entire QTL, exhibited no difference in expression between the parental lines or between NILs (not presented in Fig. 7). Notably, differential activation of candidate genes under N stress implies that they may—but not necessarily—have a causative effect on N tolerance (Peredo and Cardon 2020). These results suggest that the UPS1 allele of WEW may have an important role for improved response to N stress in IL99 and other NILs with this allele.

In the current study, we describe the introgression of QTL (QGpc.huj.uh-5B.2) derived from WEW into bread wheat following the MAS procedure as described by (Merchuk-Ovnat et al. 2016) using the “durum as a bridge” approach (Klymiuk et al. 2019). We used high-resolution QTL mapping (Fatiuka et al., 2021), and advanced genomic tools including exome capture and genotyping with 15K and 25K wheat genome arrays, to validate MAS and optimize the selection of a parent for fine mapping having the shortest introgression with low linkage drag and heterozygosity in the background of bread wheat Ruta. Trait validation in the field under three environments revealed 33% higher GPC (p < 0.05) compared to the recipient cultivar Ruta, under N stress field at the seedling stage, whereas GPC in the IL99 was unchanged when grown with adequate N-application in either water-limited or well-watered conditions. The QTL had been identified under contrasting water availabilities, with a higher proportion of explained variation (PEV) (13%) under water-limited conditions, compared to 7% PEV under well-watered conditions (Peleg et al. 2009; Fatiukha et al. 2020), suggesting that water stress may have affected N availability. On the contrary, we did not validate the high GPC in Reim which experienced low rainfall (220 mm) probably due to the supplemental irrigation of 60 mm that was applied to ensure germination, but did find a significant difference (p = 0.045) in GPD between the average normalized residues for Ruta (-0.178) and IL99 (0.0008). The higher GPC and GPD found in IL99 compared to Ruta under N stress prompted us to hypothesize that the QTL confer N stress tolerance potentially by pleiotropic effect, and genes associated with tolerance to N stress possibly found in this QTL region (Fan et al. 2019). To test our hypothesis, we developed a robust semi-hydroponic system that enables us to test severe N stress under controlled conditions to assess the rate of reduction in leaf growth parameters in IL99 compared to Ruta. The results of our N stress experiments unambiguously revealed that IL99 expresses tolerance to severe N deficiency at the seedling stage in comparison to the recurrent parent.

Fine mapping, which integrates the segregation of parental haplotypes and N stress response among recombinants, delimited the QTL of 28.55 MB to 1.29 Mb located at the center of the QTL that was shared among N stress tolerant near-isogenic lines, and which all had the WEW haplotype. Microcollinearity analysis of this region between WEW and durum and bread wheat cultivars detected collinear genes but also structural variations, as also found by (Maccaferri et al. 2019) who compared whole-genome sequences between WEW and T. durum var. Svevo. Of the 13 high-confidence genes found in this region, 11 had known functions, most are associated with N transport and N stress, as well as abiotic stress and signaling pathways. These genes or their combinations can be regarded as important candidate genes for improving N-stress tolerance in wheat.

N transporters

A candidate gene is UPS1 (TRIDC5BG005550), which is mainly known for its involvement in long-distance transport of allantoin and purine nucleotide degradation products in N-fixing and nitrate-feeding legumes (Tegeder and Perchlik 2018; Thu et al. 2020; Kaur et al. 2022; Wang et al. 2022). Stress-regulated purine gene transcription was linked to allantoin changes that improved N utilization in plants (Tegeder and Perchlik 2018; Casartelli et al. 2019). However, further studies showed that allantoin is a vital N source in plant species other than legumes, including wheat (Casartelli et al. 2019), Arabidopsis (Nourimand and Todd 2019), rice (Lee et al. 2018), and barley (Shabala et al. 2016). UPS1 and proALN were found to be crucial N sensors for N remobilization in rice (Redillas et al. 2019), and (Melino et al. 2022) showed that OsUPS1 overexpression enhances growth under low N. (Meng et al. 2023) recently confirmed the roles of TaUPS1 and TaUPS2.1 in wheat. (Li et al. 2024) found that under sufficient N supply, there is significant accumulation of ureide in roots of a high-NUE wheat cultivar and suggested that its accumulation is controlled by fine regulation of key genes involved in its metabolism. AtUPS5 recycles nutrients when plants are not under stress, and transports allantoin from roots to shoots under stress (Lescano et al. 2020). These studies demonstrated an association between ureides and high NUE under different N availability, including N stress. This association supports our RT-qPCR results showing that the expression of UPS1 increases under LN in IL99, to a higher level than in Ruta. Further studies are underway to evaluate its function under LN and FN conditions using CRISPR gene editing approach. A second transporter found in the sub-QTL was KPNB1 (TRIDC5BG005600), a main member of transport proteins that contain a short nuclear localization signal and mediate the translocation of cargo protein into the nucleus through the nuclear pore complex (Bednenko et al. 2003; Palma et al. 2019). (Bonnot et al. 2017) showed that the response of the grain subproteome to N and sulfur supply in the diploid wheat Triticum monococcum ssp. monococcum is regulated by the α- and β-subunits of importin. Z-ISO (TRIDC5BG005530) is known to be involved in antioxidant metabolism and abiotic stress in wheat (Cui et al. 2018), and is related to the NITRITE AND NITRIC OXIDE REDUCTASE U gene (Chen et al. 2010). Z-ISO accumulation varies under high salinity and N stress, potentially due to the transcriptional regulation of β-carotene-biosynthesis genes at the initial exposure to stress (Park et al. 2002; Zhu et al. 2020). Limited N availability reduces carotenoid content, impacting plant metabolic activity. (Shang et al. 2018) noted that N deficiency increases phytoene desaturase gene expression, affecting carotenoid accumulation in green algae. (Zhu et al. 2020) found upregulaton of Z-ISO genes at 3 h under N deficiency, with N deficiency and cadmium suppressing Z-ISO at 12 h while causing β-carotene accumulation.

Abiotic stress-associated genes

TRIDC5BG005540 encodes a pentatricopeptide repeat protein; these proteins are involved in mitochondrial translation in Arabidopsis, and in resistance to abiotic stress, including salinity, drought, and cold, without negatively affecting plant development (Uyttewaal et al. 2008; Jiang et al. 2015). The HKMT protein (TRIDC5BG005580) influences chromatin and DNA methylation under stress. Its direct link to the N-stress response is unclear, but it is hypothesized that HKMT-mediated chromatin changes help plants adapt through epigenetic regulation of N-sensitive genes (Perdiguero et al. 2009; Palma et al. 2010). The serine protease HtrA (TRIDC5BG005630) is known for proteolytic activity and involvement in protein quality control and degradation pathways (Clausen et al. 2011). HtrA is involved in signaling pathways and regulatory networks, influencing gene expression and stress-responsive signaling. Under N stress, it can regulate plant homeostasis and protein patterns and interact with proteins involved in N-metabolism pathways (Foucaud-Scheunemann and Poquet 2003). The TRIDC5BG005710 gene, encoding SYP132, was only found in the WEW sub-QTL. This gene is also involved in signaling, responses to abiotic stress and pathogens, cytokinesis and gravitropism, and regulates protein transport during immune signaling (Surpin and Raikhel 2004; Pratelli et al. 2004; Carter et al. 2004; He et al. 2021). SYP132 mediates tip-focused membrane trafficking for root hair-tip growth, demonstrating its importance in the development and function of root hairs in Arabidopsis (Ichikawa et al. 2014). Two other genes (TRIDC5BG005560 and TRIDC5BG005770) were associated with abiotic stress. The first activates cold-regulated protein (COR) for plant adaptation to cold and general stress. Transgenic tobacco ectopically expressing a COR gene from Saussurea involucrata demonstrated improved cold and drought tolerance in growth, germination, survival, water content, malondialdehyde, electrolyte leakage, and photosystem II efficiency (Guo et al. 2019). The second, TRIDC5BG005770, is in the MHO superfamily. It responds to abiotic stress-triggered abscisic acid and ethylene signaling, activating MHO genes. MHO enzymes may impact reactive oxygen species clearance or redox regulation in plants under stress (Wang et al. 2023).

Signal transduction

TRIDC5BG005520 belongs to the p21-activated kinases (PAKs), a family of protein kinases that play essential roles in various cellular processes, including signal transduction, cytoskeletal organization, and plant stress responses (Bokoch 2003). PAKs can be involved in nutrient signaling and uptake processes (Sun et al. 2022; Tian et al. 2023). While the specific connection between PAKs and N in wheat is not well-established, it is plausible that PAKs are involved in signaling pathways or processes related to N stress and metabolism.

N deficiency may occur in the soil at the young growth stage of wheat due to none pre-sowing N fertilization, leaching by heavy rains, or its low availability caused by other abiotic stresses (e.g., unfavorable pH, ionic stress, soil salinity or acidity, flooding, or drought), thereby, negatively affecting grain yield and quality. The adaptation to N stress at the seedling stage involve plant developmental processes, including morphological modifications of shoot and root-system architecture controlled by large changes of gene expression (Khan et al. 2017; Ru et al. 2023; Munns and Millar 2023). We dissection that delimited the QTL from 28.55 MB to a short interval of 1.29 Mb enabled us to identify potential candidate genes to LN tolerance, which may improve N uptake efficiency and GPC in N deficient environments. These include genes associated with N-stress, N-response and N transport and six genes involved in abiotic stress response. Further studies using CRISPR–Cas9 are underway to allow us to determine if and which one of the genes has a major role in the response to LN.

Cultivars that are tolerant to N deficiency can grow under lower levels of N fertilizer and improve NUE (Vishnukiran et al. 2020). Such cultivars are especially suitable for soil that tends to lose N at the beginning of vegetative growth or for organic farming. Furthermore, considering the low variability of NUE in cultivars, currently the best way for crop improvement is the introgression of traits from available wild progenitor gene pool (Munns and Millar 2023). Hence, the results of the current study demonstrate that WEW, which is known as an important source for abiotic stress improvement in wheat, can also serve as a rich source of novel genes for N-stress tolerance as a mechanism to increase GPC under low-N environments, and can contribute to sustainable agriculture.

Funding This study was funded by the US-Israel Binational Agricultural Research and Development Fund (BARD) Project IS-5198-19 and the European Community, Seventh Framework Program (FP7/ 2007-2013) under grant

agreement N°FP7-613556 (Whealbi).

Competing Interests The authors have no relevant financial or non-financial interests to disclose

Author contribution statement Conceptualization: NG, TK, and DB; Formal analysis, NG, AF, and LG; Funding acquisition: TK, DB, and TF; Investigation, NG, AF and TK; Methodology, NG and AF and CP; Project administration, DB and TK; Supervision TK and AD; Validation, NG; Writing, NG, TK; Review & editing, all co-authors.

Data availability All data supporting the reported results are included within the article or in the Supplementary Information.

Ethics approval Not applicable

Consent to participate Informed consent was obtained from all individual participants included in the study.

Consent to publish Not applicable

Informed Consent Statement: Not applicable.

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The wild emmer wheat grain protein content 5B QTL introgressed into bread wheat is associated with tolerance to nitrogen deficiency .

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Abstract

Figures

KEY MESSAGE

Introduction

Materials and methods

Trait validation under three environments

N stress phenotyping using a semi-hydroponic system

DNA extraction and KASP genotyping

Candidate gene identification and microcollinearity analysis

Real-time (RT)-qPCR

Statistical analysis

Results

Genotyping of introgression lines

Trait validation of GPC and GY under three environments

Phenotypic response to LN and FN using a semi-hydroponic system

Candidate gene identification and microcollinearity analysis

Gene expression under N deficiency tested by RT-qPCR

Discussion

N transporters

Abiotic stress-associated genes

Signal transduction

Conclusions and prospects

Declarations

References

Supplementary Files

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