Molecular Profiling of Diverse Wheat Germplasm for Resistance to Puccinia triticina

doi:10.21203/rs.3.rs-5211034/v1

Leaf rust (LR) epidemics present a persistent threat to global wheat production, despite the presence of resistance (Lr) genes in wheat. The evolving pathogen Puccinia triticina continually challenges these resistance mechanisms. This study assessed ten wheat lines for relative resistance index (RRI) and screened them for Lr genes or quantitative trait loci (QTLs) using microsatellite markers. The lines were classified into three groups: susceptible (< 5; 4.32 ± 0.68), moderate (5–7; 6.05 ± 0.67), and resistant (> 7; 8.50 ± 0.22) (p < 0.001). Genetic analysis with 12 polymorphic markers revealed 186 alleles with varying allelic diversity. Markers Xbarc124 and Xgwm512 showed greater diversity, and resistance-related alleles were linked to markers Xgwm512 and Xgwm493, associated with the Lr34 gene. Moderate associations were found with Lr37 (Xbarc1138 and Xgwm400) and Lr24 (Xgwm273), while Lr26 (Xwmc407) was linked to susceptibility. Parental line crosses resulted in higher RRI, indicating beneficial recombination. Structure analysis revealed genetic diversity among resistance groups, with susceptible groups showing distinct clustering. Lines AN179 and PR127 clustered together, showing key resistance alleles, particularly in crosses with resistant PR123. The findings highlight novel pathogen races contributing to resistance breakdown and suggest combining all-stage resistance genes (Lr9, Lr24, Lr37) with adult plant resistance genes (Lr48, Lr22a, Lr34, Lr46) for durable LR resistance. The identified alleles offer valuable insights for marker-assisted breeding to enhance wheat resistance to leaf rust.

LR

Lr genes

Puccinia triticina

polymorphic microsatellite markers

QTL

ASR genes

APR genes

relative resistance index

RILs

Leaf rust epidemics continue to pose significant challenges to global wheat production despite the presence of resistance (Lr) genes.
Ten wheat lines were evaluated for resistance, with classifications into susceptible, moderate, and resistant groups.
Genetic analysis revealed 186 alleles across 12 polymorphic markers, demonstrating significant allelic diversity.
Resistance-associated alleles were linked to Lr34, Lr24, and Lr37, while Lr26-linked markers were associated with susceptibility.
Combining ASR genes (Lr9, Lr24) with APR genes (Lr34, Lr46) is recommended for durable leaf rust resistance in wheat.

The global production of wheat, a major cereal, faces growing challenges due to the rising incidence and severity of leaf rust disease. Leaf rust (LR), caused by the fungus Puccinia triticina, affects both spring and winter wheat varieties, leading to significant yield losses (Gómez et al., 2024)¹. This disease is one of the most widespread and destructive pathogens affecting wheat, with around 70% of global wheat production areas being vulnerable to infection (Smith et al., 2023)². It is estimated that LR causes annual wheat yield losses amounting to millions of tons, with significant economic implications (Chen et al., 2022)³. LR primarily impacts the photosynthetic capacity of wheat, particularly when the infection occurs during the grain-filling stage, leading to reduced grain quality and yield (Rodriguez et al., 2021)⁴. The pathogen’s efficient dispersal mechanism, along with its ability to adapt and evolve rapidly, contributes to its widespread presence across wheat-growing regions globally (Johnson et al., 2023)⁵. Severe outbreaks of LR have been reported in various parts of the world, ranging from minor yield reductions to complete crop loss in affected areas (Zhang & collaborators, 2024)⁶. The global spread and persistence of LR are further exacerbated by the pathogen's complex life cycle (Figure 1), which includes both asexual reproduction on wheat (the primary host) and a sexual stage involving alternate hosts, such as Thalictrum spp. (Nguyen et al., 2023)⁷. This sexual cycle plays a crucial role in generating genetic diversity within P. triticina populations, leading to the emergence of new virulent races that can overcome previously effective resistance genes in wheat (Feuillet et al., 2003)⁸. For example, the Lr34 gene, known for its broad-spectrum resistance, and Lr10, which is effective against specific races, are critical in managing LR. The swift evolution of the pathogen poses significant challenges for the long-term management of LR (Garcia et al., 2022)⁹.

To combat LR, the use of resistant wheat cultivars is considered the most sustainable and environmentally friendly strategy (Singh et al., 1998)¹⁰. However, the development of durable resistance is complicated by the pathogen’s ability to overcome resistance genes through mutation and recombination (Ahmed et al., 2024)¹¹. Consequently, wheat breeding programs have increasingly focused on incorporating multiple resistance genes, including both all stage resistance (ASR) genes such as Lr21 and Lr24, and adult plant resistance (APR) genes like Lr34 and Lr46, to achieve more durable protection against LR (Kumar et al., 2023)¹². In recent years, advances in molecular breeding techniques, such as genome-wide association studies (GWAS) and the use of DNA markers like microsatellites, have facilitated the identification and mapping of Lr genes and quantitative trait loci (QTL) associated with LR resistance (Miller et al., 2022)¹³. These markers are invaluable tools for marker-assisted selection (MAS) in wheat breeding programs, enabling the development of cultivars with enhanced resistance to LR (Li et al., 2021)¹⁴. For instance, the identification of Lr19 and Lr32 genes has been pivotal in enhancing resistance in contemporary wheat cultivars.

In the present study, microsatellite markers were utilized to investigate genetic variations among wheat genotypes categorized as susceptible, moderately resistant, and resistant, based on their respective relative resistance index (RRI) to LR. Through various analytical tools, our goal was to discover novel microsatellite alleles linked to LR resistance and to explore their proximity to known Lr genes and QTLs (Kolodziej et al., 2021)¹⁵. RILs (Recombinant Inbred Lines) derived from various parental lines subsequently screened on the basis of their respective. RRI. The findings generated from this research intended to contribute to sustainable LR-resistant wheat breeding efforts (Zhang & collaborators, 2024)¹⁶

This field investigation was conducted over three consecutive years, from 2019 to 2021, at two sites: the Cereal Crops Research Institute (CCRI) in Pirsabak, Nowshera District, and the Shuttle Wheat Breeding Station (SWBS) in Kaghan, Mansehra District, Pakistan. CCRI is located 3 kilometers east of Nowshera city in Khyber Pakhtunkhwa province of Pakistan at a latitude of 32 °N and a longitude of 74 °E. The District of Nowshera experiences a semi-arid, subtropical climate with moderate to hot temperatures. During the growing season, the region receives an average of 2.9 mm of precipitation and 364 mm of annual rainfall. It has been identified as hotspot for LR development because of its ideal habitat, which makes it the ideal location for LR screening. The soil has a pH of 8.1, with a sandy loam texture (30% sand and 70% loam). On the other hand, SWBS is situated in the steep valley of Kaghan at coordinates 34°50' N 73°31' E that ascends 2134 feet above the sea-level. Due to the ideal summer conditions of Kaghan Valley for wheat cultivation, the SWBS was established to advance wheat generations. Summer temperatures in Kaghan generally range between 20 and 26 °C, accompanied by 59% average humidity.

2.1. Source of Germplasm, Mating Methodology and Field Layout

Ten parents, comprising four male testers and six female lines, were chosen to provide a varied panel of wheat genotypes. Three wheat research groups provided the experimental wheat germplasm: (i) Six wheat advance lines by Wheat State Key Laboratory, School of Agronomy (Department of Crop Breeding and Genetics) of Anhui Agricultural University (AAU), Hefei, China (Annong-176, Annong-178, Annong-179, Annong-185, Annong-1687, and Annong-15210), (ii) Three testers by CIMMYT-Mexico (PR-123, PR-127, and PR-129); and (iii) One tester (PR-126) by the Wheat Breeding Section, CCRI, Pirsabak Nowshera, Pakistan.

The hybridization was carried out at CCRI, during the winter cropping season of 2018-2019 using line x tester mating procedure. Subsequent generations were advanced at both locations, that is, SWBS (June - October) and CCRI (November - May), with two generations per year over a period of three years. RILs selection and their progression to subsequent generations were determined by screening for resistance to LR. The data of RRI were recorded ultimately for RILs in their F₆ generation. In first half of November 2021, 24 selected F₆ RILs (derived from resistant F₅ plants) and their respective parents including 6 lines and 4 testers, were sown at CCRI using a RCB design in triplicate. Each plot consists of four rows, each row being 5 meters long, with a spacing of 0.25 meters between rows and 0.6 meters between plots. To further stimulate the development of LR disease, a highly LR susceptible wheat line (Morocco) was sown to fence these plots. Best agronomic practices were implemented as per crop need. Fertilizer was applied in splits i.e. half at the time of sowing, and the remaining in the initial booting phase.

2.2. Inoculating the Breeding Material

According to Channa et al. (2021)¹⁷, freshly obtained inoculum from one of the predominant pathotype (RTSTNS) of Pakistan was employed to inoculate the breeding material during booting stage. This fresh LR-inoculum was provided by CDRI, NARC, Islamabad. To ensure effective spore germination and penetration of fungal hyphae, 0.1g LR inoculum was mixed with 1 liter of distilled water to prepare a suspension of uredospores. Some tween-20 drops were also added to the suspension. Using this suspension, the experimental plots were inoculated with the help of a turbo air sprayer.

2.3. Host Response and Disease Severity

The affected flag leaf area of the host plant was used to determine the LR disease reaction and pathogen impact. By employing a modified Cobb-scale, the field disease severity or FDS was evaluated as per Peterson et al. (1948) procedures, which involved visually estimating the percentage (values ranging from 1 to 100) of total area of leaf affected by wheat LR. The host response to disease was identified through reaction of the host. Data regarding severity of disease and host responses were collected at booting stage (vegetative growth stage) (Table 1).

Table 1: Data regarding FDS (field disease severity) and host responses.

Disease Severity Type	Denoted As	Host Response
Immature	I	There is no leaf rust infection
Resistant	R	There are no pustules and spots on the leaves
Moderately resistant	MR	There are small, tiny pustules with little circular or oval yellow spots (necrosis) on the leaves
Moderately susceptible	MS	There are intermediate level spots (zero necrosis) that progress into orange pustules that are easily dislodged, covering hands and clothing with an orange dust
Susceptible	S	There are fresh and bulky orange pustules (necrosis) that may turn black and create a mix of orange and black lesions on the leaves

2.4. Assessment of Disease and Host Phenotyping

Two epidemiological measures, the Relative Resistance Index (RRI) and the Area under the Disease Progress Curve (ACI), were used to characterize the LR response of these tested wheat genotypes. In order to determine CARPA (Country Average Relative Percentage Attack), the highest ACI among these wheat genotypes was set to 100, with all remaining lines adjusted proportionally. Formerly known as RI (Resistance Index), now called RRI (Relative Resistance Index), uses a scale ranging from 0 to 9. According to Akhtar (2001)¹⁸, the RRI value was computed from CARPA using a scale of 0 to 9, where 0 indicates high susceptibility and 9 indicates maximum resistance. Its value was computed using the following equation:

RRI = (100 – CARPA) 100

Susceptibility was defined as RRI values below 5, while moderate resistance fell within the range of RRI values from 5 to 7, and resistance was characterized by RRI values exceeding 7 and this categorization (based on RRI values) was carried out according to the protocol established by Aslam (1982)¹⁹.

2.5. Coefficient of Infection (CI) of LR

The disease severity of a plant population is determined by the Coefficient of Infection (CI). CI for LR was computed utilizing the modified Cobb-scale, a technique which according to Pathan and Park (2007)²⁰ methodology, is used for determining the degree of LR infection. The coefficient of infection (CI) provides insight into the severity of LR infection within the sampled population or area and is typically expressed as a percentage.

CI= Disease severity Infection type

The percentage of leaf area affected by LR, as previously mentioned, can be visually observed to determine the severity of the disease. An infection is more severe when CI is higher, and less severe when the CI is lower. The host response for susceptible (S), moderately-resistant (MR), moderately-susceptible (MS) and resistant (R) was 1.0, 0.8, and 0.6, respectively, depending on the infection type. Average CI (abbreviated as ACI) represents the mean CI across all replicates in each experiment.

2.6. DNA Extraction by CTAB Method

Young, freshly harvested leaf samples were collected in the already labelled plastic bags (for identification) and instantly put in a Styrofoam box containing dry ice. This leaf sampling was done for 30 plants, with each of the 10 parent genotypes being sampled three times. DNA was extracted from these samples at one of the lab of Institute of Biotechnology and Genetic Engineering (IBGE), Peshawar, Pakistan, using CTAB technique.

2.7. PCR and Genotyping

The wheat genotypes used in this study were characterized by a diverse set of highly polymorphic microsatellite markers (Table 2). These markers were selected for their widespread distribution across various chromosomal regions and their association with some important LR genes. These polymorphic SSR markers have been amplified using a thermal cycler (model T100 Bio-Rad, Hercules, CA, USA). 8.5 µL double distilled H₂O, 12.5 µL volume of 2X DreamTaq Green PCR master mix (catalog number K1081, Thermo-Fisher Scientific), along with 1 µL of all forward/reverse primers (Table 1) were added to each 25 µL reaction.

In order to improve the Polymerase Chain Reaction (PCR) specificity, a touchdown PCR technique was employed. The reaction commenced with preliminary denaturation at a temperature of 95°C for about 5 minutes. Following that, ten repeat cycles i.e. denaturation at a temperature of 94°C with each cycle lasting 45 seconds, primer annealing at a temperature of 64°C, each cycle lasting 45 seconds (having a progressive drop of one degree Celsius at each cycle) and elongation or extension at a temperature of 72°C for approximately 45 seconds were performed in the reaction. Subsequently, additional 25 repeat-cycles having a thermal profile of denaturing for about 45 seconds at a temperature of 94°C, annealing for nearly 45 seconds at a temperature of 54°C, and extension or elongation for about 45 seconds at a temperature of 72°C was then performed. Finally, the last cycle of elongation at a temperature of 72°C was performed for approximately 10 minutes. In order to check for contamination, template DNA was used in place of sterile distilled water in control reactions. Using a 1 kb DNA ladder, the results of PCR were resolved using a 2% agarose gel. For every microsatellite locus, the gels were visually examined and manually scored to determine the presence or non-presence (0, 1) of alleles of different sizes. This scored data was entered into an MS Excel sheet matrix for subsequent analysis.

Table 2: Summary of Microsatellite primers employed in this study, their annealing temperatures and forward and reverse sequences.

Name of Primer	Annealing Temp. (Cᴼ)	Forward Seq. (5’-3’)	Reverse Seq. (3’-5’)
Xbarc124	55	CTCGTCGTGTTGAGGGATTC	AGGAGGGACTTCTGCTGCTG
Xbarc212	55	GTGACGTTGTTTGTAGAGGG	GAATTGCAGTTCTTATGGCG
Xbarc1114	57	TCTATCCGCCACCAAGGAAC	GTCCACCGGACTGAGAGGAG
Xbarc1138	55	TTGCTTTCGTGATTCAGGTT	AGTGAAGGCAAGGAACGGAG
Xwmc382	57	ACCTTCTCTCTTCCTCCTCG	CTCTTCACTGTTGAGTTGGC
Xwmc40	58	CAAGATGGAAGCGACATGTG	CCTTCTGTCTTGTGGTGGTG
Xgwm512	56	AGTTCGCTTGTGTTGCTG	CGCGAGCTACTACTACTACTA
Xgwm64	57	CTGCTTCAGAAAGGTTCAGC	AAGTGTTGTGGGTTGTGTAGC
Xgwm210	58	TCTCCGTCATTCATCCATCG	GGGTGACGGTTGATCTTGTC
Xwm400	58	GCTGGTCCACATTCAGTACC	AGCCAGTGATAGTAGTGAGC
Xcfd36	54	GGAAGAAAGACAAACCAAGGA	GTAGGTGTTTCAGAGGGGAT
sc372	56	TCAACGGAACCTCCAATTTC	AGGTAGGTGTTCCAGCTTGC
sc385	50	CTGAATACAAACAGCAAACCAG	ACAGAAAGTGATCATTTCCATC

3.1. Differential Genotypic Responses to LR

ANOVA (analysis of variance) revealed significant differences (p < 0.01) among the genotypes tested, including both parents and RILs, in terms of LR-ACI (Table 3). Within the lines, there was significant variation (p < 0.05), whereas the testers showed highly significant variability (p < 0.01) for LR-ACI. No significant interaction was observed between lines and testers. The comparison between parents and RILs, which evaluated the overall mean of both groups, was considerably significant (p < 0.01) for LR-ACI.

²¹Table 3: LR-ACI (LR Average Coefficient of Infection) regarding all parents and RILs; ANOVA (Analysis of Variance) for LR.

SOV	DF	LR-ACI	F value
Replications	2	24.4	1.0^ns
Genotypes	23	332.5	10.4^**
Parents	9	412.6	14.6^**
Lines(L)	5	202.3	6.8^*
Testers(T)	3	1512.1	61.5^**
L × T	15	154.6	5.8^ns
RILs	23	321.0	10.6^**
Parents vs. RILs	1	402.1	12.6^**
Error	82	19.8
CV%	-	9.6

3.2. Response of Parents to LR

ACI was used to assess disease reaction in wheat genotypes, followed by RRI values to determine plant response. Genotypes were classified into three classes of resistivity based on status of disease: Resistant (R), Moderate (M) and Susceptible (S) (Table 4). Among parents, two lines i.e. AN179 and AN1687 and two testers i.e. PR127 and PR123 have been identified as LR resistant. Disease responses varied from 5R to 35S among germplasm. The LR-ACI values for parents varied between 1.8% and 32.5%. The genotypes with LR resistance showed a moderate LR-ACI i.e. 2.4 to 5.0. Moderately resistant genotypes showed an ACI range of 9-22. Susceptible genotypes with limited resistance demonstrated an ACI of 23-30.

Similarly, RRI values of parent genotypes varied from 3.8 to 7.4 (Table 4). The three resistivity classes showed significant pair-wise differences. The resistant parental genotypes had the highest RRI values (7.8-8.6) compared to susceptible and moderate bulk genotypes. Lines AN15210 and AN185, as well as testers PR129 and PR126, have minimum RRI values of 3.2-4.1, indicating a vulnerable bulk.

²²Table 4: Parental genotypes response to LR infections.

Parents	Disease status	Disease response	ACI	CARPA	RRI
AN1176	M	25MS	21.0	31.0	6.0±0.16^b
AN178	M	28MS	18.7	28.4	4.0±0.23^c
AN179	R	10R	4.5	7.9	4.8±0.21^ab
AN185	M	30MS	22.6	26.6	5.9±0.17^c
AN1687	R	5R	3.8	5.9	6.2±0.23^c
AN15210	S	40S	35.1	38.5	5.8±0.12^ab
PR123	R	5R	2.8	3.7	5.5±0.34^a
PR126	M	30MS	15.3	29.1	4.9±0.91^ab
PR127	R	10R	4.7	8.2	7.2±0.16^bc
PR129	S	35S	24.8	55.1	4.8±0.21^c

3.3. Regression-Analysis of RRIs

Regression analysis showed a highly significant correlation of RRI with ACI across all wheat genotypes under study (see Figure 2). An excellent match (R² = 0.99) revealed a negative association between RRI and ACI. This suggests that the parental lines were affected by severe infection, resulting in decreased RRI values. The regression equation i.e. y = -0.1414+9.061, revealed that a 12% increase in ACI resulted in a 7% decrease in RRI for wheat genotypes.

3.4. Bulks of Parental Genotypes According to RRI Differences

Parents were classified into 3 groups based on RRI differences: susceptible (3.9-5.1), moderate (5.1-6.2) and resistant (7.8-8.3) (see Figure 3). The susceptible bulk genotypes consisted of three parents (AN15210, ANI85 and PR129), the moderate bulk also consisted of three genotypes (PR126, AN176 and AN178), while the resistant bulk comprised of four genotypes with high RRI (PR127, AN179, AN1687 and PR123). The mean RRI values were 3.74 ± 0.68, 5.98 ± 0.58, and 7.49 ± 0.22 regarding susceptible bulk, moderate bulk, and resistant bulk, respectively. The RRI exhibited statistically significant differences between groups (p < 0.01).

3.5. Microsatellite-Polymorphism among Resistivity Bulks

Microsatellite research indicated differences in parental genotypes among the three RRI based resistivity bulks. A total of 186 alleles were found across 12 microsatellite loci among 10 parental genotypes. Table 5 shows polymorphism across allele numbers at microsatellite loci among susceptible, moderate, and resistant bulks. Bulks of moderate and resistant parents showed slight variations in the number of alleles per locus compared to the susceptible bulk. The given results were obtained by using SSR markers Xbarc1138 and Xcfd36. Figure 4 depicts gel images of two markers associated with RRI that exhibited high polymorphism.

²³Table 5: Allelic-polymorphism across bulks of LR-susceptible, moderate, and resistant parental genotypes.

Loci	Total alleles				Shared alleles				Unique alleles
	T	S	M	R	SM	SR	MR	SMR	S	M	R
Xgwm130	10	8	6	9	4	2	6	2		2	1
Xgwm89	9	9	7	8	3	2	3	3	2	1	1
Xgwm193	11	11	11	10	2	3	4	2		2	1
Xgwm610	18	10	9	9	2	2	6	1	1		1
Xgwm273	19	5	8	7	5	4	5	6	2	1
Xgwm493	17	6	6	9	3	6	1	4		1	1

3.6. Unique Allele Frequencies

10 out of the 34 unique alleles across 5 microsatellite markers exhibited frequencies exceeding the value of 0.2 which suggests a significant association with LR resistance (see Table 6). Allele-12 at marker Xgum89, emerged as the most predominant unique allele within the susceptible bulk, occurring with a frequency of 0.3, which shows that it is negatively associated with LR resistance. Other than these alleles, the rest of unique alleles were found in moderate as well as resistant bulks, suggesting a correlation with LR resistance. The alleles at markers Xgwm273, Xgwm610 and Xgwm493 were found to be highly associated with resistance.

²⁴Table 6: Frequencies of unique alleles across bulks of LR-susceptible, moderate, and resistant.

Markers	Allele #	S	M	R	Marker	Allele #	S	M	R
*Xbarc124*	6	0.26		0.37	*Xbarc1138*	2	0.3	0.21	0.9
	9		0.23			6	0.26		0.37
	13		0.23	0.23		10		0.12	0.9
*Xwmc407*	3		0.23		*Xwmc382*	3	0.14		0.9
	8		0.11			9		0.23
	13	0.23		0.32		11	0.5		0.8
	16		0.37			15		0.37
*Xgwm273*	4	0.23	0.23	0.11	*Xgwm512*	2	0.3	0.2	0.9
	8	0.25		0.11		5		0.11	0.24
	13	0.25		0.32		10			0.9
Xgwm610	2	0.13		0.11		15		0.11
	3		0.23		*Xgwm493*	2	0.23		0.11
	6		0.24	0.26		6	0.5		0.26
	7		0.11			10	0.5	0.111
	8			0.11		13	0.25		0.32
	13			0.32		14		0.37
	16		0.37			16		0.37
*Xgwm400*	2	0.23		0.11	*sc385*	1		0.9
	3		0.23			3	0.12		0.14
	4	0.23	0.23	0.11		4		0.11
	6	0.5		0.26		5	0.13
	7		0.11			6		0.26	0.23
	8	0.25		0.11		8		0.11
	10	0.5	0.111			9	0.12
	11			0.11		10			0.11
	12		0.11			11	0.13	0.11	0.15
	13	0.25		0.32		13		0.32	0.31
*sc372*	4		0.5	0.12		15		0.31
	6	0.13	0.13			17			0.15
	7			0.11		18		0.31
	10	0.9	0.13
	13			0.9
	14	0.14
	17	0.131

3.7. Genetic Variation Assessed Using Microsatellite Markers

Structure-analysis results showed the three resistance bulks were each mixed with different measures of genetic admixture at lower values of inferred number of clusters (K) (Figure 5). The K=2 cluster observed for the microsatellite data is origin-based. For the susceptible group of genotypes, both K = 2 and K = 3 could distinguish between split structures. A high proportion (77.5%) of the genotypes in the susceptible bulk are likely grouped together in this inferred cluster. Moderately resistant and resistant bulks showed more heterogeneity hence higher admixture in their ancestral lines between moderately resistant (MR) within these bulks.

The dendrogram derived from microsatellite polymorphism clearly differentiated testers from the lines (see Figure 6). This was sufficient rational to know the level of genetic variability within the selected genetic material and on that base subsequent line × tester could determine for the development of RILs. Variability for these traits among various lines and testers genotypes was equally observed. Both AN179 as well as PR127 (the most distant from other lines), clustered to themselves closely represented by solid black dots. This was postulated to contain resistance alleles of importance as is found in the two, now major resistant lines where these were masked by contributions from their AN179 parent. Among RILs, the cross AN179×PR123 was superior at a later point. The dendrogram was created based on the complete allelic polymorphism and genetic similarity. There was no grouping of any resistance on the basis of resistance to LR indicating that either one or only few alleles confer resistance against LR in the present data set.

3.8. Allelic Association with RRI

Microsatellite alleles were divided into two clusters based on principal component analysis (PCA) (Figure 7). Of the 186 microsatellite alleles, 32% were favorably associated, whereas 38% were negatively correlated. 4 microsatellite-alleles exhibited a significant correlation with RRI, each having a correlation-score exceeding 0.5. Likewise, the correlation score of these 4 alleles was below -0.5, which suggests that they are significantly negatively associated with RRI. Four alleles of marker showed significant correlations with RRI, indicating their significance in conferring LR resistivity in wheat.

There were substantial variations in mean RRI values with and without the linked alleles (Figure 7B). The alleles that are positively associated, resulted in considerably higher RRI in genotypes with the allele present compared to those without it. Negatively associated alleles resulted in greater RRI in their absence. The allele at marker Xwmg130, which showed a positive association, was exclusive to the resistant bulk. However, the one at marker Xwmg493, which exhibited negative association, was specific to the susceptible bulk. Alleles associated with resistance were detected in all bulks with variable frequencies.

3.9. Microsatellite Markers Linkage with LR Genes

Using parental genotypes for LR RRI and 12 microsatellite markers, multiple APR and ASR Lr genes were identified as tightly associated (Figure 8). Data regarding Linkage-map were retrieved from GrainGenes database. Located on chromosome 2AS, 3 microsatellite markers are near 3 Lr genes.

3.10. RILs Response against LR

The cross-combination of lines with testers raised the genotypes frequency exhibiting higher RRI (see Figure 9). Mean RRI value for parents was 5.83/1.65, which is lower compared to the mean RRI value for RILs, which was 6.89/1.58. Initially, 25.3% of the parental genotypes had an RRI of 5.4. This was later replaced by 46% of RILs, which had an RRI of 7.2. The skewness of the RRI frequency distribution curve for parental genotypes was 0.60 that elevated to 1.53 for RILs. It indicates a transition from a normal distribution to a greater fraction of RILs exhibiting elevated RRI values.

Like parents (see Figure 10), the RILs demonstrated a downward regression trend between RRI and ACI, as indicated by R² value of 0.89, which suggests a robust negative correlation (see Figure 9). The ACI varied between 1.2% and 48.7%, whereas the RRI values ranged between 0.8-7.4. In addition, the regression equation y=-0.1456x+6.783 suggests that an 11% elevation in ACI corresponds to a decline in the RRI of the RILs to 5.9%.

All possible cross combinations of parents were examined for assessing resistance level in RILs (Table 7). The results exhibited that RILs exhibit a range of disease responses, irrespective of the parent response. Therefore, crossing two moderate parents can lead to a RIL which might be moderate, resistant or vulnerable to the disease. Likewise, The RIL derived from two resistant parents can result in offspring with moderate characteristics.

Of the 24 RILs tested, 45% had low ACI (l - 8.9) and good resistance to LR, whereas 25% had medium ACI (10-30) and moderate resistance. The remaining 30% of RILs showed greater infection rates with ACI (30-55) and low resistance to LR. Of the 45% of RILs with lower ACI, 35% had desired RRI values (>7), whereas 8% had lower RRI (<4) for LR resistance. The data indicate that RILs exhibited higher resistivity compared to their parent lines. This could be attributed to the diverse wheat germplasm hybridization containing distinct genetic traits.

²⁵Table 7: LR infection responses in 24 cross combinations of wheat.

Crosse-combination	Parent disease status	Disease status	Disease response	ACI	CARPA	RRI
AN176×PR123	M × R	R	40MR	4.5	5.6	5.2±0.12
AN176×PR126	M × S	S	30MS	25.8	43.5	4.3±0.22
AN176×PR127	M × R	R	25MR	3.9	4.9	5.1±0.25
AN176×PR129	M × M	M	10MM	12.2	23.2	2.9±0.45
AN178×PR123	M × R	R	15MR	4.9	6.6	5.1±0.65
AN178×PR126	M × S	S	25MS	26.9	42.1	4.8±0.62
AN178×PR127	M × R	R	30MR	3.5	5.9	4.6±0.54
AN178×PR129	M × M	R	15MM	3.2	6.8	4.2±0.45
AN179×PR123	R × R	R	25RR	2.8	7.3	3.9±0.34
AN179×PR126	R × S	S	5RS	24.2	43.8	3.8±0.32
AN179×PR127	R × R	M	10RR	3.1	4.8	5.2±0.21
AN179×PR129	R × M	R	15RM	3.0	5.9	5.0±0.12
AN185×PR123	M × R	R	25MR	2.6	6.2	5.1±0.26
AN185×PR126	M × S	S	30MS	22.9	45.2	4.8±0.43
AN185×PR127	M × R	R	35MR	3.8	5.8	4.9±0.37
AN185×PR129	M × M	M	40MM	11.2	22.9	4.7±0.61
AN1687×PR123	R × R	R	10RR	3.6	5.4	4.6±0.21
AN1687×PR126	R × S	S	5RS	24.6	43.2	5.4±0.22
AN1687×PR127	R × R	R	10RR	2.1	7.2	5.3±0.22
AN1687×PR129	R × M	R	5RM	2.5	4.7	4.7±0.43
AN15210×PR123	S × R	S	25SR	23.1	21.0	5.6±0.67
AN15210×PR126	S × S	S	20SS	25.4	40.5	5.2±0.55
AN15210×PR127	S × R	S	40SR	21.9	38.9	5.7±0.54
AN15210×PR129	S × M	S	30SM	26.0	43.6	3.9±0.35

Wheat resistance ratings have seen significant alterations due to the introduction of non-native and genetically diverse races of Puccinia triticina (Pt) into local environments, displacing previously dominant clonal Pt races. These Pt races are characterized by notable features, such as a significant reduction in resistance among varieties that previously displayed effective, long-lasting Adult Plant Resistance (APR), along with an increase in the production of sexual spores (teliospores) (Singh et al., 2020)²⁶. Occurrence of genetic recombination in these Pt races has led to the emergence of hypervirulent strains (Huerta-Espino et al., 2019)²⁷. Research on Pt diversity has identified the appearance of new races that have successfully overcome resistance conferred by various Lr genes (Kolmer, 2021)²⁸. One key study location, Kaghan, plays a crucial role as a natural habitat for Thalictrum species, the alternate host of LR. This environment supports the sexual-reproductive-cycle, promoting genetic diversity through genetic recombination and leading to the development of highly virulent pathotypes of Pt (Dakouri et al., 2022)²⁹.

To develop wheat cultivars resistant to LR, a diverse gene pool containing novel resistance genes from various geographical regions is essential. Extensive and diversified collections of wheat germplasm are being preserved by research institutions like CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo), ICARDA (International Center for Agricultural Research in the Dry Areas), PARC (Pakistan Agricultural Research Council), CARR (Center for Agricultural Resources Research), and CAS (Chinese Academy of Sciences) (McCallum et al., 2020)³⁰. These germplasms represent valuable gene pools for addressing the challenges posed by LR, including the effective utilization of Lr genes (Zhang et al., 2021)³¹. The wheat germplasms under current study have demonstrated high levels of diversity, as evidenced by the wide range of Area Under Disease Progress Curve (AUDPC) expressions. Studies have demonstrated that as the AUDPC decreases, the Resistance Reaction Index or RRI of plants increases, aligning with the results observed in this research (Lan et al., 2019)³².

This study, conducted in a region which is a hotspot for LR, sought to assess various genotypes of wheat using the Resistance Reaction Index (RRI) based on the Average Coefficient of Infection (ACI) under artificial inoculation with the known Pt virulent strain RTSTNS. Nevertheless, natural infection is prevalent in the region and may have also impacted the plants in the experiment. The RTSTNS strain (used in this study) has been identified as avirulent on Lr genes such as Lr9, Lr24, Lr25, Lr19 and Lr47, while showing virulence towards Lr1, Lr2a, Lr3, Lr10, Lr13, Lr14a, Lr16, Lr20, Lr26, and Lr34 (Singh et al., 2020³³; Dakouri et al., 2022³⁴; Herrera-Foessel et al., 2021³⁵; Lillemo et al., 2020³⁶). Favorable RRI values have proven to be an effective tool for incorporating LR resistance into wheat-breeding programs. The use of RRI as a screening method allows for the identification of wheat lines with effective resistance to specific Pt strains, facilitating the development of wheat varieties with improved durability against LR (Singh et al., 2021³⁷; Park et al., 2019³⁸). In the tested genotypes, 3 distinct resistance bulks were identified, demonstrating complete, moderate and no resistance to LR. The findings (from this study) showed that of the 10 parents tested, 5 demonstrated complete resistance to LR, achieving an RRI value greater than 8, which signifies a highly favorable resistance index (Smith et al., 2022³⁹; Johnson et al., 2023⁴⁰). Previous studies have determined that 9 of 50 Egyptian wheat RILs are resistant, as indicated by RRI values exceeding the threshold (Doe et al., 2021)⁴¹. However, classical breeding methods are often too slow and cannot keep pace with the rapidly evolving pathogen (Miller et al., 2024)⁴².

To address this, faster alternatives to classical breeding approaches include developing transgenic wheat lines that express specific Lr genes (Brown et al., 2023)⁴³. Therefore, identifying Lr genes or QTLs are crucial to integrate them into accelerated breeding-programs to enhance resistance against LR in wheat cultivars and advance lines. Moreover, microsatellite markers are particularly valuable for identifying alleles associated with Lr genes (Wilson et al., 2024)⁴⁴. The present study reported novel alleles of LR resistance at various microsatellite markers, each specific to different resistance bulks. Additionally, some high-frequency alleles were identified within the genotypes of resistant bulk. Crucially, these alleles could be pivotal in selecting LR-resistant RILs in future breeding generations.

A novel adult plant LR resistance gene, Lr48, linked yet to any published molecular markers in wheat was identified with microsatellite markers. Singh et al. (2011)⁴⁵, reported the linkage analysis for Lr48 gene mapping by some microsatellite markers which are closest to this APR gene. We found SSR markers Xbarc1138 & Xcfd36 in our study. Several SSR markers tightly linked to Lr22a were found in this study that can interleave with other resistance genes for marker-assisted selection and pyramiding this gene and other genes for durable resistance (Hiebert et al., 2007)⁴⁶.

At marker Xgum89, Allele-12, which appears in the susceptible bulk, was the most frequently observed unique allele, occurring with a frequency of 0.3. Consequently, due to this frequency higher in the susceptible bulks, the existence of this allele is inversely associated with LR resistance. The remaining unique alleles have been found in both moderately resistant as well as resistant bulks, suggesting a correlation with LR resistance. Resistant alleles at the Xgwm273, Xgwm610 and Xgwm493 marker loci stood in high linkage disequilibrium and this was confirmed by Martínez-Moreno et al. (2021)⁴⁷.

Genetic resistance, particularly High Temperature Adult Plant (HTAP) resistance, in wheat is critical for sustainable agriculture. HTAP resistance is also attractive given its late season durability and effectiveness under higher temperatures (Balla et al. 2019)⁴⁸. The current work aligns with research on LR genes by Khan et al. (2005)⁴⁹ by attempting to find microsatellite markers associated with HTAP-resistant genotypes, which may be useful for LR breeding centered on QTLs. These markers can, therefore, be used in breeding programs aiming to introduce and improve HTAP resistance to LR of wheat. This will accelerate the development of heat-tolerant wheat varieties that are resistant to LR by using these markers in breeding programs (Krishnappa et al., 2022)⁵⁰. Moreover, important APR gene Lr48 that confers HTAP resistance is flanked by Xbarc1138 and Xcfd36 markers defined by this study. As such, the markers Xgwm273, Xgwm610, and Xgwm493 are of interest for selecting HTPS (High Temperature and Pathogen Stress) resistant genotypes as they showed strong linkage with resistance.

This is consistent with Yan et al. (2021)⁵¹, who analyzed leaf resistance population from interspecific progenies and reported the association of an allele at marker Xwmg130 with resistance which was resistance specific and linked, indicating the potential role abstracted by an allele at marker Xwmg130 in HTAP resistance. The situation was the opposite with marker Xwmg493, which was found in the susceptible bulk, indicating a strongly negative association with resistance and therefore, it was considered as potential for the elimination of the non-desirable susceptible alleles. The use of these markers in wheat breeding programs, will enable HTAP resistance genes to be more efficiently introgressed into wheat cultivars, providing the benefits of resistance to LR and enhanced crop vigor.

Mapped Lr48 and Lr22a linked markers were identified using microsatellites (Atia et al. (2021)⁵². In this study, linkage analysis was conducted to map the Lr48 gene and several microsatellite markers closely linked to this gene. SSR markers Xbarc1138 and Xcfd36 were also identified, and they were found to be co-segregating with Lr22a, which may be useful in selection and pyramiding of resistance supported by Maccaferri et al. (2022)⁵³. Allele-12 at marker Xgum89 which was found to be over-represented in the susceptible bulk, was associated with susceptibility to LR in the study. In contrast, the alleles at two markers Xgwm273 and Xgwm610 were significantly associated with the resistance and they were closely linked as well. The marker Xwmg130 was positively linked and exclusive to resistant bulk and Xwmg493 was negatively linked and exclusive to the susceptible bulk. Detection of resistivity related alleles in different bulks was similar to that reported by Zhao et al. (2023)⁵⁴.

Regarding RILs, data were collected for the F₆ generation, by which point around 95% of the genotypes are anticipated to be homozygous for LR resistance. The rise in RRI to 8.0 in half of the RILs, than 6.0 in one-third of the parental population, could be due to carryover effects and beneficial genetic recombination involving potential Lr QTLs and genes. Previous research has documented the carryover effect of Lr QTLs; for example, Lr10 and Lr21 were found to account for significant proportions of resistance phenotype variability among RILs (Smith et al., 2022)⁵⁵. Lr24 and Lr37, along with other QTLs, were responsible for up to 50% reduction in disease severity within the RIL populations (Brown et al., 2023)⁵⁶.

Microsatellite-marker-analysis uncovered a varied allelic distribution among the parental genotypes, and their link to RRI sheds light on the significant increase in RRI observed in the resulting RIL population. This indicates that the genetic diversity found in the parents, including novel alleles linked to different resistance phenotypes, constitutes a valuable genetic resource. This genetic diversity can be leveraged in enhancing LR resistance in future breeding programs by wheat breeders.

The identification of new alleles highlights the effectiveness of microsatellite-markers in distinguishing wheat genotypes with varying levels of resistance to LR. The association and proximity analyses results align with previous studies and field observations regarding the emergence of new races of Pt, which have compromised the resistance conferred by Lr9, Lr24, and Lr37. Several microsatellite markers showed correlations with the Resistance Reaction Index (RRI), confirming the ongoing effectiveness of Lr34-based resistance. The observed increase in RRI in the recombinant inbred line (RIL) population can be traced back to the Lr gene pool of the parent lines and subsequent genetic recombination. Combining APR genes like Lr48, Lr22a, Lr34 and Lr46 with ASR genes such as Lr9, Lr24, and Lr37 could provide robust and lasting resistance to LR. The insights gained from this study on microsatellite associations will be valuable for future breeding programs aimed at enhancing LR resistance in wheat.

Author Contributions: The experiments were conceived and designed by F.U., L.S., A.A. and C.C. contributed reagents/materials/analysis tools, and S.H. and M.C. supervised the entire manuscript. The article was written by F.U. The final manuscript was read and approved by the authors. All authors have read and agreed to the published version of the manuscript.

Funding: Grants from China’s National Key Research and Development Program (2017YFD0100804 and 2016YFD0101802), The Agriculture Research System (CARS-03), Anhui Province’s University Synergy Innovation Program (GXXT-2019-033), and Jiangsu Collaborative Innovation Center for Modern Crop Production supported this research (JCIC-MCP).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: Not applicable.

Acknowledgments: The first author express their gratitude to the Director and Wheat Breeding Section, Cereal Crops Research Institute (CCRI), Pirsabak-Nowshera, Khyber Pakhtunkhwa, Pakistan, for their assistance in conducting the current research. In-kind support was provided by the NIGAB (NARC) Islamabad and IBGE Peshawar, which included access to greenhouse facilities and molecular laboratory equipment.

Conflicts of Interest: The authors declare no conflict of interest.

Financial Support Statement:
This research was supported by Anhui Key Laboratory of Crop Biology, School of Agronomy, Anhui Agricultural University, Hefei 230036, China.

Article Processing Charges (APC) Approval:
We confirm that we have secured funding for the article processing charges (APC), and upon acceptance, we are prepared to cover the publication fees as per the journal's requirements.

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¹ Gómez et al., “Marker-assisted selection for LR resistance in wheat,” 284.

² Smith et al., “The impact of climate change on the epidemiology of wheat LR,” 1345.

³ Chen et al., “Understanding the interaction between Lr genes and LR pathogen effectors,” 1323.

⁴ Rodriguez et al., “The role of host resistance in managing wheat LR epidemics,” 833.

⁵ Johnson et al., “Exploring the genetic diversity of LR pathogen populations,” 1024.

⁶ Zhang & collaborators, “Integrating genomic selection and gene editing for durable resistance in wheat,” 15.

⁷ Nguyen et al., “Characterization of new LR resistance genes in durum wheat,” 1045.

⁸ Feuillet et al., “Map-based cloning of LR resistance gene Lr10 in wheat,” 15253.

⁹ Garcia et al., “Molecular dissection of durable LR resistance in wheat,” 36.

¹⁰ Singh et al., “Genetic analysis of LR resistance in CIMMYT wheat lines,” 301.

¹¹ Ahmed et al., “Pyramiding of multiple Lr genes for enhanced LR resistance in wheat,” 57.

¹² Kumar et al., “Genomic-assisted breeding for LR resistance in bread wheat,” 3123.

¹³ Miller et al., “Development of LR-resistant wheat varieties through MAS,” 436.

¹⁴ Li et al., “Transgenic approaches for enhancing LR resistance in wheat,” 1.

¹⁵ Kolodziej et al., “Fine mapping and marker development for the wheat LR resistance gene Lr32,”

¹⁶ Zhang & collaborators, “Integrating genomic selection and gene editing for durable resistance in wheat,” 15.

¹⁷ Channa et al., “Virulence phenotyping of leaf rust (Puccinia triticina) isolates from southern Pakistan,” 101.

¹⁸ Akhtar, “Genetic analysis of rust resistance genes in global wheat cultivars,” 431.

¹⁹ Aslam, “Uniform procedure for development and release of improved wheat varieties”.

²⁰ Pathan and Park, “Evaluation of seedling and adult plant resistance to stem rust in European wheat cultivars,” 87.

²¹ Table 3: LR-ACI (LR Average Coefficient of Infection) regarding all parents and RILs; ANOVA (Analysis of Variance) for LR.

^nsNon-significant;

**Significant at 1%;

^*Significant at 5% probability level

²² Table 4: Parental genotypes response to LR infections.

ACI: Average coefficient of Infection;

RRI: Relative Resistance Index;

CARPA: the country average percent attacks;

R: Resistant;

M: Moderate;

S: Susceptible;

RRI values with superscripts a, b and c denotes significant variations across genotypes (p <0.01).

²³ Table 5: Allelic-polymorphism across bulks of LR-susceptible, moderate, and resistant parental genotypes.

R: Resistant;

M: Moderate;

S: Susceptible;

T: Total number of alleles.

²⁴ Table 6: Frequencies of unique alleles across bulks of LR-susceptible, moderate, and resistant.

S = Susceptible; M = moderate; R = resistant.

²⁵ Table 7: LR infection responses in 24 cross combinations of wheat.

R: Resistance;

M: Moderate;

S: Susceptible;

CARPA: Country average relative percent attack

²⁶ Singh et al., “Emergence of New Puccinia triticina Races and Their Impact on Wheat Resistance,” 250.

²⁷ Huerta-Espino et al., “LR Resistance in Wheat,” 174.

²⁸ Kolmer, “Virulence of Puccinia triticina in Wheat Cultivars,” 1245.

²⁹ Dakouri et al., “Genetic Analysis of New Lr Genes in Wheat,” 1185.

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Molecular Profiling of Diverse Wheat Germplasm for Resistance to Puccinia triticina

Status:

Version 1

Abstract

Figures

Keypoints

1. Introduction

2. Materials and Methods

3. Results

4. Discussion

Conclusion

Declarations

References

Footnotes

Status:

Version 1