A novel QTL carrying NB-ARC family genes enhances grain protein content without grain weight penalty in wheat (Triticum aestivum L.).

doi:10.21203/rs.3.rs-4813038/v1

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A novel QTL carrying NB-ARC family genes enhances grain protein content without grain weight penalty in wheat (Triticum aestivum L.).

https://doi.org/10.21203/rs.3.rs-4813038/v1

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Wheat (Triticum aestivum L.) is a global food crop with low protein content of 9-12%. Enhancing grain protein content (GPC) without compromising yield is challenging largely due to the negative correlation between yield and GPC. Genome wide DNA markers, high-throughput phenotyping, genome-wide association studies (GWAS), and QTL mapping have facilitated the identification of quantitative trait loci (QTLs). This study utilized a stable recombinant inbred line F_7-9 (RIL) population, genotyped via high-depth genotyping by sequencing (GBS), and conducted multi-environmental trials to identify stable QTLs for GPC. In contrast to the inverse relationship of GPC and thousand grain weight (TGW), the RIL population showed positive correlation of 0.143 (P<0.001) with TGW. The RIL population also showed significant genetic variation in GPC, with a heritability of 0.72, and identified ten QTLs for GPC on chromosomes 2B, 5B, 5A, 4B, and 1D. Among these, QGPC.nabi-2B.2 and QGPC.nabi-5B.1 were identified as major and stable QTLs. Precise mapping of QGPC.nabi-2B.2 identified NB-ARC domain-containing proteins as potential candidate genes for GPC regulation. Three SNPs from the QGPC.nabi-2B.2 region were converted to Tetra-ARMS-PCR markers. Subsequently these markers were used to validate in Indian wheat varieties and in a genetically different RIL population. This study provides a foundation for further research into the genetic regulation of GPC in wheat and suggests that NB-ARC genes could play a significant role in improving GPC, potentially enhancing wheat's nutritional quality without yield penalty.

GPC

Grain weight

QTL analysis

RILs

NB-ARC

Wheat (Triticum aestivum L.) is the fourth major food crop with global production of ~ 820 million tonnes growing over area of over 216 million hectares (FAOSTAT, 2024). However, being consumed globally wheat is providing only 9–12% of protein (Shewry 2009). Unique properties of wheat grain proteins usually rheological properties, cohesiveness and gluten network formation makes it suitable for number of food and non-food products (Shewry 2009; Ortolan and Steel 2017). The modern demand for end-use products such as noodles, pasta, cake, biscuits and bread require higher protein quality. The breeding for high-yielding genotypes limit the enhancement of GPC due to negative correlation with yield (Oury and Godin 2007; Balyan et al. 2013; Kumar et al. 2018). However, recent studies have reported increased GPC ranging 14–16% without affecting the yield by application of higher dosage of nitrogen fertilizer (Giordano et al. 2023; Wang et al. 2023a).

Recent development in DNA markers, high throughput phenotyping, high density linkage maps, genome-wide association studies (GWAS) on larger population and high quality reference genome have endorsed quantitative trait loci (QTL) analysis for GPC ((Rapp et al. 2018; Kumar et al. 2018; Saini et al. 2020; Zhu et al. 2021). High-throughput single-nucleotide polymorphism (SNP) markers have aided the QTL analysis by developing high density genetic maps, tetra-primer amplification refractory mutation system–PCR (Tetra-ARMS–PCR), kompetitive allele specific PCR (KASP) (He et al. 2014; Medrano and de Oliveira 2014; Wang et al. 2014). These markers have been utilized effectively for QTL validation in various populations (Bui et al. 2017; Bi et al. 2024; Liao et al. 2024).

Protein metabolism is a complex pathway that include nitrate uptake, reduction, assimilation into amino acids, and translocation throughout the plant (Quraishi et al. 2017; Kumar et al. 2018). Therefore, the pathway is well regulated by activities of several proteins and transcription factors located across the genome. Consequently, QTL studies for GPC associated loci found to be present on all 21 wheat chromosomes (Quraishi et al. 2017; Kumar et al. 2018). A recent MetaQTL analysis have identified as many as 157 MetaQTLs and 7 QTL hotspot which led to identify 96 candidate genes which can be targeted for GPC improvement (Saini et al. 2022). However, GPC is controlled by multiple genomic loci acting together and has low heritability due to higher genotype x environment interactions (Balyan et al. 2013; Nigro et al. 2019). So far, very limited wheat alleles have been cloned for GPC such as NAM-B1 and TaAAP6 (Uauy et al. 2006a; Jin et al. 2018).

GPC is influenced by many factors such as genetic capabilities, nutrient availability, grain filling time and stress conditions (Surovy et al. 2020; Giordano et al. 2023). One of the major challenge for GPC improvement is it’s negative relationship with grain yield (GY) (Oury and Godin 2007; Suprayogi et al. 2009). Since the GPC and GY relationship is not quantitative trait, the approaches have been made to study the GPC deviation (GPD) which is deviation from the linear regression of GPC on GY (Monaghan et al. 2001). GPD is a heritable trait and can be used to develop high GPC lines (Bogard et al. 2013). However, simultaneous selection for GPC and GY provides more promising results (Tanin et al. 2022).

In one of major QTL for GPC, NB-ARC domain containing proteins were identified. The NB-ARC (nucleotide-binding domain shared by APAF-1, R proteins, and CED-4) gene family is primarily known to involved in plant immunity due to presence of R-gene classes such as leucine-rich repeat (LRR), leucine zipper or other coiled-coil (CC) domains and nucleotide-binding site (NBS) (Chandra et al. 2017; Pan et al. 2022). However, some NB-ARC genes such as GNP12, RPP1A, VpTNL1, RLS1 and AhRAF4 were reported to be involved in morphological changes including panicle development (Igari et al. 2008; Dolezal et al. 2014; Pan et al. 2022; Wang et al. 2023b). In wheat, the NB-ARC genes are well distributed among three sub-genomes and are found in clusters with high sequence which are likely the product of tandem duplication (Chandra et al. 2017; Andersen et al. 2020). Since the NB-ARC genes are known to involve in the pathogen associated responses, it is possible that constitutive expression of these genes due to mutation could trigger the leaf senescence and remobilize the nutrients towards the developing grain. However, no study has shown the involvement of NB-ARC genes in GPC regulation.

In this study, a stable RIL population was utilized and genotyped using the high-depth genotyping by sequencing (GBS) method. Multi-environmental trials were conducted for the population, and GBS-based SNPs were used to identify stable QTLs. The population was extensively phenotyped to identify high GPC lines without impacting grain weight. PCR-based Tetra-ARMS markers were developed for three SNPs from the identified QTLs and validated in populations from different genetic backgrounds.

Plant materials and field experiments:

The study conducted on RIL population was generated from ‘WH1105’ × ‘TAC75’ (350 F_7 − 9 lines). The recipient ‘WH1105’ is a high yielding with moderate protein content (12.3%) (pedigree MILAN/S-87230//BABAX [3589]) and the donor ‘TAC75’ is an EMS mutant genotype with high GPC (14.5%) derived from Indian bread wheat variety ‘C306’. The RIL population was sown in three different locations for two consecutive years 2021-22 and 2022-23 at Agharkar Research Institute (ARI) research farm, Hol (dist. Pune) (lat: 18.04 N, alt: 74.21 E), Chaudhary Charan Singh University (CCU), Meerut (lat: 28.97 N, alt: 77.74 E) and National Agri-Food Biotechnology Institute (NABI), Mohali. (lat: 30.66 N, alt: 76.71 E).

QTL validation was performed on Indian wheat varieties and a genetically different RIL population ‘EU60’ x ‘HD3226’ (EH) consisting 220 and 223 genotypes respectively. All the genotypes were sown in experimental plots consisted of 45 rows of 1 m length with 30 cm space and were sown by hand with recommended dose of fertilizer (80:40:40, NPK) kg per hectare. For sequencing purpose high purity genomic DNA was extracted from leaf tissue collected from young leaf of 200 RILs including parental lines by using DNeasy plant pro kit (Qiagen, Netherlands). For QTL validation purpose DNA from 220 Indian wheat varieties and 223 EH-RILs was isolated using cetyltrimethylammonium bromide (CTAB) method (Clarke 2009).

Phenotypic data and statistical analysis

Total of six agronomic traits were recorded for parents and 325 RIL (F_7 − 9) individuals grown at the multiple locations. The recorded traits included Grain Protein content (GPC), days to heading (DH), flowering time (FT), thousand grain weight (TGW), plant height (PH) and tiller number per plant (TN). The GPC data was recorded using Near Infrared Spectroscopy (FOSS, Denmark) and cross-verified for random samplings using Kjeldahl method (Velp scientifica, Italy). The measured GPC (%) was adjusted to 12% moisture content using the following formula:

Conversion Formula:

$$\:Adjusted\:GPC\:\left(\%\right)=\:\left[\frac{100-M2}{100-M1}\right]*measured\:GPC\:\left(\%\right)$$

M2 = moisture basis (12% for wheat)

M1 = original moisture (percent)

For PH measurement, length of randomly selected three plants were measured from ground to spike and taken as average for the genotype. For measurement of tiller number fixed number of seeds were sown for each genotype (30 seeds for 1 m row) and total tiller number were counted to estimate per plant tiller number. DH and FT were recorded when at least 50% individuals showed the phenotype for an individual.

Statistical analysis

Phenotypic data for six traits was collected from all the environments. The data for the replication within environment were utilized to performed Anova. R program was used to calculate the statistics. Genotype details across the environments such as minimum read (Min), maximum read (Max), range, standard deviation (SD), standard error (SE), minimum read (MinGEN) and maximum read (MaxGEN) for genotype across all environment was estimated using ‘METAN’ (Olivoto and Lúcio 2020). Standard Error of Mean (SEm), Environmental variance (Ve), Genotypic Variance (Vg), Phenotypic Variance (Vp), Environmental Coefficient of Variance, Genotypic Coefficient of Variance (GCV), Phenotypic Coefficient of Variance (PCV), Heritability (Broad Sense), Genetic Advance and Genetic Advance as percentage of mean were calculated by using package ‘Variability’(Dinesh Parmar 2020). The genotype distribution across the environment was shown using violin plot, using the package ‘Vioplot’ (Adler and Kelly 2020) and Pair-wise Pearson correlation using Best Linear Unbiased Predictors (BLUPs) values for all the traits in individual environment as well as across the environments was performed using ‘GGally’ (Hadley Wickham 2021). BLUP values were predicted using ‘METAN’ package.

Genotyping by sequencing, variant calling, and gene annotation

Pst1 and Msp1 generated GBS libraries were sequenced on Illumina Hiseq2500 (2 × 150 bp) using the standard protocol (Poland et al. 2012). The sequencing services were acquired from Eurofins Genomics, India. The raw reads were subjected for quality check by using FastQC (Andrews, S. 2010). The adapter sequences were trimmed using Trim Galore v-0.6.7 (Felix Krueger 2017). Processed reads were mapped to reference genome sequence T. aestivum assembly IWGSC CS RefSeq v2.1 using BWA-mem (Li 2013). The resultant SAM file was processed for sorting, removal of unmapped reads, indexing and further converted to BAM format using SAMtools (Li et al. 2009). The BAM files for the parental lines were used for SNPs calling and positions of all the SNPs were identified at a read depth of 25 for each of 200 individuals through SAMtools. The resulting variant call format (VCF) file was filtered for homozygous polymorphic loci using VCFtools (Danecek et al. 2011). The loci with presence in > 90% individuals were retained and annotated using the SNPeff tool (Cingolani et al. 2012). The binary database for SnpEff was constructed using the whole genome reference sequence and annotation data retrieved from T. aestivum assembly IWGSC CS RefSeq v2.1 (Genome, NCBI).

Linkage map construction and QTL analysis

After genotyping the RILs, homozygous polymorphic SNPs between TAC75 and WH1105 were filtered based on Chi-square value (χ2) showing 1:1 segregation ratio and missing proportion (< 10%) were selected for linkage mapping. The eligible SNPs were binned based on their segregation patterns using the BIN function of IciMapping 4.1 (http://www.isbreeding.net ) (ICIM). An SNP with the least missing rate in a bin was chosen to represent this bin, and if the missing rates were equal, one was chosen at random. BIN markers were tested for significant segregation distortion using a chi-square test. The sorted BINs were utilized to form linkage groups with the MAP function of IciMapping 4.2 with a logarithm of the odds (LOD) threshold score of 3.0. The algorithm of “nnTwoOpt” was used to order BIN markers by calculating the map distance from recombination frequencies with Kosambi mapping function. The criterion and window for rippling were “SARF” and “5,” respectively (Meng et al. 2015). The Chromosome centromeric positions were determined using IWGSC CS RefSeq v2.1 (Zhu et al. 2021). The genetic linkage map was drawn with the software MapChart 2.3 (https://www.wur.nl/en/show/Mapchart.htm) (Voorrips, R.E. 2002).

QTLs controlling GPC variation were independently detected for F₈ and F₉ populations. QTL analysis was performed using Windows QTL Cartographer v2.5 (Wang et al. 2007) at the LOD threshold calculated by 1000 permutations at p-value 0.05. Composite interval mapping (CIM) was employed with default parameters to compute QTL information, including PVE, and additive (a) and dominance (d) effects. QTLs that were firmly identified with similar positions and overlapping confidence intervals of (< 10 cM) were considered identical. In addition, QTLs observed in at least 4 environments were considered as the major QTLs. The QTL genetic effects including, additive, epistatic effects and their environmental interactions was performed using multi-environment trials (MET) module from ICiMapping software (v 4.1). QTLs were nominated as per the latest guidelines by Boden et al., (2023).

Precise QTL mapping

To narrow-down the identified QTL region, forty indigenous SSRs were designed using Krait tool (Du et al. 2018) to get polymorphism within identified QTL region (Fandade et al. 2024). Extreme bulks (n = 16 from each ends) for trait GPC were utilized for calculating the recombination frequency based on method described by (Zhang et al. 1994)).

$$\:C\:\left(recombination\:frequency\right)=\frac{\left(N1\:+\frac{N2}{2}\right)}{N}$$

N = Total number of plants surveyed,

N1 = Number of individuals homozygous for second parent,

N2 = Number of heterozygous individuals.

The genetic distance was calculated by utilizing the recombination frequency and the calculated by equation

$$\:X\:\left(Genetic\:distance\:cM\:\right(Kosambi\left)\:\right)\:=0.25{ln}\left(\frac{1+2C}{1-2C}\right)$$

C = Recombination frequency

The QTL interval was narrowed down to the markers showing minimum recombination frequency from both ends of the identified QTL.

Previously reported GPC-QTLs and PCR validation of identified QTL

Previously reported closely linked marker sequences of QTL/SNP related to GPC were obtained from literature survey and WheatQTLdb V2.0 (Singh et al. 2021), and identified marker sequences were further BLAST against genomes sequences of IWGSC RefSeq V2.1. to get the physical locations. Three SNPs from precise mapped region were selected and converted to Tetra-ARMS–PCR using the guidelines (Ye et al. 2001; Medrano and de Oliveira 2014). Standard PCR conditions were followed with initial denaturation at 95º C, annealing at 65º C and amplification at 72º C for 1 min each with variable primer concentration and dilution combination.

Candidate gene identification

To identify the candidate genes underlying the QTL region, flanking marker sequences of the QTL were utilised to mine the high confidence annotated genes using GrainGene genome browser (Zhu et al. 2021). The identified genes were further characterised using Wheat Expression Browser (expVIP) (http://www.wheat-expression.com/) (Ramírez-González et al. 2018) and WheatOmics (http://wheatomics.sdau.edu.com ) (Ma et al. 2021).

Phenotypic variation of GPC shows positive impact on grain weight in RIL population

The study used a Recombinant Inbred Line (RIL) population derived from a WH1105/TAC75 cross, grown in three geographically distinct regions over two consecutive years, with two replications per environment. Six agronomic traits were recorded for the parents and 325 RIL (F7-9) individuals: GPC, HD, FT, TGW, PH and TN.

GPC was normally distributed (Fig. 1) with a broad-sense heritability (H²) of 0.72, indicating significant genetic control. The parental line TAC75 had a GPC of 14.61%, while WH1105 had 12.56%. The genotype 'AM274' exhibited the lowest GPC (9.4% in E1), whereas 'AM6' had the highest (18.99% in E3). Genotypes 'AM53' (~ 16.63%) and 'AM368' (~ 10.35%) showed the most stable GPC values across environments. The population's GPC ranged from 9.4–18.99%, with a mean of 13.4%. The genetic advance as a percentage of mean (GAM) for GPC was 16.56% (Table 1).

Table 1

Statistical analysis of grain protein content (GPC) and other agronomically important traits in WH1105/TAC75 population
	GPC	TGW*	Heading	Flowering	Height	Tiller
WH1105	12.56	37.81	96	102	89	8
TAC75	14.42	42.74	87	93	112	5
Maximum	18.89	57.55	117	124	134.42	13
Minimum	9.4	11.11	77	81	62.33	3
Grand Mean	13.4	37.37	95.84	100.95	101.95	7.62
Standard Error of Mean (SEm)	0.34	1.62	1.18	0.88	2.67	0.53
Critical Difference (CD) 5%	0.94	4.5	3.29	2.46	7.42	1.48
Critical Difference (CD) 1%	1.24	5.92	4.33	3.24	9.76	1.95
Environmental Variance	0.58	15.83	8.47	4.74	42.99	1.72
Genotypic Variance	1.52	25.24	28.59	25.87	114.08	1.19
Phenotypic Variance	2.10	41.07	37.06	30.61	157.08	2.91
Environmental Coefficient of Variance	5.72	10.64	3.03	2.15	6.43	17.21
Genotypic Coefficient of Variance	9.25	13.44	5.57	5.03	10.48	14.36
Phenotypic Coefficient of Variance	10.88	17.15	6.35	5.48	12.29	22.42
Heritability (Broad Sense)	0.72	0.61	0.77	0.84	0.726	0.41
Genetic Advance	12.16	8.11	9.67	9.63	18.75	1.44
Genetic Advance as percentage of mean	16.56	21.71	10.09	9.541	18.39	18.96
*TGW = Thousand grain weight

ANOVA revealed significant variation due to genotype (G), environment (E), and G × E interaction for GPC (P < 0.001) (Table 2). GPC data, analyzed across all environments, correlated with other environments except for Environment-2, which was excluded from further analysis (Figure-S1). Other agronomic traits also exhibited normal distribution and were recorded for correlation with GPC (Figure-S2). Pearson’s correlation coefficients indicated that GPC was positively correlated with TGW with correlation of 0.143 (P < 0.001), showing no negative relationship. FT and HD were negatively correlated with GPC − 0.121 and − 0.103 respectively (P < 0.001), PH and TN showed minor correlation and were not significant (Table 3, Figure S-3).

Table 2

ANOVA for grain protein content to determine the variance across environment
Source of variation	Df **	Mean Sq	F-value	P-value
Environment	4	61.516	116.18	< 0.001
Genotype	324	7.068	13.35	< 0.001
Genotype x Environment	1296	0.908	1.71	< 0.001
Replicates x Environment	5	9.36	19.74	0.285
Residuals	985	0.529
**Df = Degree of freedom

Table 3

Pearson’s correlations between the grain protein content and other agronomic traits in the mapping population
Trait	Correlation coefficient
Days to heading (DH)	-0.121^***
Flowering time (FT)	-0.103^***
Plant height (PH)	0.041^*
Tiller number per plant (TN)	-0.009
Thousand grain weight (TGW)	0.143^***
Significance level at P < 0.05; **significance level at P < 0.001

Genotyping by sequencing (GBS) and SNP identification

GBS was performed using the double digest restriction associated DNA (ddRAD) method using PstI and MspI restriction enzymes (Poland et al. 2012). Approximately 1200 million clean reads were generated for 200 individuals, including parents, averaging 6 million reads per individual. Reads were filtered with a quality score threshold of 30 and mapped to the wheat genome (IWGSC RefSeq V2.1). A total of 1,882,167 SNPs with a minimum sequencing depth of 25 and occurrence in ≥ 90% of the samples were retained. Of these, 1,870,849 SNPs were mapped to 21 chromosomes, while 11,318 uncharacterized SNPs were excluded from further analysis. The density distribution of filtered SNPs was consistent across the wheat genome: 34.25% (644,617) in the B genome, 32.5% (613,025) in the A genome, and 32.58% (613,207) in the D genome. Twelve individuals were excluded with high SNP missing rate (> 15%), leaving 188 individuals for linkage analysis. Further screening of homozygous polymorphic loci within parents identified 40,023 SNPs, and were used for linkage mapping. The highest number of homozygous polymorphic SNPs was recorded on chromosome 2B (3,181) and the lowest on chromosome 4D (230).

Linkage mapping and QTL identification

For Linkage mapping, the BIN function from IciMapping v4.2 was used to remove redundant SNPs, resulting in 2,418 BIN IDs shortlisted based on segregation distortion and lower missing rate for linkage mapping. The 2,418 BIN IDs included 973 from genome A, 1,071 from genome B, and 384 from genome D. These were used for linkage mapping using the MAP function of IciMapping v4.2. The linkage analysis produced a high-density genetic map spanning 2,947.34 cM, with the map length for genome A being the highest (1,260.68 cM), followed by genome B (1,139.25 cM) and genome D (547.91 cM). A total of 34 linkage groups were formed: 11 each for genomes A and B, and 12 for genome D. The overall marker density was 1.22 cM per BIN marker (Table 4, Fig. 2). Total of ten QTLs were identified in the present study for the trait GPC, located on chromosomes 2B (3 QTLs), 5B (3 QTLs), 5A (2 QTLs) and one each on 4B and 1D (Fig. 3).

Table 4

Linkage map developed for WH1105/TAC75 RILs using 2418 BIN IDs representing 400023 SNPs.
Chromosome	Mapped SNP	BIN markers	linkage group	Map length (cM)*	cM per bin marker
1A	2761	164	1	191.79	1.17
2A	2537	138	1	151.75	1.1
3A	2177	131	2	199.53	1.52
4A	1847	134	2	128.16	0.96
5A	1993	103	2	189.35	1.84
6A	1793	154	1	174.88	1.14
7A	2636	139	2	225.22	1.62
A genome	15744	963	11	1260.68	1.31
1B	3059	179	2	187.13	1.05
2B	3181	224	1	197.4	0.88
3B	3337	106	3	148.75	1.4
4B	888	40	1	132.32	3.31
5B	1988	114	1	95.05	0.83
6B	2953	195	1	187.41	0.96
7B	3040	213	2	191.19	0.9
B genome	18446	1071	11	1139.25	1.06
1D	975	70	2	98.91	1.41
2D	1435	123	2	52.32	0.43
3D	811	58	2	64.98	1.12
4D	230	12	1	78.51	6.54
5D	989	66	1	72.22	1.09
6D	562	25	2	129.9	5.2
7D	831	30	2	50.57	1.69
D genome	5833	384	12	547.41	1.42
Total	40023	2418	34	2947.34	1.22
*cM = Centi morgan

The LOD scores ranged from 2.59 to 5.49 (Table 5). Among these, QGPC.nabi-2B.2 and QGPC.nabi-5B.1 were identified in six and four environments respectively, including the BLUP dataset. QGPC.nabi-2B.2 explained 5.19%-6.97% PVE with LOD values ranging from 2.59 to 3.60, while QGPC.nabi-5B.1 explained 7.15%-10.28% PVE with LOD values ranging from 3.57 to 3.96. Therefore, these two QTLs were considered major and stable for GPC. The remaining eight QTLs were detected in one or two environments, explaining 5.1%-15.42% of the PVE with LOD values ranging from 2.61 to 5.49, and were designated as minor QTLs. Additive effect values suggest that positive alleles of QGPC.nabi-2B.2 and QGPC.nabi-5B.1 were derived from the parent WH1105. The major QTL QGPC.nabi-2B.2 covered a genetic distance of 9 cM and a physical distance of 5.8 Mb, while QGPC.nabi-5B.1 covered 10 cM genetically and 31 Mb physically. Since QGPC.nabi-2B.2 was detected in all environments, including the BLUP dataset, it was considered for further analysis.

Table 5

Quantitative trait loci (QTL) analysis for grain protein content studied across five environment and BLUP data set.
sr no	QTL	Env^a	Marker	Position (cM)	Left marker	Right marker	Physical interval (Mb)	LOD^b	Add^c	PVE (%)^d
1	QGPC.nabi-1D.1	E3	M5959	32.03	M5892	M5959	8.45	2.61	0.25	6.46
1	QGPC.nabi-1D.1	E6	M5960	32.03	M5892	M5959	8.45	3.35	0.31	6.88
2	QGPC.nabi-2B.1	E1	M9585	12.71	M9533	M9585	4.44	2.96	0.71	6.48
2	QGPC.nabi-2B.1	E6	M9586	11.71	M9554	M9593	3.67	3.33	0.84	6.8
3	QGPC.nabi-2B.2	E1	M9629	17.91	M9618	M9650	2.22	3.45	0.72	6.64
		E3	M9629	17.91	M9618	M9650	2.22	2.79	0.59	5.24
		E4	M9618	20.01	M9650	M9662	3.57	2.59	0.74	5.19
		E5	M9618	16	M9593	M9618	5.8	2.78	0.3	6.95
		E6	M9629	17.91	M9618	M9650	2.22	3.6	0.85	6.97
		BLUP	M9629	17.91	M9618	M9650	2.22	2.78	0.58	5.26
4	QGPC.nabi-2B.3	E5	M12257	195	M12257	M12340	4.51	5.49	-0.36	15.42
5	QGPC.nabi-4B.1	E5	M22376	84	M22376	M22357	6.98	5.01	-0.34	13.7
6	QGPC.nabi-5A.1	E6	M24671	22.65	M24671	M24615	4.39	3.29	0.31	6.86
7	QGPC.nabi-5A.2	E3	M23555	114.1	M23555	M23561	0.54	2.91	0.26	7.19
8	QGPC.nabi-5B.1	E1	M25710	59	M25742	M25723	6.32	3.95	0.32	8.7
		E4	M25629	64	M25669	M25629	2.8	3.96	0.37	10.28
		E3	M25571	68.51	M25607	M25542	11.86	3.57	0.69	6.83
		BLUP	M25571	68.51	M25607	M25542	11.86	3.89	0.71	7.47
9	QGPC.nabi-5B.2	E1	M25536	72.51	M25542	M25511	4.25	3.61	0.75	7.15
9	QGPC.nabi-5B.2	E6	M25454	82.21	M25471	M25428	1.92	3.95	0.93	8
10	QGPC.nabi-5B.3	E3	M25927	53.11	M25909	M25764	249.06	2.63	0.6	5.1
10	QGPC.nabi-5B.3	BLUP	M25927	53.11	M25909	M25764	249.06	2.79	0.61	5.45
^a Environment, ^blogarithm of odds, ^cadditive effect, ^dphenotypic variance explained

Precise mapping of QGPC.nabi-2B.2 identifies NB-ARC domain containing proteins as candidate genes

QGPC.nab-2B.2 was covering a physical distance of 5.8 Mb. Using the physical position information of the flanking markers, SSRs were screened for polymorphism between parents (Fandade et al. 2024). Total of forty SSRs were synthesized and eight were used to narrow down the genomic region (TableS-4). The polymorphic SSRs were used to screen the extreme bulks (n = 16*2) of GPC from RIL population. The recombination frequency ranged from 0.31 to 0.63. SSR_23 showed a minimum recombination frequency of 0.31 for high GPC lines, and SSR_34 showed the same for low GPC lines (Table 6). The region spanning SSR_23 and SSR_34, considered as the minimum QTL region, which covered 2.3 Mb on chromosome 2B (Fig. 4).

Table 6

Recombinant frequency estimate for precise QTL mapping using eight polymorphic SSR markers for high (n = 16) and low GPC (n = 16) genotypes.
		High GPC					Low GPC
Sr no	SSR	P1*	P2**	H***	Recombination frequency (C)	Genetic distance cM	P1	P2	H	Recombination frequency (C)	Genetic distance cM
1	SSR_4	9	7	0	0.44	0.68	7	8	1	NL	-
2	SSR_9	10	6	0	0.38	0.49	7	9	0	NL	-
3	SSR_17	10	6	0	0.38	0.49	7	9	0	NL	-
4	SSR_18	8	8	0	0.5	0	10	5	1	0.34	0
5	SSR_22	5	9	2	0.63	0.55	9	6	2	0.41	0.58
6	SSR_23	11	5	0	0.31	0.37	6	10	0	NL	-
7	SSR_34	10	5	0	0.33	0.4	11	5	0	0.31	0.37
8	SSR_39	6	10	0	0.63	0.55	10	6	0	0.38	0.49
P1 = parent1, P2 = parent 2, H = heterozygotes, SSR = short sequence repeats, NL = Not linked

This region contained 30 high-confidence genes identified from the genome browser (Zhu et al. 2021). The most abundant class of protein was NB-ARC domain-containing protein (seven genes), followed by membrane traffic proteins (exocyst) (PC00150) (six genes). Other proteins included glycosyltransferase (PC00111) (two genes), ATP-binding cassette (ABC) transporter (PC00003) (one gene), dehydrogenase (PC00092) (one gene), transferase (PC00220) (four genes), and winged helix/fork-head transcription factor (PC00246) (one gene). Eight genes were uncharacterized (Table-S1). Expression analysis using expVIP and WheatOmics showed fewer genes having expression of more than 2 TPM and most of them belonged to the NB-ARC domain-containing protein family, marking them as important candidates. (Fig. 5)

QGPC.nabi-2B.2 shows positive effect on GPC in different genetic populations

Three SNPs A) 2B: 21949674 (G◊T) (SNP1), B) 2B: 21991706 (T◊G) (SNP2) and C) 2B: 22514403 (C◊T) (SNP3) were identified from the 2.3 mb region based on their co-segregation with trait GPC (Figure-S4). When the genotypes showing extreme GPC phenotype (25 from each end) of the RILs were studied, the identified SNPs shown to associates with GPC in the range of 67% − 80% (Fig. 6 (a)) indicating the dominance of the alleles. Subsequently these SNPs were converted to tetra ARMS-PCR primer for PCR validation (Table 7, Fig. 6 (b)). The PCR validation for the three SNP markers was carried out on Indian wheat varieties and phenotyped for 2 consecutive years (Fig. 7 (a), (b)). Similarly, another genetically different population EH-RIL was validated using two SNPs i.e., SNP-2 and SNP-3 markers, polymorphism for SNP1 was not detected in the population. Each population was divided into two groups based on the genotyping results. Groups carrying positive allele showed significantly (> 0.01) higher GPC as compared to negative allele in all of the SNPs (Fig. 7 (c)). The phenotypic variation explained by the SNPs is calculated using mean values and considered as a percentage increase in GPC. The SNPs were able to explain phenotypic variation in the range of 2.85–6.04%. Interestingly, SNP2 and SNP3 markers were able to differentiate between alleles in the EH RIL population, even though the population showed lower variation in GPC (Fig. 7 (d)).

Table 7

Tetra ARMS-PCR primer designed for screening in natural accessions and RIL population from different genetic background
SNP Name	Primer type	Primer Sequence (5'-->3')	Tm*	Product Size
2B: 21949674 (G◊T) (SNP1)	Forward inner primer (G allele)	AGCAAGGGTGGAGGAGACAAACATAATTGG	71	163
	Reverse inner primer (T allele)	GATGAATGTGCTCGTGAATCACGCAA	71	197
	Forward outer primer	GACCAAGATGTGGATGGCTCTTGGTTTC	71	304
	Reverse outer primer	CTGGTAACTGACAAGTGGCGAACCCTTG	71	304
2B: 21991706 (T◊G) (SNP2)	Forward inner primer (T allele)	ATGTGCGAGACGCAGGGTGTGCCCTAT	76	227
	Reverse inner primer (G allele)	CTTCTAGTGAGTGTCTCTTATGTGGTGTAC	61	184
	Forward outer primer	AAGGAGAAACCAGATCTAGCAGCTGCAG	68	354
	Reverse outer primer	ACTTGTTCATTTTTTCCCGAATAACCGG	68	354
2B: 22514403 (C◊T) (SNP3)	Forward inner primer (C allele)	GTGCACGCACCTACGGGAGACAATGAT	73	134
	Reverse inner primer (T allele)	GAGAGACGTACGTGGGTTAGGTCGGATTGG	73	110
	Forward outer primer	AATTCGTTACACGTCACCGGCCTGTGAA	73	187
	Reverse outer primer	ATAATGTTGCATCTGCAGGCAGCGGATT	73	187
*Melting temperature

Wheat (Triticum Spp.) is an important staple crop contributing up to 12% of world agriculture production and accounts for ~ 20% of global calorie and protein consumption (Shiferaw et al. 2013)(FAOSTAT, 2024). Wheat is used for making range of products like pasta, cake, biscuits, chapatti, bread etc. and GPC is an important trait to determine the end use quality of wheat (Sengar and Singh 2018). The crop is being targeted for continuous genetic improvement for enhanced yield and resistance to diseases. However, there is also the growing demand for nutritious wheat grains which can be achieved by improving the GPC and alteration in GPC composition (Shewry 2009; Battenfield et al. 2016). Conventional breeding have remain largely unsuccessful to improve the GPC, majorly due to low heritability, higher environmental influence, inverse relationship with grain yield and importantly the trait is known to controlled by numerous QTLs/genes with small effects (Balyan et al. 2013; Groos et al. 2003; Davies et al. 2006). Recent success in improving GPC has been limited to introgression of Gpc-B1 in bread wheat cultivars, increased expression of HOMEOBOX DOMAIN-2 due to MicroRNA resistance and a haplotype variation in TaGW2-6B have reported to enhance the GPC without yield penalty (Vishwakarma et al. 2016; Dixon et al. 2022; Bi et al. 2024). Lately, the molecular markers have been instrumental for identifying QTLs linked to GPC using various mapping populations (Kumar et al. 2018; Saini et al. 2022). However, it is still challenging to validate and utilize the identified QTLs for trait improvement in different population.

To identify the candidate genes responsible for high GPC, the present study has utilized ‘WH1105’ × ‘TAC75’ RIL population and conducted an extensive trial at three different geological locations for two consecutive years. The novel population has shown variation of GPC ranging from 9.4–18.89% with broad sense heritability of 0.72, indicating the dominance of genomic contribution as compared to environmental contribution which is more prevalent in the GPC (Balyan et al. 2013). Parental diversity has a major role in determining the efficiency and accuracy of QTL mapping outcomes, as it is typically limited in natural populations utilised for association mapping investigations (Hussain et al. 2017). The present study has utilized GBS approach to achieve efficient SNP genotyping (Poland et al. 2012). A total of 40,023 high quality homozygous SNPs distributed over 21 chromosomes were identified. Total of 2418 unique BIN IDs were utilized for linkage map construction which has resulted in formation of 34 linkage groups with cumulative length of 2947.34 cM which was comparably better than the recent reports (Luo et al. 2021; Qin et al. 2023; Sun et al. 2023).

Phenotypic correlation among investigated traits

In this study, GPC was significantly and positively correlated with TGW, which was found differing to the trend of inverse correlation of GPC with TGW (Oury and Godin 2007; Balyan et al. 2013; Kumar et al. 2018). However, such positive correlation has been reported previously (Sun et al. 2010; Simons et al. 2012; Dixon et al. 2022; Bi et al. 2024). Studies have also utilized the grain protein deviation (GPD) approach to select the GPC without negative effect on grain weight (Nigro et al. 2019). GPC has shown negative correlation with DH and FT indicates that higher FT or DH is causing lower GPC which indicates that longer grain filling time due to early onset of heading and flowering is favouring the GPC accumulation (Bogard et al. 2011; Chen et al. 2016; Kamal et al. 2019). PH and TN were not found to be correlating with GPC in our study (Table 3).

QGPC.nabi-2B.2 is a novel QTL for GPC

Considering the negative effect of GPC on wheat yield, number of studies have implemented diverse strategies to map and clone the associated genes. Physical location of flanking SNPs of the QGPC.nabi-2B.2 was compared with previous studies from the same chromosome (Table-S-2). QGPC.nabi-2B.2 was located between 19.76 Mb and 25.56 Mb (5.8 Mb) region of chromosome 2BS which was further mapped precisely to region 20.74 Mb to 23.07 Mb (2.3 Mb) which was different from previously reported QTLs. A recent metaQTL (MQTL) study by Saini et al. (2022) have reported that Chromosome 2B was carrying the maximum number of 5 MQTLs and the closest MQTL located was ranging from 4.7 Mb to 11.07 Mb. Another QTL QGpc.2B-yume was located at 242.12 Mb on chromosome 2BS with associated markers Xgwm37 and Xwmc179 (Terasawa et al. 2016). QGpc.spa-2B.2 was located at 642.92 Mb on chromosome 2BL with peak marker IWB38099 (Ruan et al. 2021) which is colocalised with QTL GPC_B2 peak marker barc101a (Habash et al. 2007). A hotspot for the chromosome 2BL was found at range of 767.65 Mb − 793.18 Mb (Zhang et al., 2021, Habash et al., 2007, Singh et al., 2022). Most promising loci would have been the gene Gpc-B2 - gene paralogous to Gpc-B1 on chromosome 2Bs at 223.15 Mb (Uauy et al. 2006b). However, Distelfeld et al. (2006) pointed out that the wheat gene Gpc-B2 and its rice ortholog have evolved to perform different functions. Comparison of physical location with previous reports suggests that QGPC.nabi-2B.2 is likely a novel QTL for GPC.

Potential candidate gene(s) for QGPC.nabi-2B.2

The identified QGPC.nabi-2B.2 spans a 2.3 Mb region with 30 high-confidence predicted genes in the Chinese Spring genome. Expression analysis using WheatOmics showed variable expression in root, stem, and spike tissues (Fig. 5). Interestingly, developing grain tissues exhibited even lower expression than other plant parts. Among the expressed genes majority belong to the NB-ARC and Exo70 family domains, marking them as important candidate genes.

An extensive study by Pan et al. (2022) on NB-ARC domain-containing proteins in rice demonstrated that most genes in the NB-ARC family were expressed in roots, panicles, and leaves. This supports our observation of lower expression in grain tissues. Flag leaf senescence mainly the chloroplast degradation is a well-known source of nitrogen which is transported towards developing endosperm (Peoples et al. 1980; Uauy et al. 2006b; Li et al. 2015). RLS1, an NB-ARC domain-containing protein with its ARM (armadillo) domain at the carboxyl terminus, is known to regulate chloroplast degradation during leaf senescence in rice (Jiao et al. 2012; Wang et al. 2023b). Additionally, NB-ARC genes are also known to regulate seed development in Arabidopsis, maize, and rice (Igari et al. 2008; Dolezal et al. 2014; Pan et al. 2022). These observations suggests that the NB-ARC family genes can be the potential target for GPC improvement.

Another important gene family identified is Exo70 family proteins that are primarily performs vesicle trafficking-related functions (Heider and Munson 2012; Cvrčková et al. 2012). Certain Exo70 genes such as Exo70B1, Exo70B2 are known to involve in endocytic and autophagy complexes to regulate membrane recycling and autophagy (Zárský et al. 2013; Brillada et al. 2021; Žárský 2022; Ortega et al. 2022). However, a report suggests that Exo70B1 mutants need a truncated NLR protein from NB-ARC family to activate the cell apoptosis in arabidopsis (Zhao et al. 2015). This highlights the importance of NB-ARC family genes.

The study has also identified 22 homozygous SNPs within QGPC.nabi-2B.2 through GBS and characterized them using SNPeff (TableS-3). Interestingly, one SNP (2B: 21949674 - G→T) with high impact was identified to be a splice donor variant at position of 520 amino acid. This variant causes intron retention in the transcript TraesCS2B03G0087200, resulting in a premature stop codon (Figure S-5). However, The resulting truncated protein may be sufficient to induce a response in wheat, similar to what is known in tomato and arabidopsis as discussed earlier (Zhao et al. 2015; De Oliveira et al. 2016).

Based on the results and the concurrent studies a hypothesized model has been proposed (Fig. 8). The model suggests that NB-ARC genes regulate seed development and leaf senescence by interacting with other proteins and thereby promotes the nutrient remobilization towards the developing endosperm. We hypothesized that the identified loci is possibly working similarly as GPC-B1 loci which has a NAC transcription factor that accelerates senescence and increases nutrient remobilization from leaves to developing grains (Uauy et al. 2006b, a). However, a network of genes may be working together instead of single candidate gene as reported earlier.

This study conducted multi-environmental trials on a recombinant inbred line (RIL) population and identified quantitative trait loci (QTLs) responsible for variation in grain protein content (GPC) through genotyping-by-sequencing (GBS). The linkage map generated from high-quality single nucleotide polymorphisms (SNPs) led to the identification of two stable QTLs: 'QGPC.nabi-2B.2' and 'QGPC.nabi-5B.1'. Among these, 'QGPC.nabi-2B.2' was characterized as a major QTL and precisely mapped to a 2.3 MB region using indigenously developed simple sequence repeats (SSRs). This QTL was validated in different genetic backgrounds. Our results identified NB-ARC family genes that have a high potential to contribute to the development of high GPC lines without negatively affecting grain weight. In summary, the identification of 'QGPC.nabi-2B.2' offers a promising resource for wheat GPC improvement.

Author contribution

JR, VF, and AM originated the experiment's concept and design and completed the manuscript. VF, AM, VS, PS, PK, AM and JK conducted the experimental procedures and analyzed the data. DS, SM and DD provided valuable assistance in data analysis. SS and MO provided space and technical assistance to conduct the multilocation trials of the genetic materials.

Disclosure

The authors declare no conflict of interest.

Data availability statement

The data underlying this article are available in the article and in its online supplementary material. Further inquiries can be directed to the corresponding author.

Acknowledgement

VF expresses sincere gratitude to the National Agri-Food Biotechnology Institute (NABI), Department of Biotechnology (DBT), Government of India (GOI), for the provision of research facilities and financial support. VF is also thankful to the Council of Scientific and Industrial Research (CSIR), India, for the Junior and Senior Research Fellowships (JRF & SRF), which enabled significant contributions to this research. Additionally, VF acknowledges the Regional Center of Biotechnology (RCB) for their support in PhD registration. The authors appreciate the NABI bioinformatics department and the Param Smriti Supercomputing Facility at NABI for their invaluable assistance and expertise in the data analysis phase of this research. Lastly, the authors are grateful for the access to online journals provided by DeLCON (DBT Electronic Library Consortium), Gurugram, India.

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FigureS1.tiff
Figure S-1: Pearson’s correlation analysis showing correlation of GPC trait across six environments and BLUP dataset.
FigureS2.tif
Figure S-2: Violine plot showing distribution of agronomic traits across six environments (E1-E6) a)days to flowering b) days to heading c) plant height d) tiller per plant e) thousand grain weight (TGW)
FigureS3.tiff
Figure S-3: Domain analysis of NB-ARC domain containing protein TraesCS2B03G0087200. The numbering indicates position of amino acid. Various domains are depicted in different colour boxes. Amino acid 520 represented by yellow bar shows the site of splice variant.
FigureS4.tif
Figure S-4: Boxplot showing GPC trait distribution of genotyped RIL lines across five environment and BLUP data set. Student’s T test has been utilized to analyse the difference between alleles. *Significance level at P < 0.05; **significance level at P < 0.01, *** significance level at P < 0.001.
FigureS5.tif
Figure S-5: Pearson’s correlation analysis among the recorded agronomic traits Days to heading, Days to flowering, Plant height, Tiller number per plant, thousand grain weight and grain protein content. *Significance level at P < 0.05; **significance level at P < 0.01, *** significance level at P < 0.001, ****significance level at P < 0.0001
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A novel QTL carrying NB-ARC family genes enhances grain protein content without grain weight penalty in wheat (Triticum aestivum L.).

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Abstract

Figures

Introduction

Materials and methods

Plant materials and field experiments:

Phenotypic data and statistical analysis

Statistical analysis

Genotyping by sequencing, variant calling, and gene annotation

Linkage map construction and QTL analysis

Precise QTL mapping

Previously reported GPC-QTLs and PCR validation of identified QTL

Candidate gene identification

Results

Phenotypic variation of GPC shows positive impact on grain weight in RIL population

Genotyping by sequencing (GBS) and SNP identification

Linkage mapping and QTL identification

Discussion

Phenotypic correlation among investigated traits

Conclusion

Declarations

References

Supplementary Files

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